Supply Chains: A Manager's Guide 9780201844634, 0-201-84463-X

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Su pply Ch a in s: A M a n a ge r 's Gu ide By David A. Taylor, Ph.D.

Publisher: Addison Wesley Pub Dat e: Sept em ber 24, 2003 I SBN: 0- 201- 84463- X •

Table of Cont ent s

Pages: 384

Today's fiercest business bat t les are t aking place bet ween com pet it ors' supply chains, wit h vict ory dependent on finding a way t o deliver product s t o cust om ers m ore quickly and efficient ly t han t he com pet it ion. For proof, j ust look t o Dell and Am azon.com , bot h of which revolut ionized t heir indust ries by changing how com panies produce, dist ribut e, and sell physical goods. But t hey're hardly alone. By revam ping t heir supply chains, Siem ens CT im proved lead t im e from six m ont hs t o t wo weeks, Gillet t e slashed $400 m illion of invent ory, and Chrysler saved $1.7 billion a year. I t 's a high- st akes gam e, and you don't have a lot of choice about playing: I f your com pany t ouches a physical product , it 's part of a supply chain- and your success ult im at ely hangs on t he weakest link in t hat chain. I n Supply Chains: A Manager's Guide, best - selling aut hor David Taylor explains how t o assem ble a killer supply chain using t he knowledge, t echnology, and t ools em ployed in supply- chain success st ories. Using his signat ure fast - t rack sum m aries and inform at ive graphics, Taylor offers a clear roadm ap t o underst anding and solving t he com plex problem s of supply- chain m anagem ent . Modern m anufact uring has driven down t he t im e and cost of t he product ion process, leaving supply chains as t he final front ier for cost reduct ion and com pet it ive advant age. Supply Chains: A Manager's Guide will quickly give m anagers t he foundat ion t hey need t o cont ribut e effect ively t o t heir com pany's supply- chain success.

Su pply Ch a in s: A M a n a ge r 's Gu ide By David A. Taylor, Ph.D.

Publisher: Addison Wesley Pub Dat e: Sept em ber 24, 2003 I SBN: 0- 201- 84463- X •

Table of Cont ent s

Pages: 384

Copyright Praise for Supply Chains: A Manager's Guide Acknowledgm ent s I nt roduct ion About t he Cover Part I . Challenges Chapt er 1. The New Com pet it ion The Thrill of Vict ory The Agony of Defeat A High St akes Gam e The New Com pet it ion Chapt er 2. The Rules of t he Gam e Facilit ies and Links Dem and, Supply, and Cash Dist ribut ion and Procurem ent Com plexit y and Variabilit y Chapt er 3. Winning as a Team JI T Supply Program s Ret ail Replenishm ent Program s The Problem wit h Program s I nsight s from Gam e Theory Winning Through Collaborat ion

Part I I . Solut ions Chapt er 4. Supply Chains as Syst em s Business Cybernet ics A Rogues Gallery of Relat ions The Dynam ics of Delay Feedback and St abilit y Chapt er 5. Modeling t he Supply Chain The Case for Models Concept ual Models Mat hem at ical Models

Sim ulat ion Models Com bining Models Chapt er 6. Supply Chain Soft ware The Manufact uring Plat form Advanced Planning Syst em s Supply Chain Applicat ions I m plicit Business Models I nt ernet - Based Syst em s

Part I I I . Operat ions Chapt er 7. Meet ing Dem and Com m unicat ing Dem and Processing an Order Assem bling t he Goods Shipping t he Order Collect ing t he Cash Accelerat ing Fulfillm ent Chapt er 8. Maint aining Supply Triggering Replenishm ent Det erm ining Order Quant it y Maint aining Safet y St ock St ream lining Replenishm ent Chapt er 9. Measuring Perform ance Measuring Tim e Measuring Cost Measuring Efficiency Measuring Effect iveness

Part I V. Planning Chapt er 10. Forecast ing Dem and Proj ect ing Trends Aggregat ing Dem and Analyzing t he Fut ure I nt egrat ing Forecast s Chapt er 11. Scheduling Supply Planning wit h ERP Opt im izing wit h APS Validat ing wit h Sim ulat ors I nt egrat ing Schedules Chapt er 12. I m proving Perform ance Set t ing Obj ect ives Avoiding Conflict s Aligning I ncent ives

I m proving Planning

Part V. Design Chapt er 13. Mast ering Dem and Knowing t he Cust om er Analyzing t he Product Shaping Dem and St abilizing Dem and Chapt er 14. Designing t he Chain Choosing a St rat egy Exploring Your Opt ions Designing t he Chain Chapt er 15. Maxim izing Perform ance I ncreasing Velocit y Pooling Risk Designing for Supply Post poning Different iat ion Not es on Sources Chapt er 1 The New Com pet it ion Chapt er 2 The Rules of t he Gam e Chapt er 3 Winning as a Team Chapt er 6 Supply Chain Soft ware Chapt er 7 Meet ing Dem and Chapt er 8 Maint aining Supply Chapt er 9 Measuring Perform ance Chapt er 10 Forecast ing Dem and Chapt er 12 I m proving Perform ance Chapt er 13 Mast ering Dem and Chapt er 14 Designing t he Chain Chapt er 15 Maxim izing Perform ance Suggest ed Readings I nt erm ediat e Level Advanced Level Collect ions of Art icles

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Copyright Many of t he designat ions used by m anufact urers and sellers t o dist inguish t heir product s are claim ed as t radem arks. Where t hose designat ions appear in t his book, and Addison- Wesley was aware of a t radem ark claim , t he designat ions have been print ed wit h init ial capit al let t ers or in all capit als. The aut hor and publisher have t aken care in t he preparat ion of t his book, but m ake no expressed or im plied warrant y of any kind and assum e no responsibilit y for errors or om issions. No liabilit y is assum ed for incident al or consequent ial dam ages in connect ion wit h or arising out of t he use of t he inform at ion or program s cont ained herein. The publisher offers discount s on t his book when ordered in quant it y for bulk purchases and special sales. For m ore inform at ion, please cont act : U.S. Corporat e and Governm ent Sales ( 800) 382- 3419 cor psales@pear sont echgr oup.com For sales out side of t he U.S., please cont act : I nt ernat ional Sales ( 317) 581- 3793 int er nat ional@pear sont echgr oup.com Visit Addison- Wesley on t he Web: w w w .aw pr ofessional.com Library of Congress Cat aloging- in- Publicat ion Dat a

Taylor , David. A., 1943– Supply chains : a m anager 's guide / David A. Taylor . p. cm . I ncludes bibliogr aphical r efer ences and index. I SBN 0- 201- 84463- X ( alk. paper) 1. Business logist ics. I . Tit le. HD38.5.T39 2004 658.7—dc21 2003056020

Copyright © 2004 by David A. Taylor All right s reserved. No part of t his publicat ion m ay be reproduced, st ored in a ret rieval syst em , or t ransm it t ed, in any form , or by any m eans, elect ronic, m echanical, phot ocopying, recording, or ot herwise, wit hout t he prior consent of t he publisher. Print ed in t he Unit ed St at es of Am erica. Published sim ult aneously in Canada. For inform at ion on obt aining perm ission for use of m at erial from t his work, please subm it a writ t en request t o: Pearson Educat ion, I nc. Right s and Cont ract s Depart m ent 75 Arlingt on St reet , Suit e 300 Bost on, MA 02116 Fax: ( 617) 848- 7047 Text print ed on recycled paper 1 2 3 4 5 6 7 8 9 10—CRW—0706050403 First print ing, Sept em ber 2003

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Praise for Supply Chains: A Manager's Guide " An excellent sum m ary of t he st at e of supply chain m anagem ent going int o t he t went y- first cent ury. Explains t he essent ial concept s clearly and offers pract ical, down- t o- eart h advice for m aking supply chains m ore efficient and adapt ive. Truly a survival guide for execut ives as t hey st ruggle t o cope wit h t he increasing com pet it ion bet ween supply chains." —Christ ian Knoll, Vice President of Global Supply Chain Managem ent , SAP AG " Through real- world case st udies and graphic illust rat ions, David Taylor clearly dem onst rat es t he bot t om - line benefit s of m anaging t he supply chain effect ively. Alt hough t he book is writ t en for m anagers, I recom m end it for everyone from t he execut ive suit e t o t he shipping floor because t hey all have t o work t oget her t o m ast er t he supply chain. But beware—you can expect m any passionat e em ployees dem anding im provem ent s in your com pany's supply chain aft er reading t his book! " —David Myers, President , WinfoSoft I nc., Form er Board Mem ber of Supply Chain Council " A com prehensive, t horoughly researched, and well- designed book t hat gives m anagers t he inform at ion t hey need in a highly readable form . I am already st art ing t o use t he t echniques in t his book t o im prove our int ernat ional dist ribut ion syst em ." —Jim Muller, Vice President of Produce Sales, SoFresh Produce " Supply chain m anagem ent is a decept ively deep subj ect . Sim ple business pract ices com bine t o form com plex syst em s t hat seem t o defy rat ional analysis: Com panies t hat form t rading part nerships cont inue t o com pet e despit e t heir best effort s t o cooperat e; sm all variat ions in consum er buying creat e devast at ing swings in upst ream dem and, and so on. I n his t radem ark fashion, Taylor clearly reveals t he hidden logic at work in your supply chain and gives you t he pract ical t ools you need t o m ake bet t er m anagem ent decisions. A m ust - read for every m anager who affect s a supply chain, and in t oday's m arket place t here are few m anagers who are

exem pt from t his requirem ent ." —Adrian J. Bowles, Ph.D., President , CoSource.net " David Taylor has done it again. Wit h his new book, David m akes supply chain m anagem ent easy t o grasp for t he working m anager, j ust as he did wit h his earlier guides t o business t echnology. I f you work for a com pany t hat is part of a supply chain, you need t his book." —Dirk Riehle, Ph.D. " David Taylor has done a m ast erful j ob of defining t he core issues in supply chain m anagem ent wit hout get t ing t rapped in t he quicksand of j argon. This concise book is well writ t en, highly inform at ive, and easy t o r ead. " —Marcia Robinson, President , E- Business St rat egies, aut hor of Services Blueprint : Roadm ap " Taylor has done a t rem endous j ob of giving readers an int uit ive grasp of a com plicat ed subj ect . I f you're new t o supply chains, t his book will give you an invaluable m ap of t he t errit ory. I f you're already am ong t he init iat ed, it will cryst allize your insight s and help you m ake bet t er decisions. I n eit her case, you can only com e out ahead by reading t his book . " —Kevin Dick, Founder of Kevin Dick Associat es, aut hor of XML: A Manager's Guide " My m ot t o for com pressing dat a is 'squeeze it t il it gags.' I n t he current business clim at e, t hat 's what you have t o do t o cost s, and Taylor shows you m any ways t o squeeze cost s out of your supply chain. He also writ es wit h t he sam e econom y: This book cont ains exact ly what you need t o m anage your supply chain effect ively. Not hing is m issing, and not hing is ext ra." —Charles Ashbacher, President , Charles Ashbacher Technologies

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Acknowledgments I 'd like t o t hank Kevin Dick, Jill Dyche, Dirk Riehle, and Jill Mizano for t heir t hought ful and const ruct ive reviews of t his book. I am especially grat eful t o Kevin, whose except ional grasp of econom ics and inform at ion t echnology was of great help t o m e in bridging t he gap bet ween t hese t wo disciplines. I am also grat eful t o John Fuller, Mary O'Brien, Tyrrell Albaugh, and t he rest of t he t eam at Addison- Wesley, t oget her wit h freelance copy edit or Carol Noble, for t aking such good care of t he book at each st age of it s developm ent . But m y deepest appreciat ion, as always, goes t o m y wife, Nina. She not only support ed and encouraged m e t hroughout t he t wo- year writ ing effort , she drew on her own out st anding skills as a writ er and business st rat egist t o great ly im prove t he finished product .

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Introduction I n May 2001, Nike announced t hat it had lost sales in t he preceding quart er because of problem s in it s supply chain. The am ount of incom e lost was im pressive: a cool $100 m illion. Three m ont hs lat er, Cisco Syst em s announced t hat it was writ ing down unusable invent ory due t o som e confusion in it s supply chain. The am ount of it s writ e- down was even m ore im pressive: $2.2 billion. I solat ed incident s? Only in t erm s of m agnit ude—supply chain failures are becom ing increasingly com m on, and t hey are cost ing com panies dearly. I n addit ion t o t heir im pact on profit s, problem s in t he supply chain have a devast at ing effect on st ock prices, causing an average loss of $350 m illion in shareholder value wit h each report ed incident . That 's a st eep price t o pay for a single m ist ake. The flip side of t his coin is t hat , as Dell and Wal- Mart dem onst rat e every day, get t ing t he supply chain right can yield a t rem endous com pet it ive advant age, allowing new players t o overt hrow ent renched indust ry leaders. Why is t he supply chain so im port ant t o success? Because it 's t he new front ier of business. Modern m anufact uring has driven m ost of t he excess t im e and cost out of t he product ion process, so t here is lit t le advant age t o be gained on t he shop floor. But supply chains are st ill not oriously wast eful and error- prone, and t hey offer huge opport unit ies for gaining com pet it ive advant age. The result is a fundam ent al shift in t he nat ure of com pet it ion. The fight for m arket dom inance is no longer a bat t le bet ween rival com panies. The new com pet it ion is supply chain vs. supply chain. What m akes t his new com pet it ion so challenging is t he level of cooperat ion it requires. To forge winning t eam s, com panies have t o t ear down t he barriers bet ween t he funct ional silos wit hin t heir organizat ions, and t hey have t o replace adversarial supplier relat ionships wit h a win- win collaborat ion across t he chain. Bringing about t his level of cooperat ion isn't easy, but t he best com panies are already doing it , and t hey're st art ing t o dist ance t hem selves from t he rest of t he pack. As a by- product of t his new com pet it ion, supply chain m anagem ent has escalat ed from a support funct ion t o a core com pet ence t hat cut s across t he ent ire com pany. Managing t he chain can no longer be left t o specialist s; in t he new com pet it ion, t he supply chain is every m anager's business. I f your com pany t ouches a physical product as it m oves t oward t he m arket , it 's part of

a supply chain, and it will succeed in t he new com pet it ion only if you and your fellow m anagers underst and how t o m ake t he chain as efficient and effect ive as it can be. This underst anding can be hard t o com e by because supply chain m anagem ent is a deep and t echnical subj ect . Most books on supply chains offer eit her sim plist ic form ulas for success, in which a single solut ion fit s every problem , or t he kind of det ailed analysis t hat only a pract it ioner could love. This book is m y at t em pt t o provide t he balanced overview you need, giving you enough inform at ion t o m ake int elligent decisions wit hout dragging you int o a m orass of det ail. Think of it as your playbook for t he new com pet it ion. The book is organized int o five part s of t hree chapt ers each, as shown in Figur e I . Part I lays out t he business challenge, Part I I describes t he t ools you need t o m eet t his challenge, and t he rem aining part s explain supply chain m anagem ent at t hree levels: operat ions, planning, and design. These last t hree part s all have t he sam e st ruct ure, wit h one chapt er each on dem and, supply, and perform ance. This com m on st ruct ure provides a unique nine- chapt er m at rix for underst anding and solving supply chain problem s. At t he back of t he book, you'll find sources for t he fact s cit ed in t he t ext , som e suggest ed readings, and a glossary of com m on t erm s.

Figure I. Organization of the Book

I assum e you're busy and don't have a lot of t im e for reading, so I use som et hing I call t he fast t rack t o help you absorb t he m at erial quickly. As you can see from t his page, t he fast t rack sum m arizes t he key point of every paragraph. This is t he fift h book I 've writ t en using t his t echnique since I developed it 15 years ago, and I cont inue t o use it because loyal readers all over t he world have t hreat ened t o shoot m e if I don't . I n fact , m any m anagers have t old m e t hat t he best t hing about m y books is t hey don't act ually have t o read t hem —t hey get everyt hing t hey need by skim m ing t he fast t rack and looking at t he drawings. I 'm never sure whet her t o be flat t ered or offended by t his observat ion, but t here it is.

Feel free t o j um p around in t he book. Part I is an execut ive briefing on chainbased com pet it ion; if all you need is t he big pict ure, here it is. Part I I is an int roduct ion t o supply chain t ools; depending on your needs, you can st udy it , skim it , or skip it . The m at rix organizat ion of t he rem aining part s allows you t o t ackle t he m at erial in slices, reading Part I V t o learn about planning, say, or Chapt ers 7, 10 , and 13 for a t our of dem and m anagem ent . Like all t echnical disciplines, supply chain m anagem ent has developed j argon t o help pract it ioners com m unicat e wit h each ot her and keep out siders at bay. To ease your way int o t he subj ect , I use specialized t erm s only as necessary and keep abbreviat ions t o a m inim um . But I also want t o give you a working vocabulary in t he subj ect , so I do int roduce t he appropriat e t erm s as t hey com e up, set t ing t hem in bold t ype and defining t hem in t he glossary. An excellent way t o furt her your underst anding of supply chains is t o experience t hem direct ly using sim ulat ion m odels of t he sort described in t his book. I f you'd like t o see supply chains in act ion, direct your browser t o w w w .su pplych a in gu ide .com . The sit e also offers in- dept h discussions of advanced t opics, reviews of books about supply chains, and links t o soft ware vendors, service providers, and ot her online resources. You can also find m y current e- m ail address t here if you would like t o drop m e a not e about t he book or ask m e a quest ion about supply chains. Dav id A. Tay lor , Ph.D. San Mat eo, Califor nia May 2003

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About the Cover The im age on t he cover of t his book is Kroeber Series # 39 by phot ographer Jay Dunit z. I n 1980 Dunit z discovered a j um ble of discarded m et al scraps left t o t he elem ent s by sculpt ure st udent s at t he Universit y of California at Berkeley. By rearranging t hese pieces and phot ographing t hem in bright sunlight , Dunit z creat ed t ransient sculpt ures of his own t hat are now preserved in his book Pacific Light , published by Beyond Words in 1989. This is t he t hird book by David Taylor t o be graced by an im age from t he Kroeber Series, t he first being his int ernat ionally acclaim ed Obj ect Technology: A Manager's Guide. When asked about his love of t hese im ages, Dr. Taylor explains: " My goal in writ ing is t o ident ify t he m ost powerful ideas, arrange t hem in t he way t hat best reveals t heir underlying st ruct ure, and convey t hem wit h as m uch clarit y as possible. I can't im agine a bet t er graphic realizat ion of t hat goal t han t he im ages of Jay Dunit z."

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Part I: Challenges

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Part I. Challenges

Chapter 1. The New Competition Th e w a y you m a n a ge t h e su pply ch a in ca n m a k e or br e a k you r com pa n y. Som e of t h e m ost spe ct a cu la r bu sin e ss su cce sse s ove r t h e pa st 2 0 ye a r s h a ve com e fr om fin din g m or e e ffe ct ive w a ys t o de live r pr odu ct s t o con su m e r s, bu t t h e r e h a ve be e n som e m a j or w r e ck s a lon g t h is sa m e r oa d. I t 's a h igh - st a k e s ga m e , a n d you don 't h a ve a lot of ch oice a bou t pla yin g; if you r com pa n y t ou ch e s a ph ysica l pr odu ct , it 's pa r t of a su pply ch a in a n d you r su cce ss h a n gs on t h e w e a k e st lin k of t h a t ch a in . W h y? Be ca u se t h e n a t u r e of com pe t it ion is sh ift in g a w a y fr om t h e cla ssic st r u ggle be t w e e n com pa n ie s. Th e n e w com pe t it ion is su pply ch a in vs. su pply ch a in .

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Chapter 1. The New Competition

The Thrill of Victory Siem ens CT of Forchheim , Germ any, m akes com put ed t om ography X- ray m achines for hospit als and diagnost ic labs all over t he world. The m achines cost about half a m illion dollars apiece and t hey are cust om - built for each cust om er. Four years ago, Siem ens CT found it self faced wit h rising cost s and price erosion t hat t hreat ened it s posit ion in t his lucrat ive m arket . The group's response was t o com plet ely reinvent t he way t hey provision, assem ble, and deliver t heir product s. They cut out t wo layers of m iddle m anagem ent , swit ched t he ent ire com pany t o t eam st ruct ures, aligned incent ives wit h supply chain success, and let creat ivit y run ram pant . Am ong ot her changes, t he t eam s t ight ened t he links wit h suppliers, elim inat ed all int erim warehousing, adopt ed j ust - in- t im e product ion t echniques, and swit ched t o airfreight deliveries for cust om ers out side of Europe. Today, Siem ens CT has an award- winning supply chain t hat set s a new st andard for best pract ices in it s indust ry. Lead t im e for t heir cust om - built m achines is down from 22 weeks t o j ust 2 weeks. The rat e of on- t im e deliveries has gone from 60% t o 99.3% , and on t im e now m eans t hat deliveries occur wit hin a t wo- hour window—an im pressive feat for a delivery t hat requires closing off a st reet and hauling in a crane. The cost of achieving t hese st ellar result s? Zero: These gains in perform ance were accom panied by a 40% reduct ion in invent ory, a 50% reduct ion in fact ory workspace, a 76% reduct ion in assem bly t im e, and a 30% reduct ion in t ot al cost s. The com pany also m anaged t o double it s out put t o 1,250 m achines a year wit hout increasing it s head count . Siem ens' st unning success would be hard t o m at ch, but t he com pany is not alone it it s willingness t o reinvent t he supply chain. At t he end of t he 1990s, t he Gillet t e Com pany, a $9 billion supplier of consum er goods, found it self losing m arket share because of escalat ing cost s. I n January 2000 it creat ed a new kind of operat ing group, com bining purchasing, packaging, logist ics, and m at erials m anagem ent in a single organizat ion wit h t he aut horit y t o com plet ely rework it s supply chain. Over t he course of t he next 18 m ont hs, t he group reduced t he t ot al invent ory in t he chain by 30% , elim inat ing 40 days' wort h of m at erials cost ing $400 m illion. The supply chain organizat ion believes t hat it is j ust now get t ing up t o speed, but it has already saved t he com pany $90 m illion.

Supply chain vict ories like t hese m ake for excit ing news, but t here is not hing new in t he t echniques t hese com panies applied. At t he end of t he 1980s, Chrysler Corporat ion was on t he ropes, ending t he decade wit h a fourt hquart er loss of $664 m illion. Desperat e for a way out of it s financial m orass, t he com pany decided t o experim ent wit h som e of t he t echniques being used by Japanese car m akers. Just as Siem ens CT and Gillet t e would do a decade lat er, Chrysler form ed cross- funct ional t eam s bringing t oget her design, engineering, m anufact uring, procurem ent , m arket ing, and finance, and it gave t hose t eam s t he aut horit y t hey needed t o reinvent t he supply chain. The t eam s cut t he supplier base in half, brought t he rem aining suppliers in on t he design of a new generat ion of cars, and developed long- t erm relat ionships based on t rust rat her t han coercion. I nst ead of ham m ering suppliers on price as it had in t he past , Chrysler asked for suppliers' help in finding ways t o save t he carm aker m oney. More surprisingly, t he com pany offered t o split t he savings wit h t he suppliers rat her t han asking t hem t o pass all t he savings on t o Chrysler. Chrysler called it s sharing program t he supplier cost reduct ion effort , or SCORE. The com pany announced SCORE in 1990 t o a highly skept ical supply base. But once suppliers realized t hat t his wasn't a t rick—t hat Chrysler was serious about part nering wit h it s suppliers and sharing t he winnings—t he ideas cam e flooding in. By 1995, t he com pany had im plem ent ed 5,300 ideas suggest ed by suppliers, for a net annual savings of $1.7 billion. The cost of developing a new vehicle dropped by as m uch as 40% , and t he t im e required for t he developm ent process fell from 234 weeks t o 160 weeks. At t he sam e t im e, Chrysler's profit per vehicle leapt from an average of $250 in t he m id1980s t o $2,110 in t he m id- 1990s, an increase of 844% . Chrysler isn't t he only com pany t hat st aved off disast er by revam ping it s supply chain. I n 1997 Apple Com put er was losing $1 billion a year and was on t he verge of bankrupt cy. The m ost visible change t he com pany m ade was t o bring back St eve Jobs, but it was radical surgery on it s supply chain t hat act ually saved t he com pany. Am ong ot her changes, Apple killed off 15 of it s 19 product s, adopt ed j ust - in- t im e product ion t echniques for t hose t hat rem ained, overhauled it s sales forecast ing syst em , and began a relent less effort t o m inim ize invent ory. Wit hin t wo years, t he com pany went from holding a m ont h's wort h of invent ory, wit h a value of $437 m illion, t o a few days' wort h, valued at j ust $25 m illion. I nvent ory went down by 94% , gross m argins went up by 40% , and Apple is st ill in business t oday. Speaking of st ill being in business, Am azon.com I nc., one of t he few surviving dot - com s, announced it s first - ever profit as of t he fourt h quart er of 2001. This profit was not so m uch a vindicat ion of t he e- com m erce m odel as it was t he result of an int ensive, yearlong effort t o fix t he com pany's sloppy supply chain. The problem s had been so bad t hat 12% of incom ing invent ory was rout ed t o t he wrong st orage locat ion, result ing in a great deal of wast ed t im e and energy as t he com pany scram bled t o t rack down it s own goods. A year lat er, aft er inst alling bet t er invent ory cont rols, t he com pany had t hat figure down t o 4% —far from perfect , but no longer crippling. Am azon also st art ed com bining it s shipm ent s t o gain econom ies of scale, sending 40% of t hose shipm ent s out in full t ruckloads and driving t hem direct ly t o dest inat ion cit ies. The result s: an 18% reduct ion in invent ory, rem oving $31 m illion wort h of idle m erchandise from Am azon's books, and a 17% reduct ion in fulfillm ent expenses, for a furt her savings of $22 m illion. These savings m ay be sm all com pared t o t he preceding exam ples, but Am azon's $5 m illion net profit clearly wouldn't have been possible wit hout t hem .

The vict ories achieved by Siem ens, Gillet t e, Chrysler, Apple, and Am azon illust rat e t he t rem endous im pact of supply chain perform ance on t he cost of doing business. These savings are vit ally im port ant , and m anagers know t his well: Cost reduct ion is t he num ber- one reason t hat com panies init iat e supply chain im provem ent s. But t here's an even bigger opport unit y here: Supply chain im provem ent s are good for t he bot t om line, but t hey can be even bet t er for t he t op line. Get t ing t he supply chain right can give a com pany a t rem endous com pet it ive advant age, and som et im es t hat advant age is enough t o overt urn an ent ire indust ry st ruct ure. The shining exam ple of t his kind of vict ory is t he way Dell Com put er syst em at ically dism ant led t he rest of t he personal com put er indust ry. Prior t o Dell, personal com put ers were m anufact ured in volum e, shipped t o ret ail st ores, and sold individually t o cust om ers—pret t y m uch like washing m achines, t elevisions, and ot her appliances. I t worked, but it required m assive am ount s of invent ory, and cust om ers were lim it ed t o a relat ively sm all set of configurat ions. Dell changed all t hat by adopt ing a direct sales st rat egy, building every PC t o order, and shipping it direct ly t o t he cust om er ( Figur e 1.1) . I nit ially a m ail- order house, Dell was one of t he first t o recognize t he pot ent ial of t he I nt ernet , selling it s first com put ers on line in 1996. Four years lat er it was doing $50 m illion a day from it s Web sit e alone. I n 2001, Dell becam e t he largest producer of personal com put ers in t he world, a posit ion it surrendered only briefly aft er t he m erger of t he form er m arket leaders, HP and Com paq.

Figure 1.1. Dell's Supply Chain Strategy

I t 's com m on knowledge t hat Dell's success was built on a com binat ion of direct sales wit h build- t o- order product ion, but Dell wasn't t he first PC com pany t o t ry t his st rat egy. What really m akes t he com pany so successful is t he way it execut es t he st rat egy. Dell is absolut ely relent less about pulling t im e and cost out of it s supply chain. Suppliers are locat ed right next t o Dell's assem bly plant s, and t hey deliver a const ant st ream of com ponent s on a j ust - in- t im e basis. Monit ors are shipped direct ly from t he com panies t hat m ake t hem and m erged in t ransit wit h Dell's own shipm ent s, arriving in m at ching Dell boxes in a single cust om er delivery ( as shown in Figure 1.1 ) . The com pany has

forecast ing and planning down t o a science, and it enj oys t he financial advant age of a negat ive cash- t o- cash t im e—it act ually get s paid for it s product s before it buys t he com ponent s. The perfect ion of t echniques such as t hese gives t he com pany a full five percent age point s of profit advant age over it s com pet it ors, a virt ually unassailable advant age in what is now alm ost a com m odit y m arket . Supply chains are as old as com m erce, but t he opport unit ies t hey now present are wit hout precedent . Modern m anufact uring has driven so m uch t im e and cost out of t he product ion process t hat t here is only one place left t o t urn for com pet it ive advant age. As business- engineering guru Michael Ham m er recent ly put it in his new book The Agenda, t he supply chain is t he last unt apped vein of business gold. The exam ples in t his sect ion m ake it clear t hat t his vein runs deep, but no one knows j ust how m uch gold is in t here because t he real pot ent ial of supply chains is j ust now being discovered. Today, supply chain m anagem ent is far m ore im port ant t han m anufact uring as a core com pet ence; so m uch so t hat it 's possible, as Nike and Cisco Syst em s have am ply dem onst rat ed, t o dom inat e t he m arket for a product wit hout owning so m uch as a single fact ory. The fut ure of supply chains looks bright indeed.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 1. The New Competition

The Agony of Defeat Cut t ing- edge supply chains are double- edged swords. Wielded wit h skill, t hey can slice open new m arket s. I m properly handled, t hey lead t o deep, selfinflict ed wounds. For all t he advant ages t hat can com e from get t ing t he supply chain right , get t ing it wrong can be cat ast rophic. By t he end of t he 1990s, Km art Corporat ion's supply chain was crippling it s abilit y t o m at ch t he prices offered by Wal- Mart and Target , and in t he discount ret ail business price is everyt hing. Worse, when t he com pany did m anage t o lure back cust om ers wit h it s Blue Light specials, t he product s weren't in t he st ores when people cam e in t o buy t hem ; t he supply chain couldn't deliver t hem in t im e for t he sale, even wit h plent y of advance warning. Km art was floundering, and it decided t hat it needed new t echnology t o solve it s problem s. I n May of 2000, t he com pany announced an unprecedent ed $1.4 billion invest m ent in soft ware and services t o overhaul it s supply chain, including warehouse m anagem ent soft ware from EXE Technologies and planning syst em s from i2 Technologies. A year and a half lat er, before t he syst em s ever went live, Km art announced t hat it was abandoning m ost of t he soft ware it had purchased and t aking a $130 m illion writ e- off. What went wrong? Nearly everyt hing, it seem s, but t he com pany did adm it t o a lack of clarit y about it s st rat egy, saying it needed t o ret hink it s supply chain st rat egy first before im plem ent ing it s syst em s. This was t he right idea, but it seem s t o have arrived lat e and left early. Not long aft er t he writ e- off, Km art announced t hat it was buying $600 m illion wort h of warehouse m anagem ent soft ware from Manhat t an Associat es, and t hat t his purchase would solve it s problem s. Perhaps in a furt her effort t o t ake som e pressure off it s supply chain, Km art also announced t hat it was closing 250 st ores. The com pany is now in bankrupt cy. Even com panies t hat once got it right can st ill t o get it wrong. Aft er years of success wit h it s SCORE program , Chrysler com plet ed t he fam ous " m erger of equals" t hat led t o Daim lerChrysler. Like t he m erger it self, t he SCORE program quickly degenerat ed, and relat ionships wit h suppliers soured. The com pany has now resort ed t o dem anding unilat eral price reduct ions from suppliers in order t o st ave off m ount ing losses. Chrysler's m om ent in t he sun has passed. Nike, t he virt ual ent erprise t hat becam e t he world's largest shoe com pany, has

also m anaged t o get it self int o t rouble wit h it s supply chain. I n February of 2001, t he com pany announced t hat it had lost $100 m illion in sales t he previous quart er because of snafus in it s supply chain. The debacle cam e right aft er t he com pany went live wit h i2 Technologies' planning syst em . Aft er a year of inst allat ion work, Nike decided it was t im e t o t hrow t he swit ch, and t he new syst em im m ediat ely creat ed havoc across t he chain. Nike blam ed i2, wit h t he chairm an com plaining t o analyst s, " This is what we get for our $400 m illion?" ( quot ed in Com put er w or ld; see t he Not es on Sources) . The vendor, in t urn, com plained t hat Nike had pushed t he syst em int o service t oo quickly and had required t oo m any cust om izat ions. Whoever is t o blam e, bot h com panies lost big. Nike's st ock dropped 20% t he day it m ade t he announcem ent , and i2's fell 22% t hat sam e day. Even Cisco Syst em s, t he paragon of supply chain m anagem ent , is capable of t he occasional m isst ep. I n May of 2001, t he com pany report ed t hat it had t o writ e off som e invent ory as unusable—t o t he t une of $2.2 billion, t he largest invent ory writ e- down in t he hist ory of business. The problem st em m ed from a breakdown in com m unicat ion up t he supply chain (Figure 1.2 ) . Cisco was com pet ing for large cont ract s in a boom ing m arket for I nt ernet hardware. Having no product ion capacit y of it s own, Cisco passed all it s ant icipat ed dem and direct ly on t o it s cont ract m anufact urers. Those cont ract ors added t his t o t he dem and t hey saw com ing from Cisco's com pet it ors, som e of which were bidding on t he sam e business, and each cont ract or looked at t he dem and independent ly, leading t o double and t riple count ing of t he sam e dem and. The result : Com ponent suppliers worked overt im e t o fill orders t hat were never placed, and Cisco wound up holding t he bag.

Figure 1.2. Cisco's $2 Billion Blunder

As t hese exam ples illust rat e, supply chain failures can be devast at ingly expensive. But t here is an even bigger price t o be paid t han t he im m ediat e im pact on cash flow. Nike and i2 bot h lost a fift h of t heir m arket value t he day Nike went public wit h it s problem s. The size of t hese drops is except ional, but t heir occurrence is not . A recent st udy conduct ed at Georgia Tech exam ined

m ore t han a t housand news report s of supply chain problem s bet ween 1989 and 1999, looking t o see whet her t hese report s had an im pact on st ock prices. The answer t hey got was a resounding yes: Com panies report ing problem s suffered an average drop in t heir st ock price of 7.5% t he day of t he announcem ent . When t he researchers exam ined t he prices six m ont hs before and aft er t he announcem ent , t hey discovered t hat t he prices act ually began t o fall well before t he announcem ent , suggest ing t hat t he bad news had a t endency t o leak, and t he prices showed no signs of recovering aft er t he fact ( Figure 1.3 ) . The t ot al drop over 12 m ont hs was 18.5% .

Figure 1.3. The Market Reaction to Supply Problems

These percent age drops are obviously large, but t he full im pact is bet t er conveyed by act ual valuat ions. On t he day of t he announcem ent , t he average drop in shareholder value for t he com pany m aking t he announcem ent was $143 m illion. Over t he course of a year, t he average loss was m ore t han $350 m illion. But even t his figure underest im at es t he t ot al loss because prices were rising at 15% per year during t hat period, so t he real im pact m ay be nearly t wice t he calculat ed am ount . But even at t he m ost conservat ive calculat ions and considering only t he one- day loss, t he researchers conclude t hat t he 1,131 supply chain problem s t hey exam ined in t heir st udy caused a loss of m ore t han $160 billion in shareholder value. Clearly, t he m arket doesn't react well t o supply chain failures. The st udy also revealed t hat invest ors don't really care who caused t he problem . When t he report ing com pany accept ed t he blam e for t he incident , it s st ock dropped 7.1% . When it blam ed it s suppliers, it s st ock dropped 8.3% . And when it blam ed it s cust om ers—usually for changing t heir requirem ent s during t he lead t im e—t he com pany's st ock dropped 10.9% . The m essage is clear: I f a com pany report s a problem wit h it s supply chain, it 's going t o get ham m ered in t he st ock m arket , regardless of who's at fault . I f anyt hing, point ing t he finger at a t rading part ner only increases t he punishm ent .

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 1. The New Competition

A High Stakes Game Why does get t ing t he supply chain right have such a big im pact on success? Because t he st akes are so high: Holding and m oving m erchandise is a very expensive proposit ion. Collect ively, U.S. com panies spend a t rillion dollars a year on t heir supply chains, j ust under 10% of t he nat ion's GDP. About a t hird of t his cost is for holding invent ory and t he rest is for m oving it around, wit h a bit of change left over for adm inist rat ion. As large as t hese figures m ay seem , t hey used t o be subst ant ially higher, t ot aling about 15% of GDP at t he beginning of t he 1980s. Deregulat ion of t he t ransport at ion indust ry coupled wit h invent ory reduct ions brought t he t ot al down t o 10% by t he early 1990s, and it has rem ained st able at t hat level ever since. The sam e percent age holds good for individual com panies, which spend an average of j ust under 10% of t heir gross incom e on supply chain funct ions. What is st riking about t he figures for individual com panies is t he t rem endous advant age t hat som e com panies have over ot hers in t his regard. A recent survey of supply chain cost s across a variet y of indust ries yielded an average of 9.8% of revenue devot ed t o supply chains, a perfect m at ch t o t he overall value. But t he survey also revealed t hat t he t op quart ile—t he 25% best perform ers—had an average cost of j ust 4.2% of revenue. These com panies spend less t han half as m uch on t heir supply chains as t he com pet it ion, giving t hem a full five- point advant age in profit s. Cont inuing surveys reveal t hat t he gap is not closing, but widening. The m essage is clear: I f your com pany is on t he wrong side of t he supply chain gap, t he sooner it m akes t he leap t he bet t er. Act ually, t he advant age is m ore dram at ic t han t hese figures m ight suggest , because in business a penny saved isn't really a penny earned. Depending on profit m argins, it is usually closer t o a nickel or a dim e. Suppose you're running a com pany wit h $100 m illion in sales, 10% supply chain cost s, and a 10% gross profit , as shown in t he first panel of Figure 1.4 . How could you increase your overall profit by 50% ? One way is t o increase sales by 50% , as shown in m iddle panel of t he figure. The ot her way is t o im it at e t he best - in- class com panies and bring your supply chain cost s down t o 5% , as shown in t he last panel. At t he level of gross m argins, t his $5 m illion savings is t he equivalent of $50 m illion in addit ional sales. This is not t o suggest t hat you wouldn't prefer t o get t he profit from growt h rat her t han cost reduct ions. But t he fact t hat a 5% reduct ion in cost s can produce t he sam e increase in profit s as a 50%

increase in sales is cert ainly a valuable insight .

Figure 1.4. Supply Chain Costs and Profit

Here is a real- world, albeit anonym ous, illust rat ion of how supply chain savings t ranslat e int o profit s. A m aj or elect ronics com pany found t hat it had $500 m illion in excess invent ory. I t s carrying cost s were 50% of t he purchase price, so it was paying $250 m illion a year t o hold t he ext ra m at erial. Given t he com pany's profit m argin of 10% , it would need $2.5 billion in addit ional earnings t o equal t he bot t om - line benefit of elim inat ing t hat excess invent ory. I n t he ret ail sect or, where profit m argins of 2% are com m on, t he im pact of savings in t he supply chain can be even m ore dram at ic. Wit h m argins t hat t hin, reducing supply chain cost s from 10% t o 8% —st ill nowhere near best - in- class perform ance—can increase profit s as m uch as doubling sales. Given t he enorm ous st akes involved, t he pressure t o pull t im e and cost out of t he supply chain is becom ing relent less, and t he dem ands are only going t o increase as everyone get s bet t er at t he gam e. I n addit ion t o t he financial drivers, several ot her fact ors are com bining t o put pressure on supply chains, including short er product life spans, fast er product developm ent , rising globalizat ion of sourcing, increasing dem and for cust om izat ion, and int ensive qualit y init iat ives such as t he Six Sigm a program . Given t he challenges involved in get t ing t he supply chain right , t his m ay not be a gam e you are eager t o play, but nobody get s t o pass on t his one. Every com pany t hat t ouches a product is part of a supply chain, and every com pany t hat is part of a supply chain has t o deal wit h t hese problem s sooner or lat er. The only choice you have is whet her t o t ackle t he problem now or wait unt il it t ackles you.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 1. The New Competition

The New Competition Very few com panies are prepared t o handle t he new pressures being placed on t heir supply chains. A recent survey of execut ives in m anufact uring com panies found t hat 91% of t hem ranked supply chain m anagem ent as eit her " very im port ant " or " crit ical" t o t he success of t heir com panies. Yet m ost acknowledged t hat t hey had problem s wit h t heir chains, and only 2% regarded t heir chains as excellent . When asked about t heir st rat egies t o im prove t heir chains, 59% report ed t hat t heir com pany had no st rat egy at all. Think about t his for a m om ent : By t heir own report s, t hese m anagers realize t hat get t ing t he supply chain right is essent ial, and t hey know t hey haven't done it yet , but m ost haven't even form ulat ed a st rat egy for at t acking t he problem . I t would be nice t o say t hat t hese result s are unusual, but t he sam e pat t ern shows up in survey aft er survey: Com panies realize t hat t hey are in t rouble wit h t heir supply chains, but t hey don't really underst and t he problem s, m uch less know how t o fix t hem . Why so helpless? There are lot s of reasons, but t he root cause seem s t o be t his: No one in t he com pany is responsible for running t he supply chain. Engineering designs t he product , m arket ing set s prices and runs prom ot ions, sales cut s deals wit h cust om ers, purchasing negot iat es wit h suppliers, m anufact uring cont rols t he invent ories, logist ics arranges t ransport at ion, account ing handles t he cash flow, and so on. All t he key act ivit ies t ake place in different groups wit h different agendas and conflict ing goals. Worse yet , m ost of t hese groups go all t he way up t o t he CEO before t hey com e under com m on m anagem ent . And t he CEO is not t he right person t o be planning and operat ing t he supply chain. Given t his level of disorganizat ion, it 's hardly surprising t hat supply chains are out of cont rol. The am azing t hing is t hat t hese chains funct ion at all. Clearly, t he first st ep t oward regaining cont rol is t o assem ble t he key decision m akers from each group and get t hem working t oget her t o find solut ions. Did you not ice t hat all t he supply chain successes described in t he first sect ion of t he chapt er st art ed out by form ing a t eam t o t ake responsibilit y for t he chain? That 's no coincidence: Cross- funct ional t eam s are a recurring t hem e in com panies t hat run good supply chains. The m ost successful com panies usually go furt her by designat ing a single t op- level execut ive who has full responsibilit y for t he chain. Even if a com pany get s it s act t oget her and form s a crack supply chain t eam ,

it 's st ill not ahead of t he gam e. Today, t he very nat ure of com pet it ion is changing, and it 's not an easy change t o absorb. Ever since t he I ndust rial Revolut ion, t he bat t les have been com pany against com pany, and t he weapons have been t he t echniques of product ion. Today, t hat gam e is largely played out . Good design, efficient product ion, and qualit y const ruct ion, while not yet universal, have becom e t he basic qualificat ions for m aking it int o t he t op ranks. Am ong t he serious players, it 's now t he supply chain t hat m akes t he difference bet ween winning and losing. Think about it t his way. From t he consum er's point of view, supply chains are irrelevant . All t he hardball negot iat ions about price and t erm s, all t he careful synchronizat ion of deliveries, all t he delays and t he scram bling t o keep product s m oving down t he chain—none of t hese t hings m at t er t o consum ers. Most of t hem don't even know what a supply chain is, m uch less appreciat e t he problem s of running one. I n t he ordinary course of event s, t he only m em ber of t he chain consum ers ever see is t he ret ailer, and t heir only sense of what lies upst ream is sum m arized in t he not ion of a brand. For t hem , it all boils down t o who can sell t hem t he best product at t he best price. From an individual com pany's point of view, t his is hardly fair. Should a m anufact urer be punished because a dist ribut or runs out of st ock? Should a ret ailer lose sales because a producer has a qualit y problem ? But t his isn't about fairness; it 's about winning a new kind of com pet it ion. Like it or not , t he fat es of all t he m em bers of a supply chain are becom ing increasingly j oined. The new com pet it ion is no longer com pany vs. com pany; it 's supply chain vs. supply chain. I f t he m em bers of a chain can work t oget her t o put t he m ost qualit y in t he consum er's hands at t he lowest price, t hey win. I f not , t hey lose. Figure 1.5 illust rat es t his point by showing how a supply chain t hat is consist ent ly cost - effect ive across t he chain can out perform chains t hat are superior t o it in any one link.

Figure 1.5. Competing Supply Chains

Cast in t his light , t he conflict ing agendas and polit ical infight ing am ong funct ional depart m ent s seem like m inor problem s. The real challenge isn't get t ing your own people t o work as a t eam ; it 's get t ing all t he com panies in

your supply chain t o form a larger t eam t hat can play and win t he new com pet it ion. But how do you even approach a problem of t his scale? I s vert ical int egrat ion t he answer? Will t he t echniques of supply chain collaborat ion do t he t rick? I s buying m ore soft ware t he solut ion? This book is here t o answer t hese quest ions, but I 'll give you a quick preview: Probably not , not likely, and no way. The new com pet it ion is a m aj or upheaval t hat is affect ing every aspect of how com panies organize and operat e. The required shift in t hinking is so great —and t he danger of not m aking t he t ransit ion is so serious—t hat t he Nat ional Research Council com m issioned a st udy t o art iculat e t he problem and help prepare Am erican m anufact urers t o m eet t he challenge. Their conclusion was t hat we are in t he m idst of a fundam ent al revolut ion in t he nat ure of business, one t hat , in t heir words, " has t he pot ent ial t o alt er t he m anufact uring landscape as dram at ically as t he I ndust rial Revolut ion." I f you want t o t hrive in t his new landscape, you have t o underst and how supply chains work—and how you can m ake t hem work bet t er. Th e ch a lle n ge of m a st e r in g you r su pply ch a in m a y be da u n t in g, bu t it 's n ot in su r m ou n t a ble . D e ll, W a l- M a r t , a n d ot h e r su pply ch a in le a de r s didn 't su cce e d be ca u se t h e y fou n d a m a gic for m u la or w e r e m a n a ge d by bu sin e ss ge n iu se s. Th e y su cce e de d be ca u se t h e y u n de r st ood t h e cor e pr oble m s of su pply ch a in s, com m it t e d t h e m se lve s t o lon g- t e r m solu t ion s r a t h e r t h a n qu ick fix e s, a n d h a d t h e st a m in a t o st ick w it h t h ose solu t ion s u n t il t h e y w or k e d. I ca n 't h e lp you w it h t h e st a m in a pa r t , bu t I ca n e x pla in t h e pr oble m s a n d sh ow you h ow t o fin d t h e be st solu t ion s. Th e n e x t ch a pt e r k ick s off t h a t pr oce ss by e x pla in in g h ow su pply ch a in s w or k a n d w h y t h e y ca n be so difficu lt t o m a n a ge .

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Part I. Challenges

Chapter 2. The Rules of the Game Su pply ch a in m a n a ge m e n t is a difficu lt ga m e t o m a st e r . I t r e qu ir e s you t o m ove a gr e a t m a n y pie ce s in ve r y spe cific w a ys, a n d you h a ve t o ch or e ogr a ph t h ose m ove s t o m a k e e a ch pie ce a r r ive in t h e r igh t pla ce a t t h e r igh t t im e . I t 's a lso a ga m e t h a t pla ys ou t on a gr a n d sca le , w it h a pla yin g fie ld t h a t spa n s t h e e n t ir e pla n e t . For t u n a t e ly, t h e r u le s of t h e ga m e —t h e de scr ipt ion s of t h e pie ce s a n d t h e w a ys t h e y m ove —a r e sim ple e n ou gh t o be su m m a r ize d in a fe w pa ge s. I n a n u t sh e ll, su pply ch a in s con sist of pr odu ct ion a n d st or a ge fa cilit ie s con n e ct e d by t r a n spor t a t ion la n e s, a n d t h e y e x ist t o su ppor t t h e flow of de m a n d, su pply, a n d ca sh . Th e difficu lt y of m a n a gin g su pply ch a in s com e s pr im a r ily fr om t h e com ple x it y t h a t cr e e ps in t o t h e ir st r u ct u r e a n d t h e va r ia bilit y t h a t ch a r a ct e r ize s t h e ir flow s. I t 's t h is com ple x it y a n d va r ia bilit y t h a t m a k e a n e a sy ga m e h a r d t o m a st e r .

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 2. The Rules of the Game

Facilities and Links A su pply ch a in is basically a set of facilit ies connect ed by t ransport at ion lanes. Figure 2.1 illust rat es one slice of t he supply chain t hat brought you t his book. Fa cilit ie s, shown as rounded rect angles in t he illust rat ion, generally fall int o one of t wo cat egories, depending on t heir prim ary funct ion: pr odu ct ion fa cilit ie s and st or a ge fa cilit ie s. Tr a n spor t a t ion la n e s, shown as arrows, are cat egorized by t heir m ode of t r a n spor t a t ion; t hey include roadways, railways, wat erways, sea lanes, air lanes, and pipelines. Viewed in t he largest cont ext , supply chains ext end from t he original e x t r a ct or s of raw m at erials, such as m ines and farm s, t o t he ult im at e con su m e r s of finished goods, t he people who act ually put t hose goods t o t heir int ended purpose.

Figure 2.1. From Tree to Book

Facilit ies cont ain cont rolled quant it ies of m at erials called in v e n t or ie s (Figur e 2.2) . Product ion facilit ies hold invent ory in t hree different form s: Ra w m a t e r ia ls in ve n t or y consist s of m at erials ready for use in product ion; w or k in - pr oce ss ( W I P) in ve n t or y includes all t he m at erials current ly being worked on; and fin ish e d goods in ve n t or y holds com plet ed product s ready for shipm ent . St orage facilit ies vary: W a r e h ou se s usually cont ain only a single kind of invent ory, but dist r ibu t ion ce n t e r s t hat do final assem bly cont ain all t hree kinds. Cr oss dock s, which are used only t o t ransfer goods bet ween t rucks, do not cont ain any separat ely m anaged invent ory. Ret ail st ores also

vary in t his regard: Cust om bicycle shops have all t hree t ypes of invent ory, warehouse- st yle st ores cont ain only one, and som e appliance st ores carry none at all.

Figure 2.2. Three Kinds of Inventory

Lanes are used t o m ove invent ory bet ween facilit ies along a part icular m ode of t ransport at ion, using a com binat ion of vehicles and cont ainers. Som e vehicles, such as t ruck t ract ors and railway engines, can be decoupled from t heir cont ainers, whereas ot her vehicles, such as delivery vans and t anker ships, have t he cont ainer built in. Decoupling is an im port ant considerat ion because it offers m ore flexibilit y in rout ing, dispat ching, t em porary st orage, and ot her t ransport at ion act ivit ies. I n t he case of pipelines, t he funct ions of t he vehicle and t he cont ainer are m erged wit h t he lane it self, wit h pum ps providing t he m ot ive force and pipes cont aining t he invent ory in t ransit . Each m ode of t ransport at ion offers a unique m ix of speed, cost , availabilit y, and capabilit y. For exam ple, shipping by air is fast , expensive, available from all large cit ies, and lim it ed t o sm all and light weight packages. By cont rast , shipping by sea is slow, cheap, available only at cit ies wit h port s, and virt ually unlim it ed wit h regard t o size and weight . There are also different volum e t rade offs wit hin each m ode. I n t rucking, it is m uch cheaper t o send fu ll t r u ck loa d ( FTL) sh ipm e n t s t han it is t o use le ss- t h a n - t r u ck loa d ( LTL) sh ipm e n t s, and t he FTL opt ion offers t ight er cont rol over t he rout ing and t im ing of t he shipm ent . However, using FTL shipm ent s requires building up m ore finished goods invent ory and m ay cause delays in shipm ent s. Sim ilar t radeoffs apply in t he ot her m odes. Shipping wit hin a lim it ed geographical region norm ally uses a single m ode from source t o dest inat ion. For larger dist ances, including m ost int ernat ional t rade, shipm ent s generally use t wo or m ore m odes, a pract ice known as in t e r m oda l t r a n spor t a t ion. For exam ple, a shipm ent m ight t ravel by rail t o t he nearest seaport , cross t he ocean by ship, and t ravel t he rest of t he way by t ruck. I nt er- m odal shipm ent s are usually enclosed in st eel cargo cont ainers t hat can be t ransferred bet ween specially fit t ed rail cars, cont ainer ships, and

t r act or - t r ailer s. Like facilit ies, t ransport at ion lanes cont ain invent ory. This in - t r a n sit in ve n t or y bridges t he gap bet ween t he shipping facilit y's finished goods invent ory and t he receiving facilit y's raw m at erials invent ory ( Figure 2.3 ) . I nt ransit invent ory is different from ot her form s in t hat it is unavailable for use, is at higher risk of loss from t heft and accident s, and is subj ect t o delays due t o vehicle breakdown and lane congest ion. Along wit h raw m at erials, work in process, and finished goods, in- t ransit invent ory represent s t he fourt h m aj or t ype of invent ory.

Figure 2.3. Inventory in Transit

The dist inct ion bet ween in- t ransit invent ory and t he t wo invent ories it connect s is oft en blurred in pract ice. Trailers or railcars are frequent ly used t o st ore finished goods at product ion facilit ies unt il full loads are produced, in which case t he goods are st ill part of t he plant 's finished goods invent ory. But if t he st orage is brief and t he dest inat ion of t he goods is det erm ined by t he choice of cont ainers, t he goods in t he cont ainer m ay be t reat ed as invent ory in t ransit as soon as t hey are loaded. Sim ilar issues com e up at t he dest inat ion, where full cont ainers m ay sit for days or weeks in a yard before being unloaded. I n one rat her perverse pract ice, railway cars are act ually kept on t he m ove, circling in wide arcs around a facilit y, unt il t here is space t o park t hem in t he yard. This is a very expensive way t o hold invent ory. Alt hough t hey don't m ake use of a separat e t ransport at ion m edium , pa ck a ge ca r r ie r s such as UPS and FedEx are com m only viewed as a dist inct m ode when m aking t ransport at ion decisions. I n realit y, t hese ca r r ie r s use a m ix of air and highway t ransport t o deliver t heir packages, using t heir own fleet s of aircraft and t rucks. As a pract ical m at t er, however, it doesn't m at t er how a package is conveyed because t hat decision is out of t he shipper's hands, so using a package carrier is viewed as an alt ernat ive on a par wit h shipping by air, land, or wat er. The t radeoffs discussed for t he ot her m odes also apply t o package carriers: They are fast , relat ively expensive, available in m ost locat ions, and lim it ed t o relat ively sm all, light weight product s.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 2. The Rules of the Game

Demand, Supply, and Cash The essent ial goal in m anaging a supply chain is t o achieve an orderly flow of goods from ext ract ors t o consum ers. I t should not be surprising, t hen, t hat t he deepest root s of t he discipline can be found in t ransport at ion m anagem ent , which is responsible for m oving finished goods t o t he next link in t he chain. Over t im e, t ransport at ion m anagem ent m erged wit h a relat ed funct ion, m at erials m anagem ent , t o form t he broader discipline of logist ics, which handles t he flow of m at erials all t he way from suppliers t hrough t he t hree int ernal invent ories and out t o cust om ers. What dist inguishes t he current discipline of su pply ch a in m a n a ge m e n t ( SCM ) from it s predecessors is t hat it is equally concerned wit h t wo ot her flows: t he flow of dem and and t he flow of cash up t he chain, as shown in Figure 2.4 . Wit hout t hese ot her flows, t he goods would never m ove: I t 's dem and t hat provides t he im pet us for t hat m ovem ent , and it 's cash t hat provides t he m ot ivat ion. The great insight of supply chain m anagem ent is t hat t he key t o m anaging t he flow of goods effect ively lies in synchronizing all t hree flows. This synchronizat ion becom es part icularly difficult when, as shown in t he " st ack" not at ion in Figure 2.4 , t here can be any num ber of organizat ions at each link of t he chain.

Figure 2.4. Three Basic Flows

The basic operat ion of a supply chain could hardly be sim pler. Dem and flows up t he chain and t riggers t he m ovem ent of supply back down t he chain. As supplies reach t heir dest inat ions, cash flows up t he chain and com pensat es suppliers for t heir goods. Nat urally, t he behavior of real- world supply chains is never quit e t his sim ple. But recognizing t he fundam ent al elegance of supply chain dynam ics provides t he best foundat ion for underst anding t he com plexit ies t hat inevit ably arise. Wit h a few except ions, such as oil m oving t hrough a pipeline, t he t hree flows in a supply chain are discret e rat her t han cont inuous. That is, t hey m ove in dist inct " packet s" t hat convey part icular quant it ies at part icular t im es. Dem and is norm ally conveyed t hrough orders, supply t hrough shipm ent s, and cash t hrough paym ent s ( Figure 2.5 ) . A great deal of supply chain m anagem ent is concerned wit h balancing t he t radeoffs bet ween t he size and t he frequency of t hese packet s. For exam ple, econom ies of scale favor infrequent orders of large quant it ies of m at erial, whereas reducing invent ory carrying cost s requires m ore frequent shipm ent s of sm aller quant it ies. For any given rat e of flow, t he sm aller t he packet s becom e, t he closer t he chain com es t o operat ing as a cont inuous flow rat her t han m oving discret e lum ps of dem and, supply, and cash across t he chain.

Figure 2.5. Packets of Demand, Supply, and Cash

As Figure 2.5 illust rat es, each exchange of dem and, supply, or cash t akes place bet ween a cu st om e r and a su p p lie r. I n t his book, t hese t erm s refer t o t he part ies involved in a t ransact ion across any link of t he chain, regardless of t heir locat ion wit hin t he chain. I n ot her words, I use t he t erm s in a relat ive rat her t han an absolut e sense, t he way t he t erm s buy er and seller are used in discussing a purchase. This is a com m on usage for t hese t erm s but it 's not universal; m any writ ers use t he t erm cust om er t o refer t o t he ult im at e consum er of t he goods, and ot hers use t he t erm supplier only for upst ream m em bers of t he chain who provide basic m at erials or assem blies. I avoid confusion in t his book by always using t he t erm s in t he relat ive sense, but you should be aware of t he inconsist ent usage in ot her discussions. Be part icularly alert t o t he differences in t he way various aut hors use t he t erm s cust om er and consum er ; t he m uddling of t hese concept s oft en leads t o point less diat ribes

about who t he " real" cust om er is. Orders t rigger t he flow of goods, but , depending on t he product ion st rat egy, t hey m ay or m ay not t rigger t heir im m ediat e product ion by a supplier ( Figur e 2.6) . I n t he m a k e - t o- st ock st rat egy, a supplier m akes product s in advance of dem and and holds t hem in finished goods invent ory, sat isfying dem and from t hat invent ory as orders com e in. I n t he m a k e - t o- or de r st rat egy, t he supplier doesn't build a product unt il it has an order in hand. There is also an int erm ediat e st rat egy, a sse m ble - t o- or de r , in which a product is part ially built in advance of dem and, but final assem bly is post poned unt il an order is received. Som e com panies use a m ix of t hese t hree t echniques, but choose one as t heir prim ary st rat egy. For exam ple, Sony uses m ake- t o- st ock, Boeing uses m ake- t o- order, and Dell uses assem ble- t o- order.

Figure 2.6. Three Strategies for Production

The choice of product ion st rat egy has a m aj or im pact on t he dynam ics of a supply chain. Wit h t he classic m ake- t o- st ock st rat egy, invent ory is produced in advance of and " pushed" down t he chain t oward consum ers so t hat it will be on hand when t hey go t o buy it . This st rat egy relies on dem and forecast s t o det erm ine how m uch invent ory t o build and where t o hold it . Wit h m ake- t oorder product ion, invent ory is " pulled" down t he chain by im m ediat e orders. Forecast s are less im port ant wit h m ake- t o- order because t here is no danger of m aking t oo m uch or t oo lit t le invent ory, t hough long- t erm forecast s are im port ant t o set t ing t he correct levels of m anufact uring capacit y. These dynam ics are oft en used t o charact erize supply chains as eit her p u sh ch a in s or pu ll ch a in s, but in realit y every chain is a m ixt ure of push and pull. As long as consum ers have a choice about what product s t hey buy and when t hey buy t hem , t he last link in t he chain is always a pull link. At t he ot her end of t he chain, t he ext ract ion of raw m at erials from t he eart h alm ost always occurs in advance of dem and for finished product s. I n effect , consum ers pull

and ext ract ors push. Som ewhere in bet ween t he t wo is t he pu sh - pu ll b ou n d a r y (Figure 2.7 ) , t he point at which t he flow of goods swit ches from being pulled by consum ers t o being pushed by ext ract ors. I n t he case of t he assem ble- t o- order st rat egy, for exam ple, t he push- pull boundary is locat ed at t he final assem bly plant .

Figure 2.7. The Push-Pull Boundary

Act ually, t he push- pull dist inct ion applies t o every link in t he chain, so it 's possible for any link t o operat e in pull m ode even t hough it is up in t he push region of t he chain. Ford's supply chain is a push chain right down t o t he dealer showroom , but it cont ains m any links t hat are pure pull. For exam ple, Johnson Cont rols builds a seat from raw m at erials and delivers it t o Ford wit hin four hours of receiving an order, allowing t he com pany t o supply seat s t o Ford based on firm orders for specific configurat ions. I n t he cont ext of a m assive supply chain involving t ens of t housands of com panies building against ant icipat ed dem and, Johnson Cont rols is able t o supply t his part icular com ponent on a pull basis. Of t he t hree prim ary flows in supply chains, cash flow is t he one t hat receives t he least at t ent ion. This is underst andable: Supply chains exist t o m ove product s t o consum ers, and orders are t he m echanism for t riggering t hat m ovem ent . But cash is t he ult im at e driver for t he ent ire process; t ake it out of t he equat ion and t he whole business would com e t o a halt pret t y quickly. Yet cash flow perform ance is t he worst of t he t hree, wit h producers rout inely t aking m ont hs t o pay suppliers for goods t hat were shipped wit hin days of being ordered. This sit uat ion is now changing, and accelerat ing t he flow of cash is com ing t o be recognized as a key elem ent of supply chain excellence. I n addit ion t o t he t hree key flows, t here is som et hing else t hat m oves across t he chain: inform at ion. Act ually, inform at ion is already im plicit in t he t hree flows: Orders represent inform at ion about im m ediat e dem and, som e product s can be t ransm it t ed as inform at ion, and even cash can be exchanged in t he form of inform at ion. But t he m ore int erest ing kind of inform at ion isn't part of t he act ual t ransact ions—it is exchanged in order t o facilit at e t hose t ransact ions. This inform at ion includes dem and forecast s, product ion plans, prom ot ion announcem ent s, and report s of all kinds. Unlike t he t hree basic flows, inform at ion can m ove across t he chain at any t im e, wit hout being part of a part icular t ransact ion, and it isn't const rained t o m ove sequent ially up or down

t he chain. I nst ead, it can be broadcast sim ult aneously t o any subset of t he chain, ensuring t hat t hey are all operat ing wit h t he sam e inform at ion at t he sam e t im e ( Figure 2.8 ) .

Figure 2.8. Information Broadcasting Across the Chain

One of t he great insight s int o t he behavior of supply chains is t hat inform at ion can oft en be subst it ut ed for invent ory. I nst ead of requiring every m em ber of t he chain t o m aint ain sa fe t y st ock t o buffer against uncert aint y in dem and, t hat uncert aint y can be reduced by sharing inform at ion t hat helps m em bers ant icipat e com ing changes in t he flows of dem and, supply, and cash. I nform at ion is usually far cheaper t han invent ory, and it has t he advant age t hat it can be in m any places at t he sam e t im e. The result : Subst it ut ing inform at ion for invent ory is a key t echnique for im proving supply chain perform ance and will be a cont inuing t hem e of t his book.

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Chapter 2. The Rules of the Game

Distribution and Procurement Alt hough t he basic elem ent s of supply chains can be com bined in an infinit e variet y of ways, t here are t wo basic pat t erns t hat account for m ost of t he st ruct ure. To see t hese pat t erns, consider how a supply chain looks from t he perspect ive of a single plant . Every facilit y downst ream of t hat plant is a dest inat ion for it s finished goods and form s part of t he plant 's dist r ibu t ion n e t w or k . Every facilit y upst ream is a source of supplies, and form s part of it s pr ocu r e m e n t n e t w or k . These t wo net works of t he supply chain are radically different from t he plant 's perspect ive. Som e plant s ship only t o a single dest inat ion, but t his is rare. The norm al pat t ern is for each plant t o serve as m any dest inat ions as necessary t o sat isfy dem and wit hin a part icular geographical region. These dest inat ions, in t urn, m ay ship t he goods onward t o st ill m ore dest inat ions, and so on, unt il t he product s event ually reach t heir ult im at e consum ers ( Figure 2.9 ) . The successive layers of t his supply chain pat t ern are com m only referred t o as e ch e lon s, and t hey are num bered out ward from t he plant as shown in Figur e 2.9.

Figure 2.9. Echelons in Distribution

The business problem addressed by t his port ion of a supply chain is dist ribut ion, which is basically a m at t er of choreographing t he flow of finished goods from t he plant t o consum ers in a way t hat sat isfies dem and in a cost effect ive m anner. When m ult iple echelons are under t he cont rol of a single com pany, dist ribut ion m anagers oft en t ry t o m aint ain an orderly dist ribut ion net work by using only t he links shown in Figure 2.9 ; t hat is, shipm ent s are not norm ally allowed t o skip echelons, and each dest inat ion receives shipm ent s from only one facilit y in t he echelon above it . Alt hough t hese const raint s sim plify t he m anagem ent of a dist ribut ion net work, t hey do not produce t he m ost cost - effect ive solut ions. Const raint s on dist ribut ion pat t erns are now being relaxed as m ore sophist icat ed t ools becom e available for designing and operat ing dist ribut ion syst em s. As you m ight expect , t he difficult y of m anaging dist ribut ion goes up dram at ically as t he num ber of dest inat ions increases. Wit h m ore facilit ies t o serve, t he available invent ory has t o be divided m ore finely, increasing t he risk of not having t he right am ount of product at any one facilit y. I n addit ion, t he t im e and expense of handling t he goods increases wit h each echelon. On t he ot her hand, t ransport at ion cost s go down wit h m ore echelons because product s can t ravel m uch of t he dist ance in larger, m ore econom ical shipm ent s. Finding t he right balance bet ween t hese opposing forces is one of t he key t radeoffs in dist ribut ion design. Looking upst ream , j ust t he opposit e pat t ern is observed. Alt hough it is possible for a plant t o obt ain all of it s supplies from a single source, t his rarely happens. Ordinarily, t he plant receives supplies from m ult iple sources, each of which receives it s supplies from m ult iple sources, and so on, up t o t he point where t he raw m at erials are obt ained direct ly from ext ract ors ( Figure 2.10) . The successive layers of t his supply chain pat t ern are called t ie r s. Like echelons, t iers are num bered out ward from t he plant .

Figure 2.10. Tiers in Procurement

The business funct ion support ed by t his port ion of a supply chain is procurem ent , which involves choreographing t he flow of raw m at erials and subassem blies from t heir suppliers t o t he plant in a t im ely, cost - effect ive m anner. As shown in t he illust rat ion, procurem ent net works t end t o be less orderly t han dist ribut ion net works, wit h overlapping sources being t he rule rat her t han t he except ion. Like dist ribut ion, procurem ent becom es m ore difficult t o m anage as t he num ber of sources increases. The essence of successful procurem ent is having everyt hing arrive as close t o a product ion dat e as possible wit hout paying m ore t han is necessary t o achieve t hat end. Sim ply by t he laws of chance, t he m ore suppliers involved, t he m ore likely it is t hat at least one of t hem will m iss it s delivery dat e and delay a product ion run. I n addit ion, t he cost of placing orders and m aking paym ent s goes up wit h t he num ber of suppliers, as does t he overhead of m anaging t he addit ional relat ionships. As wit h echelons on t he dist ribut ion side, adding t iers on t he procurem ent side also increases t he t ot al t im e and expense required t o bring product ion m at erials t o t he plant . The basic dist ribut ion and procurem ent pat t erns described in t his sect ion can t ake on a wide variet y of configurat ions. Most im port ant , t he sources and dest inat ions m ay t hem selves be plant s, each of which has it s own dist ribut ion and procurem ent net work. When t here are m ult iple layers of plant s, t he dist ribut ion and procurem ent pat t erns overlap and t he dist inct ion bet ween t hem blurs. For any one plant , t he pict ure is reasonably clear, but for t he supply chain as a whole, it can becom e quit e com plicat ed. An im port ant considerat ion in analyzing supply chains is ident ifying ownership boundaries. A sequence of facilit ies owned by t he sam e com pany m akes up it s in t e r n a l su pply ch a in, and t he links out side of t he ownership boundary are it s e x t e r n a l su pply ch a in (Figure 2.11) . I nt ernal supply chains oft en run m ore sm oot hly t han ext ernal chains because t hey can be cent rally cont rolled, and no buying and selling are required t o m ove t he goods. One of t he big advant ages of t he classic st rat egy of ve r t ica l in t e gr a t ion, in which a single com pany owns as m uch of t he supply chain as it can acquire, is t hat it pit s an int ernal supply

chain against t he com pet it ion's harder- t o- m anage ext ernal chains.

Figure 2.11. Internal and External Supply Chains

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 2. The Rules of the Game

Complexity and Variability The basic elem ent s of supply chains—t he st ruct ures, dynam ics, and pat t erns described in t he preceding sect ions—are sim ple. Yet , as illust rat ed by t he exam ples in Chapt er 1, real- world supply chains are not oriously difficult t o m anage, and t hey are liable t o cat ast rophic failure. This cont rast bet ween principle and pract ice invit es a crucial quest ion: Where does t he difficult y com e from ? Underneat h t he m any sym pt om s and t heir im m ediat e causes, t here appear t o be t wo root causes t o t he difficult y of m anaging supply chains: com plexit y and variabilit y. This last sect ion of t he chapt er t akes t he m easure of each. The com plexit y begins wit h t he way t he t hree prim ary flows relat e t o one anot her. I n principle, it 's sim ple—orders t rigger shipm ent s, and shipm ent s t rigger paym ent s. I n pract ice, t he relat ionship of orders t o shipm ent s and paym ent s quickly becom es t ort uous (Figure 2.12) . A single product ion run generat es orders t o m any different suppliers, and t hese orders are usually com bined wit h orders for ot her product ion runs t o achieve econom ies of scale in purchasing.

Figure 2.12. Orders, Shipments, and Payments

The shipm ent s fulfilling t hese orders m ay furt her com bine orders t o reduce t he cost of t ransport at ion, but large orders m ay also be split across t wo or m ore shipm ent s, and backordered it em s are oft en sent in st ill lat er shipm ent s. I nvoices usually cover m ult iple shipm ent s, paym ent s m ay cover m ult iple invoices, and so on. The sim ple linkages am ong t he t hree basic flows are quickly obscured by t hese groupings and regroupings. Anot her source of com plexit y is t he way supply chains are m anaged, wit h different groups handling each of t he t hree basic flows ( Figure 2.13) . On t he cust om er side of a t ransact ion, orders m ight be placed by a cent ralized purchasing depart m ent , shipm ent s received by various local assem bly plant s, and paym ent s m ade by a regional account ing depart m ent . On t he supplier side, orders m ight be received by sat ellit e sales offices, shipm ent s m ade from regional dist ribut ion cent ers, and paym ent s received by t he account ing office of a parent firm . All of t hese groups operat e according t o different —and all t oo oft en, deeply incom pat ible—agendas, and no one group is responsible for t he out com e of t he ent ire t ransact ion.

Figure 2.13. Different Groups Handling the Flows

Com plexit y is also creat ed by t he proliferat ion of docum ent s associat ed wit h orders. For each purchase order generat ed by a cust om er, a corresponding sales order is generat ed by t he supplier—despit e t he fact t hat t he m aj orit y of t he inform at ion in t he t wo docum ent s is ident ical—and bot h m ust be m at ched against any governing cont ract s t o m ake sure all of t heir t erm s are being honored. Each shipm ent result ing from t he order requires it s own docum ent at ion, including pa ck in g slips, bills of la din g, a dva n ce sh ippin g n ot ice s, and t he like, and t he billing and paym ent cycle generat es yet anot her t rail of paper. All of t hese docum ent s m ust reference t he cont rolling purchase and sales orders, and all t he m appings am ong t he docum ent s m ust ( or should) be carefully t raced so t hat bot h com panies are cert ain t hat what was ordered was shipped, and t hat what was shipped was paid for. And t hese are j ust t he docum ent s t hat flow bet ween t he com panies; t he num ber of docum ent s required wit hin each com pany can be m uch larger. Yet anot her source of com plexit y is t he st ruct ure of t he chain it self. The ideal supply chain is neat ly organized int o echelons and t iers, as described in t he preceding sect ion, and all t ransact ions follow an orderly subset of links. I n pract ice, t hese layered pat t erns are oft en obscured by a m aze of ad hoc links and sequences t hat are crucial t o t he operat ion of t he chain but m ake it very difficult t o underst and, m uch less m anage. This is rarely by design; m ost chains are never act ually designed. Rat her, t hey evolve over t im e t hrough a series of independent decisions—open a plant here, add four m ore suppliers for a com ponent over t here, shut down t his warehouse inst ead of refurbishing it , and so on—few of which t ake t he " big pict ure" int o account . The second core challenge of supply chains is coping wit h variabilit y. No m at t er how well m anaged, all business act ivit ies exhibit nat ural variabilit y in t heir durat ion, qualit y, and ot her at t ribut es. Daily sales, delivery t im es, product ion yields, defect rat es, m aint enance t im es, and a t housand ot her aspect s of supply chains all vary around som e average value. For som e purposes, it is sufficient t o know t his average and plan for it . But real- world supply chains don't ever " see" average values; what t hey deal wit h every day are t he act ual values t hat m ake up t hose averages. The m ore variabilit y t here is in t hose values, t he m ore difficult and expensive it is t o run t he chain.

A great deal of supply chain m anagem ent is devot ed t o coping wit h t his variabilit y. I nvent ories of finished goods act , in part , as a buffer against variabilit y in dem and, and raw m at erial invent ories offer com parable prot ect ion against variabilit y in supply. Case in point : An audit of a m aj or ret ailer found it needed $200 m illion in safet y st ock j ust t o cover variabilit y in it s vendors' deliveries—a very expensive way t o com pensat e for poor reliabilit y. Redundant sources, such as alt ernat e suppliers and t ransport at ion opt ions, provide furt her prot ect ion against variat ion in t he availabilit y of m at erials and services. The list is long: Qualit y assurance program s at t em pt t o reduce t he variabilit y in product qualit y, forecast ing at t em pt s t o predict variat ion in dem and, and so on. All of t hese effort s have som e value in t he at t em pt t o cope wit h variabilit y, but each ext ract s it s own cost s. Supply chains are part icularly vulnerable t o t he effect s of variabilit y because t hey involve long sequences of int erdependent act ivit ies. A relat ively sm all delay in an upst ream process, for exam ple, can cascade down t he ent ire supply chain, t hrowing off product ion schedules and disrupt ing any num ber of deliveries. Sim ilarly, variat ion in t he level of supply for upst ream com ponent s relat ive t o downst ream dem and can wreak havoc on a chain, as t he elect ronics indust ry graphically illust rat es wit h it s sporadic chip short ages. Just as variabilit y in supply can am plify down t he chain, variabilit y in dem and can am plify back up t he chain ( Figure 2.14) . The classic exam ple of t his de m a n d a m plifica t ion is a st udy conduct ed by Proct er & Gam ble in t he early 1990s t o invest igat e peculiar fluct uat ions in t he dem and for raw m at erials used in it s Pam pers brand of diapers. These fluct uat ions puzzled t he com pany because babies generally go t hrough diapers at a fairly const ant rat e. Sure enough, a check of sales showed only m inor, random variat ions in t he ret ail sales of Pam pers. I t t urns out t hat t hese sm all variat ions were being am plified up t he supply chain, producing large swings at t he level of raw m at erials. The causes of t his effect —which P&G dubbed t he bu llw h ip e ffe ct —are now well underst ood and easily count ered ( see Chapt er 13 ) , but dem and am plificat ion cont inues t o be a serious problem in m any chains.

Figure 2.14. Demand Amplification

The problem s associat ed wit h com plexit y and variabilit y are bot h exacerbat ed by scale. I n t he early st ages of indust rializat ion, supply chains consist ed m ost ly of local com panies working t oget her t o bring goods t o m arket , and com plex

m appings am ong t he t hree flows were not serious im pedim ent s t o com m erce. Today, wit h supply chains including t housands of com panies spanning t he ent ire planet , com plexit y and variabilit y have devast at ing effect s on bot h t he efficiency and effect iveness of t he supply process. The reasons for t his are not subt le; it 's a sim ple m at t er of m echanics. As t he num ber of cont ribut ors t o a finished product goes up, t he likelihood of errors and delays inevit ably escalat es, and t he ensuing disrupt ions becom e increasingly severe wit h each addit ional link in t he chain. Supply chains aren't likely t o get any sm aller in t he years t o com e, but bot h com plexit y and variabilit y can be great ly reduced. The com plexit y of m odern supply chains is ult im at ely a self- inflict ed wound, t he product of business pract ices t hat dat e back t o t he I ndust rial Revolut ion. Alt hough variabilit y it self is a fact of life, t here is a ready arsenal of weapons t o prevent it from at t acking supply chains. The real business challenge doesn't lie in com plexit y and variabilit y t hem selves, but in t he failure t o recognize t he havoc t hey wreak on supply chains and m ake t he necessary correct ions. I f you underst and t he im port ance of at t acking t hese problem s, and choose your weapons carefully, you can beat t hem . Th a t w a s a w h ir lw in d t ou r of su pply ch a in s, bu t it ga ve you a qu ick look a t t h e m a j or la n dm a r k s a n d sh ow e d you t h e la y of t h e la n d, w h ich sh ou ld h e lp you k e e p you r be a r in gs a s you e x plor e t h is r e gion fu r t h e r . M or e im por t a n t , you n ow u n de r st a n d t h e fu n da m e n t a l pr oble m s of su pply ch a in s a n d a r e r e a dy t o se e h ow t h e y ca n be solve d, w h ich is t h e su bj e ct of t h e t h ir d a n d fin a l ch a pt e r of Pa r t I .

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Part I. Challenges

Chapter 3. Winning as a Team I f com ple x it y a n d va r ia bilit y a r e w h a t m a k e su pply ch a in m a n a ge m e n t a h a r d ga m e t o m a st e r , t h e n t h e be st t a ct ics a r e t h ose t h a t le a d t o sim plicit y a n d st a bilit y. I n de e d, m ost of t h e in n ova t ion s in su pply ch a in m a n a ge m e n t ove r t h e pa st 2 0 ye a r s h a ve a t t e m pt e d t o bot h sim plify a n d st a bilize t h e flow of de m a n d, su pply, a n d ca sh . Th e se in n ova t ion s in clu de t h e e x t e n sion of j u st - in - t im e m a n u fa ct u r in g t e ch n iqu e s ou t t o t h e su pply ch a in , plu s a va r ie t y of spe cia lize d pr ogr a m s for m a n a gin g t h e r e ple n ish m e n t of r e t a il in ve n t or ie s. Un for t u n a t e ly, t h e ga in s pr odu ce d by t h e se pr ogr a m s h a ve oft e n com e a t t h e e x pe n se of ot h e r lin k s in t h e ch a in , a n d t h a t doe sn 't im pr ove t h e com pe t it ive n e ss of t h e ch a in a s a w h ole . A br ie f look a t ga m e t h e or y r e ve a ls w h y t h e se pr ogr a m s a r e fa llin g sh or t a n d poin t s t h e w a y t o t h e w in n in g st r a t e gy: in t e gr a t in g t h e m e m be r s of t h e su pply ch a in in t o a sm oot h ly fu n ct ion in g t e a m by m a k in g su r e t h a t e ve r y m e m be r 's w in con t r ibu t e s t o t h e su cce ss of a ll t h e ot h e r s.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 3. Winning as a Team

JIT Supply Programs Of t he m any effort s t o im prove t he flow of raw m at erials int o product ion facilit ies, m ost have involved ext ending t he reach of t he j u st - in - t im e ( JI T) m a n u fa ct u r in g m et hod upst ream t oward suppliers. One of t he key elem ent s of t he JI T approach is elim inat ing excess invent ory t hroughout t he product ion process by t im ing t he m ovem ent of m at erials t o each workst at ion t o arrive j ust at t he m om ent t hey are needed for t he next operat ion. This pract ice m inim izes invent ories t hroughout t he product ion process, helping m anufact uring com panies reduce holding cost s, m inim ize obsolescence, and im prove t heir ret urn on asset s. These benefit s have led t o t he widespread adopt ion of JI T t hroughout indust ries t hat use repet it ive product ion t echniques. Of t he t hree invent ories held in product ion facilit ies, t he work- in- process ( WI P) invent ory is m ost easily reduced using JI T. But WI P is usually t he sm allest and least expensive of t he invent ories, and t ackling t he ot her t wo requires changing t he way suppliers deliver raw m at erials and cust om ers receive finished goods. I n order t o bring down t he invent ory of raw m at erials, JI T producers work wit h t heir suppliers t o swit ch over from large shipm ent s of m at erials t hat go t o cent ral receiving facilit ies t o sm all, frequent shipm ent s t hat go direct ly from t rucks t o t he fact ory floor ( Figure 3.1 ) . The change is a dram at ic one, oft en t aking a com pany from m ont hly orders and shipm ent s t o m ult iple shipm ent s a day wit h precisely t im ed arrivals. Most JI T producers have a sim ilar program on t he out bound side, using sm all, frequent deliveries t o m inim ize t heir invent ory of finished goods.

Figure 3.1. Just-in-Time Supply

As soon as m anufact urers begin t o m ake t hese kinds of changes, JI T quickly expands from a product ion init iat ive t o a m uch broader program t hat requires syst em at ic changes in supply chain m anagem ent . Toyot a, t he com pany t hat pioneered t he JI T m et hod in t he 1970s, was keenly aware of t his aspect of it s program , and it worked closely wit h it s suppliers t o convert t heir operat ions t o JI T as well, precisely coordinat ing t he flow of goods from suppliers t o product ion plant s. I n order t o support t he close relat ionship required by t his new kind of product ion, Toyot a used a uniquely Japanese form of j oint part nership, called a k e ir e t su , wit h it s key suppliers. I n Toyot a's case, t he keiret su involved t aking a 20% t o 50% equit y posit ion in each supplier and replacing 20% of it s key execut ives wit h Toyot a personnel. JI T pract ices offer im port ant insight s int o how supply chains can be im proved. Alt hough t he apparent focus of JI T is on reducing invent ory, t he t rue spirit of t he m et hod is a syst em at ic pursuit of qualit y, one aspect of which is elim inat ing any unnecessary com plexit y. I n t he case of supply chain t ransact ions, t his philosophy has led t o a m uch needed st ream lining of t he order- shipm ent paym ent cycle. I nst ead of accum ulat ing large orders m ixing m any different kinds of m at erials, producers place m any orders for individual m at erials, oft en paying for t hese m at erials on delivery rat her t han accum ulat ing lum p sum s. I n addit ion, a great deal of docum ent at ion has been st ripped away. For exam ple, t radit ional orders are oft en elim inat ed in favor of cont inuously updat ed delivery schedules, and billing docum ent s m ay be elim inat ed alt oget her. One of t he great cont ribut ions of JI T t o supply chain m anagem ent is t o provide a clear dem onst rat ion of j ust how sim ple t he basic flows can becom e. Along wit h reducing com plexit y, t he JI T philosophy of qualit y also seeks t o reduce variabilit y in every st age of product ion. To t his end, each operat ion is analyzed, refined, and rehearsed unt il it can be com plet ed bot h quickly and consist ent ly. I n t he case of supply chains, t his level of rigor not only accelerat es t he m ovem ent of goods, it also adds an unprecedent ed level of precision t o deliveries. This precision allows invent ories of raw m at erials t o be reduced t o a fract ion of t heir norm al levels wit hout causing shut downs on t he line. Of course, not every form of variabilit y can be elim inat ed, and herein lies t he downside of JI T: I t can m ake supply chains so fragile t hat any int errupt ion in t he flow of supplies brings t he ent ire chain t o a halt . Toyot a learned t his in 1997 when a fire at one of it s suppliers shut down Toyot a's product ion lines for an ent ire week. The following year, st rikes in t wo GM part s plant s led t o t he shut down of alm ost all of t he com pany's assem bly plant s wit hin a m at t er of

days. A year lat er, seven Daim lerChrysler plant s and t hree GM plant s were forced int o half- shift s when flooding in one supplier's plant creat ed a short age of a single part . Aft er t he t errorist at t acks of Sept em ber 11, 2001, m any plant s in t he Unit ed St at es had t o be closed due t o breakdowns in t he t ransport at ion syst em . Ford, for exam ple, shut down five Nort h Am erican plant s due t o part s short ages, m any of t hem due t o delays in bringing t rucks across t he Canadian bor der . Shut downs such as t hese can quickly wipe out t he savings associat ed wit h reduced invent ory levels. For a large m anufact urer, having a plant shut down can cost as m uch as $10,000 a m inut e. Given t his kind of financial im pact , m any firm s t hat adopt ed JI T wholeheart edly are now ret hinking t heir posit ion and t aking a m ore conservat ive approach. Honda, for one, now has a policy of m aint aining dual suppliers for all it s raw m at erials. Ford, while reaffirm ing it s com m it m ent t o it s JI T program in t he wake of t he t errorist at t acks, im m ediat ely began developing plans t o st ockpile engines and ot her key part s at som e U.S. plant s. Even wit h appropriat e risk m anagem ent , JI T isn't t he right approach for every supply chain. I t doesn't work in j ob shops, which do not use product ion lines, and it 's not relevant t o process m anufact uring. Even wit hin it s nat ural dom ain, repet it ive product ion, it 's not a good choice for low- volum e product s or for product s wit h uncert ain dem and. But t hese are lim it at ions, not defect s; for t he right kind of product ion environm ent , JI T can lead t o dram at ic im provem ent s. More im port ant , however, is t he way t he JI T effort illust rat es how m uch can be done t o reduce com plexit y and variabilit y in supply chains. JI T's em phasis on sim plicit y and consist ency can be used t o advant age at every link of t he chain, regardless of whet her ot her aspect s of t he t echnique are em ployed.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 3. Winning as a Team

Retail Replenishment Programs The second m aj or class of supply chain program s deals wit h t he dist ribut ion side, and is concerned wit h replenishing ret ail invent ories. Hist orically, t he link bet ween ret ail st ores and t heir im m ediat e suppliers has been a difficult j unct ure in t he supply chain. I n t he past , ret ail invent ories were m anaged by independent st oreowners, who oft en lacked sophist icat ed t ools for forecast ing dem and and planning replenishm ent . Yet t his is precisely t he point in t he chain t hat can be t he hardest t o m anage because it is t he first point t o feel t he im pact of changing consum er preferences. I t is also t he point where t he chain becom es visible t o t he consum er, so it 's crit ical t o m anage it well. I f t he desired product isn't on t he shelf when a consum er walks in t o buy it , even t he m ost perfect sequence of supply operat ions is a failure. The first generat ion of ret ail replenishm ent program s was based on shift ing t he cont rol of invent ories ( Figure 3.2 ) . I n t he t radit ional arrangem ent , ret ailers m anage t heir own invent ories and replenish t hem as t hey see fit . The problem wit h t his arrangem ent is t hat producers are oft en in a bet t er posit ion t han ret ailers t o t rack em erging pat t erns in dem and. I n addit ion, producers can rem ove cost and uncert aint y from t his link in t he chain by cent ralizing cont rol of t he replenishm ent process. One way t o leverage t hese advant ages is con sign m e n t , in which producers ret ain bot h ownership and cont rol over invent ories of t heir product s at a ret ailer's sit e. Consignm ent has proved t o be an effect ive t ool for selling product s t hat ret ailers m ight not be willing t o carry on convent ional t erm s, but it 's not t he first choice for producers because t hey have t o wait longer before t hey get paid for t heir product s.

Figure 3.2. Inventory Management Relationships

A m ore recent developm ent , ve n dor - m a n a ge d in ve n t or y ( VM I ) , is shown in t he m iddle row of Figure 3.2 . The innovat ive aspect of VMI is t he way it separat es cont rol from ownership, bot h of which usually t ransfer at t he sam e t im e. I n VMI , a producer receives cont inuous updat es on a ret ailer's invent ory level and replenishes it as needed, wit h t he ret ailer t aking ownership of t he goods on delivery. This gives producers bet t er visibilit y of sales of t heir product s, helping t hem ant icipat e dem and and bet t er plan supply. The ret ailers benefit because t hey no longer have t o t rack invent ory levels or place orders for product s under a VMI program . They also save m oney because t hey usually need less invent ory, som et im es as lit t le as half of what t hey would ot herwise keep in st ock. I n addit ion t o VMI , several ot her program s have been developed t o sm oot h t he flow of goods t hrough ret ail st ores. One of t he earliest was t he q u ick r e spon se ( QR) program , an effort on t he part of t he apparel indust ry in t he 1980s t o com bine som e of t he t echniques of JI T wit h t echnologies for m onit oring invent ory levels in real t im e. As shown in Figure 3.3 , elect ronic poin t of sa le ( POS) syst em s aut om at ically capt ured dat a about clot hing sales as t hey occurred, t hen t ransm it t ed t his dat a t o producers using e le ct r on ic da t a in t e r ch a n ge ( ED I ) connect ions. Producers responded wit h daily shipm ent s of pre- t agged it em s t hat could go direct ly from t heir t rucks t o t he selling floor.

Figure 3.3. The Quick Response Program

I n t he lat e 1980s, t he apparel indust ry rolled out an ext ension of t he QR program known as con t in u ou s r e ple n ish m e n t ( CR) . As shown in Figure 3.4 , t his program incorporat ed VMI for bet t er invent ory cont rol, and it int roduced j oint forecast ing so t hat producers and ret ailers could pool t heir underst anding of consum er dem and t o bet t er predict fut ure sales. Anot her im port ant aspect of t his program was t hat a replenishm ent agreem ent act ed as a st anding purchase com m it m ent . This allowed m em bers of t he program t o elim inat e individual purchase orders alt oget her, furt her st ream lining t he replenishm ent pr ocess.

Figure 3.4. Retail Replenishment Programs

I n 1993, t he grocery indust ry launched it s own version of cont inuous replenishm ent , calling it t he e fficie n t con su m e r r e spon se ( ECR) program . ECR's m aj or cont ribut ion was t he addit ion of ca t e gor y m a n a ge m e n t , which organizes prom ot ion and replenishm ent act ivit ies around groups of product s t hat consum ers view as roughly equivalent in sat isfying t heir needs. This addit ion helps grocery st ores det erm ine t he best m ix of product s t o put on t heir shelves t o m ake sure t heir cust om ers' needs are m et even if t here are occasional short ages. This program also encourages t he use of act ivit y- based cost ing ( described in Chapt er 9) t o det erm ine t he profit abilit y of each product cat egor y . Like t he JI T program s described earlier, ret ail replenishm ent program s reflect a cont inuing effort t o sim plify and st abilize supply chain flows. For exam ple, t he elim inat ion of orders in cont inuous replenishm ent rem oved a m aj or source of t im e and cost t hat added no value t o t he end consum er. These program s also pioneered im port ant t echniques for coping wit h variabilit y, including som e t hat aren't em ployed in t he JI T effort . Most not ably, t he use of real- t im e dat a on sales allows ret ailers t o respond quickly t o variat ions in consum er buying pat t erns, and t he addit ion of j oint forecast ing allows ret ailers t o prepare for som e of t hese shift s before t hey hit t he st ores. The m ost am bit ious replenishm ent program t o dat e is colla bor a t ive pla n n in g, for e ca st in g, a n d r e ple n ish m e n t ( CPFR) , a m ult i- indust ry effort t hat was form alized in 1998 (Figure 3.5 ) . Alt hough CPFR is not a direct ext ension of any of t he preceding program s, it draws on t he experience gained

wit h all t hree. Being t he first clean- sheet design since t he com m ercializat ion of t he I nt ernet , CPFR abandons EDI and privat e net works in favor of I nt ernet com m unicat ion. I n addit ion t o t he direct com m unicat ion of real- t im e dat a, t rading part ners use cent ralized inform at ion servers t o view and updat e shared plans and forecast s.

Figure 3.5. The CPFR Program

I n short , t he CPFR program relies on advanced, I nt ernet - based t ools t o pool inform at ion about dem and and supply, allowing t rading part ners t o coordinat e t heir invent ory decisions and sm oot h t he flow of goods across t he chain. The use of such t ools offers im port ant advant ages, but it also requires com panies t o m ake subst ant ial invest m ent s in new t echnologies. Anot her obst acle is cult ural: CPFR requires com panies t o share highly det ailed inform at ion about t heir operat ions, and m any are reluct ant t o do t hat . CPFR is beginning t o win convert s, but it 's t oo soon t o t ell how widely t he program will be em braced.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 3. Winning as a Team

The Problem with Programs All of t he program s described in t his chapt er were int roduced wit h great fanfare, and t here are solid st at ist ics t o dem onst rat e t hat each of t hem has succeeded in reducing invent ories and accelerat ing t he flow of goods across t he chain. These glowing reviews are bolst ered by cont inuing report s in t he business press about t he rem arkable econom ies produced over t he past t wo decades t hrough t he relent less reduct ion of invent ory. There's j ust one problem wit h t hese im pressive result s: They m ay not be real. Last year, a t eam of researchers at Ohio St at e Universit y conduct ed a com prehensive analysis of t he invent ory levels report ed by U.S. corporat ions over t he past 20 years, and t hey reached a st art ling conclusion: The Great I nvent ory Reduct ion of t he lat e t went iet h cent ury never happened. The st udy did reveal a m odest overall decline in t ot al invent ory since 1980, but m ost of t hat was due t o a sm all num ber of indust ries t hat m ade st ruct ural changes in t heir supply chains. For exam ple, t he elim inat ion of dist ribut ors and ret ailers in t he direct sales m odel perfect ed by Dell, t oget her wit h ot her advanced supply chain t echniques, allowed t he com put er indust ry t o cut it s t ot al invent ories in half over t he 20- year period. These are t ruly im pressive gains, and t hey have cont ribut ed t o t he dram at ic reduct ions in prices wit hin t his indust ry. But for ot her indust ries, including t he t wo t hat have m ost ardent ly pursued ret ail replenishm ent program s—apparel and grocery—invent ory levels have rem ained absolut ely flat over t he life of t hose pr ogr am s. What 's going on here? Are t hese program s j ust a sham ? No; t he problem is subt ler t han t hat . The invent ory levels of t he com panies part icipat ing in t hese program s have, in fact , dropped, but it now appears t hat m ost of t hose reduct ions were achieved by displacing invent ory wit hin t he chain rat her t han act ually elim inat ing it . This m ay be good for t he com panies report ing success, but it 's hard on ot her m em bers of t heir chains, and it does not hing t o m ake t hose chains m ore efficient or com pet it ive overall. These program s m ay be int ended t o creat e a new level of cooperat ion in t he supply chain, bringing com panies t oget her as t rue t rading part ners, but , as oft en happens in business, t he benefit s of t hat cooperat ion appear t o accrue m ost ly t o t he dom inant part y. The renowned success of Wal- Mart in m ast ering it s supply chain provides a

good case in point . Through a variant of t he classic vert ical int egrat ion st rat egy, Wal- Mart has largely elim inat ed t he dist ribut ors, carriers, and ot her m iddlem en t hat used t o int ervene bet ween producers and ret ail out let s ( Figur e 3.6) . The scale of t his effort is st aggering: Wal- Mart 's t rucks carry 50 m illion pallet s of goods each week t o 500 m illion square feet of ret ail space t o serve 15 m illion cust om ers a day. Wit h econom ies of scale such as t hese, Wal- Mart has been able t o elim inat e a great deal of excess cost in it s supply chain. These efficiencies are reflect ed in t he nat ional dat a: Ret ail is one of t he few sect ors t hat has m ade dram at ic progress in reducing it s t ot al invent ory, neat ly paralleling t he rise of m ega- ret ailers such as Wal- Mart .

Figure 3.6. The Wal-Mart Model

Wal- Mart 's m assive scale also allows it t o dict at e t erm s t o m anufact urers, reversing t he hist orical dom inance of producers in t he supply chains for consum er goods. For exam ple, com panies t hat want access t o Wal- Mart 's vast ret ail channel have t o ship large volum es of goods t o m any different locat ions, m eet precise delivery schedules wit h high reliabilit y, and react inst ant ly t o changing levels of dem and t hroughout t he Wal- Mart em pire. These requirem ent s t ranslat e direct ly int o increased invent ories of finished goods, and t hat 's exact ly what t he dat a show. I n t he indust ries t hat serve m egaret ailers such as Wal- Mart , invent ories of finished goods have not j ust rem ained flat , t hey have act ually gone up over t he last 20 years. Of course, producers can com pensat e for t his pressure t o som e ext ent by st ream lining t heir int ernal operat ions and put t ing pressure on t heir own suppliers for m ore prom pt perform ance, reducing t heir invent ories of raw m at erials and work in process. And t hat 's j ust what t he dat a indicat e; it is reduct ions in raw m at erials and WI P invent ories t hat have kept t ot al invent ories from rising. Of course, increasing t he pressure on suppliers t o hold invent ory t o t he last m inut e and respond rapidly t o dem and signals requires t hem t o keep m ore finished goods on hand, and so on, up t he chain. I n short , t he dram at ic reduct ions in invent ory achieved at t he ret ail level have com e, in large part , from pushing invent ory up t he chain, not from t aking it out of t he chain. This pat t ern of pushing invent ory up t he chain is also found in JI T program s. Here again, requiring suppliers t o m ake precisely t im ed deliveries and respond rapidly t o changing consum pt ion reduces a producer's invent ory of raw m at erials at t he cost of forcing suppliers t o hold m ore finished goods t o buffer variabilit y in dem and. The st andard response t o t his problem is for t he

suppliers t o adopt JI T as well, but t hat only works if cust om ers and suppliers precisely synchronize t heir operat ions. When U.S. com panies first adopt ed JI T in t he 1980s, t hey som et im es found t hat t ot al invent ory cost s went up rat her t han down. The problem wasn't wit hin t he four walls: Bot h cust om ers and suppliers ran exem plary JI T shops, each keeping on- sit e invent ory t o a m inim um . The problem lay in t he link bet ween t hem . I n order t o handle coordinat ion problem s, com panies oft en kept invent ory in t hird- part y warehouses t o provide a buffer st ock ( Figure 3.7 ) . The invent ory hadn't been elim inat ed aft er all; it had j ust been m oved t o m ore expensive facilit ies.

Figure 3.7. Hidden Inventory in JIT

One im port ant difference bet ween program s at t he product ion level and t hose at t he ret ail level is t hat producers are in t he m iddle of t he chain rat her t han at t he end, so t hey have t he opt ion of pushing invent ory downst ream as well as upst ream (Figure 3.8 ) . Not surprisingly, t his is exact ly what happens. The best exam ple com es from t he aut om obile indust ry; having sort ed out m ost of t he supplier aspect s of JI T, U.S. aut o plant s now operat e wit h as lit t le as t hree hours of invent ory on hand. But t he invent ory of cars and t rucks sit t ing at dealerships now runs as high as t hree m ont hs' wort h of supply. JI T m ay be a success for t he aut om akers, but it isn't m aking t heir supply chains m ore efficient . Of all t he ways in which t he indust ry could hold invent ory, finished goods is by far t he m ost expensive form .

Figure 3.8. Producer Displacing Inventory

Viewed in t he larger cont ext of t rade relat ionships, t his pat t ern of pushing t he burden up and down t he chain rat her t han elim inat ing it alt oget her is not surprising. Alt hough adj acent m em bers of a supply chain are oft en called t rading part ners, m ore oft en t han not t his is a euphem ism t o draw at t ent ion away from a relat ionship t hat rem ains econom ically adversarial. No m at t er how m uch t hey m ay wish t o cooperat e, t he bot t om line is t hat t he m em bers of a supply chain are in com pet it ion wit h each ot her t o increase t heir share of t he consum er's dollar. When com pet it ion bet w een chains drives down prices, t he com pet it ion w it hin chains heat s up as each m em ber of t he chain t ries t o m aint ain it s profit m argins. I f t here is any im balance of power wit hin t he chain—and t here alm ost always is—t he profit s event ually gravit at e t o t he power players, and t he sm aller players have t o t ake what t hey can get . Supply chain relat ionships don't have t o be like t his. When com panies act as t rue t rading part ners, working t oget her t o pull t im e and cost out of t he chain, t hey can creat e a sit uat ion in which everyone m akes m ore m oney. Chrysler's SCORE program —at least in it s early years—was an excellent exam ple of how m uch can be achieved t his way. The com pany's $1.7 billion in savings didn't com e out of it s suppliers' hides; suppliers saved m oney right along wit h Chrysler. The savings cam e from finding bet t er ways t o build a car. What m ade t his program different is t hat SCORE fost ered t rue innovat ion rat her t han j ust escalat ing t he com pet it ion for a fixed am ount of m oney. The Ohio St at e researchers m ent ioned at t he beginning of t his sect ion reached t he sam e conclusion, based on t heir st udy of nat ional dat a. I n t heir words, " effort s t o increase efficiency t hrough t he exercise of power sim ply change t he locat ion of t he inefficiency." The only way t o get genuine im provem ent s is t o redesign t he supply chain t o increase it s efficiency as a whole. The idea of replacing com pet it ion bet ween t rading part ners wit h cooperat ion, creat ing win- win relat ionships, is so obvious and so oft en repeat ed t hat it no longer has m uch currency. At t em pt s t o build such relat ionships can and do succeed, but failure is t he m ore com m on result , and t oday's m anagers are right t o be suspicious of t rading part ners t hat t alk about building win- win relat ionships wit hout showing where t he addit ional winnings will com e from . They know t hat no m at t er how friendly t hings get , t here will always be a dollar- for- dollar t radeoff bet ween t heir profit s and t hose of t heir " part ners," so cooperat ion will never t ruly replace t he nat ural com pet it ion bet ween t hem . The dilem m a, t hen, is t his: Adj acent m em bers of a supply chain m ay have very real opport unit ies t o increase t heir shared profit s, but t he underlying t ension over how t he profit s are divided can prevent t hem from realizing t hose opport unit ies. And even if t hey do find a way t o increase t heir t ot al profit , t hey

m ay do so by pushing invent ory or ot her cost s ont o ot her m em bers of t he chain. This sit uat ion m akes any at t em pt t o im prove t he perform ance of t he chain as a whole a difficult proposit ion at best . The only way out of t he dilem m a is som ehow t o separat e t he effect s of cooperat ion from t hose of com pet it ion, recognizing t hat bot h exist and devising a way t o dist ribut e t he profit s from cooperat ion in a m anner t hat is fair t o all part ies. That 's hard t o do under t he best of circum st ances, but t he t echniques of gam e t heory can m ake it a lit t le bit easier.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 3. Winning as a Team

Insights from Game Theory When t rading part ners com pet e wit h each ot her over a fixed sum of m oney, t hey are playing what gam e t heorist s call a ze r o- su m ga m e. I n zero- sum gam es, t here's a fixed am ount of m oney at st ake, and players com pet e t o see who can win t he largest share. I n Figure 3.9 , t wo players, A and B, are com pet ing for st akes of $100. The range of possible out com es, from A t aking everyt hing t o B get t ing it all, form s t he diagonal line labeled t he win- lose line in t he diagram . The out com e of t he gam e is a single point on t his line. For clarit y—t hese aren't st andard t erm s—I 'll call t he line describing t he possible out com es t he t radeoff curve, and I 'll refer t o t he point describing t he out com e as t he t radeoff point . I n t he case of a zero- sum gam e, t he t radeoff curve is t he sam e as t he win- lose line, and m ovem ent of t he t radeoff point along t his line represent s com pet it ion in it s purest form . Most supply chain t ransact ions play out as zero- sum gam es, wit h t he t wo part ies vying wit h each ot her t o push t he out com e in t heir direct ion along t he win- lose line.

Figure 3.9. A Zero-Sum Game

I f t here are ways in which t he part ies involved in a t ransact ion can influence t he t ot al winnings in addit ion t o det erm ining how t hey divide up t hose winnings, t he t ransact ion t urns int o a non- zero- sum gam e. A non- zero- sum gam e can go eit her way, depending on t he relat ionship bet ween t he t wo part ies. I f t hat relat ionship is cooperat ive, t he part ies can push t he t radeoff curve up int o t he win- win region, as shown in t he left panel of Figure 3.10. I f t he relat ionship is ant agonist ic, t hey can do each ot her m ore harm t han good, m oving t he t radeoff curve down int o t he lose- lose region.

Figure 3.10. Non-Zero-Sum Games

The core cont ribut ion of gam e t heory t o econom ics is t he insight t hat few business t ransact ions are rest rict ed t o pure com pet it ion. Much of what we t hink of as win- lose t ransact ions are act ually m uch richer t han t his. The focus of t he following discussion is on m oving t rading relat ionships up int o t he win- win range, but t hat shouldn't obscure t he fact t hat relat ionships oft en degenerat e int o lose- lose proposit ions. I t is all t oo easy for t he adversarial aspect s of com pet it ion t o dom inat e a relat ionship, even t o t he point where harm ing t he ot her part y becom es m ore im port ant t han winning t he gam e. This is oft en seen in t he com pet it ion bet ween supply chains, where price wars and ot her form s of " cut t hroat " com pet it ion can plunge com panies int o t he lose- lose region. But it is also found w it hin supply chains, as evidenced by t he hidden JI T invent ory shown in Figure 3.7 and in t he higher carrying cost s of invent ory at aut o dealers rat her t han plant s. One of t he dangers of t hinking of t rading relat ionships as zero- sum gam es is t hat it is all t oo easy for st ruggles along t he win- lose line t o slide off t he line int o t he lose- lose region. On a m ore posit ive not e, t rading part ners t hat want t o im prove t heir com bined profit s rat her t han j ust fight over a fixed am ount of m oney can look for ways t o change t heir relat ionship int o a posit ive- sum gam e. This is not t o say t hat t hey can elim inat e t he elem ent of com pet it ion alt oget her; no m at t er how far t hey push t he t radeoff curve int o t he win- win region, t here can st ill be a st ruggle over who get s t he lion's share of t he winnings. The difference is a m at t er of em phasis rat her t han kind. I n a cooperat ive gam e, t he players focus on how t o increase t heir t ot al winnings and relegat e t he allocat ion of t hose winnings t o a secondary concern. I n a com pet it ive gam e, t he winnings are considered fixed and t he allocat ion is everyt hing. This is why Chrysler's SCORE program was so successful. I t com plet ely recast t he relat ionship t o focus on cooperat ion and provided a sim ple set of m echanics t o resolve t he com pet it ive elem ent . Current prices were t aken as a given, and reduct ions in t hose prices were lim it ed t o act ual savings result ing from im proved t echniques. That lim it ed t he com pet it ive elem ent t o t he am ount of t he savings, and t he program was very flexible wit hin t hat range. I n t he early days, Chrysler oft en accept ed what ever savings a supplier chose t o pass on, wit hout quest ioning t he act ual am ount . Som e suppliers no doubt kept m ore t han half of t he savings, but ot hers passed along m ost or even all of t he savings in an effort t o win m ore business. Since everyone was winning at t his point , no one worried t oo m uch about keeping score. The first lesson t o be drawn from gam e t heory, t hen, is t hat t rading part ners should place m ost of t he em phasis on m axim izing t he t ot al winnings. The m ore successful t hey are in t his effort , t he less im port ant t he allocat ion of t hose winnings becom es. This oft en requires a m aj or shift in t he way cust om ers and suppliers view t heir relat ionship, and m aking t hat shift is oft en harder t han finding opport unit ies for savings. I n fact , st udies of why supply chain part nerships so oft en fail reveal t hat t he failure is usually due m ore t o at t it udes t han econom ics. I t t akes a sust ained effort t o build a posit ive- sum relat ionship, but , at least for key links in t he chain, t he ret urn on t hat invest m ent of t im e and energy can be am ong t he best in business. Of course, t he quest ion of j ust where t o place t he t radeoff point in any given exchange doesn't ever go away, and even t he best of relat ionships can becom e t ense when t here is freshly m int ed m oney lying on t he t able. There are m any ways t o resolve t his quest ion, but t he preferred choice should always be t o pick t he point t hat m axim izes t he t ot al winnings, com pensat ing for any inequit ies

t hrough som e ot her exchange. This is not only t he best " average" out com e across t he t wo com panies, it is also t he way t o m axim ize t he com pet it iveness of t heir supply chain. This is best seen t hrough an exam ple. Suppose a cust om er and supplier are each spending $5 a unit t o verify t he qualit y of a cert ain com ponent . Figur e 3.11 shows how t his sit uat ion can be represent ed as a zero- sum gam e. I n t his case t he cooperat ive region is in t he lower left rat her t han t he upper right because t he com panies benefit by reducing cost s, whereas in t he earlier exam ple t hey benefit ed by increasing profit s. The t radeoff curve in t he diagram represent s t he result s of a j oint st udy showing t hat a cooperat ive inspect ion program could elim inat e several redundant operat ions, reducing t he t ot al expendit ure on qualit y cont rol. According t o t he st udy, t he t radeoff curve is asym m et rical; t he largest savings will be realized if t he supplier t akes on m ore of t he burden of qualit y assurance because t his elim inat es t he addit ional expense of shipping and ret urning defect ive com ponent s. Assum ing t he com panies can agree on t his program , how should t hey split t he savings?

Figure 3.11. Allocating Cost Savings

I n t he real world, t he m ost likely out com e is t hat t he cust om er would express out rage at having t o spend so m uch t o com pensat e for poor qualit y and would insist t hat t he supplier get it s act t oget her and elim inat e t he defect s. But suppose t hat , in t he spirit of cooperat ion, t he t wo agree t o share t he savings equally, choosing t he t radeoff point labeled equal savings. This isn't a bad choice; bot h com panies spend less m oney on qualit y cont rol, and t he t ot al cost s for t he com ponent go down by $2, allowing t he supply chain t o im prove it s m argins. But a bet t er choice would be t o pick t he point t hat m axim izes t he t ot al savings. I n t his exam ple, t he t wo com panies can shave an addit ional $1

per com ponent off t heir com bined cost s if t he supplier act ually increases it s t ot al cost . This m ay not be fair t o t he supplier, but t his inequit y is easily rect ified by having t he producer com pensat e t he supplier in ot her ways. The sim plest solut ion is for t he producer t o pay m ore for com ponent s shipped under t he new qualit y program . This last point —t hat t he cust om er can com pensat e t he supplier for it s added expense t hrough side paym ent s or som e ot her exchange—reveals anot her im port ant cont ribut ion of gam e t heory. Alt hough it m ay m ake sense for com panies t o view spot purchases and ot her isolat ed t ransact ions as zero- sum gam es, t hat kind of t hinking breaks down when it com es t o sust ained relat ionships, which span m ult iple t ransact ions and include m ult iple t radeoffs. Even if a com pany insist s on applying zero- sum logic t o an ent ire relat ionship, it is st ill bet t er off choosing opt im al point s for individual t ransact ions and m aking up t he difference elsewhere. But t he best relat ionship is achieved by set t ing t he com pet it ive com ponent aside long enough t o explore t he full benefit s t hat can be realized t hrough cooperat ion. There is always a way t o balance out t he books lat er if one part y doesn't realize it s full share of t he benefit s in a part icular t ransact ion. Anot her key insight from applying gam e t heory is t hat decisions such as t hese can't be m ade int uit ively; t hey are sim ply t oo com plex for t hat . Even a t rivial exam ple of t he sort shown in Figure 3.11 out st rips our abilit y t o discover t he best solut ion by t hinking in t erm s of who " ought " t o carry a cost or what a fair division of savings m ight be. The key t o t aking win- win relat ionships out of t he realm of warm fuzzies and m aking t hem a working realit y is t o use form al m odels t o find opt im al values. For som e decisions, a sim ple spreadsheet showing cost t radeoffs is enough; ot hers m ay require m odeling t he ent ire supply chain. Chapt er 5 provides an overview of t he various kinds of m odels and t heir applicat ions; t he im port ant point here is sim ply t hat m odeling is an indispensable t ool for m aking t he com plex decisions required in supply chain m anagem ent .

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 3. Winning as a Team

Winning Through Collaboration Alt hough supply chain m anagem ent has com e a long way from it s origins in t ransport at ion m anagem ent , t he discipline st ill t ends t o reflect t he original focus on m anaging t he flow of goods across a single link in t he chain. As t he exam ples in t his chapt er illust rat e, it is all t oo easy for such point solut ions t o sim ply push problem s up or down t he chain rat her t han act ually solving t hem . Even when t wo or m ore t rading part ners cooperat e t o im prove t heir overall posit ion, t hey oft en do so at t he expense of ot her m em bers of t he chain. I n gam e t heory t erm s, t hey are creat ing a local posit ive- sum gam e, but t heir cooperat ive relat ionship m ay act ually drive t heir int eract ions wit h ot her m em bers of t he chain int o t he lose- lose region. This is not t he way t o build a winning chain. The new com pet it ion bet ween supply chains isn't based on t he effect iveness of individual links; it 's based on t he abilit y of t he chain as a whole t o bring bet t er product s t o t he m arket fast er and cheaper t han ot her chains. The key t o doing t his is t o apply t he logic of gam e t heory across t he ent ire chain, pushing t he chain as a whole as far as possible int o t he win- win region. This can only happen if all t he m em bers of t he chain are willing t o play as a t eam , opt im izing t he t radeoffs at every link in order t o pull t im e and cost out of t he chain. I n effect , t he m em bers of t his t eam need t o plan and act wit h t he int egrit y of a single organizat ion, working t oget her t o sim plify and st abilize t he flow of dem and, supply, and cash across t he chain. This pooling of int erest s, t his synergy of planning and act ing, is t he essence of supply chain int egrat ion. Succinct ly put , supply chain int egrat ion m eans t hat t he m em bers of t he chain com e t oget her t o form a larger whole, one in which t he part s are carefully aligned and synchronized so t hat t he chain behaves as a single, coordinat ed sy st em . Supply chain int egrat ion isn't an all- or- none proposit ion: I t varies in bot h form and degree, as shown in Figure 3.12. The classic form , shown on t he left side of t he figure, is vert ical int egrat ion, in which all t he m em bers of t he chain are owned by t he sam e com pany. Vert ical int egrat ion is st ill pract iced in som e segm ent s of t he chain, as seen in Wal- Mart 's ownership of t he dist ribut ion channel, but it 's hard t o achieve across t he ent ire chain t oday because so m any com panies are involved. Henry Ford was a great believer in vert ical int egrat ion, and he m ade sure his com pany owned everyt hing from rubber t rees t o sales

lot s. Today, Ford's supply chain includes m ore t han 100,000 com panies. Even if it were possible for Ford t o own all t hose com panies, t he inevit able overhead and bureaucracy would negat e m ost of t he advant ages of com m on ownership.

Figure 3.12. Strategies for Integration

Today, it is far m ore com m on for com panies t o focus on t heir core com pet ence and cooperat e wit h ot her com panies t o assem ble com plet e supply chains. But t he form of t hat cooperat ion varies widely, as shown in Figure 3.12. The keiret su is forged by est ablishing overlapping ownership and m anagem ent am ong form erly independent t rading part ners, as described earlier in t his chapt er. I t generally achieves levels of int egrat ion nearly as good as t hose of vert ical int egrat ion, but t his m ay be due as m uch t o Japanese cult ure as t o t he business st ruct ure. The diam et rical opposit e of vert ical int egrat ion is t he ad hoc supply chain shown in t he lower right of Figure 3.12, a group of independent ly owned com panies bound only by need and m arket m echanism s. This kind of chain requires t he least governance and is t he m ost flexible, in t hat it s m em bership can change wit h each t ransact ion. But it would be hard t o envision a less int egrat ed solut ion t o t he problem s of coordinat ing a chain. At t em pt s t o gain a high degree of int egrat ion wit hout com prom ising independent ownership—an approach called vir t u a l in t e gr a t ion—are shown as m ovem ent up t he right side of Figure 3.12. Part nership agreem ent s bet ween adj acent m em bers of t he chain are t he usual first st ep t oward vert ical int egrat ion, but t hey are at best a part ial solut ion because t hey only span a single link. True int egrat ion requires t he m em bers of a supply chain t o coordinat e t he flow of dem and, supply, and cash across t he chain as a whole, not j ust across a single link. As indicat ed in Figure 3.12, t he current push for collaborat ion across t he chain represent s t he nat ural convergence of t wo m aj or t rends in supply chain

m anagem ent . One t rend is away from com m on ownership and t oward independent com panies. The ot her t rend is away from ad hoc t ransact ions and t oward t ight er int egrat ion. The place where t hose t wo t rends m eet —t he spot m arked wit h t he bull's- eye—is t he goal of supply chain collaborat ion: a t eam of com panies achieving a high degree of int egrat ion across t he supply chain while ret aining independent ownership and cont rol. Supply chain collaborat ion isn't a new idea; JI T, quick response, efficient consum er response, and t he ot her program s described in t his chapt er are all early form s of collaborat ion, but t hey are lim it ed t o a sm all subset of t he larger supply chain. I n t he fut ure, collaborat ion has t o span enough links in t he chain t o t ruly pull t im e and cost out of t he chain, not j ust displace it wit hin t he chain. Achieving t his level of collaborat ion will require m anagers t o t ake a m uch wider perspect ive on t he supply chain t han t hey do t oday, t hinking of t heir com panies as part of a larger whole rat her t han t he cent er of t he business universe. This won't com e easily; one recent survey revealed t hat m ore t han 80% of all supply chain init iat ives are com plet ely cont ained wit hin a single com pany, and m ost of t he rem ainder deal only wit h im m ediat e t rading part ners. Anot her survey, report ed in Supply Chain Managem ent Review ( see Not es on Sources) , reinforces t he point wit h t his rat her bleak conclusion: " We did not find a single incidence of ext ensive analysis of t he t ot al supply chain t o underst and t he int er- relat ionships or t o set t he goals," adding t hat " ...no com pany has a m odel of t he supply chain on which t o t est different m odes of operat ion or t he im pact of different st rat egies." This m ay be a bleak conclusion for t he supply chain indust ry as a whole, but it represent s a t rem endous opport unit y for com panies t hat are ready t o m ove t o t he next level. I nt egrat ing a supply chain t hrough collaborat ion m ay not be easy, but you don't need t o get your chain anywhere near t he bull's- eye t o score a big win. Given t he current st at e of supply chains, j ust m aking progress in t hat direct ion can be enough t o give you a solid com pet it ive advant age. I m agine a perfect ly int egrat ed chain as a cham pion m arat hon runner, clicking off a st eady st ream of six- m inut e m iles by m aint aining perfect synchrony in every m ovem ent . The corresponding im age for a convent ional chain would be Dr. Frankenst ein's m onst er lurching down t he village lane, st ruggling t o m ake an ad hoc assem bly of m uscles propel it s body forward. I f t hat 's t he com pet it ion, you don't have t o be an Olym pic runner t o com e in first . I f you can walk, you can win. Th e e sse n t ia l m e ssa ge you sh ou ld t a k e a w a y fr om Pa r t I is t h is: Su pply ch a in s a r e t h e n e w a r e n a of cor por a t e com pe t it ion , t h e cor e pr oble m in m a n a gin g su pply ch a in s is de a lin g w it h com ple x it y a n d va r ia bilit y, a n d colla bor a t ion a m on g t r a din g pa r t n e r s is e sse n t ia l t o copin g w it h t h e se pr oble m s. Th is is t h e m ission ; sh ou ld you ch oose t o a cce pt it , you w ill n e e d som e spe cia lize d t ools t o h e lp you su cce e d. Pa r t I I pr e se n t s t h e se t ools by ( 1 ) e x pla in in g h ow t o look a t su pply ch a in s fr om a syst e m s pe r spe ct ive , ( 2 ) sh ow in g you t h r e e diffe r e n t w a ys t o m ode l su pply ch a in s, a n d ( 3 ) givin g you a qu ick t ou r of su pply ch a in soft w a r e . On ce you h a ve t h e se t ools in h a n d, you 'll be r e a dy t o m a st e r you r ow n su pply ch a in .

Te a m - Fly

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Te a m - Fly

Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Part II: Solutions

Te a m - Fly Top

Te a m - Fly

Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Part II. Solutions

Chapter 4. Supply Chains as Systems I n t e gr a t in g a su pply ch a in r e qu ir e s a sse m blin g a n a d h oc colle ct ion of fa cilit ie s in t o a coh e r e n t syst e m t h a t ca n fu n ct ion w it h a sin gle pu r pose . I n or de r t o su cce e d in t h is e ffor t , you n e e d t o k n ow som e t h in g a bou t syst e m s—h ow t h e y a r e de sign e d, h ow t h e y w or k , a n d h ow t h e y a r e con t r olle d. I n sh or t , you n e e d a lit t le syst e m s t h e or y. Th is m a y sou n d lik e a n a bst r a ct su bj e ct of lim it e d r e le va n ce t o you r n e e ds, bu t n ot h in g cou ld be fu r t h e r fr om t h e t r u t h . As a m a n a ge r , you de a l w it h som e of t h e m ost com ple x syst e m s on e a r t h e ve r y da y, a n d you r e x pe r ie n ce h a s a lr e a dy give n you a ba sic u n de r st a n din g of h ow t h e se syst e m s w or k . Th e pr oble m w it h t h is u n de r st a n din g is t h a t it 's la r ge ly in t u it ive , m a k in g it h a r d t o u se in solvin g n e w pr oble m s. Th is ch a pt e r w ill h e lp you h on e t h ose in t u it ion s in t o pow e r fu l bu sin e ss de sign t ools.

Te a m - Fly Top

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 4. Supply Chains as Systems

Business Cybernetics The form al st udy of syst em s dat es back t o t he 1940s wit h t he em ergence of cybernet ics, which t ook insight s gained from t he invent ion of com put ers and applied t hem t o ot her dom ains. I n cybernet ics, a syst em is viewed as an assem bly of com ponent s t hat int eract t o produce collect ive behavior. Com put ers are syst em s, of course, but so are plant s, anim als, ecologies, nat ions, com panies, fact ories, and, yes, supply chains. The key insight of cybernet ics is t hat t here are com m on principles across all t hese different kinds of syst em s, principles t hat help explain t he behavior of each. Knowing som et hing about syst em s in general really does help you underst and business syst em s in part icular. One of t he key cont ribut ions of cybernet ics was t he insight t hat all syst em s can be seen as t ransform ing input s int o out put s. When syst em s are const ruct ed by people, as supply chains are, t hey are usually designed t o produce out put s t hat have great er im m ediat e value t han t he input s. For exam ple, com put ers t ake in large volum es of dat a and dist ill it int o useful inform at ion; fact ories consum e raw m at erials and produce finished goods; hum an beings t ake in food and t ransform it int o...well, som e im provem ent s are less obvious t han ot hers. I n t his case, t he out put of int erest is t he energy ext ract ed from t he food, which in t urn is t ransform ed int o physical m ovem ent and ot her form s of work. Nat ural syst em s, such as ecologies, are usually self- regulat ing, and at t em pt s t o cont rol t hem oft en do m ore harm t han good. Syst em s m ade by people, on t he ot her hand, are designed t o be cont rolled and m onit ored so t hat t heir perform ance can be im proved over t im e. Cont rol is achieved by regulat ing t he flow of input s, and m onit oring involves m easuring t he result ing out put s. I n effect , t hese syst em s have t he equivalent of knobs on t heir input s and gauges on t heir out put s; changing t he set t ings of t he knobs changes t he readings on t he gauges ( Figure 4.1 ) . I nside t he syst em , a num ber of com ponent s—which m ay be syst em s in t heir own right —int eract t o t ransform t he input s int o t he out put s. I f t he arrangem ent of t he com ponent s in t he illust rat ion suggest s t he st ruct ure of a supply chain, t hat 's probably not a coincidence.

Figure 4.1. A System

Not ice in Figure 4.1 t hat not all t he input s have knobs, and not all t he out put s have gauges. Even in t he best - designed syst em s, t here are usually som e input s t hat can't be cont rolled by t he people operat ing t he syst em . I n t he case of supply chains, econom ic cycles and nat ural disast ers can have a profound im pact on perform ance, but t hese are out side t he span of cont rol. Econom ist s call t hese input s e x t r in sic fa ct or s because, in cont rast t o in t r in sic fa ct or s such as plant capacit y and budget allocat ions, t hey originat e from out side t he boundaries of t he syst em . Sim ilarly, it m ay not be possible t o m easure every out put of a syst em . For exam ple, m easuring t he cont ribut ion t o consum er value added by each st age of a product ion process is highly desirable but not oriously difficult in m ost indust ries. Even if it is possible t o m easure every out put , syst em s usually have so m any out put s t hat it 's not cost - effect ive t o m easure t hem all. The preferred approach, t hen, is t o m easure t he set of out put s t hat are m ost helpful in m onit oring and cont rolling t he syst em . The problem of choosing t he best set of out put s t o m easure is part icularly difficult in t he case of supply chains ( see Chapt er 9) . Wit h j ust t hese few concept s in place, it 's already possible t o see why an underst anding of syst em s is useful in m anaging supply chains. I n essence, each m anager in t he chain is given responsibilit y for a set of knobs, and each one sees t he readings on a set of gauges. The goal is for everyone t o set t heir knobs j ust right in order t o m axim ize t he out put s of t he chain. That isn't going t o happen wit hout som e shared underst anding of how t he set t ings affect t he operat ion of t he chain, t oget her wit h som e coordinat ion of t he changes t o get t he best overall perform ance. Figure 4.2 illust rat es how t his works by showing t he relat ionships am ong t hree key processes in m anaging syst em s: underst anding, predict ion, and cont rol. Underst anding provides t he insight s necessary for you t o predict how a syst em will behave in response t o changes t o it s input s. Predict ion, in t urn, allows you t o cont rol t he syst em by m aking t he best com binat ion of adj ust m ent s. Com paring predict ed wit h act ual result s deepens your underst anding of t he syst em , allowing you t o m ake m ore accurat e predict ions and im proving your cont rol. Toget her, t hese core processes form t he heart of any successful m anagem ent process.

Figure 4.2. Understanding, Prediction, and Control

Of t he t hree processes, underst anding is arguably t he m ost im port ant , yet it is also t he m ost neglect ed. I nst ead, t he em phasis proceeds in t he ot her direct ion: Cont rol is t he prim ary concern, predict ion is invoked only as needed t o im prove cont rol, and underst anding is viewed as an incident al by- product rat her t han t he prim e m over of t he sequence. This reversal of priorit ies m ay be necessary in t he short run, but it is self- defeat ing in t he long haul. The im age t hat com es t o m ind is driving a t andem t ruck down t he freeway in reverse, m aking wild correct ions t o t he st eering in order t o com pensat e for going about t he m at t er backward. This book—not t o m ent ion m y ent ire career—is devot ed t o get t ing underst anding back out in front where it belongs. To be fair, som e syst em s are so well designed t hat very lit t le underst anding is required t o cont rol t hem . Cont em porary cars epit om ize such syst em s, at least in regard t o t he basic cont rols. The harder you press on t he gas pedal, t he fast er t he car goes. The m achinery and soft ware t hat int ervene bet ween t his input and t he result ing out put have becom e ext rem ely com plex over t he years, but t he m apping bet ween t he t wo is so st raight forward t hat operat ors don't need t o know a t hing about t he int ernals of t he syst em . I n com put er t erm s such syst em s are referred t o as being user- friendly, a st at e t hat rem ains an elusive goal for com put ers t hem selves. Supply chains are anyt hing b u t user- friendly. The basic m echanics, as described in Chapt er 2, are pret t y sim ple, but t he behavior of t he chain as a whole can be very difficult t o underst and, m uch less predict and cont rol. One of t he recurring t hem es of t his book is t hat even t he m ost benign at t em pt s t o cont rol supply chains, such as offering quant it y discount s t o encourage volum e purchases or running prom ot ions t o increase sales, can have wholly unint ended and oft en disast rous effect s on perform ance. When it com es t o syst em s of t his level of com plexit y, underst anding is not a luxury; it 's a necessit y.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 4. Supply Chains as Systems

A Rogues Gallery of Relations One of t he m ost basic charact erist ics of syst em s is t he way in which t hey m ap values on t he input s t o values on t he out put s. This m apping, or r e la t ion , can t ake on a variet y of different t ypes, which range from t he m ost st raight forward t o t he t ruly bizarre. This sect ion int roduces you t o t he various kinds of relat ions you m ight encount er, using a device I call t he Rogues Gallery of Relat ions. To see why you need t o underst and relat ions, im agine cont rolling t he syst em shown in Figure 4.3 . I t 's about as sim ple as a syst em can get , wit h j ust a single com ponent , a single input , and a single out put . The values of t he input and t he out put bot h range from 0 t o 100. The input has a knob and t he out put has a gauge, so you have com plet e cont rol of t he syst em 's input and full knowledge of it s out put . The com ponent it self could have any degree of int ernal com plexit y, but we'll t reat it as a " black box" —all t hat m at t ers is t he relat ionship bet ween t he input and t he out put . One possible relat ionship is shown graphically in t he bot t om of t he figure. As you t urn t he knob from 0 t o 100, t he out put goes from 20 t o 80. Wit h a bit of pract ice, you could quickly adapt t o t his cont rol and produce any available out put on dem and. Over t im e, it would becom e as aut om at ic as using t he gas pedal in your car.

Figure 4.3. The Simplest System

This syst em is easy t o underst and and operat e because t he relat ion bet ween t he input and out put is so sim ple. Unfort unat ely, relat ions in real- world syst em s are rarely t his sim ple. To see som e ot her relat ions t hat m ight have been lurking in t his syst em , t ake a look at t he relat ions shown in Figure 4.4 . Each panel illust rat es a part icular kind of relat ion, t oget her wit h t he nam e m ost com m only used for t hat t ype. All of t hese relat ions are found in supply chain syst em s, and knowing which one you are dealing wit h when you are changing an input is essent ial t o achieving good cont rol. As you proceed from left t o right in t he diagram , t he relat ions becom e increasingly difficult t o underst and and cont rol, which is why I call t hem rogues. A brief rundown on each rogue will help you recognize it and deal wit h it successfully.

Figure 4.4. The Rogues Gallery of Relations

The relat ion shown in Panel A of t he gallery is called a linear relat ion because t he m apping of input s t o out put s is described by a st raight line. This is t he relat ion seen in Figure 4.3 , and it isn't really a rogue at all; it has every desirable qualit y, and it is t he best - behaved relat ion you could possibly hope for. Linear relat ions are easy t o underst and, easy t o predict , and—best of all—easy t o cont rol because increasing t he input by a const ant am ount always

produces t he sam e, const ant increase in t he out put . The world would be a m uch m ore orderly place if all relat ions were of t his clean, linear variet y. Unfort unat ely, linear relat ions are j ust one special case. All t he ot her rogues in t he gallery are decidedly nonlinear. The m onot onic relat ion in Panel B of t he gallery is not as well behaved. The only rest rict ion on t his relat ion is t hat increasing t he input never reduces t he out put . Beyond t his, t here are no guarant ees regarding t he shape of t he curve. I t could rise slowly, t hen plat eau for a while, t hen shoot up st eeply, and so on. This m akes it m uch harder t o use t he knob t o cont rol t he out put because a sm all adj ust m ent in t he knob could produce a big change in t he out put in one part of t he range and lit t le or no change in anot her. The sam ple curve shown in Panel B illust rat es a syst em t hat is m uch m ore sensit ive in t he m iddle of it s range t han it is near t he ends. The effect of repet it ion on brand recognit ion oft en exhibit s t his kind of relat ion, showing lit t le or no increase unt il a cert ain t hreshold is reached, t hen rising quickly t o a sat urat ion point . The cont inuous relat ion illust rat ed in Panel C is even less well behaved; t he only guarant ee wit h t his relat ion is t hat t he out put will rise or fall sm oot hly wit h changes in t he input , wit hout any sudden j um ps. But t he act ual m apping can t ake on any form what ever. Cont inuous relat ions m ake cont rol harder st ill because increasing t he input can drive t he out put higher, push it lower, or leave it unchanged. Unless you have som e pret t y good insight s int o how a syst em works, about t he best you can do wit h t his relat ion is sweep t he knob back and fort h and wat ch t he gauge, t rying t o find t he spot t hat gives you t he best out put . Many com panies find t hem selves doing t his in t rying t o m anage t he relat ion bet ween price and profit , which usually follows a curve like t he one shown in Panel C. Up t o a cert ain point , raising prices increases revenue and profit s go up. Beyond t hat point , furt her increases result in lost sales and profit s st art t o go back down. Finding t he price t hat produces t he largest profit s is rarely an easy process. The single- valued relat ion shown in Panel D is st ill harder t o work wit h because even t he sm allest change in input can produce a huge leap in t he out put , wit h no sm oot h t ransit ion bet ween successive levels. The only t hing you can count on wit h t his relat ion is t hat it will always produce t he sam e out put for any given input . Beyond t hat , anyt hing goes. This rogue is quit e com m on in supply chains, and it 's alm ost always a m onst er of our own creat ion. For exam ple, quant it y discount s int roduce discont inuit ies in t he relat ion bet ween price and quant it y, so t hat increasing t he quant it y by a single it em can cause an abrupt change in price for all t he it em s in an order, possibly even reducing t he t ot al cost rat her t han increasing it as expect ed. This kind of behavior m ay not seem so bad sim ply because it 's fam iliar, but quant it y discount s are am ong t he pract ices t hat m ake supply chains hard t o predict and cont rol. The m ult i- valued relat ion illust rat ed in Panel E is t he worst of rogues because it doesn't even prom ise t o give you t he sam e out put for a given input . Wit h t his relat ion, a sm all change t o t he input can not only produce a sudden leap, it can shift t he relat ion over t o anot her curve alt oget her, so t hat reversing t he change doesn't put t hings back t he way t hey were. This relat ion m ay seem so perverse t hat it should never be perm it t ed in supply chains, but it 's t here whet her we like it or not . I n fact , t he exam ple curve shown in Panel E is a nat urally occurring pat t ern in t he dem and for fashion- based product s, as explained in Chapt er 10 . Research on hum an t hinking and decision m aking reveals t hat we have a great

deal of t rouble wit h t he rogues described above. Sim ply put , we nat urally assum e t hat all syst em s are linear in nat ure, and we are very bad at det ect ing and underst anding any ot her kind of relat ion. Nonlinear relat ionships are quit e com m on in supply chains, so you will have t o overcom e your nat ural inclinat ions if you want t o m ast er supply chain m anagem ent . I will help you wit h t his t hroughout t he book by point ing out nonlinear relat ionships whenever t hey appear, and by showing som e of t he ways in which t he assum pt ion of linearit y is built int o our t hinking about supply chains.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 4. Supply Chains as Systems

The Dynamics of Delay The range of behavior t hat can be observed wit h j ust a single com ponent barely hint s at what can happen when t wo or m ore com ponent s are com bined. Even t he sim plest com binat ions can produce behavior t hat is surprising and, for t he purpose of underst anding supply chains, quit e revealing. Figure 4.5 shows t hree com ponent s hooked t oget her t o form a chain, wit h t he out put of each becom ing t he input of t he next . The com ponent s don't act ually do anyt hing; as t he relat ions below each com ponent indicat e, t hey j ust pass t heir input s t hrough t o t heir out put s wit hout changing t hem in any way.

Figure 4.5. Combining Components

This syst em behaves ident ically t o t he single- com ponent syst em explored in t he preceding sect ion ( see Figure 4.3 ) ; t he sequence of values generat ed by t he knob—a sequence t hat is oft en called t he signal—is im m ediat ely placed on t he final out put , j ust as it is in t he sim pler syst em . I t only t akes a t iny alt erat ion t o m ake t his syst em behave different ly from t he sim pler one: a sm all delay from t he t im e a com ponent receives a change in it s input t o t he t im e t hat change is reflect ed in it s out put . Figure 4.6 illust rat es t he im pact of t his delay by plot t ing t he input s t o t he t hree com ponent s over t im e. The original signal, labeled A in t he figure, is fait hfully replicat ed by t he ot her t wo com ponent s, but t he levels at t he t hree com ponent s are no longer t he sam e at any given t im e. I n t echnical t erm s, t he com ponent s are now said t o be

out of phase wit h each ot her. All syst em s involve som e delays, so it is norm al for t heir com ponent s t o be out of phase. I n supply chains, delays occur in all t hree flows—dem and, supply, and cash—and t hey can range anywhere from m inut es t o m ont hs.

Figure 4.6. The Effects of Delay

To see what kind of confusion t hese phase shift s can cause in a supply chain, im agine t hat Com ponent s A, B, and C are a ret ailer, producer, and supplier, respect ively, and t hat t he signal of int erest is t he level of dem and being experienced by t he chain. At t he t im e labeled t in Figure 4.6 , dem and at t he producer is right on t he average value, but dem and at t he ret ailer is below average, and t he supplier is experiencing unusually high dem and. Based on t he m ost current dat a, each com pany m ight reach t ot ally different conclusions about how t he chain ought t o be responding t o current dem and. I f any com pany t ries t o m ake a correct ion on it s own, it is alm ost cert ain t o t hrow t he ot her t wo out of balance. I f phase shift s were always as obvious as t he ones shown in Figure 4.6 , t hey could be det ect ed and handled rat her easily. But real- world supply chains are never t his kind. Even if t he original signal is t ransm it t ed fait hfully all t he way up t he chain, t he am ount of t he delay int roduced by each com ponent varies bot h wit hin and across com ponent s. I t t akes very lit t le variat ion of t his sort t o t urn t he neat curves of Figure 4.6 int o wild, unpredict able swings. A furt her com plicat ion is t hat t he original dem and signal never varies in t he sm oot h, cyclical m anner shown in t he figure; it usually carves out a j agged pat t ern t hat has lit t le or no hint of regularit y ( see Chapt er 10 ) . The result : Phase shift s are rarely apparent even in t he best of circum st ances. All t hat t he m em bers of t he chain know is t hat t hey are experiencing different levels of dem and, and t here m ay be no way t o know whet her t hose are sim ple delay effect s or real disagreem ent s t hat are cause for concern. As puzzling as t he effect s of delay m ight be, m uch m ore confusion is int roduced if t here is any dist ort ion of t he signal from one com ponent t o t he next . Realworld syst em s oft en show a pat t ern of increasing dist ort ion as signals t ravel upst ream , wreaking havoc am ong upst ream com ponent s. Have you ever

wondered why dense freeway t raffic lurches along in waves of accelerat ion and braking rat her t han j ust flowing at a single, slow rat e? Traffic st udies have revealed t hat t hese waves can be t riggered by j ust one or t wo drivers overreact ing t o t he cars in front of t hem , t riggering a ripple of exaggerat ed responses t hat spreads and am plifies for m any m iles behind t hem . Dist ort ions of incom ing signals can com e from any num ber of sources, and t hey can be int roduced accident ally or int ent ionally. I n supply chains, t he fam iliar econom ies of scale represent a com m on source of dist ort ion: Cust om ers order m ore t han t hey need in order t o get a quant it y discount , producers run larger bat ches t han necessary t o reduce unit cost s, and so on. Such decisions m ay save m oney in im m ediat e operat ions, but t he dist ort ions t hey cause in t he signals for dem and, supply, and cash ext ract a m uch higher cost t han m ost com panies realize. To see t he problem in act ion, im agine t hat each com ponent in t he chain shown in Figure 4.5 increases t he signal it receives by 50% . The result would be larger and larger swings of t he signal as it m oves up t he chain, as shown in Figure 4.7 . This is precisely what happens in t he phenom enon of dem and am plificat ion described in Chapt er 2. The bullwhip effect t hat caused t he wild swings in t he supply chain for Pam pers wasn't a st range aberrat ion of t his part icular chain, but a nat ural out com e of t radit ional pract ices found in all supply chains. Put anot her way, dem and am plificat ion is a problem of our own creat ion, one we have woven int o t he very fabric of supply chain pract ices. The only sure way t o get rid of t he problem is t o elim inat e t he pract ices t hat cause it ( see Chapt er 13 ) .

Figure 4.7. Combining Delay with Amplification

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 4. Supply Chains as Systems

Feedback and Stability I n t he syst em s discussed so far in t his chapt er, t he signals all t ravel in t he sam e direct ion, from t he input s t oward t he out put s. Alt hough such syst em s exist , t hey are rare; m ost real- world syst em s have addit ional pat hways t hat carry signals upst ream as well, from out put s back t o t he input s of earlier com ponent s ( Figure 4.8 ) . Such signals are called f e e d b a ck because t hey feed inform at ion about t he out put back int o t he input , creat ing a loop in t he syst em t hat wouldn't be t here ot herwise. Given t hat syst em s wit hout feedback can be so hard t o underst and, adding a loop of t his sort m ay seem like a perverse t hing t o do, but it t urns out t hat t he proper use of feedback is crit ical t o producing useful, effect ive syst em s.

Figure 4.8. Introducing Feedback

Feedback can t ake on m any form s. The m ost basic kind of feedback sim ply t akes a port ion of t he out put and m ixes it in wit h t he incom ing signal, as shown in t he upper link of Figure 4.8 . The m ore com m on kind of feedback in supply chains, shown in t he lower link, uses a separat e signal t hat com m unicat es inform at ion about t he current out put t o an upst ream com ponent rat her t han redirect ing part of t he original signal. Feedback can be ent irely aut om at ic, or it can require hum an int ervent ion, as it does when an operat or m onit ors a gauge and adj ust s an input knob t o achieve a desired out put . I n supply chains, using feedback effect ively involves m any people working t oget her t o analyze out put s and m odify input s. The purpose of feedback is t o provide inform at ion about current out put t o t he upst ream port ions of a syst em , allowing t hem t o t une t heir behavior t o bet t er

regulat e t hat out put . To see how t his works, im agine t hat t he ext ernal signal going int o Com ponent A in Figure 4.8 is rising at a const ant rat e. Wit hout feedback, t he out put will also rise at t he sam e const ant rat e. However, if t he out put of Com ponent B includes a feedback signal t o A t hat causes it t o am plify it s response t o t he incom ing signal, t hen t he out put of A will go up at an everincreasing rat e. This kind of feedback is called posit ive fe e dba ck because it am plifies t he incom ing signal st rengt h. The result of posit ive feedback is an ever- accelerat ing increase in t he out put level, as shown in t he left panel of Figure 4.9 . I f you have ever been at a present at ion where som eone t urned t he m icrophone am plifier up t oo high, you know exact ly what happens wit h posit ive feedback—t he signal j ust get s st ronger and st ronger unt il it overloads t he syst em .

Figure 4.9. Two Kinds of Feedback

Now im agine alt ering t he feedback m echanism so t hat t he out put of B is used t o decrease A's response t o t he incom ing signal rat her t han increase it . This arrangem ent is called n e ga t ive fe e dba ck because it dam pens incom ing signals. Wit h negat ive feedback, each increase in t he original signal has a sm aller effect on t he out put , as shown in t he right panel of Figure 4.9 . This kind of feedback t ends t o keep a syst em wit hin set bounds rat her t han pushing it t oward ext rem e values. As t he exam ples suggest , t he t wo kinds of feedback have radically different effect s on a syst em . Posit ive feedback encourages m ovem ent in a part icular direct ion and act s t o prom ot e unbounded growt h. For exam ple, com pound int erest on a bank account feeds t he int erest back int o t he principal, causing it t o generat e m ore int erest during t he next period, and so on. The sam e principle explains t he exponent ial growt h of st art - up com panies, m arket s, populat ions, and t he like; it only t akes a lit t le posit ive feedback t o t ranslat e a m odest rat e of growt h int o an exponent ial explosion. By cont rast , negat ive feedback lim it s m ovem ent in a part icular direct ion, and it is m ost frequent ly used t o prom ot e st abilit y in a syst em . A regressive t ax syst em is an exam ple of negat ive feedback because it reduces t he increase in net incom e as gross incom e goes up. Negat ive feedback in econom ic syst em s is oft en expressed as t he law of dim inishing ret urns, in which each addit ional

dollar invest ed in an act ivit y produces a sm aller ret urn t han t he previous one. Of t he t wo kinds of feedback, negat ive feedback is used m uch m ore ext ensively in t he design of syst em s because of it s abilit y t o keep a syst em wit hin reasonable operat ing bounds. Feedback is t he lifeblood of supply chains, and m any of t he supply chain init iat ives described in Chapt er 3 are designed t o im prove t he flow of feedback up t he chain. One of t he advant ages of vendor- m anaged invent ory, for exam ple, is t hat it let s suppliers direct ly m onit or invent ory levels in dist ribut ion cent ers and ret ail st ores, giving t hem m uch earlier feedback on t he flow of product s and allowing t hem t o t une t heir product ion accordingly. The use of point - of- sale syst em s in t he quick response program im proves t his feedback by pushing t he flow gauge all t he way out t o t he cash regist er and det ect ing t he m ovem ent of goods t he m om ent it occurs. I n addit ion t o facilit at ing t he flow of goods down t he chain, feedback facilit at es t he flow of dem and and cash back up t he chain. I n fact , t he signals t hat m ake up t he feedback loops of supply chains can becom e so int erwoven t hat it no longer m akes sense t o t ry t o t ease t hem apart . Are t he sales dat a flowing upst ream from ret ailers giving feedback on t he flow of goods, or are t hey act ually providing early inform at ion ( som et im es called feed forward) about t he dem and t hat will soon flow up t he chain? The difference isn't wort h debat ing; t he im port ant point is t hat free exchange of inform at ion across supply chains provides t he feedback necessary t o regulat e all t hree flows across t he chain. The great power of feedback in supply chains is t hat it reduces uncert aint y by giving com panies advance inform at ion about upcom ing variat ions in dem and and supply, allowing t hem t o bet t er cope wit h t hese variat ions. Wit hout t his advance not ice, t he only prot ect ion against variabilit y in supply and dem and is t o hold enough invent ory t o handle t he great est dem and and t he lowest supply t hat are likely t o occur, and invent ory is a very expensive form of insurance. The insight t hat inform at ion can reduce t he need for invent ory has led t o syst em at ic effort s wit hin m any indust ries t o replace invent ory wit h inform at ion wherever possible. I ndeed, subst it ut ing inform at ion for invent ory is one of t he m ost vit al aspect s of supply chain m anagem ent , and t echniques for achieving t his goal are provided t hroughout t his book. Th is ch a pt e r pr ovide s on ly t h e br ie fe st glim pse of a ve r y de e p su bj e ct , bu t it 's e n ou gh t o h e lp you m a n a ge su pply ch a in s m or e e ffe ct ive ly. Th e m ost im por t a n t in sigh t is t h e r e la t ion sh ip a m on g u n de r st a n din g, pr e dict ion , a n d con t r ol: You h a ve t o u n de r st a n d you r ch a in in or de r t o pr e dict it s be h a vior , a n d you r a bilit y t o pr e dict is w h a t a llow s you t o ga in con t r ol. I n e a ch of t h e se cor e pr oce sse s—u n de r st a n din g, pr e dict ion , a n d con t r ol—you m a n ipu la t e in pu t s a n d m on it or ou t pu t s t o se e w h a t h a ppe n s, a n d you r su cce ss de pe n ds in la r ge pa r t on h ow you se le ct t h e in pu t s a n d ou t pu t s you w a n t t o w or k w it h . An ot h e r k e y t o su cce ss is be in g pr e pa r e d t o cope w it h in pu t - ou t pu t r e la t ion s ot h e r t h a n t h e w e ll- be h a ve d lin e a r r e la t ion w e a ll n a t u r a lly a ssu m e t o be a t w or k . You a lso n e e d t o be a r in m in d t h e im por t a n ce of fe e dba ck in su pply ch a in s, m a k in g su r e t h a t you k e e p e n ou gh in for m a t ion flow in g a cr oss you r ch a in t h a t it ca n r e spon d t o ch a n gin g con dit ion s qu ick ly a n d e ffe ct ive ly.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Part II. Solutions

Chapter 5. Modeling the Supply Chain Th e e x a m ple s in Ch a pt e r 4 sh ow t h a t e ve n t h e sim ple st of syst e m s ca n ge n e r a t e su r pr isin gly com ple x be h a vior . H ow , t h e n , a r e m a n a ge r s t o u n de r st a n d su ch com ple x bu sin e ss syst e m s a s su pply ch a in s a n d m a n a ge t h e m e ffe ct ive ly? Th e a n sw e r , in a w or d, is m ode lin g. Th e on ly w a y t o u n de r st a n d com ple x syst e m s is t o con st r u ct sim plifie d m ode ls of t h e m , pla y w it h t h e m ode ls t o se e h ow t h e y w or k , a n d t h e n a pply w h a t you le a r n t o t h e r e a l- w or ld syst e m . You m a y n ot h a ve t h ou gh t a bou t it t h is w a y, bu t you a lr e a dy do t h is w it h con ce pt u a l m ode ls a ll t h e t im e , e ve n if t h e m ode ls a r e on ly in you r m in d. Th is ch a pt e r sh ow s you h ow t o u se t h e se m e n t a l m ode ls m or e e ffe ct ive ly, a n d it in t r odu ce s t w o m or e pow e r fu l k in ds of m ode ls—m a t h e m a t ica l a n d sim u la t ion m ode ls—t h a t you ca n u se a s pr e cision t ools for pr e dict ion a n d con t r ol. You don 't h a ve t o k n ow h ow t o bu ild t h e se m or e a dva n ce d m ode ls, bu t k n ow in g w h a t t h e y a r e a n d w h e n t o u se t h e m is vit a l t o m a n a gin g a su pply ch a in e ffe ct ive ly.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 5. Modeling the Supply Chain

The Case for Models A m odel is not hing m ore t han a sim plified represent at ion of a real- world syst em . This represent at ion can t ake a wide variet y of form s, including verbal explanat ions, whit eboard diagram s, m at hem at ical equat ions, physical st ruct ures, and com put er program s. All t hese m odels serve a com m on purpose: They t ake a syst em t hat m ay be hard t o underst and or dangerous t o m anipulat e, and t hey render it in a form t hat is easier t o underst and and safer t o play wit h. Models help wit h all t hree of t he key business processes described in Chapt er 4: underst anding, predict ion, and cont rol ( Figure 5.1 ) . Building a m odel of a syst em requires t hat you analyze t he syst em t o ident ify it s key com ponent s, figure out how t hose com ponent s work, and t hen reassem ble t hem in a way t hat replicat es t he essent ial behavior of t he syst em . That basic sequence of analysis and synt hesis is t he surest way t o underst and com plex syst em s. I t 's also t he m et hod underlying m ost scient ific discoveries, engineering solut ions, and business innovat ions.

Figure 5.1. Modeling Supply Chains

Once you have assem bled a m odel, you can use it as a t est bed t o generat e predict ions about how t he syst em it represent s would behave under a variet y of condit ions. Would building a new warehouse in Om aha reduce t ransport at ion cost s as m uch as expect ed? Could t he current supply chain support a 15% increase in dem and? What would be t he im pact on cash flow of ext ending bet t er credit t erm s t o key cust om ers? I t 's possible t o answer " what if" quest ions such as t hese by changing t he real- world syst em , but m odels provide answers fast er, less expensively, and wit h a lot less risk t o t he com pany. Predict ions generat ed by business m odels, in t urn, increase your underst anding of t he real- world syst em , and you can use t hat increased underst anding t o furt her im prove t he qualit y of t he m odel and it s predict ions. To see how im port ant predict ions are t o helping you im prove your supply chain, t hink back t o t he exam ple in Chapt er 3, in which a cust om er and a supplier want ed t o creat e a shared win by reducing t heir t ot al inspect ion cost s. The t radeoff curve shown in Figure 3.11 revealed t hat t he best arrangem ent was for t he supplier t o spend a dollar m ore on inspect ion, allowing t he cust om er t o spend four dollars less. Where did t his t radeoff curve com e from ? I t could only be t he result of a m odel t hat t ook int o account t he operat ions required for qualit y assurance, t he cost of t hose operat ions, and t heir net effect s on qualit y. I n pract ice, a com pany wouldn't act ually draw t his t radeoff diagram once it had t he m odel; it would sim ply use t he m odel t o find t he lowest - cost solut ion. The only reason for drawing t he diagram would be t o help people underst and why t he new inspect ion program is a win for bot h com panies. Models are also used t o cont rol real- world syst em s, as shown on t he right of Figure 5.1 . This use of m odels is less obvious t han t he ot her t wo because t he m odels used in cont rol are usually im plicit —t hat is, t hey are em bedded in t he design of business syst em s, but are never com m unicat ed t o t he people who own and operat e t hose syst em s. This problem is part icularly acut e in t he case of soft ware. All t he various kinds of supply chain soft ware described in t he next chapt er are based on very specific m odels about how product ion, dist ribut ion, replenishm ent , sales, and ot her business processes are carried out .

Unfort unat ely, m ost of t he com panies t hat buy t his soft ware have no idea what t hose m odels are unt il aft er t hey inst all t he syst em and discover t hat t he em bedded m odels don't support t he way t hey do business. The im pact of t his m ism at ch can range from a m inor nuisance t o a com plet e failure of t he syst em , which is cert ainly a devast at ing way t o learn about t he im port ance of business m odels. Business m odels com e in a wide variet y of form s, but m ost of t hem fall int o one of t he t hree broad cat egories shown in Figure 5.2 . Con ce pt u a l m ode ls use diagram s and descript ions t o represent a business syst em . They can be creat ed on whit eboards, com put er screens, or t he backs of envelopes, and t hey provide sim ple, fam iliar st ruct ures for reasoning about t he business. M a t h e m a t ica l m ode ls represent a business in t erm s of form ulas and procedures, and t hey are solved by evaluat ing t hose form ulas or procedures under a part icular set of assum pt ions. Sim u la t ion m ode ls use soft ware obj ect s t o represent t he com ponent s of a business, and t hey are solved by " running" t he m odel t o see what happens when t he obj ect s int eract wit h each ot her. Mat hem at ical and sim ulat ion m odels are oft en referred t o as for m a l m ode ls because t hey have st rict form s and generat e num erical predict ions, in cont rast t o t he inform alit y of concept ual m odels.

Figure 5.2. Three Kinds of Models

The dist inct ions am ong t hese t hree kinds of m odels are not hard and fast —hybrid form s are com m on. But t he t hree t ypes do represent t hree fundam ent ally different approaches t o m odeling, and each offers a unique set of capabilit ies and lim it at ions. Because concept ual m odels are t he easiest t o build and underst and, t hey are t he best choice for achieving a shared underst anding of t he supply chain, part icularly when m anagers are involved in t he m odeling process. Mat hem at ical m odels are t he m ost powerful, and t hey are best used t o predict and opt im ize t he perform ance of t he chain. Sim ulat ion m odels are t he m ost flexible, and t hey should be used t o st udy t he behavior of a m odel under t he m ost realist ic business condit ions. As a m anager, you don't need t o know how t o use m at hem at ical m odels and sim ulat ions; t hese form al m odels are usually im plem ent ed in soft ware, and specialized skills are required t o set t hem up and run t hem . But you do need t o know how t o use concept ual m odels because you are already applying t hem , whet her well or badly, and you need t o know what t o expect of t he ot her t wo t ypes and when t o t rust t heir out put . The next t hree sect ions provide a quick

t our of t he t hree kinds of m odels, and t he final sect ion offers som e guidelines for using t hem t o solve supply chain problem s.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 5. Modeling the Supply Chain

Conceptual Models The concept ual m odel is by far t he sim plest of t he t hree t ypes. This sort of m odel is basically a descript ion of a business syst em and is usually expressed as som e com binat ion of diagram s and explanat ions. To a large ext ent , t he form at depends on t he experience of t he m odelers—t hose wit h t he m ost t raining in m odeling usually rely on det ailed diagram s wit h form al not at ion t o reduce am biguit y. By cont rast , t hose wit h lit t le or no t raining t end t o express t heir m odels as verbal descript ions m ixed wit h st ories about how t he business works—st ories t hat can oft en be form alized as scenarios. Alt hough generally less precise t han diagram s, descript ions and scenarios oft en capt ure t he nat ure of t he business in a way t hat form al diagram s cannot . The best concept ual m odels are usually a m ix of diagram s, descript ions, and scenarios. Regardless of how you express a concept ual m odel, t he key is t o find t he right balance bet ween precision and ease of com m unicat ion. For syst em s analyst s t rained in t he use of ent it y relat ionship ( ER) diagram m ing, form al ER diagram s and det ailed scenarios m ay be j ust t he right t ools. For m anagers who have never engaged in business m odeling before, t he right balance m ay be a com binat ion of sim ple diagram s and inform al explanat ions. But even wit h m anagers, som e convent ions are necessary t o m ake t he diagram s and explanat ions m ake sense. Ot herwise t he out put of t he process m ay cont ain m ore m yt h t han m odel. The diagram s in t his book generally follow t he convent ions of convergent engineering, a m odeling t echnique I developed specifically t o help m anagers form ulat e useful business m odels. I n t his approach, a business syst em consist s of t hree basic kinds of obj ect s: organizat ions, processes, and resources. As shown in Figure 5.3 , each of t hese obj ect s plays a different role in t he m odel, and t he t hree relat e t o each ot her in ways t hat bot h const rain t he m odel and m ake it m ore underst andable at t he business level. Briefly put , organizat ions own resources and execut e processes; processes consum e one set of resources and generat e anot her set ; and resources are t he source of all cost and value in t he syst em . There is m uch m ore t o t he approach t han t his, of course ( see m y earlier book, Business Modeling wit h Obj ect Technology ) , but t his one- sent ence sum m ary illust rat es what I view as an appropriat e level of form alism for m anagers, and it should m ake t he illust rat ions m ore m eaningful t o you as well.

Figure 5.3. Organizations, Processes, and Resources

Concept ual m odels can be developed by individuals, but for syst em s t hat cross organizat ional boundaries, as supply chains inevit ably do, t he best approach is t o assem ble a t eam of represent at ives from all t he groups involved and ham m er out t he m odel t oget her. Many soft ware t ools have been designed t o support t his group design process, but low- t ech t ools are oft en t he m ost effect ive. Personally, I 've always got t en t he best result s from a com binat ion of whit eboard diagram s and 5x7 index cards. Each card represent s one of t he organizat ions, processes, or resources required for t he m odel, and part icipant s t ake t urns role- playing t hese obj ect s as t hey int eract in t he operat ion of t he business. The result ing process is highly engaging, oft en cont ent ious, and always educat ional as part icipant s discover t hat each has a radically different underst anding of how t he business act ually works. Once t he group has assem bled a consensus m odel out of it s various conflict ing perspect ives, it has a solid foundat ion on which t o build a bet t er syst em . Alt hough concept ual m odels form t he basis for underst anding syst em s, t hey are of lit t le value in predict ion and cont rol. I t should be clear from t he preceding chapt er t hat even t he sim plest m odels can produce surprising int eract ions as soon as t wo or m ore com ponent s are hooked t oget her, and our m inds are sim ply not equipped t o ext rapolat e t he effect s of t hese int eract ions. When we do t ry t o puzzle out t he behavior of a syst em , m ost of us t acit ly assum e t hat all t he relat ions involved are linear. For reasons t hat psychologist s are st ill t easing out , it is ext rem ely difficult for us t o ext rapolat e t he behavior of nonlinear int eract ions, so we nat urally t end t o work wit hin our lim it at ions and oversim plify real- world relat ionships. Going beyond t hese lim it at ions requires us t o t urn t o m ore powerful kinds of m odels.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 5. Modeling the Supply Chain

Mathematical Models Rem em ber t hose word problem s you hat ed as a kid? They went som et hing like t his: I f a boat m oving upst ream in a river flowing at 2 m iles an hour t akes 4 hours t o t ravel 3 m iles, how m any people were in t he boat ? These exercises were designed t o t each you how t o generat e and apply m at hem at ical m odels. And despit e t his early t raining, you do use m at hem at ical m odels t oday. You j ust don't do it form ally. For exam ple, suppose your boss asks you how m uch it would cost t o run bat ches of 1,000, 2,000, or 3,000 audio CDs. You know t hat it cost s $1,000 t o set up t he run and a dollar t o m ake each CD once t he run begins, so your t ot al cost would be $1,000 plus $1 t im es t he num ber of CDs, giving you $2,000, $3,000, and $4,000 for t he t hree quant it ies. I n working up t hose num bers, you used one of t he m ost com m on m at hem at ical m odels in all of business—a linear m odel. I n effect , your m odel predict s a linear relat ion bet ween cost and t he num ber of CDs produced, as shown in Figure 5.4 .

Figure 5.4. Production Cost by Volume

As t his exam ple suggest s, a m at hem at ical m odel is act ually a special kind of syst em , one in which relat ions are specified using equat ions. The preceding exam ple used t he linear relat ion, t he best - behaved rogue in t he gallery of relat ions described in Chapt er 4 ( see Figure 4.4 ) . But in t his case t he relat ion isn't j ust a graph of observed values—it 's a m at hem at ical equat ion t hat specifies a procedure for generat ing t hose values, as shown in Figure 5.5 . The equat ion is act ually a recipe for carrying out t he calculat ion: First m ult iply t wo num bers t oget her, t hen add a t hird num ber t o t he result .

Figure 5.5. The Linear Model

Like all syst em s, m at hem at ical m odels have input s and out put s. I n t he linear m odel shown in Figure 5.5 , t he input is represent ed by t he let t er x and t he out put by y. As you t wist t he knob t o vary t he value of x, t he reading for y m oves up t he graph in a st raight line. The ot her t wo quant it ies, labeled a and b, are called pa r a m e t e r s, and t hey are used t o adj ust t he m odel t o a part icular set of circum st ances. I n t he linear m odel, a changes t he angle of t he line and b changes it s height . I n Figure 5.5 , a and b are bot h set t o 1. I n Figur e 5.4, a is $1, t he unit cost , and b is $1,000, t he set up cost . Where do t he values of param et ers com e from ? Param et ers are very int erest ing in t his respect : They can act eit her as input s or as out put s, depending on how you want t o use t he m odel. I f you already know what t hese param et ers are, as you did in t he product ion cost ing exam ple, you ent er t hose values as set up input s before you run t he m odel, t hen feed in values of x t o see what kind of graph t hey produce. I f you don't know t heir values but already have som e dat a plot t ed, you can do it t he ot her way around—give t he m odel t he dat a and look for t he param et er values t hat produce t he best fit t o t he dat a. For exam ple, if you didn't know t he set up and unit cost s but did know t he t ot al cost s for 2,000, 3,000, and 4,000 CDs, you could plot t he graph shown in Figure 5.4 and t hen read t he values of t he param et ers right off t he graph. The linear m odel is a part icularly sim ple t ype, m aking it easy t o underst and and apply. The form ulas used in m at hem at ical m odels are oft en com plex and hard t o underst and, part icularly when t he m odels use som e of t he m ore roguish relat ions such as nonm onot onic relat ions. But t he basic pat t ern rem ains t he sam e: All relat ions are expressed as equat ions, and any num ber of relat ions can be com bined t o creat e m odels of any size. There m ay be a long series of st eps required t o solve a large m odel, wit h specialized t echniques for

curve fit t ing when t hat process is applied, but t he basic operat ion of t he m odel rem ains t he sam e. Calculat ing num erical solut ions for anyt hing but t he sim plest of m odels can quickly becom e t edious, but t his grunt work is alm ost always done by com put ers. The m ost com m on t ool for m at hem at ical m odeling is a spreadsheet program such as Microsoft Excel. Spreadsheet s st art ed as out t ools for account ant s, but t heir prim ary use t oday is in building business m odels. These m odels usually deal wit h financial flows, but t he num bers can j ust as easily express t he flow of supply or dem and. For exam ple, spreadsheet s are oft en used t o build dem and forecast s, as described in Chapt er 10 . Of course, spreadsheet s aren't t he only t ools for im plem ent ing m at hem at ical m odels—m any of t he supply chain applicat ions described in t he next chapt er use specialized m at hem at ical m odels t o perform t heir calculat ions. Given t he difficult y of building and using m at hem at ical m odels, t here has t o be a good reason t o use t hem , and t here is. Unlike concept ual m odels, where t he behavior of t he m odel can be a subj ect of m uch debat e, m at hem at ical m odels are unam biguous; you plug in t he num bers as input s and you get clear, quant it at ive result s. That 's a powerful advant age in dealing wit h com plex syst em s in which behavior can oft en be hard t o underst and, m uch less predict . There is, however, an even bet t er reason for using m at hem at ical m odels: I n m any sit uat ions, t hey can not only t ell you what out put you can expect from a given set of input s, t hey can t ell you what input s t o use in order t o produce t he best possible out put . This rem arkable abilit y, known as opt im iza t ion , can be a t rem endous t ool for m aking decisions about how t o run a supply chain. What opt im izat ion does is a lot like curve fit t ing, but inst ead of looking for param et er values t hat m at ch a given set of out put s, opt im izat ion looks for values t hat produce t he best out put s, and you get t o t ell it what const it ut es " best ." For exam ple, you could m odel t he way profit depends on bot h price and sales, including t he int eract ion bet ween t he t wo, t hen solve t he m odel m at hem at ically t o find t he price t hat m axim izes your profit . I n supply chain m anagem ent , t he m ost com m only used opt im izat ion t echnique is lin e a r pr ogr a m m in g ( LP) . Linear program m ing is an ext rem ely powerful m anagem ent t ool, one t hat com es about as close t o m agic as anyt hing in business. Linear program m ing can be done in Excel, using it s built - in opt im iz e r, but t he really powerful LP opt im izers are found in t he supply chain design and planning t ools described in t he next chapt er. Skilled m odelers use t hese syst em s t o const ruct m odels t hat include t housands of param et ers, represent ing such fact ors as hist orical dem and levels, plant and warehouse capacit ies, m at erial and labor cost s, t ransport at ion rat es, and required service levels. They t hen run t he m odels t o discover t he best m ix of product s t o build at each plant , t he m ost cost - effect ive sources for each cust om er region, and ot her opt im al values. There is a price t o be paid for all t his power: Linear program m ing m akes som e rat her st ringent sim plifying assum pt ions about t he real- world syst em . As t he nam e linear program m ing suggest s, one assum pt ion is t hat all relat ions be of t he well- behaved linear form , so all t he t rue rogues in t he gallery are banished. But in sit uat ions where t he assum pt ions com e reasonably close t o t he realit y, t he opt im izat ions provided by linear program m ing can be im m ensely valuable. There are also variant s of linear program m ing t hat relax som e of t hese

const raint s. These alt ernat ives usually t ake longer t o com put e solut ions, but t hey are st ill guarant eed t o produce opt im al solut ions.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 5. Modeling the Supply Chain

Simulation Models As t he preceding paragraph suggest s, m uch of t he power of m at hem at ical m odels com es from dist illing com plex relat ionships down int o relat ively sim ple m at hem at ical form s. For syst em s wit h known relat ionships t hat can be capt ured in equat ions, m at hem at ical m odels are usually t he best way t o go. But som et im es t he relat ions am ong com ponent s don't conform t o sim ple equat ions. Alt ernat ively, t hey m ay fit an equat ion j ust fine, but t here is no way of knowing what t hat equat ion m ight be. I n such cases, sim ulat ion m odels are usually a bet t er approach. Like a m at hem at ical m odel, a sim ulat ion is a special kind of syst em , wit h input s, out put s, and param et ers. The difference is t hat sim ulat ions are a bit m ore lit eral t han m at hem at ical m odels: They j ust t ry t o im it at e t he behavior of a syst em 's com ponent s rat her t han dist illing t hat behavior down t o an equat ion. I n essence, building a sim ulat ion consist s of program m ing a num ber of soft ware obj ect s t o act out t he roles of real- world obj ect s, t hen running t he syst em t o see how t hose obj ect s int eract wit h each ot her under realist ic business condit ions. The obj ect s represent cust om ers and suppliers, orders and shipm ent s, m at erials and product s, vehicles and cont ainers, and all t he ot her elem ent s of supply chains described in t his book. I n t he program , t hese obj ect s affect each ot her j ust as t hey do in t he real world: Cust om er obj ect s creat e order obj ect s and send t hem t o supplier obj ect s, which ship product obj ect s using vehicle and cont ainer obj ect s, and so on. I n a good sim ulat ion, t hese obj ect s are m odeled at a fine level of det ail. The m ore det ailed and accurat e t he sim ulat ion, t he m ore precise and reliable t he predict ions about supply chain perform ance. As wit h m at hem at ical m odels, sim ulat ions can be const ruct ed using a variet y of t ools. A really sim ple sim ulat ion can be conduct ed wit h som et hing as low- t ech as index cards, wit h people holding t he cards and act ing out t he roles of t he various obj ect s. Sim ulat ions can also be expressed in soft ware using convent ional program m ing languages. However, t he m ost cost - effect ive approach is usually t o use a com m ercial sim ulat ion syst em . These syst em s include graphical t ools for building m odels, aut om at ed rout ines for t est ing t hem under different condit ions, and report ing t ools for analyzing t he result s. Alt hough general- purpose sim ulat ors are available and can be used, t he best choice is a specialized t ool built j ust for sim ulat ing supply chains. These dedicat ed sim ulat ors include prebuilt obj ect s for all t he usual supply chain

elem ent s, allowing powerful sim ulat ions t o be assem bled and t est ed wit h a m inim um of effort . Once you have const ruct ed a sim ulat ion m odel, you t est it by running it as shown in Figure 5.6 . First , you init ialize t he obj ect s in t he syst em by set t ing t heir param et ers t o reflect real- world product ion capacit ies, shipm ent t im es, m at erial and labor cost s, ret ail prices, and t he like. You t hen st art t he sim ulat or and feed it a sequence of input s as t hey would occur in real t im e, including shift ing levels of dem and, seasonal variat ions in price, and so on. As t he m odel runs, it generat es out put s indicat ing t he speed wit h which dem and is sat isfied, t he am ount of invent ory held at each facilit y, t he t ot al cost t o operat e t he syst em , and ot her m easures of supply chain perform ance.

Figure 5.6. Running a Simulation

Up t o t his point , running a sim ulat ion m odel is a lot like running a m at hem at ical m odel: You set up t he param et ers, pass in t he input s, and see what you get in t he way of out put s. But sim ulat ions go furt her t han m ost m at hem at ical m odels in t hat t hey allow t he param et ers and input s t o vary about som e average value rat her t han being locked down t o a fixed value. I t is possible t o incorporat e t his kind of variabilit y int o m at hem at ical m odels, but in syst em s as large as supply chains, t he com put at ional power required is usually t oo great for t hese t echniques t o be of m uch value. Sim ulat ors, on t he ot her hand, incorporat e variabilit y quit e nat urally—an im port ant advant age because sales, shipm ent s, prices, and count less ot her aspect s of supply chains vary quit e a bit in real- world supply chains, and t his variabilit y has a m aj or im pact on how t he chains perform . I t 's not j ust a m at t er of causing variabilit y in t he out put s, alt hough t hat cert ainly is one out com e. The m ore im port ant concern about variabilit y is how it affect s t he way you m anage t he chain. As an exam ple of t his, consider a kind of variabilit y t hat is especially problem at ical for supply chains: variabilit y in dem and. I m agine t hat you have a product —a part icular kind of sofa, say—t hat is selling at a rat e of 100 a week. You can sim ply use t hat value as a param et er of t he m odel, but t hat sim plificat ion glosses over a very im port ant realit y: Som e weeks you sell m ore t han 100, and som e weeks you sell less. I n any given week you have t o be ready t o handle t he act ual sales for t hat week, not j ust t he average sales, which m eans you need t o m aint ain ext ra invent ory t o buffer variabilit y in dem and. How m uch invent ory? I t depends on how m uch variabilit y you have. I f your weekly sales vary quit e a bit , like t hose shown in Case A of Figure 5.7 , you need t o be able t o supply as m any as 150 sofas a week t o avoid running

out . I f you have only half t his am ount of variabilit y, as shown in Case B, t hen 125 sofas will probably be enough. I n eit her case, having j ust t he average num ber of 100 sofas on hand pret t y m uch guarant ees t hat you will run out of st ock at least half t he t im e. A m odel based only on average dem and would not produce a viable business result .

Figure 5.7. Variability in Weekly Demand

The way m odels handle t his sort of variabilit y is by using dist ribut ions. I f you t ake a large num ber of dat a point s of t he sort shown in Figure 5.7 and plot t he t ot al num ber of t im es you get sales of each num ber of sofas, t he result will be dist ribut ions of values like t he ones shown in Figure 5.8 . The part icular dist ribut ion shown follows a very com m on form , called t he norm al dist ribut ion, which has a m at hem at ical form ula wit h j ust t wo param et ers: t he m ean, which is t he form al nam e for t he num erical average, and t he st andard deviat ion, which is a m easure of t he variabilit y. I n Figure 5.8 , bot h dist ribut ions have a m ean of 100, but t he upper one has a st andard deviat ion of 15, t wice t hat of t he lower figure. To capt ure t he variabilit y in weekly dem and, t hen, you t ell t he m odel t o use t he norm al dist ribut ion and give it t wo param et ers—t he m ean and t he st andard deviat ion—rat her t han j ust t he m ean. Given t his richer input , t he m odel can random ly vary t he level of dem and t o reflect t he way it varies in realit y, producing a m uch m ore accurat e sim ulat ion.

Figure 5.8. Distributions of Weekly Demand

Adding variabilit y t o a sim ulat ion m akes it m ore accurat e, but it also com plicat es m at t ers because t he result s of running t he m odel now have a random elem ent t o t hem . I f t he out put can vary each t im e you run a m odel, you can't j ust run it once and t ake t he result s as definit ive. I nst ead, you have t o run it m any t im es and average t he result s t o see how t he m odel is m ost likely t o behave under a realist ic range of circum st ances. This t echnique of running a m odel m any t im es wit h random values is called t he M on t e Ca r lo m e t h od , in recognit ion of t he role t hat chance plays in t he out com es. I t m ay sound t edious t o do m any runs and pool t he result s, but sim ulat ion t ools handle all t hat aut om at ically. The only real cost t o t he m ult iple runs is t he t im e spent wait ing for t he result s. A Mont e Carlo series provides a det ailed look at how a supply chain design will perform under a single set of realist ic business condit ions. This m ay be sufficient t o validat e t he result s of m at hem at ical m odeling, but it doesn't do anyt hing t o im prove t hose result s. The way t o do t hat is t o vary t he design in som e syst em at ic m anner, running a Mont e Carlo series on each variat ion and com paring t he result s. For exam ple, you could seek t he opt im al level of safet y st ock under a variet y of dem and sit uat ions by sim ulat ing each level and com paring t he result s. Alt ernat ively, you m ight want t o com pare t he cost and benefit s of varying t he num ber of warehouses over a specified range. Alt hough sim ulat ions are bet t er t han m at hem at ical m odels at exploring t he effect s of variabilit y, t hey aren't as good at finding opt im al solut ions. The best you can do wit h a sim ulat or is t o vary t he value of one or m ore param et ers in a syst em at ic way and look for t he one t hat gives you t he best fit . That can be t edious, but m ost sim ulat ors support a t echnique called h ill- clim bin g t o accelerat e t he process. Rat her t han t ry out every possible value of a param et er, t he sim ulat or st art s wit h a value provided as input , runs t he m odel t o det erm ine how well it perform s using t hat value, and t hen explores nearby values t o see if it can im prove on t hat perform ance. For exam ple, if t he param et er illust rat ed in Figure 5.9 was init ialized at a value of 30, t he

sim ulat or would t ry out values j ust above and below 30, quickly discovering t hat only an increase in t he value im proved perform ance. Through a series of such t est s, it would gradually hom e in on 50 as t he best value.

Figure 5.9. Hill-Climbing to Find the Best Value

Hill- clim bing is a great t im e- saver in sim ulat ion work, but t here is no guarant ee t hat it will find t he best possible value. The m ost com m on problem , as shown in Figure 5.9 , is t hat it can converge on a solut ion t hat is t he best wit hin a region, but fail t o find a bet t er solut ion t hat lies fart her out . I n t he exam ple, init ial values below 80 will t end t o m ove t oward 50 as t he best choice, even t hough a bet t er result can be obt ained wit h a value of 110. There are variat ions of hill- clim bing t hat increase t he likelihood of finding t he best value, but t he fact rem ains t hat , unlike opt im izers, sim ulat ors are not guarant eed t o find t he best possible solut ion.

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Chapter 5. Modeling the Supply Chain

Combining Models The fact t hat t hree very different kinds of business m odels are available begs t he quest ion of which one is best . The answer t o t hat quest ion depends on t wo t hings: The nat ure of t he problem you're t rying t o solve, and t he kind of answers you are looking for. This final sect ion of t he chapt er offers a few suggest ions about when and how t o use each of t he t hree kinds of m odel. The m ost im port ant considerat ion is t o apply each kind of m odel t o t he problem s it is best at solving. I f your goal is t o underst and how t he current supply chain works and explore ways t o im prove it , t he best choice m ay be a well- st at ed concept ual m odel. I n fact , int roducing m at hem at ics or sim ulat ions can oft en do m ore harm t han good by clouding t he essent ial issues wit h irrelevant det ail. I f, on t he ot her hand, t he problem is one of choosing am ong a num ber of well- defined alt ernat ives whose behavior can be st at ed in t he form of equat ions, not hing can m at ch t he power of a m at hem at ical m odel. I f t he behavior of t he chain can't be reduced t o fam iliar equat ions, or if t he problem at hand concerns t he effect s of variabilit y on perform ance, a sim ulat ion is usually t he best choice. Because t he t hree kinds of m odels have com plem ent ary st rengt hs, m any problem s are best at t acked wit h t wo or m ore m odels in com binat ion. Concept ual m odels are usually t he best place t o st art because t hey require t he least am ount of t raining t o use, and t hey can be indispensable in developing a shared underst anding of a problem and it s possible solut ions. The best st art ing point is usually a concept ual m odel because it provides a quick way t o ident ify t he key inform at ion required for building a form al m odel. Once t he form al m odel has been explored, t he result s can be m apped back t o t he concept ual m odel, wit h appropriat e m odificat ions, t o com m unicat e t he key insight s t o ot her m anagers. I f it isn't clear based on t he problem which kind of form al m odel t o use, consider using bot h (Figure 5.10) . One opt ion is t o use a m at hem at ical m odel t o find an opt im al solut ion, t hen use a sim ulat ion t o m ake sure t he result s are robust across t he m any kinds of variabilit y t hat can affect t he perform ance of t he syst em . The ot her opt ion is t o use a sim ulat or t o get a bet t er feel for how t he supply chain works, t hen use t he result s t o form ulat e a m at hem at ical m odel suit able for opt im izat ion. The ideal is t o m ove fluidly am ong all t hree kinds, using each t o gain insight s about t he ot her unt il t he best solut ion

em erges.

Figure 5.10. Combining the Types

All t hree kinds of m odels—including concept ual m odels—require special t raining in order t o use t hem effect ively. Unfort unat ely, t raining in t he use of m odels alm ost always focuses on one t ype t o t he exclusion of t he ot hers, or even on a single m et hod wit hin a t ype. Syst em s analyst s are com m only t rained in t he use of st ruct ured concept ual m odels such as ent it y- relat ionship diagram m ing, financial analyst s and operat ions analyst s are usually t rained t o use m at hem at ical m odels, and so on. I n looking for specialist s t o help you in your own m odeling effort s, it 's im port ant t o find people who are fluent in all t hree t ypes. To paraphrase t he old saying, if you hire som eone who only has a ham m er, all your problem s will look like nails. Regardless of who const ruct s your form al m odels, you should be careful about delegat ing concept ual m odels. At t his level, t he m ost im port ant m odelers in t he com pany are you and your fellow m anagers. You are t he ones wit h first hand knowledge of how t he supply chain is const ruct ed, t he real- world experience in how well it 's working, and t he responsibilit y t o m ake it work bet t er. The best way t o st art any effort t o m odel t he supply chain is by gat hering t oget her t he m anagers who m ake t he chain work, bringing in a professional facilit at or, and building a concept ual m odel. Once you have t hat m odel in hand, t hen you can bring in specialist s t o t ranslat e it int o a form al m odel and analyze it in furt her det ail. M a n y m a n a ge r s fin d t h e m se lve s r e lu ct a n t t o t a p t h e pow e r of bu sin e ss m ode lin g, oft e n in t h e m ist a k e n be lie f t h a t t h e y a r e n 't qu a lifie d t o do it . Aft e r m or e t h a n 1 5 ye a r s of fa cilit a t in g bu sin e ss de sign se ssion s, I ca n sa y w it h con fide n ce t h a t n ot on ly a r e ope r a t ion a l m a n a ge r s com pe t e n t t o do t h e w or k , t h e y a r e t h e on ly on e s w h o u n de r st a n d t h e bu sin e ss w e ll e n ou gh t o do t h e j ob. On ce t h e y ge t st a r t e d, m ost m a n a ge r s fin d t h a t , de spit e t h e h a r d w or k a n d con t e n t iou s de ba t e s t h a t gr ou p m ode lin g e ffor t s m a y in volve , t h e y love t h e pr oce ss be ca u se it t a ps t h e ir n a t u r a l a bilit ie s for or ga n izin g r e sou r ce s a n d solvin g pr oble m s. So don 't r e sist t h e ide a of m ode lin g j u st be ca u se it 's for e ign t o you ; it 's for e ign t o m ost m a n a ge r s a t fir st . An d r e m e m be r t h a t t h e on ly w a y t o ga in a com pe t it ive a dva n t a ge is by doin g t h e t h in gs t h e ot h e r gu y isn 't w illin g or a ble t o do. Le ve r a gin g t h e pow e r of bu sin e ss m ode ls is on e of t h e m ost dir e ct , cost - e ffe ct ive w a ys of im pr ovin g you r

a bilit y t o w in t h e n e w com pe t it ion be t w e e n su pply ch a in s.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Part II. Solutions

Chapter 6. Supply Chain Software Fift y ye a r s a go, su pply ch a in s w e r e de sign e d a n d m a n a ge d u sin g t h e t im e - h on or e d t ools of pa pe r a n d pe n cil, w it h a lit t le h e lp fr om ca lcu la t or s. Toda y, it w ou ld be a lm ost u n t h in k a ble t o ope r a t e a la r ge su pply ch a in w it h ou t e x t e n sive soft w a r e su ppor t . Bu t t h e r e is a be w ilde r in g a r r a y of soft w a r e t o ch oose fr om , a n d pick in g t h e w r on g pa ck a ge ca n br in g you r su pply ch a in t o a st a n dst ill. Th is ch a pt e r pr ovide s a gu ide d t ou r of su pply ch a in soft w a r e , st a r t in g w it h cla ssic m a n u fa ct u r in g syst e m s a n d w in din g u p w it h spe cia lt y a pplica t ion s de sign e d for spe cific su pply ch a in pr oble m s. Th e t ou r con clu de s w it h a look a t h ow t h e I n t e r n e t is ch a n gin g t h e w a y t r a din g pa r t n e r s coor din a t e t h e flow of de m a n d, su pply, a n d ca sh a cr oss t h e su pply ch a in .

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 6. Supply Chain Software

The Manufacturing Platform The easiest way t o underst and t he m any form s of supply chain soft ware is t o view t hem in t he cont ext of t he business processes t hey support . The m at rix in Figure 6.1 cat egorizes t hese processes in a way t hat reflect s t he st ruct ure of t his book. The rows of t he m at rix correspond t o t he operat ional, planning, and design levels of m anagem ent , and t he colum ns reflect processes concerned wit h supply, product ion, and dem and. All of t he processes shown in t he m at rix are discussed in t he appropriat e chapt ers in Part s I I I t hrough V. I n t he present chapt er, I t ake t he processes as given and focus on t he soft ware.

Figure 6.1. The Supply-Chain Management Matrix

Because supply chains are concerned wit h m oving m anufact ured goods, t he funct ions of supply chain soft ware are a nat ural ext ension of exist ing

m anufact uring syst em s. Today, t he dom inant soft ware in m anufact uring com panies is t he e n t e r pr ise r e sou r ce pla n n in g ( ERP) syst em . Alt hough t he em phasis of ERP is on t he int ernal operat ions of a m anufact uring organizat ion—t he act ivit ies t hat t ake place " inside t he four walls" —m any of t he applicat ions included in ERP packages are direct ly relevant t o supply chain act iv it ies. ERP syst em s can be difficult t o underst and because t hey have becom e so large and com plex over t he years, incorporat ing a t rem endous array of funct ions and t ouching alm ost every area of a m anufact uring com pany. However, t he essence of ERP can be underst ood by placing a handful of key m odules in t he cont ext of t he m anagem ent m at rix, as shown in Figure 6.2 . The heart of an ERP syst em is a set of planning m odules t hat t ranslat e ant icipat ed dem and int o plans for m anaging supply, product ion, and dist ribut ion. The ot her m odules help a com pany im plem ent t hese plans by providing com put erized support for purchasing, receiving, sales, and ot her operat ions.

Figure 6.2. Modules of an ERP System

The basic flow of ERP- based planning is shown in Figure 6.2 . Using hist orical and expect ed sales as input , t he dist ribut ion requirem ent s planning ( DRP) m odule builds a dist ribut ion plan t hat indicat es how m any product s of each t ype need t o be at each locat ion in each period. The result ing plan is passed as input t o t he m ast er product ion scheduling ( MPS) m odule, which works out when product ion will have t o occur in order t o m eet t he dist ribut ion schedule. The MPS m odule t hen calls on t he services of t wo ot her m odules t o validat e it s schedule: The m at erial requirem ent s planning ( MRP) m odule m akes sure t hat all t he necessary m at erials and com ponent s can be acquired in t im e, and t he capacit y requirem ent s planning ( CRP) m odule checks t o see whet her t he available product ion facilit ies will be able t o perform t he work. Alt hough t he focus of ERP is on product ion planning, several of t he m odules

include t ools for supply chain m anagem ent . The m at erial requirem ent s plan generat ed by t he MRP m odule can be fed direct ly int o t he purchasing syst em as a schedule of proposed purchases, and t he dist ribut ion plan generat ed by t he DRP m odule is used t o choreograph shipm ent s of finished goods t hrough an echelon dist ribut ion syst em . I n addit ion, t he receiving and shipping m odules handle t he flow of m at erials in and out of t he com pany, and t he invent ory cont rol m odule m onit ors t he current st ocks of raw m at erials, work in process, and finished goods. Alt hough m any com panies use ERP syst em s t o m anage t heir supply chains, ERP by it self is rarely t he best opt ion. ERP was developed t o m anage t he act ivit ies wit hin a single product ion facilit y, and it doesn't lend it self t o planning act ivit ies t hat span m ult iple facilit ies. Most ERP packages can, in fact , be used for m ore t han one plant , but t hey plan t he act ivit ies of each plant individually rat her t han developing an int egrat ed plan t hat m akes t he best use of all t he plant s. Anot her concern is t hat , as explained in Chapt er 11 , t he scheduling t echnique used by ERP was designed for t he cont rolled environm ent of a m anufact uring facilit y, and it lacks t he flexibilit y necessary t o handle t he m ore dynam ic requirem ent s of supply chains.

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Chapter 6. Supply Chain Software

Advanced Planning Systems The m ost im port ant applicat ion direct ly aim ed at m anaging supply chains is t he a dva n ce d pla n n in g a n d sch e du lin g ( APS) syst em . As wit h ERP syst em s, APS syst em s include a large num ber of m odules t hat can be com bined in various ways, and not every vendor offers t he sam e select ion of m odules. However, t he m odules shown in Figure 6.3 are com m on t o m ost APS syst em s, and t hey give a fair represent at ion of APS capabilit ies. A com parison of t his figure wit h Figure 6.2 quickly reveals t he m ost im port ant difference bet ween ERP and APS: Whereas ERP support s t he lower t wo layers of t he process m at rix, planning and operat ions, APS focuses on t he upper t wo layers, com bining planning wit h design.

Figure 6.3. Modules of an APS System

Unlike ERP, which is prim arily concerned wit h product ion facilit ies, APS t akes a net work of supply chain facilit ies as it s st art ing point . Set t ing up an APS syst em involves using t he net work design m odule t o ent er a det ailed descript ion of t he chain, including it s facilit ies, t ransport at ion links, and ot her charact erist ics. Once t his inform at ion is in place, t he planning process follows t he arrows shown in Figure 6.3 . First , t he dem and planning m odule forecast s t he dem and for each product in each region. The m ast er planning m odule t hen com bines t his forecast wit h t he capabilit ies of t he chain as described t o t he net work design m odule, developing an overall plan for m oving supplies t hrough t he chain. I n order t o develop t hat plan, it calls on t he services of t hree specialized m odules t o analyze t he im pact of t he m ast er plan on m at erials, product ion capacit y, and dist ribut ion requirem ent s. APS offers a num ber of advant ages over ERP, including a m ore flexible scheduling syst em t hat can handle t he m ore varied requirem ent s of supply chain m anagem ent . The m ost com pelling advant age of an APS syst em , however, is t hat it is based on m at hem at ical m odels t hat support opt im izat ion, including t he linear program m ing m et hod described in Chapt er 5. These m odels are used for design as well as planning, so an APS gives you t he opport unit y t o opt im ize not only t he schedule of operat ions but also t he very st ruct ure of your supply chain. The opt im izat ion is done against any m easures of perform ance you choose t o specify, including cost , cust om er service, and pr ofit abilit y . Alt hough APS syst em s offer sophist icat ed planning and scheduling capabilit ies, t hey don't provide t he operat ional m odules necessary t o t ranslat e t hese plans int o act ion. The usual solut ion t o t his problem is t o link APS syst em s int o exist ing ERP syst em s. The m ost effect ive way t o com bine t he t wo applicat ions is t o use a single APS syst em t o plan t he m ovem ent of goods across a num ber of product ion facilit ies, each of which is m anaged by a local ERP syst em (Figur e 6.4) . This approach offers t he best of bot h worlds, com bining t he advant ages of ERP and APS t o provide a level of int egrat ion t hat isn't possible wit h eit her t ype alone.

Figure 6.4. APS Integrating Multiple ERP Systems

Linking APS and ERP syst em s is a subst ant ial undert aking ( see Chapt er 11 ) , and t he cooperat ion bet ween t he t wo kinds of syst em s is st ill som ewhat lim it ed. However, t his sit uat ion is already beginning t o change for t he bet t er. ERP vendors have a hist ory of absorbing new applicat ion cat egories t hat affect ent erprise- level planning, and t hey are already hard at work rolling in supply chain soft ware. Several vendors now offer APS m odules t hat com m unicat e wit h t heir exist ing ERP m odules, and ot her vendors are likely t o follow suit in order

t o rem ain com pet it ive. I n t he m eant im e, be caut ious about ERP vendors t hat claim t o support supply chains; t hey could be offering anyt hing from a full suit e of APS m odules t o an ad hoc collect ion of t he support ing applicat ions described in t he next sect ion. Chapt er 11 , which provides a m ore det ailed descript ion of how ERP and APS m odules work t oget her, should give you enough inform at ion t o m ake an inform ed choice.

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Chapter 6. Supply Chain Software

Supply Chain Applications I n addit ion t o ERP and APS, t here are several ot her kinds of applicat ions t hat serve t he needs of supply chain m anagem ent . Figure 6.5 shows som e of t he key m odules of a warehouse m anagem ent syst em . As t he diagram indicat es, t hese packages focus prim arily on operat ions, offering j ust enough planning funct ionalit y t o sm oot h t he flow of invent ory t hrough t he facilit y. I n addit ion, t here are no m odules for product ion because t hat 's not a t radit ional funct ion of warehouses, alt hough t his is beginning t o change ( see Chapt er 15 ) . The m odules on t he supply side are concerned wit h aut om at ing t he process of receiving incom ing goods and assigning t hem t o t he appropriat e st orage locat ions, and t he m odules on t he dem and side are concerned wit h assem bling out bound orders and preparing t hem for shipm ent . The m at erials handling m odule bridges t he gap bet ween t he t wo set s of m odules, and t he yard m anagem ent m odule governs t he m ovem ent s of vehicles, cont ainers, and invent ory held in st aging areas adj acent t o t he warehouse.

Figure 6.5. Warehouse Management Modules

Anot her im port ant class of supply chain soft ware is t he t ransport at ion m anagem ent syst em ( Figure 6.6 ) . A com plet e syst em includes everyt hing from net work design t ools down t o operat ional applicat ions for t racking shipm ent s, scheduling drivers, and det erm ining how m uch it will cost t o run a shipm ent bet ween any t wo point s. Because t ransport at ion requirem ent s differ across indust ries and m odalit ies—scheduling t anker ships is quit e different from t racking t he locat ions of rail cars—t ransport at ion syst em s are usually highly specialized for individual m arket s.

Figure 6.6. Transportation Management Modules

Warehouse and t ransport at ion m anagem ent syst em s have been around for m any years. The newest generat ion of soft ware includes t he applicat ions shown in Figure 6.7 . One of t hese, cust om er relat ionship m anagem ent ( CRM) , is designed t o int egrat e all cust om er- cont act act ivit ies, including sales, service, and support . Newer st ill is t he logical count erpart of CRM, supplier relat ionship m anagem ent ( SRM) . CRM and SRM are usually lim it ed t o int eract ions wit h im m ediat e t rading part ners, so t hey each span only a single link in t he supply chain. However, som e of t he m ore advanced CRM packages include t he abilit y t o support relat ionships wit h cust om ers' cust om ers, and it seem s likely t hat SRM packages will be ext ended in a com parable m anner.

Figure 6.7. Emerging Applications

One of t he m ost recent and excit ing developm ent s is t he em ergence of t he supply chain visibilit y applicat ions shown in t he upper part of Figure 6.7 . These applicat ions t rack t he m ovem ent of invent ory as it flows t hrough t he chain, providing graphical displays t hat show expect ed and act ual levels at each locat ion. A closely relat ed cat egory is supply- chain event m anagem ent soft ware, which offers t he abilit y t o define business rules t hat t rigger when specified event s occur ( or fail t o occur) . This soft ware allows supply chain m anagers t o focus t heir at t ent ion on m anaging except ions rat her t han having t o personally m onit or every m ovem ent and com pare it against plan. Som e of t he m ost sophist icat ed syst em s for supply chain m anagem ent are used in t he design of t he chain it self. Alt hough t his capabilit y is built int o APS syst em s, it is also available in st and- alone packages. Som e of t hese syst em s use m at hem at ical m odels t o find opt im al designs, and ot hers use sim ulat ors t o const ruct highly realist ic m odels. The best syst em s offer a m ix of t hese approaches, allowing each kind of m odel t o com pensat e for t he lim it at ions of t he ot her.

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Chapter 6. Supply Chain Software

Implicit Business Models A crit ical yet rarely underst ood aspect of t he soft ware syst em s described in t his chapt er is t hat t hese syst em s are all based on m odels of t he supply chain. Som e of t he m odels are explicit and direct ly m odifiable by users. Supply chain design syst em s, in part icular, display a concept ual m odel of a chain in a graphical form t o help designers underst and and alt er t he st ruct ure of t he chain. These syst em s t hen t est t he qualit y of a design by expressing it in t he form of a m at hem at ical or sim ulat ion m odel and evaluat ing t hat m odel t o see how well it perform s. Designing a supply chain is, in effect , an exercise in m odeling. Unfort unat ely, t he explicit , m odifiable m odels used in design syst em s are t he except ion rat her t han t he rule. Alt hough planning and operat ional syst em s also rely on m odels, t hese m odels are usually buried deep in t he soft ware and cannot be m odified. ERP syst em s, for exam ple, are based on a m odel in which quant it ies of goods are produced or t ransport ed during fixed int ervals called t im e bucket s, which are m ost com m only weeks. Product ion and dist ribut ion plans are const ruct ed by working backward from t he required quant it ies of finished goods t o figure out how m uch purchasing and product ion has t o occur in each t im e bucket . I n t his m odel, all work is done on t he last possible dat e given t he current const raint s on m at erials and capacit y. How did t his m odel com e t o be t he st andard t echnique for product ion planning? I n large part , it is a hist orical art ifact t hat has been codified in t he archit ect ure of m anufact uring soft ware. The m odel was developed back in t he days before com put ers, when all planning was done on blackboards or large sheet s of grid paper, and it reflect s t he lim it at ions of hum an planners t rying t o cope wit h large m at rices of num bers. The earliest planning syst em s sim ply t ranslat ed t he m anual procedure int o a program , relieving people of t he need t o m ove quant it ies from one t im e bucket t o anot her by repeat edly erasing and rewrit ing num bers. This was a m aj or breakt hrough at t he t im e because it great ly reduced t he t im e necessary t o const ruct a workable plan. But it did not hing t o im prove t he planning process it self, which t o t his day is st ill based on doing everyt hing as lat e as possible, and it has no abilit y t o find opt im al solut ions based on business concerns such as cost . Com pared t o t he power of cont em porary m odels, t he m odel underlying ERP scheduling is pret t y sim plist ic. But once t his m odel was t ransferred t o a com put er, it was never changed.

A weak m odel wouldn't be so bad if it could be m odified, but syst em s wit h im plicit , hard- wired m odels don't offer t hat opt ion. The result is t hat using one of t hese syst em s requires a com pany t o adapt it s business t o t he soft ware rat her t han t he ot her way around. This problem is m ost apparent wit h ERP syst em s because t hey aut om at e so m any core business funct ions, but it 's also t rue of m ore recent syst em s such as t he CRM packages, which are based on im plicit m odels of how com panies int eract wit h t heir cust om ers. Many com panies insist t hat vendors m odify t heir soft ware t o conform t o t he way t hey act ually do business, but t hese cust om izat ion effort s oft en lead t o failed inst allat ions and m aint enance night m ares. The spect acular failure of Nike's cust om ized APS syst em described in Chapt er 1 illust rat es t he perils of t his appr oach. The presence of im plicit business m odels in supply chain soft ware is a problem wit hout a good solut ion. Minor t weaks m ay help, but deep cust om izat ion is rarely successful. I n m ost cases, t he choice com es down t o " t heir way or no way," and t hat 's not t he kind of decision you should have t o m ake when buying a m ult im illion- dollar syst em . But what are t he alt ernat ives? Supply chain soft ware is now so large and com plex t hat building your own syst em is rarely a viable opt ion, and t rying t o run a large chain wit hout soft ware is a nonst art er in t oday's fast - paced m arket s. The best you can do is t o be aware t hat im plicit business m odels are lurking inside all com m ercial soft ware packages and carefully choose t he m odel t hat com es closest t o fit t ing t he way you do business. Once you've found t he closest m at ch, be prepared t o change your operat ions t o conform t o t he m odel. I t m ay be a galling choice, but t he alt ernat ives are worse.

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Chapter 6. Supply Chain Software

Internet-Based Systems The biggest change t aking place in supply chain soft ware t oday is t he m ove t o t he I nt ernet . The I nt ernet does not , as was widely believed at t he end of t he last cent ury, lead t o a fundam ent ally different econom y, nor does it alt er t he basic dynam ics of supply chains. Physical goods st ill have t o get from place t o place, and t he I nt ernet doesn't alleviat e t he need t o choreograph t hat m ovem ent as precisely as possible. What t he I nt ernet does do is provide a vast ly im proved com m unicat ions m edium for coordinat ing t his m ovem ent of goods. Like t he t elegraph, t he t elephone, and t he fax m achine before it , t he prim ary im pact of t he I nt ernet is on t he speed, and not t he nat ure, of business processes. As wit h t hese earlier t echnologies, t his im pact is proving t o be bot h deep and pervasive. Boiled down t o it s essence, supply chain m anagem ent consist s of choreographing t he flow of dem and, supply, and cash. Two of t hese flows—dem and and cash flow—can be m oved ent irely t o t he I nt ernet (Figur e 6.8) . Orders consist ent irely of t ext ual dat a t hat is readily t ransm it t ed in elect ronic form , and paym ent s can be m ade using elect ronic funds t ransfer ( EFT) . Moreover, all t he support ing inform at ion t hat is passed up and down t he chain—forecast s, plans, not ices, and t he like—can be shift ed t o t he I nt ernet as well. Wit h t he except ion of act ually shipping t he goods, every funct ion of t he supply chain can be perform ed fast er, cheaper, and m ore accurat ely using t he I nt ernet . The advant ages are profound, and t he I nt ernet is rapidly becom ing t he st andard m edium for supply chain m anagem ent .

Figure 6.8. Moving to the Internet

For product s t hat consist prim arily of inform at ion, it is possible t o m ove t he flow of supply t o t he I nt ernet as well, creat ing a fully elect ronic supply chain. Newslet t ers, books, designs, pict ures, soft ware, m usic, and ot her inform at ion product s can be packaged as pure dat a and delivered alm ost inst ant ly anywhere in t he world. Not only is e le ct r on ic dist r ibu t ion fast er and cheaper for such product s, it prom ises t o change t he very definit ion of what it m eans t o deliver a product . For exam ple, m usic can be delivered each t im e it is list ened t o rat her t han being st ored by t he consum er bet ween uses, and soft ware can be cont inuously updat ed by a vendor t o reflect bug fixes and enhancem ent s. These kinds of changes are already t aking place in m any inform at ion- based pr oduct s. To dat e, t he use of t he I nt ernet t o com m unicat e inform at ion about t he supply chain has been ham pered by a lack of st andards for packaging dat a. I t 's relat ively easy wit h t oday's t echnology t o send an e- m ail request ing inform at ion about a product , or t o place an order by select ing t hat product on a Web sit e. But consider a com pany like I ngram Micro, which handles 60 m illion t ransact ions a day. To m ove even a fract ion of t hat t raffic ont o t he I nt ernet requires a lot m ore t han hum an- readable m essages and Web pages; it requires t aking people out of t he loop ent irely and let t ing m achines do all t he work. The key t echnology for m aking t hat happen is now in place wit h t he advent of XM L, t he ext ensible m arkup language. What XML does, in essence, is em bed t ags in dat a t o ident ify each elem ent of t he dat a and give it m eaning. Using XML m eans prices can be t agged as prices, discount t erm s as discount t erm s, and so on. XML can be used t o m ake Web pages readable by m achines, and it can be used in m essages t o allow m achines t o com m unicat e direct ly wit h each ot her. For exam ple, a price list wit h st andard quant it y discount s m ight be post ed on t he Web in XML form , whereas an order request ing a part icular quant it y m ight be sent as an XML- form at t ed m essage. I n neit her case is it necessary for hum an beings t o be involved in t he process. Because XML is easy t o code and decode, applicat ions soft ware can easily handle t asks like preparing price list s, reading t hese list s t o find t he best quant it y break, generat ing purchase orders, and so on. Given it s sim plicit y and clarit y, XML is rapidly becom ing t he lingua franca for dat a exchange over t he I nt ernet . I n addit ion t o placing labels on individual pieces of inform at ion, XML allows dat a t o be assem bled int o nest ed st ruct ures. For exam ple, an order can be defined as a nest ed st ruct ure consist ing of a header, a body, and a foot er, as

shown in Figure 6.9 . Each of t hese elem ent s can, in t urn, be defined in t erm s of m ore basic elem ent s, unt il t he st ruct ure reaches t he level of sim ple t ext . Moving in t he opposit e direct ion, orders can funct ion as elem ent s in st ill larger st ruct ures, such as cont ract s, and so on.

Figure 6.9. An XML Order

Of course, t wo com panies have t o agree on all t hese st ruct ures before t hey can st art sending orders back and fort h, and t hat 's where st andards bodies com e in. I t 's a slow process t o achieve agreem ent even on a seem ingly sim ple st ruct ure like an order, and in m any indust ries t wo or m ore groups are proposing conflict ing st andards. Many com panies have avoided t he wait by j ust t ransferring t he convent ions of t he elect ronic dat a int erchange ( EDI ) st andard over t o t he I nt ernet . But EDI is overkill for t he I nt ernet , and despit e years of st andardizat ion it st ill has at least a dozen dist inct dialect s. For t ruly universal com m unicat ion, sim pler form at s are called for, and t hese are now beginning t o em er ge. XML can be used t oday in Web pages and m essages, but it s great est pot ent ial lies in allowing applicat ions t o int eract over t he I nt ernet wit hout any hum an involvem ent what soever. I n order for t his t o happen, applicat ions need t o be able t o " call" each ot her over t he I nt ernet , ask for part icular services, and receive t he result s of t heir request s. That capabilit y requires anot her layer of prot ocols on t op of XML t o handle such t asks as locat ing t he appropriat e applicat ion, discovering it s capabilit ies, subm it t ing a request in t he required m anner, and m aking sense of t he response. The desire t o link applicat ions over t he I nt ernet is st rong, and prot ocols have com e int o exist ence surprisingly quickly. The essent ial capabilit ies, collect ively known as W e b se r vice s, are already operat ional in real syst em s, and t heir use will likely skyrocket over t he next five t o t en years.

The I nt ernet is changing supply chain m anagem ent at every level. To dat e, m ost of t he changes have com e at t he operat ional level, wit h m ore and m ore t ransact ions t aking place elect ronically. At t he planning level, com panies are already exchanging forecast s and product ion plans over t he I nt ernet , and XML will soon becom e t he com m on form at for t hese exchanges. At t he design level, t he I nt ernet is widely used t o exchange files cont aining product designs, and t he event ual conversion of t hose files t o XML will gradually m ake t he design process m ore int eract ive. As t hese changes t ake effect , supply chain m anagem ent will be t ransform ed in a profound way. As XML and Web services becom e widely adopt ed, all t he rout ine int eract ions required t o run a supply chain will shift t o t he I nt ernet . Because program s will com m unicat e direct ly wit h ot her program s, t hese t ransact ions will occur below t he level of hum an awareness and t hey will happen fast er t han a person could even follow. Freed from t he m undane t asks of placing orders and updat ing schedules, t he people who run supply chains will be able t o operat e at a m uch higher level, set t ing goals for t he chain and analyzing it s perform ance. I n effect , running a supply chain will becom e as aut om at ic as walking; inst ead of t hinking about how t o get every m uscle t o m ove in j ust t he right way, you'll be free t o focus on where you are going and how t o get t here. I t 's not quit e t he vision of t he m arat hon runner clicking off six- m inut e m iles, but it 's a long way from Frankenst ein's m onst er st ruggling down t he village lane. Th e r e 's a lot of soft w a r e ou t t h e r e for su pply ch a in s, a n d a sse m blin g t h e be st syst e m for you r ch a in is n o sm a ll t a sk . Th e m ost im por t a n t de cision you 'll m a k e is t h e ch oice of de sign a n d pla n n in g syst e m s, a n d a cqu ir in g t h e opt im izin g ca pa bilit ie s of APS—e it h e r a s pa r t of a n ERP pa ck a ge or se pa r a t e ly—sh ou ld be a h igh pr ior it y. W h e t h e r you n e e d syst e m s t o m a n a ge w a r e h ou se s a n d t r a n spor t a t ion syst e m s de pe n ds e n t ir e ly on you r ch a in , bu t you sh ou ld de fin it e ly look in t o t h e n e w visibilit y a n d e ve n t m a n a ge m e n t pa ck a ge s. Be ca u t iou s a bou t cu st om e r a n d su pplie r r e la t ion sh ip syst e m s, h ow e ve r , a s t h e y t a k e a r a t h e r pr ovin cia l vie w of t h e su pply ch a in , a n d t h e y se e m t o be pa r t icu la r ly pr on e t o t h e im plicit - m ode l pr oble m . Fin a lly, m a k e su r e t h a t a n y syst e m s you bu y a r e r e a dy t o ope r a t e ove r t h e I n t e r n e t , u sin g a s m a n y of t h e a dva n ce d se r vice s a s possible . Ot h e r w ise , you a r e lik e ly t o fin d you r syst e m s cr a w lin g r a t h e r t h a n r u n n in g a ga in st t h e com pe t it ion .

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Part III: Operations

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Part III. Operations

Chapter 7. Meeting Demand Th is ch a pt e r m a r k s t h e t r a n sit ion fr om su pply ch a in con ce pt s a n d t ools t o a n e x a m in a t ion of h ow ch a in s a r e a ct u a lly m a n a ge d, be gin n in g w it h t h e ir da ily ope r a t ion s. Th e m ost ba sic ope r a t ion is fu lfillm e n t , t h e pr oce ss of sa t isfyin g t h e im m e dia t e de m a n d for pr odu ct s. As sh ow n in Figu r e 7 .1 , t h e fu lfillm e n t cycle be gin s w it h a n or de r fr om a cu st om e r a n d e n ds w h e n pa ym e n t is r e ce ive d for t h e de live r e d goods. I n e ffe ct , fu lfillm e n t r e pr e se n t s a com ple t e cycle of de m a n d, su pply, a n d ca sh flow a cr oss a sin gle lin k of t h e ch a in . Th is ch a pt e r e x a m in e s e a ch of t h e com pon e n t pr oce sse s fr om t h e su pplie r 's pe r spe ct ive , in clu din g pr oce ssin g t h e or de r , a sse m blin g t h e goods, sh ippin g t h e or de r , a n d ge t t in g pa id.

Figure 7.1. The Fulfillment Cycle

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Chapter 7. Meeting Demand

Communicating Demand The way orders are t ransm it t ed from cust om ers t o t heir suppliers has changed m any t im es over t he years, const ant ly m oving t oward fast er m edia, but t he inform at ion cont ained in orders is as old as business. Essent ially, an order answers t he classic W quest ions: Who is doing t he buying and selling, what is being request ed, where is it t o be delivered, and when is it supposed t o arrive? Alt hough sim ple t o st at e, all of t hese quest ions can have com plex answers. I n t he case of w h o, t here m ay be j ust t wo part ies—t he cust om er and t he supplier. As described in Chapt er 2, however, t he t hree different flows involved in an order—dem and, supply, and cash flow—are usually handled by different groups wit hin t he buying and selling organizat ions, so t here can easily be six or m ore part ies involved in a t ransact ion, each of which m ust be t reat ed as a different operat ional or legal ent it y. Even for a relat ively sim ple t ransact ion, j ust uniquely ident ifying t he various part ies is m ore t han m any order syst em s can do. Specifying t he what of an order can also becom e com plicat ed. A single order usually request s a variet y of different product s, and it 's not sufficient t o sim ply nam e t hese product s. Off- t he- shelf goods m ust be specified using unique ident ifiers such as part num bers, universal product codes ( UPCs) , or st ockkeeping unit ( SKU) num bers. Som e of t hese ident ifiers have been st andardized wit hin part icular indust ries, but m any are unique t o each organizat ion, requiring one or bot h part ies t o perform a t ranslat ion bet ween t he t wo ident ificat ion syst em s. Orders for cust om ized goods m ust cont ain very det ailed specificat ions regarding dim ensions, com posit ion, m at erial qualit y, and t he like. St ipulat ing t he quant it ies of t hese product s—norm ally a m at t er of j ust specifying a num ber and a unit —can also be a challenge when t he t wo com panies m easure t he sam e goods in different ways ( pounds vs. bales, for exam ple) or use different m easurem ent syst em s ( m et ric vs. cust om ary) . The answer t o t he where quest ion can also be com plex. I t m ay be a single locat ion, or it m ay be m ult iple locat ions, wit h a different m ix of product s going t o each dest inat ion. These dest inat ions are m ore t han j ust addresses—each can have it s own receiving capabilit ies, hours of operat ion, and ot her charact erist ics. A furt her considerat ion is t hat , for com panies t hat operat e m ult iple plant s, product s can com e from m ult iple locat ions, so shipm ent s of t hese product s m ay need t o be split or m erged in t ransit . I n t hat case, t he

product s will have t o go by way of a com m on int erm ediat e dest inat ion such as a cross dock or dist ribut ion cent er. Figure 7.2 illust rat es a dist ribut ion cent er t hat m erges shipm ent s from t hree fact ories t hen breaks t hem out for delivery t o t wo assem bly plant s.

Figure 7.2. Merging and Splitting Shipments

Even t he quest ion of when can int roduce com plicat ions. Alt hough usually specified as a sim ple dat e, delivery t arget s are act ually int ervals of t im e. Hist orically, t he int erval was im plicit , wit h t he dat e indicat ing t he last accept able day for delivery. Given t he current em phasis on m inim izing invent ory, however, t he dat e oft en specifies an act ual day on which delivery should occur, and delivery prior t o t hat dat e m ay be no m ore accept able t han delivery aft er it . Wit h JI T operat ions, t he int erval is usually specified m ore precisely by adding a t im e t o t he dat e, and t he supplier m ay be penalized if a delivery arrives as lit t le as 30 m inut es lat e. Then t here's t he quest ion of whet her all t he goods have t o m eet t he sam e delivery dat e. At one ext rem e, a cust om er can st ipulat e t hat t he order is sh ip com ple t e , m eaning t hat all t he it em s m ust arrive in a single delivery, wit hout t he opt ion t o backorder it em s t hat are current ly out of st ock. At t he ot her ext rem e, each it em could have it s own schedule of deliveries, causing a single order t o be spread out over m any deliv er ies. I n addit ion t o answering t he who, what , where, and when quest ions, an order can also address how quest ions: how t he goods are t o be packaged, how t hey are t o be shipped, t heir form and qualit y on receipt , and so on. Finally, alt hough not norm ally included in an order, why inform at ion is becom ing increasingly im port ant t o m aint aining synchronizat ion in a supply chain. For exam ple, m any m anufact urers now share t heir product ion schedules wit h t heir suppliers. I nst ead of sim ply requiring t hat m at erials arrive on t heir dock at a specified t im e, t hey let t heir suppliers know what t hey are building and when, allowing t he suppliers t o m ake bet t er decisions about priorit ies in t he event t hat deliveries fall behind schedule. This is clearly a lot for a single docum ent t o com m unicat e, and orders have evolved a com m on st ruct ure for packaging t his inform at ion. As shown in Figur e 7.3, an order consist s of t hree basic part s: a header, a body, and a foot er. The

header specifies such generic inform at ion as t he part ies involved, t he crit ical dat es for t he t ransact ion, and t he t erm s of paym ent . The body cont ains line it em s, each of which specifies a quant it y of product t o be delivered t oget her wit h t he price per unit and t he ext ended price for t he quant it y request ed aft er any discount s are applied. The foot er cont ains financial inform at ion t hat depends on t he cont ent of t he line it em s, such as t he t ot al price, t axes, and delivery charges.

Figure 7.3. The Structure of an Order

Not all of t his inform at ion appears on t he order t hat init ially t riggers dem and. The order t ransm it t ed by t he cust om er usually t akes t he form of a purchase order, which doesn't norm ally include prices and ot her financial t erm s. This inform at ion is added by t he supplier and ret urned t o t he cust om er in t he form of a sales order, which represent s a com m it m ent on t he part of t he supplier t o provide t he goods on t he indicat ed t erm s. I f t he cust om er agrees wit h t he t erm s added by t he supplier, including any changes in t he product s, quant it ies, and dat es, t he order becom es binding on bot h part ies. I f not , it becom es t he subj ect of negot iat ion. Alt hough t he basic st ruct ure shown in Figure 7.3 is sufficient for m ost orders, it oft en requires ext ension t o handle m ult iple deliveries. The m ost com m on form of ext ension is t o add anot her level of st ruct ure underneat h t he line it em s t o specify separat e deliveries, as shown in t he left panel of Figure 7.4 . I n som e indust ries, t his nest ing is invert ed t o place line it em s below delivery dat es, as shown in t he right panel of Figure 7.4 . This form , com m only known as a cu st om e r sch e du le, is usually found in JI T environm ent s, where it is im port ant t o see at a glance what it em s are arriving in each delivery.

Figure 7.4. Three-Level Orders

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Chapter 7. Meeting Demand

Processing an Order Orders can be t ransm it t ed in a variet y of form s and can m ake use of several different m edia ( Figure 7.5 ) . Hist orically, orders were eit her sent in t he m ail or dict at ed over t he t elephone. Wit h t he invent ion of t he fax m achine, paper orders could be sent over t elephone lines, great ly speeding t he delivery process. Sim ilarly, t he developm ent of t he elect ronic docum ent int erchange ( EDI ) prot ocol allowed com panies t o t ransm it orders in seconds over privat e net works. However, EDI t echnology was expensive, and only large com panies were able t o afford it . The I nt ernet now m akes elect ronic orders easy and affordable for com panies of all sizes.

Figure 7.5. Receiving Orders

Because all t hese t echniques are st ill in use, com panies m ust be able t o handle orders received in m ost if not all of t hese m odes. The first challenge is t o get all t he orders int o a com m on form , a t ask t hat is best handled by order m anagem ent soft ware. I n t he case of m ail, phone, and fax orders, t here is lit t le

choice but t o key t he orders in m anually, as shown in Figure 7.5 . Com panies t hat use EDI are able t o accept orders direct ly int o t heir order m anagem ent syst em s wit hout m anual ent ry, but I nt ernet orders are not yet sufficient ly st andardized t o perm it full aut om at ion. Many com panies cont inue t o accept orders over t he I nt ernet , print t hem out , and m anually re- ent er t hem int o t heir order m anagem ent syst em s. The advent of XML and Web services, as described in Chapt er 6, should rem edy t his problem , but it will t ake a few years for t he rem edy t o work. Once an order is " in t he syst em ," a supplier begins a sequence of act ivit ies t hat can range from t he m erely com plex t o t he t ruly Byzant ine. Figure 7.6 illust rat es t he m aj or st eps in order processing, shown in t he sequence t hey would m ost com m only be carried out . But every com pany operat es a lit t le different ly, and t he sequence can vary even wit hin a com pany. I f a supplier received a m illion dollar order from a relat ively sm all cust om er, it would very likely m ove t he credit check up t o t he front of t he process.

Figure 7.6. Major Steps in Order Processing

Ordinarily, t he first st ep in t he process is t o check all t he ent ries in t he order, t o m ake sure t hey are reasonable and valid. Many of t hese checks are handled aut om at ically by t he order m anagem ent syst em , which filt ers out such basic errors as let t ers in fields t hat require num bers, and values t hat lie out side of t ypical ranges. The syst em can also m ake sure t hat t he cust om er is already known t o t he supplier and has a line of credit t o back up it s orders. Most syst em s can review t he individual line it em s t o m ake sure t hat all product s are properly ident ified and quant ified, and som e can assist wit h any m apping t hat m ight be required bet ween t he product ident ifiers or unit s of m easure used by t he cust om er and t he supplier. Once t his aut om at ic validat ion is com plet e, orders are usually given a " reasonableness" check by an account represent at ive t o see whet her t he goods request ed are of t he sam e t ype and quant it y t his cust om er norm ally orders. The next st ep, configurat ion, is required only if t he m ix of product s on an order is int ended t o be used t oget her. This process t ypically involves t wo different checks, one for com pat ibilit y and t he ot her for com plet eness. As t he nam es suggest , t he first check m akes sure t hat t he com ponent s will work t oget her

properly, and t he second ensures t hat t he cust om er receives everyt hing necessary for t he assem bled syst em t o funct ion as expect ed. Because configurat ion isn't required in m ost indust ries, order m anagem ent syst em s only include configurat ion checking capabilit ies if t heir t arget m arket requires t hem . Wit h highly sophist icat ed product s such as large com put ers or t elecom m unicat ions swit ches, suppliers oft en const ruct specialized knowledgebased syst em s t hat use business rules t o check configurat ions for com pat ibilit y and com plet eness. The pricing st ep is oft en a com plex process in it s own right , involving a sequence of sophist icat ed t asks. Alt hough order m anagem ent syst em s provide support for m any of t hese t asks, pricing can st ill involve considerable t im e and effort , and it 's t he source of m any errors and com plaint s in large t ransact ions. The m aj or t asks are:

1 . D e t e r m in in g t h e cor r e ct u n it pr ice— I f t he supplier prices it s product s according t o region, m arket , and ot her fact ors, select ing t he appropriat e price t o use can require t he applicat ion of m ult iple rules, som e of which m ay conflict wit h each ot her. 2 . Applyin g a ppr opr ia t e discou n t s— Once t he unit price is det erm ined, it is reduced by any discount s t hat m ight apply based on com pany policies, cust om er cont ract s, product prom ot ions, and ot her considerat ions. Discount ing is it self a dark science; m ult iple discount s m ay be applicable, and som e discount s can be com bined wit h each ot her according t o various form ulas, while ot hers are m ut ually exclusive. 3 . Com pu t in g t h e e x t e n de d pr ice — This st ep can be as sim ple as m ult iplying t he discount ed price t im es t he quant it y request ed, but it usually requires t he use of one or m ore quant it y discount schedules, wit h discount s being applied eit her t o all t he unit s purchased or j ust t o t hose beyond each price- break quant it y. The order t ot al m ay be subj ect t o yet anot her discount based on t he dollar am ount and/ or t he current cum ulat ive purchases of t he cust om er. 4 . Ca lcu la t in g a ddit ion a l ch a r ge s— Det erm ining t he appropriat e shipping fees, t axes, t ariffs, and ot her charges can add anot her layer of com plexit y, part icularly in t he case of int ernat ional sales. Sim ply det erm ining how t o ship t he order m ay require checking t he cost of several different t ransport at ion opt ions and picking t he one t hat best conform s t o com pany policies and cust om er cont ract s. The applicat ion of addit ional charges also t ends t o vary by indust ry and by com pany pract ice, m aking it difficult t o use off- t he- shelf order syst em s t o com put e t hese charges. Once t he order t ot al is known, t he next st ep is t o m ake sure t hat t he cust om er has enough credit t o cover t he purchase. I n a full- feat ured credit m anagem ent syst em , each cust om er is assigned a m axim um am ount of credit , and out st anding purchases are subt ract ed from t his m axim um t o det erm ine t he available credit for new purchases ( Figure 7.7 ) . Alt hough sim ple in principle, t his approach requires an int egrat ion of account ing and order m anagem ent syst em s t hat is st ill t he except ion rat her t han t he rule, and suppliers m ay have t o be cont ent wit h sim ply checking t o see whet her a credit flag has been set for t he cust om er. An addit ional feat ure t hat 's lacking in m any syst em s is t he abilit y t o use a separat e part y, such as t he cust om er's parent organizat ion, as a credit

source, accruing all out st anding charges against t hat com m on source.

Figure 7.7. Checking Credit

Alt hough t he pract ice is far from universal, m ost com panies like t o be sure t hey can deliver on an order before com m it t ing t o it . I n t he case of product s m ade t o st ock, current and planned invent ory m ay be checked t o m ake sure t hat t he product is a va ila ble t o pr om ise ( ATP) t o t he cust om er. For product s t hat are m ade or assem bled t o order, t he plant is checked t o ensure t hat it is ca p a b le t o pr om ise ( CTP) t he product s. I n eit her case, t he supplier has t he opt ion t o reserve t he invent ory or capacit y in quest ion or sim ply t o t ake a chance t hat it won't also be prom ised t o anot her cust om er. Alt hough earm arking invent ory is cert ainly preferable, it requires an except ional level of int egrat ion wit h product ion and invent ory m anagem ent syst em s. At present , t he best t ools for perform ing real- t im e ATP and CTP checks are t he advanced planning and scheduling ( APS) syst em s described in Chapt er 6. Unlike ordinary invent ory m anagem ent syst em s, APS syst em s can evaluat e alt ernat ive sources for a product and det erm ine t he best source based on business rules. For exam ple, t hey can check several different warehouses t o see which one can deliver a product by t he required dat e at t he lowest t ot al cost t o t he chain, or com pare several different plant s against out sourcing opt ions t o decide where t o have a product assem bled. Not only does t his realt im e ATP and CTP capabilit y keep you from default ing on your com m it m ent s, it also allows you t o m ake m ore aggressive com m it m ent s when you have capacit y t o spare, im proving your abilit y t o win bids against com pet it ors t hat lack t his inform at ion. The last st ep in order processing is get t ing t he order approved. Depending on t he size of t he order, it m ay go t hrough an int ernal review and approval wit hin t he supplier before being sent t o t he cust om er for approval. Once t he order has been confirm ed by t he cust om er, t he dem and phase is com plet e, and t he process of filling t he order begins. Even wit h a good order processing syst em , t he sequence of event s described in t he preceding paragraphs is oft en slow, labor int ensive, and prone t o errors.

One of t he enduring goals of product com panies has been t o fully aut om at e order processing, reducing t he t im e, cost , and m ist akes associat ed wit h t his act ivit y. Now t hat orders can be t ransm it t ed over t he I nt ernet in st andard XML form at , it will soon be possible t o perform m any of t hese t asks by sending t he order t o exist ing syst em s and request ing services from t hem . For exam ple, an incom ing order could be sent sim ult aneously t o an invent ory m anagem ent syst em for an ATP check, t o an account ing syst em for a credit check, t o a cont ract s syst em for a com pliance check, and t o a pricing syst em for appropriat e unit and volum e pricing. Because XML allows any applicat ion t o read t he order direct ly, it is not necessary for all of t hese funct ions t o be carried out in a single, st and- alone order m anagem ent syst em .

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Chapter 7. Meeting Demand

Assembling the Goods The supply phase of t he fulfillm ent cycle begins wit h t he select ion of one or m ore facilit ies t o fill t he order. This is usually an easy decision; t he supplier j ust ships t he goods from t he st orage facilit y closest t o t he cust om er. But , as always, t here are variat ions t o t ake int o account . One variat ion has t o do wit h product ion st rat egies; m ake- t o- order product s, by t heir very nat ure, are creat ed on dem and at a product ion facilit y, so t hey won't be found wait ing in a warehouse. Anot her variat ion arises from t he cent ralizat ion of order m anagem ent , in which a com pany provides a single order point for t wo or m ore of it s operat ing divisions. I n t his case, an order m ight be dispat ched t o several different locat ions, each of which would ship it s port ion of t he order independent ly. I f t he cust om er has request ed t hat t he order be shipped com plet e, t his m ay require t hat t he various shipm ent s be m erged in t ransit and com bined in a single delivery. A com plex sequence of act ivit ies is required t o prepare an order for shipm ent . For clarit y, I 'll describe t hese act ivit ies in t he cont ext of a warehouse. The sam e act ivit ies occur when pulling invent ory from fact ories, st ockroom s, and ot her st orage locat ions, but t hey are m ore easily underst ood in t he specialized environm ent of t he warehouse. All of t hese act ivit ies are support ed elect ronically by warehouse m anagem ent syst em s. The layout of a t ypical warehouse is illust rat ed in Figure 7.8 . Alt hough not all warehouses are arranged in t he linear fashion shown here, m ost are organized int o t he five different areas shown: a receiving dock, a bulk st orage area, a picking area, one or m ore assem bly areas, and a shipping dock. Each area is dedicat ed t o a different funct ion and has specialized equipm ent t o support t hat funct ion. A com m on variat ion of t he st ruct ure shown is t o bend t he linear form int o a U shape t o allow a com m on set of docks t o support bot h receiving and shipping. The m ost im port ant considerat ion in t he layout is m aint aining a high rat e of flow am ong t he areas. I t is not unusual for a warehouse t o have 50,000 pallet s in st orage and t o m ove hundreds of pallet s t hrough t he syst em every hour.

Figure 7.8. Layout of a Warehouse

As shown in Figure 7.8 , warehouses use bot h push and pull dynam ics t o cont rol t he flow of st ock from t he receiving dock t o t he shipping dock, wit h t he push- pull boundary locat ed at t he picking area. When st ock arrives at t he warehouse, m ost of it is unloaded and placed in bulk st orage, but a port ion of it m ay be unpackaged and placed in t rays, shelves, or bins in t he picking area. These m ovem ent s are all push act ivit ies because t hey queue up product in advance of dem and. Subsequent act ivit ies are pull- based because t hey do not occur unt il t he warehouse receives an order. When it is t im e t o ship an order, t he warehouse m anagem ent syst em generat es a pick list indicat ing t he quant it ies of each it em included in t he order. An em ployee called a picker t akes t his list and ret rieves t he it em s in t he sequence t hey appear on t he list , which is designed t o t ake t he picker on t he short est pat h t hat t ouches each of t he it em s. This is an im port ant opt im izat ion: Picking can account for as m uch as half t he labor cost s in a warehouse, and pickers spend up t o 70% of t heir t im e m oving from one locat ion t o t he next . Even sm all im provem ent s in t he picking process can yield m aj or savings. Wit h relat ively sm all, light weight it em s, picking is usually done by hand. For larger product s, pickers use hand t rucks, m ot orized loaders, or ot her equipm ent t o gat her and m ove st ock. I n som e facilit ies, conveyor syst em s m ove t he st ock, aut om at ically rout ing packages t o t heir dest inat ions. I n ot hers, pickers st and in place and t he st ock is brought t o t heir posit ion by carouselst yle dispensing syst em s. Regardless of how t he gat hering is done, t he out com e is t he sam e: The picked st ock is deposit ed wit h t he picking slip in it s designat ed assem bly area. Once t he st ock is in t he assem bly area, ot her workers perform any final operat ions t hat m ight be needed prior t o shipm ent . These operat ions are usually m inor, oft en consist ing of a quick visual inspect ion and t he addit ion of a label. However, it is becom ing increasingly com m on t o perform final product assem bly at warehouses, a pract ice t hat m akes it easier for producers t o cust om ize t heir product s t o local requirem ent s ( see Chapt er 15 ) . For com panies using t his pract ice, t he assem bly area looks m ore like a m iniat ure plant t han a st aging area.

Aft er t he order is assem bled, it is packaged for shipm ent . Depending on t he product , t here m ay be up t o t hree levels of packaging ( Figure 7.9 ) . The pr im a r y pa ck a ge is t he box, can, blist er pack, or ot her cont ainer t hat holds t he act ual product . The se con da r y pa ck a gin g is usually a cart on t hat groups a st andard num ber of prim ary packages t oget her for convenient handling. Except where warehouses perform final assem bly, m ost product s com e prepackaged wit h t hese t wo levels of prot ect ion. The t hird layer, t he t r a n spor t pa ck a gin g , is usually a pallet in com binat ion wit h a prot ect ive covering such as a polyuret hane sheet . Large shipm ent s of a product are norm ally loaded ont o fu ll pa lle t s, which cont ain only one kind of product . For sm aller shipm ent s, t he various product s going t o a single dest inat ion are loaded ont o m ix e d pa lle t s.

Figure 7.9. Three Levels of Packaging

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Chapter 7. Meeting Demand

Shipping the Order What happens next depends on whet her t he supplier uses a carrier for it s shipm ent s or operat es it s own fleet of vehicles. I f a carrier is used, t here is not hing m uch t o do once t he vehicles are loaded but wait for confirm at ion t hat t he delivery was m ade on t im e. I n t he case of a privat e fleet , t here is a good deal of planning t o be done even before t he loading begins. This sect ion describes how a supplier operat es it own vehicles, assum ing for t he sake of clarit y t hat t hese vehicles are t rucks. This assum pt ion is realist ic given t hat t rucking account s for 70% of all shipping in t he Unit ed St at es by dollar volum e, and t rucking present s som e int erest ing problem s t hat don't norm ally arise in t ransport by ot her m odes. The basic problem t o be solved is finding t he best rout e t o t ake from t he supplier's warehouse t o t he dest inat ion facilit y. When cust om ers order in full t ruckload ( FTL) quant it ies, rout ing is usually a sim ple m at t er of finding t he short est pat h bet ween t wo point s. However, wit h deliveries in m et ropolit an areas slight ly longer rout es can som et im es yield short er driving t im es, and cert ain rout es m ay becom e part icularly slow during peak t raffic periods. The m ain problem wit h FTL shipm ent s, however, is figuring out what t o do wit h t he t ruck once it has m ade it s delivery. Because driving it back t o t he warehouse em pt y is a wast e of fuel and driver t im e, com panies are const ant ly looking for ba ck h a u ls, which are shipm ent s in t he opposit e direct ion t hat m ake use of t he capacit y provided by t he t ruck. I n t he absence of a backhaul, t rucks usually go t o t he nearest dist ribut ion cent er rat her t han ret urning t o t heir origin. A special problem arises when orders require shipm ent s from m ult iple facilit ies. The sim plest solut ion is t o m ake t he shipm ent s independent ly but coordinat e t hem such t hat t hey arrive on t he sam e day. But t his approach j ust t ransfers t he cost of com bining shipm ent s t o t he cust om er, and m any cust om ers obj ect t o t he pract ice. The alt ernat ive is a m e r ge in t r a n sit , in which t he m ult iple shipm ent s are sent t o a dist ribut ion cent er close t o t he cust om er sit e, reloaded ont o a single t ruck, and sent on as a single delivery. The m ost cost - effect ive way t o achieve t his is t o use a t echnique called cr oss dock in g, in which goods are m oved direct ly from a receiving dock t o a shipping dock wit hout int erm ediat e st orage. Cross docking was originally developed by Wal- Mart , which uses it wit h great effect iveness in t ransport ing goods t o it s nat ional chain of discount st ores. Cross docking is usually perform ed in specialized facilit ies of t he sort illust rat ed in Figure 7.10.

Figure 7.10. A Cross-Docking Facility

One of t he biggest challenges of shipping is t racking orders while t hey are in t ransit from a supplier t o a cust om er. Suppliers oft en t ransm it an advance shipping not ice ( ASN) t o let a cust om er know when it s order goes out , but get t ing inform at ion on t he progress of a shipm ent has been difficult if not im possible. Federal Express showed t he way of t he fut ure when it added barcodes t o all it s packages and scanned t hose codes each t im e a package m oved from one facilit y t o t he next . Today, anyone wit h a t racking num ber and access t o t he I nt ernet can t rack a FedEx shipm ent anywhere in t he world. More recent ly, global posit ioning syst em s ( GPS) and radio frequency ( RF) t ransponders allow cont inuous m onit oring of vehicles and cont ainers right down t o t he level of individual boxes. These t echnologies can also be used t o det ect breakdowns and rerout e vehicles t o avoid congest ion or synchronize deliv er ies. Part of an efficient t racking syst em is aut om at ing t he ent ry of inform at ion regarding each shipm ent . Not only does t his save t im e, it dram at ically reduces er r or s. Figure 7.11 shows t he result s of a U.S. Depart m ent of Defense st udy of error rat es for different t echniques of dat a capt ure, including handwrit t en, keyboard ent ry, opt ical charact er recognit ion ( OCR) , barcoding, and RF ( radiofrequency) t ransponders. The rat es shown are t he average num ber of errors for each 30 m illion charact ers ent ered. I t is im m ediat ely clear from t he t able why barcodes and RF t ransponders are t he t echniques of choice for shipm ent t r ack ing.

Figure 7.11. Error Rates for Data Capture

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Chapter 7. Meeting Demand

Collecting the Cash Once an order has been delivered t o t he cust om er, t he t hird and final phase of t he fulfillm ent cycle begins—get t ing paid. The first st ep in t his process is det erm ining t he act ual am ount due. I n m ost cases, t his is sim ply t he t ot al at t he bot t om of t he supplier's sales order, which represent s t he am ount t he cust om er agreed t o pay. However, t he figure on t he order m ay not be final, in which case furt her com put at ion is needed t o det erm ine t he am ount due. For exam ple, m any bulk goods are sold by weight or volum e, and t he quant it y of t he shipm ent isn't finalized unt il t he shipm ent is weighed or m easured at t he receiving end. Once t he am ount due has been det erm ined, t he supplier generat es an invoice for t hat am ount . I nvoices have t o reference t he cust om er's purchase order, t he supplier's sales order, or bot h. I f m ult iple orders have been shipped t o t he sam e cust om er during a relat ively short int erval, t hey are usually covered under a single invoice t o reduce paperwork. Most invoices are st ill sent by m ail, but t he sam e t echnologies t hat allow orders t o t ravel over t he I nt ernet are now being applied t o invoices, so invoices should st art t o m ove a lit t le fast er over t he next few years. I n an ideal world—at least from a supplier's point of view—all invoices would be paid prom pt ly and accurat ely. I n t he real world, cust om ers usually t ake as m uch t im e t o pay as t hey can get away wit h because t his gives t hem t he use of t he cash in t he int erim . As an inducem ent for prom pt paym ent , invoices usually include paym ent t erm s along t he lines of " 2% 10 Net 30," which requires paym ent in 30 days but offers a 2% discount if paym ent is received wit hin 10 days. This discount for prom pt paym ent usually produces t wo bat ches of paym ent s, one averaging 15 days and claim ing t he discount , and t he ot her averaging 45 days. I n m ost cases, t he only requirem ent s for get t ing paid are an invoice and a m odicum of pat ience. I f t he cust om er doesn't pay wit hin t he t im e allowed, t he next st ep is t o include t he balance due on a m ont hly st at em ent wit h a rem inder of past due am ount s. I f t hat fails t o produce result s, one or m ore friendly phone calls t o t he bill- t o cust om er m ay do t he t rick. I f not , t he supplier m ay place a hold on t he cust om er's credit unt il paym ent is fort hcom ing. I n supply chains wit h est ablished t rading part ners, it is rare for paym ent problem s t o escalat e beyond t his point , but t he next st ep would be t o init iat e legal act ion or

t urn t he account over t o a collect ion agency. Com pared t o t he urgency t hat usually accom panies t he first t wo phases of t he fulfillm ent process, t he paym ent phase can be a rat her leisurely int eract ion. Figure 7.12 offers a t ypical fulfillm ent t im eline t o illust rat e t his point . I n t he exam ple shown, a supplier spends an average of t hree days processing each order, and it t akes about a week t o deliver t he goods. The average t im e for it t o fill an order—called t he fu lfillm e n t le a d t im e —is t herefore 10 days. The account ing depart m ent generally invoices com plet ed orders in about 5 days, and t he average age of t he com pany's account s receivable is 45 days. For t his com pany, t he process of get t ing paid consum es 50 of t he 60 days t hat m ake up t he fulfillm ent cycle.

Figure 7.12. Fulfillment Cycle Timeline

Hist orically, t he cash port ion of t he fulfillm ent cycle has not been viewed as a crit ical aspect of supply chain m anagem ent . The t ask of collect ing paym ent is sim ply delegat ed t o t he account ing depart m ent , which applies it s own policies and procedures for collect ing paym ent s. But cash flow isn't m erely t he last of t he t hree flows—it 's t he one t hat m ot ivat es t he ot her t wo. Many suppliers are beginning t o quest ion whet her com pressing fulfillm ent lead t im e wit hout accelerat ing t he flow of cash is an ent irely balanced proposit ion. Profit depends on t he effect ive use of all resources, and cash is t he ult im at e resource because it is t he prim ary m eans of acquiring ot her resources. As described in Chapt er 9, t ying up cash in any area of a business inflict s an opport unit y cost because t his cash could be used for ot her purposes. I n t he present inst ance, having cash sit t ing in receivables for four t o six weeks inflict s a t rem endous penalt y on a com pany. To get a feeling for t he m agnit ude of t his penalt y, suppose t he supplier in Figure 7.12 does $600 m illion dollars in sales and has an opport unit y cost of 14% . Wit h 45- day receivables, t he com pany float s $75 m illion t o it s cust om ers, wit h an opport unit y cost of $10.5 m illion a year. Depending on it s m argins, t his com pany could be devot ing as m uch as a 20% of it s operat ing profit s t o financing it s cust om ers' purchases. Of course, suppliers can easily j ust ify t his float because t hey m ake it up by

float ing t he cost s of t heir own supplies. This is j ust t he way t he gam e is played. But t his is anot her exam ple of t he kind of zero- sum gam e described in Chapt er 3; t here is only so m uch cash flowing up t he supply chain, and t here can't be any net gain across t he chain from t rading part ners slowing down it s m ovem ent . I n fact , t his gam e slides down int o t he lose- lose region because none of t he act ivit ies involved in billing and collect ing cont ribut e any value t o t he end product . From t he perspect ive of t he supply chain as a whole, t hese are wast ed dollars, pure and sim ple.

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Chapter 7. Meeting Demand

Accelerating Fulfillment I n light of all t he operat ions necessary t o fill an order, it should com e as no surprise t hat so m any com panies have t rouble wit h fulfillm ent . Many of t he pract ices, including t he rit ual exchange of sim ilar but slight ly different docum ent s at each st age, dat e back t o t he early days of com m erce. On t op of t his t radit ional foundat ion, m any com plicat ions have been const ruct ed in t he nam e of im proved sales and cust om er service, including t iered pricing, quant it y discount s, revolving credit , special t erm s, cust om configurat ion, and specialized packaging. The result is a com plex process t hat t akes a long t im e t o carry out , incurs a great deal of expense, and provides lot s of opport unit y for errors. These problem s t end t o frust rat e everyone involved in fulfillm ent , including not only your cust om ers but also t he people in your com pany who serve t hose cust om ers. To reduce t he frust rat ion and m eet t heir obj ect ives, sales st aff and cust om er service represent at ives oft en find ways t o subvert t he process t o bet t er serve t heir own cust om ers. For exam ple, if t here isn't enough st ock on hand t o fill an order, a service rep m ight call a friend at t he warehouse and arrange t o have his cust om er receive goods originally int ended for anot her cust om er. I n a m ore ext rem e case, t he service rep m ight convince a plant m anager t o push a product ion run forward in order t o m eet a cust om er requirem ent . Such pract ices, som et im es sanct ioned under t he nam e of expedit ing, provide an excellent exam ple of local opt im izat ion hurt ing t he chain as a whole. They m ay speed up delivery of a single order, but t hey do so by t hrowing off ot her orders and creat ing addit ional work t o resolve t he conflict s and confusion t hey produce. Expedit ing individual orders is clearly not t he best way t o solve t he fulfillm ent problem , but finding a bet t er way t o fix t hese problem s can be a challenge. Many com panies have applied t he t echniques of business process reengineering t o im prove t heir fulfillm ent process, but usually wit h lim it ed success. The problem t hey ult im at ely com e up against is t hat t he pract ices t hat m ake fulfillm ent slow and com plex are difficult t o change, in part because cust om ers have com e t o expect a cert ain way of doing business. Sim plifying t he flow of paper, elim inat ing pricing t iers, or skipping t he confirm at ion st ep on rout ine orders m ay all m ake perfect sense from your point of view, but convincing your cust om ers t o accept t hese changes is anot her m at t er. And even if you do bring your cust om ers around, t he savings you realize from such increm ent al

im provem ent s m ay be out weighed by t he cost of bringing about t he change. The alt ernat ive t o increm ent al im provem ent is radical change—sim ply t hrowing out t he old way of doing business and com ing up wit h a dram at ically st ream lined process. I t m ay seem paradoxical, but radical change is oft en easier t o achieve t han increm ent al change. For exam ple, rat her t han asking your cust om ers t o alt er t he way t hey place t heir orders, it m ay be sim pler t o elim inat e orders alt oget her in favor of aut om at ed replenishm ent . This is t he approach t aken by JI T, vendor m anaged invent ory, and several ot her program s described in Chapt er 3, and it has worked quit e well in pract ice. I n a sense, orders st ill exist , but t hey have becom e so sim plified and st andardized t hat t hey bear lit t le resem blance t o t he orders of old. I n a JI T environm ent , for exam ple, t he " order" m ay t ake t he form of a plast ic bin t hat holds a part icular set of part s. Processing t his order couldn't be sim pler: When a bin arrives, you ret urn it full. Anot her exam ple of radical change is t he m ove t oward inst ant paym ent . Many com panies t hat have adopt ed JI T pract ices are now applying t he sam e t echniques t o t he billing and paym ent cycle. As each shipm ent of supplies arrives at t he cust om er sit e, scanning in t he delivery aut om at ically t riggers an elect ronic deposit in t he supplier's bank account . No invoices, no st at em ent s, no paym ent t erm s, no collect ions, no credit checks, and no float . I nst ant paym ent not only elim inat es a great deal of cost on bot h sides of t he t ransact ion, it wraps t he business up a lot fast er. Given t he sluggish pace wit h which cash norm ally flows up t he chain, t he fulfillm ent cycle can be accelerat ed by a fact or of 5 or 10 wit h t his t echnique. Making radical changes of t his sort clearly requires deep changes in t he nat ure of your relat ionship wit h cust om ers. You can't j ust call t hem up one day, welcom e t hem t o your new inst ant - paym ent plan, and read off an account num ber. Changes like t hese have t o com e as part of a larger package t hat redefines t he relat ionship, and t he package has t o be at t ract ive t o bot h part ies. For exam ple, inst ant paym ent is one way t o com pensat e JI T suppliers for t he ext ra cost associat ed wit h m aking frequent , sm all deliveries. Look in g ba ck a t t h e slow , com ple x , la bor - in t e n sive pr oce sse s t h a t go in t o fillin g a sin gle or de r , it m a y be h a r d t o im a gin e ge t t in g fr om w h e r e w e a r e t oda y t o t h e ide a l of a u t om a t ic su pply ch a in s de scr ibe d in t h e la st ch a pt e r . Th e pr oble m isn 't figu r in g ou t a be t t e r w a y t o h a n dle fu lfillm e n t ; t h a t 's m ost ly a m a t t e r of st r ippin g a w a y m u ch of w h a t 's t h e r e a n d st r e a m lin in g t h e r e st t o t h e poin t w h e r e it ca n be ca r r ie d ou t by soft w a r e . Th e h a r d pa r t is cu lt u r a l; t h e com ple x it ie s of fu lfillm e n t a r e w ove n in t o t h e ve r y fa br ic of you r or ga n iza t ion , a n d you r r e la t ion sh ips w it h cu st om e r s a r e pr e dica t e d on t h e m a ze of discou n t s a n d bu yin g opt ion s t h e y've com e t o e x pe ct . I t w ou ld be n e a r ly im possible t o dism a n t le t h is e n t ir e syst e m a n d st ill k e e p you r com pa n y in bu sin e ss. Bu t you ca n se t u p a pa r a lle l syst e m , on e t h a t offe r s low e r cost a n d fa st e r se r vice t o cu st om e r s w h o opt for a u t om a t e d or de r s a n d pa ym e n t s. Th e n le t t h e t w o syst e m s com pe t e , a n d n a t u r a l se le ct ion w ill do t h e r e st .

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Part III. Operations

Chapter 8. Maintaining Supply Ju st a s fu lfillm e n t de live r s pr odu ct s t o m e e t cu st om e r de m a n d, so r e ple n ish m e n t a cqu ir e s t h e m a t e r ia ls n e ce ssa r y t o bu ild t h ose pr odu ct s. Th e r e ple n ish m e n t cycle , sh ow n in Figu r e 8 .1 , in volve s t h e sa m e a ct ivit ie s a s t h e fu lfillm e n t cycle , bu t vie w s t h e m fr om t h e pe r spe ct ive of t h e cu st om e r r a t h e r t h a n t h e su pplie r . Give n t h is pe r spe ct ive , t h e ch a pt e r focu se s on t h r e e k e y qu e st ion s t h a t dr ive pu r ch a sin g de cision s: w h e n t o pla ce a n or de r , h ow m u ch t o bu y a t a t im e , a n d h ow m u ch in ve n t or y t o k e e p on h a n d. Th e a n sw e r t o t h e la st qu e st ion r e ve a ls a dist u r bin g t r u t h : Th e r e is n o a m ou n t of in ve n t or y t h a t w ill pr e ve n t st ock ou t s, so se t t in g in ve n t or y le ve ls is ba sica lly a m a t t e r of r isk m a n a ge m e n t . Th e fin a l se ct ion t u r n s t o t h e qu e st ion of h ow you ca n im pr ove r e ple n ish m e n t by r e m ovin g t im e a n d cost fr om t h e pr oce ss.

Figure 8.1. The Replenishment Cycle

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Chapter 8. Maintaining Supply

Triggering Replenishment Each t im e a facilit y fulfills an order, it reduces it s invent ory of finished goods. Sooner or lat er, t hat invent ory has t o be replenished. Behind t his self- evident t rut h lie t hree key quest ions: 1 . When should invent ory be replenished? 2 . What quant it y should be ordered wit h each replenishm ent ? 3 . How m uch invent ory should be m aint ained on sit e? The answers t o t hese t hree quest ions const it ut e what is known as a r e ple n ish m e n t policy. The first t hree sect ions of t his chapt er address t he t hree quest ions in t urn, st art ing wit h t he quest ion of when t o place an order. There are several opt ions for deciding when t o replenish invent ory. One solut ion is sim ply t o wait unt il t he current invent ory is exhaust ed. This pract ice is usually t he result of inat t ent ion rat her t han int ent ion, but it is act ually t he best policy for it em s t hat have short er lead t im es t han " need" t im es. I f a supplier can buy or build a product in a week and it s cust om ers are happy wit h 10- day lead t im es, t hen t hat supplier has t he very at t ract ive opt ion of operat ing wit h no st ock on hand. I t would only m aint ain invent ory of t he product if t here were ot her benefit s, such as reducing cost s by ordering or building in quant it y. At t ract ive as it m ight be, t he zero- invent ory solut ion is rarely an opt ion because cust om ers want t o t ake delivery fast er t han t he replenishm ent lead t im e. Ordinarily, t hen, a facilit y has t o order st ock in advance of dem and and keep enough on hand t o fill orders from it s invent ory. To do t his, it has t o m onit or it s invent ory levels and place a new order while it st ill has enough st ock t o avoid running out before t he order arrives. This m onit oring can t ake one of t wo form s, called periodic review and cont inuous review. Wit h a pe r iodic r e vie w policy, invent ory is count ed at fixed int ervals and an order is placed whenever t he count falls below a preset r e or de r poin t ( ROP) . Under a con t in u ou s r e vie w policy, t he count is m onit ored at all t im es and an order is placed as soon as t he count hit s t he reorder point . Under bot h review policies, invent ory levels describe a sawt oot h pat t ern over

t im e, wit h gradual declines as invent ory is consum ed followed by sudden j um ps when replenishm ent st ock arrives. Wit h cont inuous review, a new order is t riggered whenever t he st ock reaches t he reorder point , as shown in Figur e 8.2. St ock cont inues t o dim inish during t he r e ple n ish m e n t le a d t im e , but t he reorder point is set high enough t o m inim ize t he frequency of st ock ou t s, in which sales are lost due lack of invent ory. The periodic review policy produces a sim ilar pat t ern, but orders wait unt il t he next invent ory count occurs rat her t han being placed im m ediat ely. The facilit y cont inues t o deplet e it s invent ory during t his addit ional wait , so it has t o set t he reorder point higher t o com pensat e for t he delay. The result : Periodic review requires m ore invent ory t han cont inuous review.

Figure 8.2. Inventory Levels Under Continuous Review

Alt hough cont inuous review is m ore efficient t han periodic review, it is also m ore expensive because it requires an accurat e invent ory count at all t im es. Hist orically, periodic review was t he preferred m et hod because it avoids t his added expense. However, t here are ways t o get t he benefit s of cont inuous review wit hout t he cost of const ant count ing. A sim ple exam ple of t his is t he t wo- bin syst em used in fact ories t o m aint ain a supply of part s at a workst at ion. I n t his schem e, an operat or draws part s from one bin while t he ot her is being refilled by an upst ream workst at ion. As each a bin is em pt ied, it is sent back for a refill, t riggering anot her cycle of t he replenishm ent process. I t 's a sim ple, elegant way t o m onit or count s wit hout count ing, and it 's easily ext ended out t o suppliers. I n t he aut om ot ive indust ry, specially shaped, reusable bins are sent t o suppliers t o t rigger t he replenishm ent of part s kit s at assem bly st at ions. I n addit ion t o creat ing a precise flow of part s from suppliers, t hese bins facilit at e assem bly by present ing t he part s t o assem blers in a st andard m anner, and t hey elim inat e t he wast e associat ed wit h t em porary packaging. Wit h t he advent of com put erized invent ory cont rol syst em s, t he t ask of count ing has becom e t rivial, so cont inuous review is now t he m et hod of choice. I n order for t hese cont rol syst em s t o work, however, t hey m ust be not ified

every t im e invent ory is reduced. I n ret ail facilit ies, t his is usually handled by point - of- sale ( POS) syst em s t ied t o t he cash regist ers. I n fact ories, it m ay be accom plished by put t ing barcodes on com ponent s or punching count ers at st ock cages. These t echniques don't elim inat e t he need for m anual count ing alt oget her; invent ory is always subj ect t o sh r in k a ge t hrough t heft or loss. But m anual count s are used only for verifying and adj ust ing t he com put er count s, which fully aut om at e t he process of t riggering orders. The abilit y t o swit ch from periodic t o cont inuous review is a good exam ple of how inform at ion can replace invent ory, producing subst ant ial savings. I n t his case, t he inform at ion is not hing m ore t han a single num ber indicat ing t he current count . But t his num ber can be wort h a lot : I n one com parison, convert ing from periodic t o cont inuous review reduced required invent ory from 1,570 t o 906 unit s. Given t he escalat ing cost s of invent ory, it 's usually a good deal cheaper t o hold t he count t han t o hold t he invent ory.

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Chapter 8. Maintaining Supply

Determining Order Quantity The preceding sect ion answered t he quest ion of when t o replenish invent ory, and t his answer provides t he basis for t ackling t he second quest ion: What quant it y should be ordered wit h each replenishm ent ? That quant it y hinges on t he relat ive cost s of placing an order and holding invent ory. The or de r cost is t he basic cost of placing and receiving an order, independent of t he quant it ies involved. The h oldin g ( or ca r r yin g) cost is t he cost of st ockpiling invent ory in advance of consum ing it . I t includes t he cost s of st orage and handling, t he opport unit y cost of t he capit al t ied up in t he invent ory, t he loss of value due t o obsolescence or spoilage, and t he cost of insuring against risks such as fire and t heft . These t wo cost s t end t o push t he order quant it y in opposit e direct ions. I ncreasing order quant it ies reduces order cost s because fewer orders are required t o buy a given am ount of invent ory, but it also drives up holding cost s by increasing average invent ory levels. Conversely, ordering in sm aller quant it ies reduces holding cost s at t he expense of order cost s. Finding som e sort of balance bet ween t he t wo is im port ant because neit her cost is t rivial. Manufact uring com panies usually have a significant percent age of t heir capit al asset s t ied up in invent ory, and t hey want t o bring t hat percent age down. But placing an order generally cost s $100 t o $150, and t hat adds up fast given t he volum e of orders required t o keep a plant running. The obvious quest ion at t his j unct ure is whet her t his t radeoff has a sweet spot , a quant it y at which t he sum of t hese t wo cost s is m inim ized. I t does, and t he spot can be found using a m at hem at ical m odel known as t he e con om ic or de r qu a n t it y ( EOQ) . The equat ion for t his m odel is sim ple, but it 's easier t o see how t he m odel works by looking at a graph. As Figure 8.3 indicat es, holding cost s increase linearly wit h quant it y, while order cost s decrease inversely wit h quant it y. The t ot al of t hese t wo cost s, shown in t he t op curve, drops wit h increasing quant it y and t hen rises again. The lowest point on t his curve is t he EOQ, and it is easily calculat ed using st andard form ulas. For any given com binat ion of order cost and holding cost , t he EOQ gives t he exact size of t he order t hat should be placed t o m inim ize t he t ot al expense of buying and holding invent ory.

Figure 8.3. Inventory Cost Curves

The EOQ m odel provides a good exam ple of how t he kinds of relat ions described in Chapt er 4 show up in real- world problem s. The relat ion for holding cost over quant it y is linear ( t he first rogue in t he rogues gallery of relat ions shown in Figure 4.4 ) , and t he relat ion for order cost is m onot onic ( t he second rogue) . Com bining t hese t wo produces a t hird relat ion t hat is cont inuous ( t he t hird rogue) , so t hree of t he five rogues in t he gallery appear in t his one m odel. I n real- world applicat ions of EOQ, t he fourt h rogue also pops up when quant it y discount s int roduce discont inuit ies in t he t ot al- cost curve, changing it from a sm oot h line t o a scalloped one t hat j um ps downward at price breaks. These discont inuit ies com plicat e t he EOQ m odel and m ake it t edious t o calculat e; m ost buyers j ust run t he regular EOQ calculat ion and bum p t he quant it y up if it 's close t o a price break. The EOQ m odel was developed m ore t han 80 years ago and has been in cont inuous use since. I n recent years, however, it s use fulness has been called int o quest ion because it doesn't t ake int o account fluct uat ions in dem and, incent ives for buying invent ory in advance of need, t he effect s of request ing m ult iple product s on a single order, and a host of ot her fact ors t hat can influence order quant it ies. More im port ant , t his kind of local opt im izat ion overlooks higher- level opport unit ies for reducing cost s, such as sim plifying t he order process or elim inat ing orders alt oget her. But t he EOQ m odel is st ill around for t he sim ple reason t hat , in t he absence of a m ore com prehensive plan, it gives a quick and reasonably good answer.

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Chapter 8. Maintaining Supply

Maintaining Safety Stock The t wo preceding sect ions dealt wit h t he quest ions of when t o replenish invent ory and how m uch t o buy on each order. The present sect ion t ackles t he t hird quest ion: How m uch invent ory should a facilit y m aint ain on sit e? The quant it ies calculat ed in t he EOQ m odel provide a lower bound on t his num ber; t here should always be enough st ock on hand t o sat isfy dem and during t he replenishm ent lead t im e. But t hese calculat ions are based on average figures for dem and and supply. I n realit y, t he num ber of unit s sold varies from day t o day, shipm ent s are delivered lat e, goods arrive in an unusable condit ion, and so on. I f any of t hese event s causes a st ockout , cust om er orders go unfilled. There is no m argin for error in t he basic EOQ m odel. The st andard solut ion t o t his problem is t o hold excess invent ory—safet y st ock—t hat is used t o avoid st ockout s when dem and is great er t han expect ed or supplies arrive lat e. The role of safet y st ock can be seen graphically in Figure 8.4 , which adds a dose of realism t o Figure 8.2 by illust rat ing som e variabilit y in dem and and supply. I n t he exam ple shown, safet y st ock is needed in t he first cycle t o cover a higher level of dem and, as reflect ed in t he st eeper consum pt ion line. I t 's also needed in t he t hird cycle t o com pensat e for a lat e shipm ent , as reflect ed in t he ext ended lead t im e.

Figure 8.4. Adding Safety Stock

I n short , a com pany needs t o m aint ain enough invent ory t o support it s norm al operat ions—a quant it y known as t he cycle st ock—plus enough safet y st ock t o cover variat ions in supply and dem and. Using t he EOQ m odel causes t he cycle st ock t o vary bet ween zero and t he EOQ level, averaging out t o half t he value of t he EOQ. That leaves t he quest ion of how m uch safet y st ock is required t o avoid st ockout s. The answer, unfort unat ely, is " m ore t han you could possibly afford." Managers usually hat e answers like t hat , so it 's im port ant t o explore t he reason behind t his conclusion. I t t urns out t o be t he work of one of t he t wo core problem s of supply chains: variabilit y. As described in Chapt er 5, variabilit y in m ost quant it ies conform s t o t he norm al dist ribut ion, a recurring pat t ern t hat describes how far values are likely t o deviat e from t he average. As you m ay recall, t his dist ribut ion is described by j ust t wo param et ers: t he m ean and t he st andard deviat ion. Given t hose t wo param et ers, t he norm al dist ribut ion predict s t he num ber of t im es each possible value is likely t o occur. For exam ple, Figure 8.5 shows t he likelihood of each level of dem and for a product wit h a m ean dem and of 100 unit s a week and a st andard deviat ion of 10 unit s. Just looking at t he curve, it 's clear t hat dem and is rarely going t o be less t han 70 unit s a week or m ore t han 130.

Figure 8.5. Distribution of Weekly Demand

The problem wit h t hat conclusion is t he word rarely. The norm al dist ribut ion has t he unfort unat e charact erist ic t hat t he ends of t he dist ribut ion never quit e drop t o zero. No m at t er how far out t he value, t here is always som e probabilit y of t hat value occurring. I n Figure 8.5 , t he likelihood of experiencing a dem and of 140 unit s is very sm all—only a fract ion of a percent —but it 's not zero. Neit her is t he probabilit y of 150 unit s, nor even 200. Which m eans t hat no am ount of safet y st ock is enough t o ent irely elim inat e t he possibilit y of a st ockout . This isn't a failure of t he norm al dist ribut ion as a m odel; it 's an accurat e descript ion of what happens in t he real world. The norm al dist ribut ion sim ply reflect s t he profound im pact of variabilit y on planning and perform ance t hroughout t he supply chain. Given t hat st ockout s can't be elim inat ed alt oget her, t he best t hat safet y st ock can do is reduce st ockout s t o an accept able level. The st andard procedure is t o set a t arget level of product availabilit y, called t he cu st om e r se r vice le ve l ( CSL) , t hen adj ust t he safet y st ock t o m eet t hat level. Nat urally, higher CSLs are bet t er, but set t ing t hem t oo high can be very expensive because safet y st ock rises exponent ially wit h t he service level. Figure 8.6 illust rat es t he rapid rise in safet y st ock required t o hit it em fill rat es in t he high 90s, which is where m ost com panies would like t o keep t hem . For t his part icular product , sim ply increasing t he fill rat e by a half a point , from 97.5% t o 98% , requires holding nearly t hree t im es t he safet y st ock. This is a high price t o pay for such a sm all im provem ent given t he high cost s of holding invent ory.

Figure 8.6. Required Levels of Safety Stock

Given t hat increasing invent ory yields such rapidly dim inishing ret urns on service level, how should you go about choosing t he right level? The ideal would be t o have a form ula com parable t o t he EOQ t o t ell you t he level at which t he cost of holding ext ra invent ory j ust offset s t he cost of st ockout s. But how do you calculat e t he cost of a st ockout ? I f a cust om er is willing t o accept a backorder, t he cost is t he expense of t he backorder. I f t he cust om er t urns t o anot her supplier for t his one purchase, t he cost is t he loss of t he revenue from t he sale. I f t he cust om er t urns t o anot her supplier and never com es back, t he cost is t he lost revenue for all fut ure sales t o t hat cust om er. Few com panies have a good handle on t he likelihood of t hese various out com es, m uch less an accurat e way of est im at ing lost revenue from fut ure sales. Most sim ply set a service- level t arget som ewhere in t he high 90s and adj ust safet y st ocks accor dingly . Using fill rat e as t he m easure of t he cust om er service level doesn't quit e resolve t he m at t er because fill rat e can be m easured in m ore t han one way. I n t he exam ple above, it was im plicit ly defined as t he abilit y t o fill orders for a single product from st ock—what is oft en called t he it e m fill r a t e. But business cust om ers rarely order j ust one product ; t hey generally place m ult i- line orders t hat cover a num ber of product s. Alt hough t his reduces t he cost of order processing on bot h sides of t he t ransact ion, it also m akes it harder for suppliers t o fill t he orders because t he probabilit y of filling an ent ire order—t he or de r fill r a t e —is roughly t he num eric product of t he it em fill rat es for all t he lines on t he order. Figure 8.7 illust rat es graphically how quickly t he order fill rat e drops as t he num ber of it em s on t he order goes up. Even wit h an it em fill rat e of 95% , t he order fill rat e drops t o 60% wit h as few as 10 line it em s. Wit h 20 it em s, barely a t hird of out going orders ship com plet e.

Figure 8.7. Predicting Order Fill Rates

Let m e put t his anot her way: When cust om ers require com plet e shipm ent s, suppliers have t o m aint ain m uch higher invent ory levels t han t hey would ot herwise require. For exam ple, t o hit an order fill rat e of j ust 90% on 10- line orders, a supplier would have t o have an it em fill rat e of 99% , and t hat calls for a great deal of safet y st ock. This safet y st ock is ent irely devot ed t o coping wit h variabilit y in cust om er dem and. But t here is a vicious cycle at work here: Receiving incom plet e orders is one of t he sources of variabilit y on t he cust om er side t hat t hese cust om ers are t rying hard t o cont rol, so t hey are increasingly insist ent on com plet e shipm ent s. This is why t he problem of variabilit y is so insidious: Trying t o reduce it at any one link in t he chain m ay j ust push it up or down t he chain, oft en am plifying it in t he process. The only real solut ion t o t his problem is for t rading part ners t o work t oget her t o rem ove variabilit y across t he chain rat her t han t rying t o cope wit h it t hrough point solut ions such as added safet y st ock.

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Chapter 8. Maintaining Supply

Streamlining Replenishment Like t he fulfillm ent process described in Chapt er 7, replenishm ent has becom e increasingly com plex over t he years, producing corresponding increases in t he t im e, cost , and errors associat ed wit h t he process. These problem s, in t urn, oft en lead people wit hin a com pany t o t ry t o beat t he syst em by speeding up t heir own orders, usually at t he expense of ot her orders t hat m ay be equally urgent . I n addit ion t o slowing down t he replenishm ent process as a whole, t hese expedit ing bat t les lead t o ext ra work and m ay also incur surcharges for rush orders. As in t he case of fulfillm ent , t he solut ion is not t o bypass t he fulfillm ent syst em but t o im prove it , and radical change in t his syst em m ay produce bet t er result s t han at t em pt s at increm ent al refinem ent . Here again, radical changes require t he cooperat ion of t rading part ners, but t hat cooperat ion is usually easier t o obt ain in t he case of replenishm ent because you have a lot m ore clout in your dealings wit h suppliers t han you do wit h cust om ers. I n general, t he furt her upst ream you can push t he changes, t he m ore likely you are t o rem ove t im e and cost from t he chain as opposed t o j ust pushing it around wit hin t he chain. The act ual changes should, of course, be closely aligned wit h t he changes used on t he fulfillm ent side so t hat t he flow t hrough your own com pany is as sm oot h as possible. I f your cust om ers are dem anding sm aller, m ore frequent shipm ent s for JI T product ion, t hen it m akes sense t o m ove replenishm ent in t he sam e direct ion, accelerat ing t he flow of goods t hrough your facilit ies rat her t han becom ing t he point at which bat ch shipm ent s convert t o a flow m ode. By m aking t he sam e changes on bot h sides of your supply chain, you have t he best opport unit y t o creat e an arrangem ent t hat is m ore econom ical for everyone. The shift t o frequent , sm all shipm ent s is a good exam ple of how t his balancing act works. I f all you do is reduce t he size of shipm ent s t o and from your t rading part ners, everyone will get hurt because you will all lose econom ies of scale. The t radeoff expressed by t he EOQ m odel st ill applies—t here is no escaping it —so, unless you m ake ot her changes, t he savings in holding cost s don't offset t he increased cost of placing addit ional orders ( see Figure 8.3 ) . The t rick t o m aking t he econom ics work is t o reduce t he order cost s by sim plifying and st andardizing t he order process, preferably by elim inat ing orders alt oget her. I n a chain t hat uses ret urnable bins, for exam ple, processing an

" order" am ount s t o lit t le m ore t han refilling t he bin. I t 's only by reducing order cost s t o an absolut e m inim um t hat you bring t he EOQ down t o where frequent deliveries are cost - effect ive for everyone. Collaborat ing wit h suppliers t o int egrat e operat ions and st ream line replenishm ent is a significant undert aking, and t he effort should be reserved for suppliers t hat provide crit ical m at erials, such as cust om com ponent s and st andard it em s t hat are subj ect t o short ages. For widely available com m odit ies, such as nut s and bolt s, t he classic approach of dealing wit h several qualified suppliers and shopping for price m ay st ill be t he best opt ion. This is t he area where t he I nt ernet is having t he great est im pact on t he replenishm ent process. Hist orically, com m odit y suppliers were select ed by digging t hrough st acks of cat alogs t o find out who offered t he required product s, checking price books t o com pare cost s, sort ing t hrough flyers t o see whet her anyone had a prom ot ion in progress, and calling t he supplier t o check availabilit y. Today, searchable e le ct r on ic ca t a logs on t he Web reduce t he t im e and t edium of t his process t o a fract ion of it s form er level, significant ly reducing order cost s for cat alog purchases. Som e of t hese cat alogs are host ed by suppliers and are specific t o t heir product s, but ot hers are m ore like dist ribut or cat alogs, m erging product s from m ult iple suppliers and organizing t hem by t ype. These cat alogs becom e even m ore useful when t hey are incorporat ed int o e le ct r on ic e x ch a n ge s, Web- based m arket places in which buyers and sellers conduct business wit hout having t o leave t heir desks or even pick up t heir phones. Suppliers post t heir product s on t he exchange, which m erges t hem int o a com m on cat alog and provides t ools for searching and com parison ( Figure 8.8 ) . Cust om ers browse t he cat alog, add t heir select ions t o a " shopping cart ," t hen fill in a short form t o place t heir orders. I n addit ion t o handling t ransact ions, exchanges oft en provide added services such as qualifying buyers and sellers, providing inform at ion on t he st at e of t he m arket , and even support ing collaborat ion am ong supply chain part ners.

Figure 8.8. An Electronic Exchange

Alt hough m ost exchanges work from t he prices set by suppliers, som e host e le ct r on ic a u ct ion s, in which supplies are sold t o t he highest bidder ( Figur e 8.9) . I n a norm al auct ion, suppliers post product s on t he exchange, and cust om ers post bids indicat ing how m uch t hey are willing t o pay for t hose product s. At t he end of t he bidding period, t he exchange aut om at ically com pares t he bids and not ifies t he bidders of t he out com e. I n a r e ve r se a u ct ion, t he roles are swit ched: Cust om ers post request s for quot es ( RFQs) for specific product s, suppliers subm it bids, and t he sale goes t o t he lowest bidder rat her t han t he highest . While norm al auct ions t end t o push prices up, reverse auct ions generally have t he opposit e effect and are oft en favored by cust om ers for t hat reason.

Figure 8.9. Electronic Auctions

Elect ronic exchanges com e in a variet y of form s, depending on t heir access and ownership st ruct ures. Pu blic e x ch a n ge s require only t hat part icipant s be qualified t o buy and sell t he m at erials handled by t he exchange, and t hey are usually host ed by independent organizat ions t hat specialize in m anaging a m arket . Pr iva t e e x ch a n ge s are accessible t o com panies t hat have been approved for m em bership, and m ay charge a fee for use of t he exchange. Privat e exchanges can be independent organizat ions, or t hey can be host ed by one or m ore of t he t rading part ies. I n t he lat t er case, t hey are som et im es referred t o as ca pt ive e x ch a n ge s. I n several indust ries, t here has been a rapid evolut ion of st ruct ure from public t o privat e exchanges, t hen t o capt ive exchanges as a few dom inat e players ext end t heir m arket cont rol t o include t he elect ronic channel. A recent Forrest er Research report indicat es t hat 42% of com panies are now doing business on privat e exchanges, com pared wit h only 11% doing business on public exchanges. Exchanges—like so m any t echnology innovat ions—have fallen vict im t o t he hype- and- snipe cycle, in which analyst s m ake grossly exaggerat ed forecast s for new product cat egories and t hen declare t hese cat egories t o be failures when t heir predict ions aren't m et . I n t he case of exchanges, t he only t hing t hat failed was t he idea t hat vent ure- funded st art - ups could insert t hem selves bet ween est ablished t rading part ners and t ake a piece of t he act ion. As soon as t he pot ent ial of exchanges becam e apparent , t he m aj or buyers and sellers set up t heir own capt ive exchanges, bypassed t he st art - ups, and t ook t heir m arket s back. A good exam ple of t his is Covisint , t he purchasing exchange launched by U.S. aut om akers in t he last quart er of 2000. The exchange handled $129 billion wort h of purchases in t he first half of 2001. I t 's cle a r t h a t r e ple n ish m e n t h a s be com e j u st a s com ple x a s fu lfillm e n t , w h ich isn 't su r pr isin g give n t h a t t h e t w o a r e r e a lly t h e sa m e pr oce ss vie w e d fr om diffe r e n t pe r spe ct ive s. I n a ddit ion t o w or k in g w it h su pplie r s t o se t u p a fu lly a u t om a t e d or de r syst e m , a s su gge st e d a t t h e e n d of Ch a pt e r 7 , t h e r e a r e se ve r a l ot h e r t h in gs you ca n do t o im pr ove you r r e ple n ish m e n t pr oce ss. On e is t o dr ive dow n you r or de r cost s t o m a k e it m or e e con om ica l t o pla ce sm a lle r , m or e fr e qu e n t or de r s, t h e r e by r e du cin g you r r e qu ir e d le ve ls of bot h cycle st ock a n d sa fe t y st ock . An ot h e r is t o r e du ce va r ia bilit y in you r con su m pt ion , fu r t h e r r e du cin g you r n e e d for sa fe t y st ock . Bu t t h e m ost pow e r fu l t e ch n iqu e is t o su bst it u t e in for m a t ion for in ve n t or y w h e r e ve r you ca n , a u t om a t in g in ve n t or y cou n t s a n d con st a n t ly u pda t in g you r su pplie r s so t h a t t h e y ca n a n t icipa t e you r n e e ds a n d h e lp you m in im ize you r in ve n t or ie s.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Part III. Operations

Chapter 9. Measuring Performance On e of t h e k e ys t o im pr ovin g su pply ch a in ope r a t ion s is h a vin g a solid se t of m e a su r e s in pla ce t o m on it or pe r for m a n ce . Th e ch a lle n ge h e r e is m a k in g good ch oice s a m on g t h e doze n s of m e a su r e s a va ila ble . Som e com pa n ie s t r y t o m e a su r e t oo m u ch , ove r w h e lm in g t h e m se lve s w it h da t a t h a t n e ve r qu it e for m a coh e r e n t pict u r e . Ot h e r s m e a su r e t oo lit t le , r e lyin g on on e or t w o in dica t or s t h a t don 't r e fle ct t h e fu ll spe ct r u m of pe r for m a n ce . Th is t e n de n cy t o focu s t oo n a r r ow ly is e x a ce r ba t e d by m a n a ge m e n t fa ds su ch a s cycle - t im e r e du ct ion in t h e 1 9 9 0 s a n d t h e cu r r e n t obse ssion w it h in ve n t or y ve locit y. Ju st a s t h e r e is n o e a sy a n sw e r t o a ll su pply ch a in pr oble m s, t h e r e is n o m a gic m e a su r e for im pr ovin g pe r for m a n ce . Th is ch a pt e r in t r odu ce s a fr a m e w or k for u n de r st a n din g a n d se le ct in g su pply ch a in m e a su r e s ba se d on fou r br oa d ca t e gor ie s: m e a su r e s of t im e , m e a su r e s of cost , m e a su r e s of e fficie n cy, a n d m e a su r e s of e ffe ct ive n e ss. You w ill n e e d a t le a st on e a n d pr oba bly se ve r a l m e a su r e s fr om e a ch ca t e gor y if you w a n t t o ge t t h e be st pe r for m a n ce ou t of you r ch a in .

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Chapter 9. Measuring Performance

Measuring Time Tim e is t he easiest m easure t o capt ure because it involves not hing m ore t han t aking t wo readings and perform ing a subt ract ion ( Figure 9.1 ) . The t im es of great est int erest in supply chains are process t im es, which m easure t he am ount of t im e required for key business processes t o run from init iat ion t o com plet ion. As t he exam ples in Figure 9.1 suggest , t he processes involved can be m easured on any scale from seconds t o m ont hs.

Figure 9.1. Measuring Time

Figure 9.2 illust rat es som e of t he process t im es involved in t he fulfillm ent act ivit ies of supply chains. The overall fulfillm ent process, shown at t he t op of

t he figure, can be broken down int o com ponent processes dealing wit h t he support ing flows of dem and, supply, and cash. Alt hough t he t ot al durat ion of t he fulfillm ent process is crit ical t o cash flow, it is rarely m easured direct ly. Rat her, t he prim ary concern is wit h t he fu lfillm e n t le a d t im e , which is t he sum of t he first t wo phases. The t hird phase is usually handled by t he account ing depart m ent , where it is m easured indirect ly in t he aging of account s receivable. Fulfillm ent lead t im es vary considerably across indust ries, but a t ypical lead t im e for an order t hat has not been previously scheduled is in t he range of t wo t o t hree weeks. Of t hat t im e, several days m ight be spent processing t he order, several m ore days assem bling t he order, and t he rem aining t im e t aken up by t ransport at ion.

Figure 9.2. Fulfillment Times

Figure 9.3 shows a breakdown of t he replenishm ent process int o t he sam e t hree phases. I n t his process, t he prim ary concern is wit h r e ple n ish m e n t le a d t im e , which is m easured from t he t im e a request for goods is subm it t ed for purchasing t o t he t im e t hose goods are available for use. Of necessit y, t his t im e includes t he supplier's fulfillm ent lead t im e, which t o t he cust om er is sim ply a wait ing period. Because t he supplier's lead t im e is such a visible part of t he fulfillm ent lead t im e, com panies oft en equat e t he t wo and at t em pt t o reduce lead t im es by dem anding fast er fulfillm ent from t heir suppliers. However, it is rare t hat a cust om er's purchasing and receiving processes can't be accelerat ed as well.

Figure 9.3. Replenishment Times

Tim es t hat aren't direct ly t ied t o a single business process are usually referred t o as int ervals. As indicat ed in Figure 9.1 , one such int erval is t he t im e t hat elapses bet ween orders from a part icular cust om er, a m easure t hat m ight range from hours in a JI T environm ent t o weeks in a t radit ional product ion operat ion. Anot her int erval of int erest is t he ca sh - t o- ca sh t im e, which is usually m easured in days. As can be seen in Figure 9.4 , t his int erval doesn't correspond direct ly t o any one process. Rat her, it begins when raw m at erials are paid for, which happens lat e in t he replenishm ent process, and ends when paym ent is received for t he finished goods, near t he com plet ion of t he fulfillm ent process.

Figure 9.4. Cash-to-Cash Times

Hist orically, cash- t o- cash t im es have not received as m uch at t ent ion in supply chains as ot her, m ore visible t im e- based m easures such as lead t im es. However, com panies now recognize cash as a vit al supply chain asset t hat needs t o be recovered and put back int o play as quickly as possible, so t his m easure is being used wit h increasing frequency. Cash- t o- cash t im es t ypically run about 70 t o 90 days, but efficient com panies get t his num ber below 60 days, and t he best keep it under 30. As described in Chapt er 1, Dell has act ually driven it s cash- t o- cash t im e int o t he negat ive range, receiving paym ent from it s cust om ers before paying it s suppliers. The t hird exam ple of an int erval shown in Figure 9.1 , m achine cycle t im e, raises t he quest ion of how t he popular m easure of cycle t im e fit s int o t his fram ework. Originally, cycle t im e referred t o t he int erval bet ween repet it ions of a periodic process, which is not necessarily t he sam e as t he durat ion of t hat process. For exam ple, a product ion line wit h a cycle t im e of 30 seconds would produce t wo product s a m inut e, but t he t im e required t o assem ble any one product —t he t im e it t akes m at erials t o get from one end of t he line t o t he ot her—m ight be 20 m inut es. Cont em porary usage confounds t hese t wo m easures, applying t he t erm cycle t im e t o process durat ions as well as repet it ion int ervals. Given t his confounding, you m ay want t o be caut ious when you see t he t erm cycle t im e t o be sure you underst and exact ly what is being m easured. I n t his book, I avoid confusion by avoiding t he t erm . Anot her approach t o m easuring t im es is t o invert t hem and express t hem as sp e e d, which is a dist ance divided by a unit of t im e. I n supply chains, speed is used m ost ly for assessing t he t ransport at ion funct ion, reflect ing eit her act ual perform ance or lane charact erist ics. Speed values range from t he leisurely pace of ocean freight ers t o t he blur of part s flying t hrough pneum at ic t ubes. When speed t akes on a part icular direct ion, it is called velocit y. Recent ly, in ve n t or y ve locit y has becom e t he t erm of choice for describing t he speed at which m at erial flows t hrough a supply chain, and t he current em phasis is on finding ways t o increase t hat velocit y. This is an excellent goal ( see Chapt er 15 ) , but invent ory velocit y is m ore of a m et aphor t han a m easure. People who t alk about invent ory velocit y—m ost not ably Michael Dell—aren't really describing t he speed at which invent ory is t ransport ed. Rat her, t hey're referring t o t he am ount of t im e it t akes t o t ransform raw m at erials int o finished goods. I f you look for act ual m easurem ent s of invent ory velocit y, you'll com e up em pt y- handed; what you'll act ually find are report s of such t radit ional m easures as invent ory t urns or days on hand, as defined lat er in t his chapt er. The last t ype of m easure shown in Figure 9.1 is t hroughput , which is defined as unit s of work divided by a unit of t im e. This t ype of m easure is really a variant of speed, but it 's concerned wit h how quickly work is perform ed rat her t han how quickly som et hing m oves. For supply chains, t hroughput is usually of m uch m ore int erest t han speed. Exam ples of t hroughput include product s produced per week, orders processed per day, gallons of out put per hour, and it em s picked per m inut e. As wit h all m easures, m easurem ent s of t im e can reflect a part icular inst ance of a process or int erval—t he t im e t o deliver an im port ant order, say—or t hey can sum m arize a num ber of t hese inst ances, such as t he t im e required t o deliver all t he orders received at a given facilit y during t he prior m ont h. When m easurem ent s are aggregat ed, t hey are usually report ed as a single num ber—som et im es a t ot al, but usually an average value such as t he m ean. This is a dangerous pract ice because reducing a group of m easurem ent s t o a

single num ber m asks t he variabilit y am ong t hose m easurem ent s, and coping wit h variabilit y is one of t he deepest challenges in supply chain m anagem ent . Figure 9.5 illust rat es t his point by showing t he dist ribut ions for t he fulfillm ent lead t im es of t wo suppliers. Supplier A requires an average of 17 days t o fill it s orders, whereas Supplier B requires 19 days on average. Based on t hese average values, Supplier A clearly offers bet t er perform ance. But t he act ual m easurem ent s underlying t hese averages t ell a different st ory: A is m uch less consist ent in it s delivery t im es t han B, wit h lead t im es as lit t le as 9 days and as long as 25 days. Early deliveries cause you t o hold invent ory longer t han necessary, and lat e deliveries require you t o increase invent ory levels t o avoid st ockout s, so any deviat ion from t he request ed delivery dat e requires you t o hold m ore invent ory. Depending on your holding cost s, you m ay find t hat t he consist ency of Supplier B m akes it a bet t er choice even t hough it 's a lit t le slower t han A on average.

Figure 9.5. Two Lead-Time Distributions

I t 's also im port ant t o underst and what happens t o variabilit y when you com bine processes t o form larger processes, as shown in Figures 9.2 and 9.3. Do t he variat ions in t he com ponent t im es cancel each ot her out , m aking t he t ot al t im e a m ore st able m easure? Or do t hey add up, m aking it less st able? I f t he t im es for t he com ponent processes are reasonably independent of one anot her, t he variabilit y in t he dist ribut ion of t he t ot al t im e is t he sum of t he variabilit y in all t he com ponent t im es. I n effect , all t he variabilit y t hat is picked up along t he way is accum ulat ed in t he dist ribut ion for t he t ot al t im e. The business m essage is t his: I f you want t o m inim ize variabilit y in your supply chain, you have t o m inim ize it in every single process along t he way.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 9. Measuring Performance

Measuring Cost The second m aj or cat egory of m easures deals wit h cost s, which com e in a wide variet y of t ypes. Com pared t o t he sim plicit y of m easuring t im e, m easuring cost is considerably m ore difficult , and com plex issues can arise when you t ry t o m ix or com bine different t ypes of cost . Figure 9.6 provides exam ples of five different t ypes, but t hese are only m eant t o illust rat e som e of t he ways in which cost s can arise. There are cert ainly ot her kinds of cost s you will need t o consider, and t he ones shown are not m ut ually exclusive. For exam ple, m ost periodic cost s are also indirect cost s, and t he expense of correct ing an error m ight include cost s of every ot her t ype in t he list .

Figure 9.6. Measuring Cost

Direct cost s, t he first t ype, are t hose you can at t ribut e direct ly t o t he product ion of finished goods. As shown in Figure 9.7 , t his cat egory includes t he cost of raw m at erials t oget her wit h t he cost of t he processes necessary t o acquire t hese m at erials, t ransform t hem int o finished goods, and deliver t hem t o cust om ers. Measuring direct cost s accurat ely is difficult because few account ing syst em s provide t he necessary inform at ion, but m ost m anufact uring com panies have a reasonable handle on t heir direct cost s.

Figure 9.7. Direct Costs

I ndirect cost s, t he second t ype, are t hose t hat are necessary t o run your com pany but t hat can't be at t ribut ed direct ly t o t he creat ion of a part icular product . As shown in Figure 9.8 , t hese include t he cost s of purchasing and m aint aining t he equipm ent used in producing goods, t he cost s of building and operat ing t he facilit ies t hat house t his equipm ent , and t he cost s of running all t he support organizat ions t hat are essent ial t o a m anufact uring ent erprise. I ndirect cost s are relat ively easy t o m easure because t hey usually correspond t o m aj or account ing syst em cat egories. The challenge lies in figuring out how t o allocat e t hese cost s t o finished goods. I f you are going t o m ake m oney on your product s, t he selling price has t o recover not only t heir direct cost s but also t heir fair share of t he indirect cost s.

Figure 9.8. Indirect Costs

The m ost syst em at ic approach t o allocat ing indirect cost s is a ct iv it y - b a se d

cost in g ( ABC) . I n t his approach, indirect cost s are allocat ed t o product s by way of act ivit ies and t he resources t hey require. Translat ing slight ly t o m at ch t he t erm s I use in t his book, t he idea is t hat all cost s are ult im at ely due t o t he use of resources by processes. I f you can t race back all t he resources required by a product ion process, t herefore, you can det erm ine it s act ual cost . Resources t hat are fully consum ed by a process appear as direct cost s, and t he process absorbs t heir cost s in full. Resources t hat are required for t he process but t hat aren't consum ed by it appear as indirect cost s, and a pro rat a port ion of t hese indirect cost s is charged t o t he process. I n Figure 9.9 , a print ing process consum es ink, paper, and labor, so it pays t he full freight for t hese resources. I t requires t he press but does not consum e it , so t he process only accrues a cost proport ionat e t o t he t im e it m onopolizes t he press. Sim ilarly, t he print ing process m akes use of a print shop and requires t he office t o m anage t he paperwork, so t hose resources also pass on a port ion of t heir t ot al cost .

Figure 9.9. Costing a Printing Process

I n effect , act ivit y- based cost ing seeks t o t ranslat e indirect cost s int o direct cost s. This t ask becom es increasingly problem at ical as access t o resources becom es m ore indirect . Working out t he cost cont ribut ion of equipm ent m ay be difficult , but allocat ing t he cost of facilit ies is even harder, and figuring out how t o allocat e t he overhead of back- office st aff funct ions can be a highly creat ive endeavor. Despit e t hese obst acles, act ivit y- based cost ing has proved quit e useful in assessing t he profit abilit y of individual product lines, and it oft en yields surprising insight s. So m uch of product cost is hidden by indirect ion t hat , wit hout t he kinds of analysis called for by act ivit y- based cost ing, it 's hard t o know which product s are act ually generat ing a profit . A part icularly im port ant kind of indirect cost is opport unit y cost , which is t he loss of revenue t hat could have been realized from an alt ernat ive use of t he funds invest ed in a process. I f you invest $200,000 in a product ion run and don't recover t hat invest m ent unt il six m ont hs lat er, you have lost t he opport unit y t o use t hat m oney for som e ot her area of t he business. Opport unit y cost is logically equivalent t o paying int erest on t he m oney t hat 's t ied up in t he process, but it uses a higher rat e because opport unit y cost is based on t he ret urn you could get from t he best use of t he m oney in your operat ions, and com panies com m only put t his figure in t he range of 10% t o 15% . At t hese

rat es, t he product ion run in t he exam ple would incur an opport unit y cost of up t o $15,000, and you would have t o recover t hat cost along wit h t he original $200,000 before you could realize a profit . A t hird kind of cost is t he expense at t ribut able t o errors in supply chain processes. These errors include incorrect quant it ies, invalid product subst it ut ions, inaccurat e prices, invent ory st ockout s, lat e shipm ent s, deliveries t o t he wrong locat ion, dam aged goods, and m issing it em s, t o nam e j ust a few. The m ost obvious error cost is t he expense of running correct ive processes, such as handling ret urns, expedit ing replacem ent s, reworking defect s, and handling set t lem ent s. Because t hese correct ive processes are usually ad hoc and t im e int ensive, t hey are generally m ore expensive t han t he original process, causing t he t ot al cost of a t ransact ion t o m ore t han double. Less obvious error cost s include such long- t erm consequences as t he loss of fut ure business from cust om ers who change suppliers due t o process failures, as well as dam age t o t he com pany's reput at ion if t hese failures becom e frequent . These kinds of cost s are, of course, m uch harder t o m easure. Like all m easures, cost s can be expressed eit her as a sim ple num ber or as a rat io t o som e ot her num ber, usually a m easure of t im e or work. As shown in Figure 9.6 , cost m easures based on t im e include such periodic cost s as annual int erest and m ont hly rent , while cost s based on work include m easures like t ransport at ion cost per m ile, cost per order processed, and cost per cubic foot of space. The advant age of st at ing cost s in absolut e t erm s is t hat t hey all have t he sam e unit s and can be added and subt ract ed, as t hey are in financial st at em ent s. Relat ive cost s, on t he ot her hand, are m ore useful for com paring perform ance on t he sam e process across organizat ions, or across t im e periods wit hin t he sam e organizat ion, because t hey fact or out t he effect s of volum e. Som e t ypical cost rat ios used in supply chains are selling cost s as a percent of sales, t ransport at ion cost per m ile, and cost per cubic foot of st orage.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 9. Measuring Performance

Measuring Efficiency Cost s, alt hough crit ical t o supply chain perform ance, fail t o capt ure an im port ant aspect of supply chains: t he efficiency wit h which a chain ut ilizes it s resources. I f facilit ies, vehicles, equipm ent , and ot her asset s are not used at or near t heir full capacit y, t heir indirect cost s m ust be spread across fewer product s, raising t he cost of each. Sim ilarly, supplies m ust be consum ed as quickly as possible t o m inim ize holding cost s, which are a m aj or com ponent of direct cost in supply chains. The purpose of t he t hird cat egory of m easures is t o assess t he efficiency wit h which a chain ut ilizes it s asset s ( Figure 9.10) .

Figure 9.10. Measuring Efficiency

Of t he m any asset s required for supply chains, invent ory usually receives t he m ost at t ent ion because it inflict s such a heavy financial burden. Several m easures are used t o m onit or invent ory levels, including current and average count s, but t he m ost widely used m easure is t he in ve n t or y t u r n ove r r a t io , also called in ve n t or y t u r n s. The t urnover rat io for a product is t he annual sales of t hat product divided by t he average quant it y on hand. For exam ple, a product selling 60 unit s a year wit h an average invent ory of 10 unit s has a t urnover rat io of 60/ 10 = 6. I ndust ries differ great ly in t heir invent ory t urns, but a t urnover rat io of 6 is fairly t ypical. Wit hin indust ries, t urns can vary by a

fact or of 4 or 5, so if 6 is t he average it would be com m on t o see som e com panies t urning t heir invent ory only 2 or 3 t im es a year while ot hers were t urning t heirs 10 or 12 t im es a year. Alt hough t he t urnover rat io is t he m ost com m on m easure for convent ional product ion, com panies t hat have adopt ed JI T product ion usually find t hat t he m easure becom es unwieldy. For exam ple, Lear Corporat ion, which builds aut om ot ive int eriors for U.S. carm akers, t urns it s inbound invent ory bet ween 120 and 214 t im es a year. When t urns get t his high, it 's bot h easier and m ore precise t o m easure invent ory in t erm s of da ys on h a n d, which is t he num ber of days t he invent ory would last given norm al consum pt ion. I n t he case of Lear, t he com pany is operat ing wit h one t o t wo days of invent ory on hand. Alt hough t he t wo m easures provide t he sam e inform at ion, t he days- on- hand m easure put s t he inform at ion in a m ore m eaningful form for com panies t hat go t hrough invent ory as quickly as Lear does. I f you reduce t he am ount of invent ory you keep on hand for a given product , individual it em s in t hat invent ory m ove t hrough t he chain fast er. This is what is m eant by increasing t he velocit y of invent ory, and it 's an excellent idea even t hough, as not ed earlier, velocit y doesn't act ually represent a new m easure. One int erest ing approach t o quant ifying t he not ion of velocit y is t o m easure t he am ount of t im e product s spend being processed in som e way, including t ransport at ion as well as t ransform at ion, t hen divide t hat by t he t ot al t im e t he product s spend in t he chain. This rat io indicat es t he relat ive am ount of t im e t he product is act ually m oving t hrough t he chain rat her t han j ust sit t ing t here t aking up space. A num ber of st udies indicat e t hat , despit e at t em pt s t o accelerat e invent ory m ovem ent , product s in t he pipeline st ill spend t he m aj orit y of t heir t im e sit t ing around. I t 's not uncom m on t o find t im e- in- process result s in t he range of 10% t o 20% , and one st udy of t he aut om ot ive indust ry in England found t hat st eel part s for cars spend only about 3% of t heir t im e in process. A relat ed st udy of t he aut om ot ive assem bly process found t hat , of t he 40 days required t o m anufact ure a car, only one and a half days—less t han 4% of t he t ot al t im e—are act ually spent assem bling and t est ing t he vehicle. To m ost m anagers, t hese figures seem shockingly low. Where does all t he t im e go? One way t o answer t his quest ion is t o plot t he t im e in process against t he passage of calendar t im e and look for t he flat spot s. Figure 9.11 illust rat es t his t echnique by showing t he m ovem ent of a product t hrough t wo links of a supply chain. As you can see, t he product spends a large percent age of it s t im e sit t ing in raw m at erials or finished goods invent ories, and t he bulk of t he rem aining t im e is spent in t ransit . Most of t he act ive processing of t his product t akes place in t wo spurt s, in t he t hird and nint h weeks, while t he product is act ually on t he floor of a product ion facilit y. But even in t hese periods t he product spends m ost of it s t im e wait ing, get t ing no m ore t han an hour's wort h of at t ent ion in t he course of a day. A det ailed chart of t hose periods would reveal a m iniat ure version of t he pat t ern in Figure 9.11; t he product would be seen t o spend m ost of it s t im e sit t ing in queues or m oving bet ween workst at ions, wit h relat ively lit t le t im e in process.

Figure 9.11. Mapping Time in Process

The second t ype of efficiency m easure shown in Figure 9.10 deals wit h t he use of fixed capacit y such as facilit ies and m achinery. The m ost im port ant m easure is t he load, which represent s t he percent age of capacit y t hat is in use at any t im e. Since capacit y represent s a fixed cost , you'd like t o keep t he load high so you can am ort ize t his cost over as m any product s as possible, reducing t he cost per unit . But if you want t o ret ain som e flexibilit y t o handle varying levels of dem and, you can't run your chain at 100% capacit y except for brief spurt s. Finding t he right balance point —whet her it 's 80% or 98% —depends in large part on t he degree of variabilit y you need t o cope wit h in your chain. Ot her m easures of capacit y ut ilizat ion are expressed as t he work perform ed by a unit of capacit y, such as t he quant it y of product creat ed per square foot of plant space, or t he num ber of orders processed per cust om er represent at ive. As wit h ot her m easures, expressing t he use of capacit y in t erm s of rat ios is helpful when m aking com parisons across facilit ies, or across t im e wit hin a single facilit y. Unlike absolut e m easures of capacit y, t hese rat ios aut om at ically adj ust for any differences in t he volum e of work across facilit ies or over t im e. The t hird kind of efficiency m easure shown in Figure 9.10 is concerned wit h t he use of capit al, which is part icularly im port ant because it is t he m edium for acquiring ot her resources. The m ost com m on m easure for assessing t he efficient use of capit al is t he ret urn on invest m ent ( ROI ) rat io, obt ained by dividing net profit by t he capit al required t o produce t hat profit . An alt ernat ive m easure is t he cash t urnover rat io, defined as annual sales divided by cash in use. The t urnover rat io is direct ly analogous t o invent ory t urns, and it m easures t he efficiency wit h which a com pany m oves cash t hrough t he business. Anot her im port ant m easure for t racking t he use of cash is t he casht o- cash t im e described earlier in t his chapt er.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 9. Measuring Performance

Measuring Effectiveness While efficiency is crit ical t o profit abilit y, it is of lit t le value unless it is accom panied by anot her qualit y: effect iveness. Unlike efficiency, which is concerned wit h t he econom ical use of resources, effect iveness reflect s how well a process achieves it s business obj ect ives. The t wo qualit ies are oft en confused, but t he dist inct ion is sim ple: Efficiency m easures how well you use what you have, and effect iveness m easures how well you get what you want . Effect iveness is a concern for all t he processes involved in replenishm ent , product ion, and fulfillm ent , but t he fulfillm ent end of t he business usually receives t he m ost at t ent ion because it 's t he m ost visible t o cust om ers. To put t he m at t er blunt ly, it doesn't m at t er how good you are at purchasing and product ion if you can't deliver product s t o your cust om ers in a t im ely, reliable m anner. Given t his focus, t he m ost im port ant m easures of effect iveness are concerned wit h cust om er service levels ( Figure 9.12) .

Figure 9.12. Measuring Effectiveness

Cust om er service can be m easured in a variet y of different ways. I n years past , cust om er service level ( CSL) was usually defined in t erm s of proxim it y—t he percent of cust om ers wit hin 400 m iles of a warehouse, say—under t he t acit assum pt ion t hat holding invent ory close t o t he cust om er was t ant am ount t o

good service. A m ore com m on m easure t oday is t he on - t im e de live r y rat e, t he percent age of orders t hat arrive at t he cust om er sit e wit hin a cert ain t im e lim it . That t im e lim it can eit her be a fixed period, such as next - day delivery, or it can be t he prom ised dat e on t he order; t he choice depends on what it t akes t he keep t he cust om er happy. I t em and order fill rat es, described in Chapt er 8, are also com m on m easures of CSL; in t his case, t he choice bet ween t he t wo is det erm ined by whet her cust om ers require com plet e shipm ent s or find part ial shipm ent s accept able. Best - in- class com panies generally keep bot h t heir ont im e delivery rat es and t heir it em fill rat es in t he high 90s. For average t o poor com panies, bot h figures can drop down int o t he 70% t o 80% range. However it is defined, t he CSL m et ric can be applied in t wo ways: Som et im es it is a m easure, and ot her t im es it is a const raint . Som e com panies sim ply m easure t heir CSL and use it t o m onit or t heir perform ance across product s, regions, and t im e. Ot hers specify a t arget CSL and use t his t arget as a const raint on t he supply chain, im proving t he chain unt il t he t arget level is reached. For exam ple, you m ight set a t arget CSL of having 97% of your orders ship com plet e by t he prom ised dat e, t hen work on your chain unt il you hit t hat t arget . Of course, any m easure can be used as bot h a t arget and an out com e, but t his dual usage is part icularly com m on in t he case of CSL because it plays such a defining role in supply chain perform ance. Before you can int erpret a CSL figure for a com pet it or, you need t o underst and not only how t hey define t he m easure, but also whet her t he report ed value is a result or an obj ect ive. Maint aining good fill rat es and delivering orders on t im e are vit al t o good cust om er service, but it 's possible t o hit t arget levels for t hese m et rics and st ill have problem s wit h fulfillm ent : I t em s m ay be shipped t hat weren't ordered, product s m ay be incorrect ly labeled or packaged, t he order m ay be priced incorrect ly, support ing docum ent at ion m ay be m issing, goods m ay be dam aged in t ransit , or t he ent ire order m ay arrive precisely on t im e in perfect condit ion at t he wrong locat ion. To help cont rol t hese kinds of errors, m any firm s are now adopt ing a pe r fe ct - or de r m easure as t heir st andard of cust om er service. This m et ric records t he percent age of orders t hat ship com plet e, arrive on t im e, cont ain t he correct goods, are free of dam age, and have accurat e paperwork. The perfect - order m easure is a dem anding st andard, but it 's t he right m easure for a com pany t hat aspires t o excellent service. The ot her way t o m easure effect iveness is t o m easure cust om er sat isfact ion, which can be m onit ored eit her passively or act ively. Passive m easures consist m ost ly of count ing com plaint s, ret urns, request s for adj ust m ent s, and ot her indicat ions of t rouble. Act ive m easures solicit feedback from cust om ers who m ight ot herwise rem ain silent . Passive m easures are t he m ost com m on, but act ive m easures are t he m ost effect ive. I t doesn't t ake m uch; get t ing your cust om ers t o rat e your service on a 10- point scale or even t o m ark a checkbox on delivery form s asking " Are you sat isfied wit h t his order?" can produce a good baseline for t racking sat isfact ion while requiring a m inim um of effort on t he cust om er's part . When m ore inform at ive m easures are needed, cust om er surveys and int erviews are t he appropriat e inst rum ent s. Cat erpillar, which is renowned for it s cust om er service, sends out nearly 90,000 surveys a year—and pays very close at t ent ion t o t he result s. The ult im at e m easure of effect iveness, of course, is cust om er ret ent ion. I f you have a growing base of loyal cust om ers t hat buy your product s in everincreasing quant it ies, you're clearly doing som et hing right . I f a lot of cust om ers t ry you once and t hen m ove on, or if you st art t o see increased t urnover in

your cust om er base, it 's t im e t o call som e form er cust om ers and ask t hem for honest feedback on why t hey t ook t heir business elsewhere. This can be hard t o do, and m any m anagers never get around t o picking up t he phone. But t he inform at ion is absolut ely vit al t o im proving your supply chain, and j ust asking t he quest ion m ay be enough t o convince a cust om er t o give you anot her t ry. Give n a ll t h e diffe r e n t w a ys you ca n m e a su r e you r su pply ch a in , h ow sh ou ld you go a bou t ch oosin g t h e be st se t of m e a su r e s? Th e r e is on ly on e good a n sw e r t o t h a t qu e st ion . M e a su r e s a r e poin t le ss u n le ss t h e y h e lp you m ove t ow a r d spe cific obj e ct ive s, so t h e cor r e ct w a y t o ch oose you r m e a su r e s is t o w or k ba ck w a r d fr om you r obj e ct ive s. Bu t a discu ssion of obj e ct ive s lie s in t h e r e a lm of pla n n in g, n ot ope r a t ion s, a n d pla n n in g is t h e focu s of Pa r t I V. Aft e r discu ssin g t h e pla n n in g a spe ct s of de m a n d a n d su pply in Ch a pt e r s 1 0 a n d 1 1 , I w ill r e t u r n t o t h e su bj e ct of pe r for m a n ce in Ch a pt e r 1 2 , a n d e x pla in h ow t o u se obj e ct ive s t o u n ify pla n n in g, ope r a t ion s, a n d m e a su r e m e n t in t o a con sist e n t pr ogr a m for im pr ovin g su pply ch a in pe r for m a n ce .

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Part IV: Planning

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Part IV. Planning

Chapter 10. Forecasting Demand M a n a gin g a su pply ch a in r e qu ir e s pla n n in g t h e pr odu ct ion a n d m ove m e n t of goods m a n y m on t h s in t o t h e fu t u r e . Th a t 's a difficu lt pr oce ss in it se lf ( se e Ch a pt e r 1 1 ) , bu t t h e de e pe r pr oble m is t h a t you ca n 't k n ow in a dva n ce h ow m a n y pr odu ct s cu st om e r s w ill bu y, so a ll of you r pla n s a r e ba se d on gu e ssw or k . Th e fir st st e p in pla n n in g a su pply ch a in , t h e r e for e , is t o u se t h e t e ch n iqu e s of de m a n d for e ca st in g t o m a k e you r gu e sse s a s a ccu r a t e a s possible . For st a ble pr odu ct s w it h a lon g sa le s h ist or y, you ca n u se st a n da r d m ode ls t h a t ide n t ify t r e n ds a n d pr oj e ct t h e m in t o t h e fu t u r e , a s de scr ibe d in t h e fir st se ct ion of t h e ch a pt e r . You ca n a lso gr ou p sim ila r pr odu ct s t oge t h e r t o im pr ove t h e a ccu r a cy of you r for e ca st s, u sin g t e ch n iqu e s de scr ibe d in t h e se con d se ct ion . Bu t if you 'r e t r yin g t o for e ca st sa le s of a n in n ova t ive pr odu ct w it h n o sa le s h ist or y, you 'll n e e d a diffe r e n t se t of t e ch n iqu e s, a s e x pla in e d in t h e t h ir d se ct ion . Re ga r dle ss of h ow you a r r ive a t you r for e ca st s, t h e be st w a y t o im pr ove t h e m is t o w or k w it h you r t r a din g pa r t n e r s t o de ve lop in t e gr a t e d for e ca st s spa n n in g e ve r y lin k in t h e ch a in . Th a t t opic is cove r e d in t h e fin a l se ct ion of t h e ch a pt e r .

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Chapter 10. Forecasting Demand

Projecting Trends For a product wit h a known sales hist ory, t he best guide t o fut ure sales is past perform ance. Using t he t echniques of t im e - se r ie s a n a lysis, you can apply st andard form ulas t o analyze a sales hist ory, ext ract inform at ion about recurring pat t erns, and use t hose pat t erns t o proj ect sales int o t he fut ure. To see how t his works, look at t he upper panel in Figure 10.1, which plot s m ont hly sales figures for a part icular product over t he past t hree years. There are clearly pat t erns here: There is an overall increase in sales from one year t o t he next , but t he sales appear t o be relat ively flat wit hin each year, and t he am ount of variabilit y from one m ont h t o t he next appears t o be roughly const ant over t im e. The t im e- series analysis of t hese dat a shown in t he lower panel confirm s t hese im pressions and reveals t hat dem and act ually varies in a syst em at ic way over t he course of each year, wit h higher sales in t he spring. The analysis also m akes a clear predict ion about t he sales you can expect in each m ont h of t he com ing year.

Figure 10.1. A Time-Series Analysis

Tim e- series t echniques can be as sim ple or as sophist icat ed as you like. For a product wit h a flat sales curve, your forecast is j ust t he past m ont h's sales. For product s t hat show a sim ple t rend over t im e, it m ay be sufficient t o use a m ovin g a ve r a ge t o predict t he next m ont h's sales. But if t he product 's hist ory shows a m ore com plex pat t ern of t he sort seen in Figure 10.1, or if you want t o forecast sales furt her int o t he fut ure t han t he next m ont h, t hen you need t o use t he full m odel. This m odel analyzes a sales hist ory int o four dist inct com ponent s, as shown in Figure 10.2: 1 . The le ve l com pon e n t is a single value t hat represent s average sales. All ot her com ponent s are variat ions around t his level. 2 . The t r e n d com pon e n t is a st raight line t hat reflect s t he overall t endency for sales t o increase or decrease. 3 . The se a son a l com pon e n t is a curve t hat capt ures t he rise and fall in sales over t he course of each year. 4 . The r a n dom com pon e n t represent s all ot her variat ion in dem and, regardless of it s cause, and has no syst em at ic pat t ern over t im e.

Figure 10.2. Components of Demand

The first t hree com ponent s are called t he syst e m a t ic com pon e n t s of dem and because t hey behave consist ent ly over t im e and can be predict ed. Each of t hese com ponent s is represent ed by a param et er in t he t im e- series m odel. When you run a t im e- series analysis, t he m odel first est im at es t hese param et ers by adj ust ing t hem t o fit t he hist orical sales dat a as closely as possible, t hen uses it s est im at es t o proj ect fut ure sales. By definit ion, t he random com ponent can't be predict ed, but t he m odel does est im at e t he m agnit ude of t hat com ponent and proj ect it forward as well, allowing you t o ant icipat e t he range of dem and you are likely t o encount er. Most forecast ing t ools illust rat e t his range visually by drawing con fide n ce in t e r va ls on t he forecast plot , as shown in Figure 10.3. I n t he exam ple, t he likelihood of act ual dem and being wit hin t he range indicat ed by t he t wo bars is 90% , wit h only a 10% probabilit y t hat it will fall eit her above t he t op bar or below t he bot t om bar. So you can be pret t y confident t hat act ual dem and will fall wit hin t he int erval shown.

Figure 10.3. A Forecast with Confidence Intervals

The m ost dist ant period for which you generat e a forecast is called t he for e ca st h or izon . Given t he way t he t im e- series m odel works, you can set t he forecast horizon as far in t he fut ure as you like. However, t he accuracy of t he forecast falls off dram at ically as you look furt her out , as you can see from Figure 10.3. For t he com ing m ont h, t he expect ed dem and is 130 unit s, and will m ost likely fall bet ween 120 and 140. By cont rast , t he confidence int erval out at t he horizon runs from 75 t o 230 unit s, a range of m ore t han 3: 1. I n pract ice, it rarely m akes sense t o set t he forecast horizon m ore t han 12 t o 18 m ont hs in t he fut ure. You can increase t he accuracy of your forecast s subst ant ially by updat ing t hem cont inuously based on current sales, a t echnique known as d y n a m ic for e ca st in g. I n years past , when forecast ing was done by hand, t he m ore com m on pract ice was st a t ic for e ca st in g, in which a forecast was generat ed and t hen used as is t hrough t he forecast horizon. Now t hat forecast ing is fully aut om at ed, m ost com panies use dynam ic forecast ing. To see t he advant age of t his approach, im agine t he forecast in Figure 10.3 scrolling t o t he left each m ont h, wit h t he confidence int erval for each m ont h shrinking dram at ically as t he m ont h get s closer t o t he present . The business advant age of forecast ing is t hat it elim inat es predict able variabilit y from your fut ure dem and st ream , allowing you t o plan product ion m uch m ore precisely. To see t his advant age in act ion, consider t wo firm s t rying t o predict t he sam e flow of dem and over t he course of t he com ing year (Figur e 10.4 ) . The dem and exhibit s a great deal of variabilit y, as evidenced by t he spread of it s dist ribut ion, but m ost of t hat variabilit y is due t o a pat t ern of increasing sales com bined wit h seasonalit y, as shown in Figure 10.3. Com pany A doesn't use forecast ing, so it has t o be prepared t o handle t he full range of possible dem and levels across t he ent ire year. This is an expensive proposit ion, requiring bot h increased safet y st ock and reserve product ion capacit y. Com pany B uses forecast ing t o elim inat e t he known sources of variabilit y, placing narrow const raint s around t he act ual dem and it will have t o cope wit h in any given m ont h. This allows Com pany B t o get by wit h very lit t le safet y

st ock and no reserve capacit y, giving it a subst ant ial financial advant age over Com pany A.

Figure 10.4. Removing Uncertainty from Variability

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Chapter 10. Forecasting Demand

Aggregating Demand The preceding sect ion explains how t o forecast dem and for a single product , but in pract ice you would generat e forecast s for individual product s only in special sit uat ions—for exam ple, when you are deciding whet her t o int roduce a new product or ent er a new m arket . Set t ing t hose sit uat ions aside, t he cost of generat ing separat e forecast s for t housands of different product s would be prohibit ive, and t he st andard procedure is t o group sim ilar product s t oget her when m aking forecast s. This t echnique—called a ggr e ga t ion—m ight seem like it would degrade t he qualit y of t he forecast s because it would ignore differences am ong t he individual product s. I n fact , j ust t he opposit e is t rue: These a ggr e ga t e for e ca st s, as t hey are called, are act ually m ore reliable because t hey are based on larger sam ples of cust om er behavior. Why does sam ple size m ake a difference? Whenever you use a sm all num ber of sam ples t o generat e predict ions about a larger populat ion, you run t he risk of sam pling error—t hat is, of picking a sam ple t hat doesn't happen t o represent t he populat ion as a whole. One of t he basic laws of st at ist ics is t hat t he likelihood of sam pling error goes down as t he sam ple size goes up. To cit e a fam iliar exam ple, a sam ple of 10,000 vot ers provides a far m ore reliable forecast of an elect ion result t han a sam ple consist ing of only 10 vot ers. The sam e reasoning holds for forecast ing dem and. I f you sell 200,000 product s a year from a cat alog of 10,000 SKUs, each product has j ust 20 sales a year on average, and t hat 's not enough t o support a reliable forecast . But if you group t hose SKUs int o 100 cat egories, t hen each cat egory will have 2,000 sales on average, and t hat gives you enough dat a t o m ake solid forecast s. I n addit ion t o aggregat ing dem and across product s, forecast s also aggregat e dem and across cust om er t ype, geographical region, and ot her fact ors. Also, t he fact t hat forecast s are based on t he num ber of sales wit hin each forecast ing period m eans t hat sales hist ories are aut om at ically aggregat ed across t im e. Seen in t his light , t he choice of forecast ing period t akes on new im port ance. When a forecast is based on large quant it ies of dat a, it is possible t o get reliable forecast s down t o t he level of weeks or even days. Forecast s wit h sparse dat a, on t he ot her hand, should use m ont hs or even quart ers as t heir t im e period. There are st andard form ulas for det erm ining t he m ost appropriat e period t o use wit h any given sam ple size. One of t he m ost im port ant considerat ions for aggregat ing product s int o groups

is t he overall level of sales. I t has long been recognized t hat in m ost com panies, a handful of product s account for t he m aj orit y of sales. This phenom enon is known inform ally as t he " 80: 20 rule," which st at es t hat 80% of sales com e from 20% of t he product s. A m ore form al t echnique, called Pa r e t o An a lysis, uses t hree cat egories, wit h a breakdown of 80% A product s, 15% B, and 5% C. I n addit ion t o reflect ing t he classic 80: 20 rule, Paret o Analysis also expresses t he observat ion t hat half t he product s of a com pany usually account for 95% of t he com pany's sales ( Figure 10.5) . There's no part icular reason why t he percent ages should com e out t his way, and t he sales of your product s could cert ainly follow a different curve. But Paret o Analysis produces t he result s shown in Figure 10.5 wit h rem arkable consist ency across com panies in m any different indust ries, so you shouldn't assum e t hat your com pany is an except ion unt il you do t he analysis on your own sales.

Figure 10.5. Pareto Analysis of Demand

Given t hat a sm all num ber of product s account for t he m aj orit y of your revenues, you should invest m uch m ore effort in forecast ing product s in t he A cat egory, eit her by forecast ing t hem individually or by aggregat ing t hem int o sm all groups wit h sim ilar A product s. Dem and for t hese product s is crit ical t o t he success of your com pany, and having sufficient dat a at t he it em level is rarely a problem wit h t hese product s because t heir sales num bers are so high. Conversely, you should aggregat e t he 50% of t he product s t hat account for only 5% of sales int o large groups t o reflect t heir relat ively sm all cont ribut ion t o sales and t heir correspondingly low dat a densit y. When com bining product s for aggregat e forecast s, be careful not t o m ix product s wit h different sales pat t erns, as reflect ed in t heir t im e- series com ponent s. For exam ple, don't pool seasonal product s wit h nonseasonal product s because t hat would underest im at e t he effect s of season on t he seasonal goods, and it would forecast seasonal pat t erns for product s t hat do not exhibit t hem . By t he sam e t oken, you shouldn't com bine seasonal product s wit h different peaks; grouping bat hing suit s and parkas in t he sam e aggregat e forecast could cause bot h product s t o appear as const ant , year- round sellers,

m issing t he seasonal com ponent alt oget her. Many m anufact urers use t he t echniques of group t echnology, in which sim ilar kinds of product s are m ade wit h t he sam e core com ponent s and t he sam e product ion operat ions. Oft en, t he differences am ong product s wit hin a group aren't int roduced unt il lat e in t he product ion process, perhaps in final assem bly. For such com panies, aligning forecast ing groups wit h product ion groups is quit e beneficial because t he aggregat e forecast s aut om at ically det erm ine t he m at erial requirem ent s for all shared com ponent s. Given t hat different iat ion occurs relat ively lat e, it m ay also be possible t o put off buying t he different iat ing com ponent s unt il j ust before product ion is com plet ed, when forecast s for individual product t ypes are m ore accurat e. This t echnique, known as post ponem ent , is described in Chapt er 15 . Aggregat ion across cust om ers is usually done eit her by region or by t ype. Aggregat ing dem and by region has t he advant age t hat it t ends t o group cust om ers t hat exhibit t he sam e seasonalit y, st yle, and fashion preferences, as t hese variat ions usually have a st rong regional com ponent . I n addit ion, it provides a head st art on dist ribut ion planning because it groups expect ed dem and according t o it s dest inat ion. The alt ernat ive t o using cust om er region is t o use cust om er segm ent s defined by such charact erist ics as dem and volum e, required cust om er service level, order frequency, and ot her buying habit s. This is anot her good place t o apply Paret o Analysis, which oft en reveals t hat 80% of t ot al sales com e from j ust 20% of t he cust om er base, and t hat half of t he cust om er base account s for only 5% of sales. As wit h product s, you should t ry forecast ing your sales t o cust om ers in t he A group individually, and you can safely lum p all of t he cust om ers in t he C group t oget her wit hout sacrificing m uch accuracy. One concern you m ight have about aggregat e forecast s is t hat t hey seem t o t hrow away inform at ion about individual product s, inform at ion t hat m ay be im port ant t o you. This really isn't t he case, t hough—it j ust t akes an ext ra st ep t o get t hat inform at ion back. I f you know t hat a product norm ally account s for 12% of t he sales of a group in an aggregat e forecast , t hen you j ust m ult iply t hat forecast by 12% t o get back t he it em forecast . Figure 10.6 illust rat es t his process for t he first t hree it em s in an aggregat e forecast spanning four quart ers; as t he t ot al sales go up, forecast s for t he individual product s also rise in keeping wit h t heir percent of sales. At first glance, it m ay seem like t his process is at t em pt ing t o get back a level of precision t hat was given up when m oving t o an aggregat e forecast . But t here is no sleight of hand here. I f t he aggregat ion is done properly, all t he it em forecast s share t he sam e dem and pat t ern as a group, differing only in t heir overall levels. The percent ages provide j ust t he right inform at ion t o pull out t hese levels.

Figure 10.6. Breaking Out Item Forecasts

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Chapter 10. Forecasting Demand

Analyzing the Future The t echniques of t im e- series analysis described so far are powerful, but t hey aren't t he answer t o every forecast ing problem . New product s, which have no sales hist ory, obviously require a different approach. I f a product is sim ilar t o exist ing product s, it m ay be possible t o proj ect it s sales by t aking a percent age of an exist ing aggregat e forecast . I f not , alt ernat ive t echniques m ay be necessary t o predict it s sales. Sim ilarly, it m ay be necessary t o supplem ent t im e- series analysis wit h ot her t echniques for product s t hat are subj ect t o changing m arket forces, such as increasing cust om er expect at ions or t he em ergence of new com pet it ors. I n sit uat ions where t im e- series analysis isn't enough, forecast ing requires t he use of old- fashioned, cause- and- effect reasoning. This reasoning generat es num bers, and it m ay involve a form ula or t wo, but —unlike t im e- series analysis—it is m uch m ore art t han science, and it s m et hods are not nearly as well est ablished. Accordingly, t he t echniques described in t his sect ion are known as su bj e ct iv e or j u dgm e n t a l t e ch n iqu e s. The general approach for subj ect ive t echniques is t o consider all t he business influences t hat m ight affect fut ure sales, est im at e t heir individual effect s, and t hen com bine t hem t o form a predict ion. Most of t hese influences are ext rinsic fact ors, as defined in Chapt er 4, because t hey lie out side your im m ediat e cont rol. As Figure 10.7 illust rat es, t he ext rinsic fact ors include t he st at e of t he econom y, t he charact erist ics of t he m arket for t he product being forecast , and t he needs and want s of t he cust om ers who will buy t he product . I nt rinsic fact ors, such as your own decisions about pricing and prom ot ions, also play a r ole.

Figure 10.7. Overview of Forecasting Factors

The m aj or effect of general econom ic fact ors is t o act as a m ult i plier on sales: A robust , expanding econom y generally increases sales, and a weakening econom y reduces sales. Because t he effect of t he econom y on sales is sim ple t o m odel, it is relat ively easy t o incorporat e int o predict ions. Usually, adj ust m ent s are m ade t o forecast sales based on t he current values of one or m ore econom ic indicat ors. Market fact ors are harder t o incorporat e because t hey int eract in com plex ways. These fact ors include changes in t he size of t he m arket , t he act ions of com pet it ors, and t he effect s of changing st yles and fashions. The best way t o predict changes in m arket size and share is t o apply t rend analysis t echniques from st at ist ics and use t he result s t o adj ust sales forecast s. Act ions of com pet it ors are nearly im possible t o predict , so t he best a com pany can do in t his regard is t o run a series of " what if" scenarios t o det erm ine how vulnerable it s sales are t o a variet y of com pet it ive m aneuvers. Predict ing t he effect s of st yle and fashion oft en com es down t o st icking a wet finger in t he air. The m ost crit ical set of ext rinsic fact ors shown in Figure 10.7 are t he requirem ent s and obj ect ives of t he t arget cust om ers for a product . For est ablished product cat egories, cust om ers usually know what t hey want and can art iculat e t heir requirem ent s if asked. For new or em erging product s, where rapid innovat ion is occurring, cust om ers m ay not have form ulat ed t heir requirem ent s in any syst em at ic way. I n t his sit uat ion, it is usually bet t er t o focus on t he cust om ers' obj ect ives t o see which pot ent ial product s would be m ost at t ract ive t o t hem . I n eit her case, t here is no subst it ut e for asking t he cust om er, using a com binat ion of surveys, focus groups, and int erviews. The only int rinsic fact ors shown in Figure 10.7 are t he act ions of your own com pany wit h regard t o t he posit ioning, pricing, and prom ot ion of your product . Because t here are so few int rinsic fact ors com pared t o ext rinsic fact ors, you need t o use t hem t o full advant age t o influence dem and. Exact ly how you use t hem depends on your supply chain st rat egy. For exam ple, if you

seek t o be t he price leader in your indust ry, t hen your price has t o be low enough t o induce cust om ers t o bring t heir business t o you. The use of int rinsic fact ors t o influence dem and is a m aj or issue in supply chain design, and it is explored in det ail in Chapt er 13 . The biggest challenge in dem and forecast ing is predict ing t he sales of a product t hat breaks new ground. As shown in Figure 10.8, innovat ive product s go t hrough a lifecycle t hat is charact erized by slow sales as cust om ers decide whet her t o adopt t he product , t hen a period of rapid growt h as t he product cat ches on, followed by st able or declining sales aft er t he product has est ablished it self in t he m arket . The difficult ies in forecast ing innovat ive product s lie in predict ing how soon a product will ent er it s growt h phase, how quickly sales will t ake off, and how high t hey will event ually go. Forecast ing t hese num bers is a high- st akes gam e. I f you overest im at e how well a product will be received, you'll be st uck wit h excess product ion capacit y and unsold invent ories. Underest im at e it and you'll be faced wit h angry cust om ers, expensive m easures t o accelerat e product ion, and opport unit ies for com pet it ors t o gain m arket share.

Figure 10.8. Lifecycle of Innovative Products

One reason t hat predict ing t he sales of innovat ive product s is so difficult is t hat t he behavior of sales levels over t im e can be ext rem ely com plex. I n Chapt er 4 I int roduced t he kinds of relat ions t hat can be found in syst em s in t erm s of five " rogues," beginning wit h t he well- m annered linear relat ion and ending wit h t he nast iest rogue of all, t he m ult i- valued relat ion (Figure 4.4 , Panel E) . Well, t his is one place where t hat part icular rogue is known t o m ake an appearance, and it com es in t he form of a phenom enon called t he t ippin g poin t. The t ipping point was originally discovered in t he st udy of epidem ics of cont agious diseases, but it has now been shown t o apply t o m any ot her kinds of " cont agious" syst em s as well, including crim e levels in big cit ies, t rends in t he st ock m arket , and consum er buying pat t erns. Tipping point behavior com es about t hrough t he int eract ion of people who

com m unicat e som e kind of " germ " t o each ot her, eit her t he lit eral germ of a disease or t he germ of an idea about crim e, t he econom y, or a desirable product . Once a cert ain t hreshold of " infect ed" people is reached—t he t ipping point —t he likelihood of infect ion rises precipit ously and t riggers an epidem ic. Figure 10.9 illust rat es how t his works by plot t ing t he sales of a new kind of product —a Web wat ch, say—against t he num ber of people current ly wearing t he wat ch. Sales st art out on t he lower curve and increase gradually as m ore and m ore people st art wearing t he wat ch, j ust as you'd expect . But once a cert ain num ber of people are wearing t he wat ch, t he likelihood of ot hers being " infect ed" wit h t he desire t o own t his wat ch t akes a sudden leap, and sales shift over t o an ent irely different growt h curve. St ranger st ill, t he sales st ay on t he upper curve even as t he wat ch becom es passé, and t hey don't fall back t o t he lower curve again unt il t he wat ch has alm ost disappeared from wrist s.

Figure 10.9. The Tipping Point

Tipping point behavior—for diseases as well as ideas—is now well underst ood and can be reproduced using a sim ple m at hem at ical m odel of com m unicat ion. What is not well underst ood is how t o predict whet her a part icular out break of an infect ious idea or disease will reach t he t ipping point and t rigger an epidem ic. The im port ant t hing t o underst and about t ipping point s, t herefore, is sim ply t hat t hey exist , and t hat t hey can lead t o t ot ally unexpect ed leaps in dem and, as well as t o sudden failures of dem and even aft er long runs of popularit y. Tipping point behavior is m ost likely wit h highly innovat ive product s, and it 's seen m ost oft en when t he decision t o buy t he product is heavily influenced by fashion—t he classic recipe for fads. I f you sell t his t ype of product , don't be surprised if sales suddenly explode on you. And if t hey do, expect t hem t o im plode j ust as suddenly aft er a m odest but st eady decline in sales.

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Chapter 10. Forecasting Demand

Integrating Forecasts A good way t o im prove t he reliabilit y of dem and forecast s is t o have m ult iple analyst s generat e forecast s independent ly and t hen com bine t heir result s. The problem here is figuring out how t o int egrat e t he forecast s in a m eaningful way. One solut ion is sim ply t o average t hem all t oget her, but t his can be risky. Just as aggregat ing across seasonal product s wit h different peaks can cancel out t he effect s of seasonalit y, averaging independent forecast s can m ask pat t erns t hat are evident in each forecast but t hat don't align precisely across forecast ers. The bet t er approach is t o t ry t o underst and t he reasoning behind each forecast and som ehow com bine t he reasoning rat her t han j ust t he num ber s. The obvious solut ion—j ust get t he forecast ers t oget her in a room and let t hem hash it out —doesn't always work well. Experience indicat es t hat t hese discussions quickly becom e a cont est of wills, and t he " int egrat ed" forecast usually t urns out t o m at ch t he forecast of t he m ost assert ive analyst . A bet t er approach is t o use t he D e lph i t e ch n iqu e, in which analyst s reach a consensus wit hout ever m eet ing as a group. I nst ead, t he analyst s subm it t heir forecast s and t heir support ing rat ionales in writ ing t o a neut ral part y, who creat es a sum m ary com parison of t he forecast s wit hout revealing t heir aut hors. Analyst s t hen m odify t heir forecast s as t hey t hink appropriat e given t he views of t heir anonym ous colleagues, and t he process repeat s unt il consensus is achieved. Alt hough t im e consum ing, research shows t hat t his t echnique produces subst ant ially m ore obj ect ive and reliable forecast s. Com bining forecast s t o gain consensus is difficult wit hin a single depart m ent , but t he problem becom es harder st ill when forecast s are generat ed by different depart m ent s. Many depart m ent s have a st ake in predict ing dem and, including m arket ing, sales, product ion, dist ribut ion, finance, and personnel. Unfort unat ely, t hese groups have different perspect ives on dem and, use different t echniques for predict ing it , and have different incent ives for how high t hey'd like t he forecast s t o be. The Delphi t echnique can work here as well, but few com panies m ake t he effort t o unify t heir forecast s. I nst ead, t hey j ust let each depart m ent forecast independent ly and plan accordingly, an alm ost cert ain form ula for depart m ent al discord and corporat e confusion. I f com bining forecast s across depart m ent s is t oo difficult for m ost com panies, it should com e as no surprise t hat fewer st ill t ake t he next st ep and int egrat e

forecast s wit h t hose of ot her com panies in t he supply chain. Yet t he failure t o do so is one of t he m ost pernicious problem s in supply chain m anagem ent because it im pairs bot h t he efficiency and t he effect iveness of t he ent ire chain. Here's why: When each of t he suppliers in a chain forecast s t he needs of it s im m ediat e cust om ers, every com pany in t he chain winds up forecast ing som eone else's dem and, as shown in Figure 10.10. This leads t o a lot of wast ed energy because each com pany is forecast ing a different version of t he sam e underlying dem and. Worse, link- by- link forecast s can int roduce errors t hat cascade and am plify up t he chain.

Figure 10.10. Link-by-Link Forecasting

A bit of reflect ion on where dem and ult im at ely com es from suggest s a m uch bet t er approach. When a supply chain is viewed as a whole, t here is only one t rue source of dem and: t he consum ers of final product s. All ot her dem and—for raw m at erials, subassem blies, int erm ediat e product s, and t he like—ult im at ely derives from consum er purchases. To reflect t his dist inct ion, consum er dem and is known as in de pe n de n t de m a n d. All of t he purchases m ade by com panies upst ream of consum ers depend in som e way on consum ers' choices, so t hese purchases are called de pe n de n t de m a n d . The m odern view of forecast ing is t hat only t he independent dem and should be forecast , and t hat all ot her dem and should be derived from t hese forecast s. Having each com pany focus it s forecast ing effort s on independent dem and does not , in it self, elim inat e redundant forecast ing. Case in point : I t 's com m on for m anufact urers and ret ailers t o each m ake t heir own forecast s of consum er dem and, and bot h t ypes of com panies j ust ify t he pract ice by claim ing t hat t hey have a bet t er underst anding of consum er buying habit s. But t he m ost powerful approach is for supply chain part ners t o collaborat e t o build a shared forecast , com bining t heir differing perspect ives on consum er behavior in order t o obt ain t he m ost reliable predict ions of fut ure sales ( Figure 10.11) . Here again, form al processes such as t he Delphi t echnique m ay be required t o m ake sure t hat shared forecast s t ruly reflect t he predict ions of all t he part icipat ing com panies.

Figure 10.11. Joint Forecasting

Collaborat ive forecast ing neat ly addresses t he problem s described at t he beginning of t his sect ion. First , duplicat ion of effort is elim inat ed, oft en reducing t he overall forecast ing effort by 80% or m ore. Second, t here is no cascade of errors up t he chain t o dist ort dem and. The m ost dram at ic benefit , however, is t he im provem ent of forecast ing accuracy t hat result s from sharing knowledge about consum er behavior. There are sales pat t erns t hat m anufact urers can see t hat dist ribut ors and ret ailers cannot , but t here are ot her aspect s of consum er behavior t hat can only be observed close up. When supply chain part ners com bine t heir unique perspect ives t o im prove t heir underst anding of independent dem and, t hey can do a m uch bet t er j ob of ant icipat ing t he needs of t he consum ers who keep t he chain in business. There are m any obst acles t o collaborat ive forecast ing. Sales forecast s are usually considered highly confident ial, and sharing t his dat a requires a degree of t rust and openness t hat sim ply isn't com pat ible wit h t he adversarial relat ionship t hat has long charact erized cust om ers and suppliers. But t he com pet it ive advant ages of int egrat ing t he supply chain are driving deep changes, and t he long- st anding barriers t o cooperat ion are crum bling in t he wake of JI T, quick response, cont inuous replenishm ent , and ot her indust ry program s ( see Chapt er 3) . The t im e for collaborat ive forecast ing has arrived, and m ost com panies seem ready t o accept it . For e ca st in g syst e m s pr ovide pow e r fu l t ools for a n t icipa t in g t h e de m a n d t h a t is a bou t t o be pla ce d on you r su pply ch a in , bu t u sin g t h e se t ools e ffe ct ive ly r e qu ir e s t h a t you k n ow w h e n t o a pply t h e m . Tim e se r ie s a n a lysis pr odu ce s t h e m ost pr e cise pr e dict ion s, bu t a ll it doe s is look for pa t t e r n s in t h e sa le s h ist or y a n d pr oj e ct t h ose pa t t e r n s in t o t h e fu t u r e . For m a t u r e pr odu ct s in st a ble m a r k e t s, t h a t m a y be a ll you n e e d. For ot h e r pr odu ct s, you n e e d t o look be yon d st a t ist ica l pa t t e r n s a n d e x a m in e ca u se - a n d- e ffe ct r e la t ion sh ips t o pr e dict sa le s. I t t a k e s a lot of w or k t o ge n e r a t e good for e ca st s, bu t t h e a bilit y t o t u n e you r su pply pla n t o m a t ch fu t u r e sa le s offe r s a n e x ce lle n t r e t u r n on you r in ve st m e n t in t h e pr oce ss. Th e a dva n t a ge s ca n be a m plifie d if you w or k w it h you r t r a din g pa r t n e r s a n d pool you r in sigh t s t o ga in a be t t e r u n de r st a n din g of t h e u lt im a t e sou r ce of de m a n d, t h e bu yin g h a bit s of t h e con su m e r s a t t h e e n d of you r ch a in .

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Part IV. Planning

Chapter 11. Scheduling Supply On ce you h a ve a de m a n d for e ca st in h a n d, you n e e d t o figu r e ou t t h e m ost cost - e ffe ct ive w a y t o sa t isfy t h e e x pe ct e d de m a n d. Th is ch a pt e r e x pla in s h ow you ca n u se ERP, APS, a n d sim u la t ion syst e m s t o pla n t h e pr odu ct ion a n d m ove m e n t of goods a cr oss you r ch a in . Alt h ou gh m ost com pa n ie s t e n d t o r e ly on j u st on e k in d of syst e m , t h e m ost e ffe ct ive a ppr oa ch is t o com bin e t h e t h r e e , u sin g e a ch syst e m t o solve t h e pr oble m s it h a n dle s be st . As w it h ot h e r a r e a s of su pply ch a in s, t h e bigge st ch a lle n ge is m e r gin g t h e pla n s of in dividu a l com pa n ie s in or de r t o a ch ie ve a n in t e gr a t e d solu t ion for t h e ch a in a s a w h ole .

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Chapter 11. Scheduling Supply

Planning with ERP Supply chain planning st art s wit h a concept ual m odel of what has t o be done, det erm ines t he t im e required by each com ponent process, and t hen schedules each process in a way t hat com plet es t he sequence at t he right t im e. At t he broadest level, m eet ing dem and consist s of t hree core processes: procuring t he necessary m at erials, producing t he goods, and dist ribut ing t hem t o cust om ers ( Figure 11.1) . To keep t he discussion clear, I t reat t hese processes as st rict ly sequent ial, so t hat each process is t riggered by t he com plet ion of t he preceding process. This is a correct represent at ion of how scheduling works, but in pract ice t here could be a fair degree of overlap am ong t hese t op- level processes. For exam ple, product ion m ight begin as soon as m at erials st art t o arrive, even t hough t he procurem ent process is st ill under way. Sim ilarly, t he out put of product ion would norm ally be placed in t he dist ribut ion channel as it cam e off t he line rat her t han wait ing unt il t he ent ire run was com plet e.

Figure 11.1. Scheduling the Core Processes

The act ual t echniques used and t he goals pursued differ subst ant ially depending on t he soft ware used t o perform t he scheduling. The first t hree

sect ions of t his chapt er explain how product ion plans are const ruct ed using ERP, APS, and sim ulat ion- based planning syst em s, in t hat order. As you read t hrough t hese sect ions, bear in m ind t hat t hese are com plem ent ary syst em s, not alt ernat ive solut ions as t hey are oft en port rayed. Each has st rengt hs and lim it at ions, and t he best pract ice is t o use t wo or m ore syst em s in com binat ion t o perfect your plans. There are t wo broad approaches t o scheduling, called forward scheduling and back scheduling. For w a r d sch e du lin g begins wit h a st art dat e and adds processes in t he order t hey will be execut ed, scheduling each process t o st art as t he preceding process com plet es (Figure 11.2, left panel) . This kind of scheduling is m ost appropriat e when t he st art dat e is known and t he com plet ion dat e has t o be det erm ined from t he result s of t he scheduling effort . When a com pany has a required com plet ion dat e and needs t o figure t he necessary st art dat e, t he back- scheduling approach is t he m ore nat ural choice. Ba ck sch e du lin g aligns t he com plet ion of t he last process wit h t he t arget com plet ion dat e, t hen adds processes in reverse order of t heir execut ion ( Figure 11.2, right panel) .

Figure 11.2. Forward and Back Scheduling

Ent erprise resource planning ( ERP) syst em s, t he operat ional foundat ion of cont em porary m anufact uring ( see Chapt er 6) , are based on t he backscheduling approach. As shown in Figure 11.3, t he first st ep of an ERP run is t o feed a dem and forecast int o t he DRP ( dist ribut ion requirem ent s planning) m odule, which works backward from t he required delivery dat es t o figure out when finished goods need t o be shipped. DRP passes t he required shipping dat es t o t he MPS ( m ast er product ion scheduling) m odule, which det erm ines when product ion needs t o st art on each bat ch of product s in order t o be ready for shipm ent . MPS t hen passes t hese dat es t o t he MRP ( m at erial requirem ent s planning) m odule, which det erm ines when t he required raw m at erials have t o be ordered. The last m odule in t he chain, t he CRP ( capacit y requirem ent s planning) m odule, det erm ines when t he necessary labor and equipm ent will have t o be available t o perform t he work.

Figure 11.3. The ERP Scheduling Process

The operat ion of t hese m odules is com plicat ed by t he fact t hat each product in t he dem and forecast is norm ally com posed of m ult iple raw m at erials. Moreover, t he process of assem bling t hese m at erials is usually not a single operat ion, but rat her a sequence of operat ions in which t he st ruct ure of t he end product is built up out of subassem blies or int erm ediat e m ixt ures. I n order t o handle t hese com plicat ions, product s are described in t erm s of t wo docum ent s, which are st ored in elect ronic form by t he ERP syst em and accessed by t he planning m odules as required. The bill of m a t e r ia ls ( BOM ) is a nest ed list of all t he raw m at erials t hat go int o t he product , st ruct ured according t o t he subassem blies of t he product . Sim ilarly, t he bill of ope r a t ion s ( BOO) uses it s own nest ed st ruct ure t o describe t he sequence of operat ions necessary t o creat e each com ponent of t he product . As shown in Figure 11.3, t he MRP m odule uses t he BOM t o det erm ine t he t im ing and quant it ies of m at erials required for product ion, and t he CRP m odule uses t he BOO t o det erm ine t he labor and equipm ent necessary t o perform t he work. The first t wo m odules in t he planning sequence, DRP and MPS, are ent irely driven by requirem ent s. That is, t hese m odules work backward from t he necessary com plet ion dat es t o calculat e when purchasing and product ion should begin wit hout regard for t he feasibilit y of t hose st art ing dat es. Once t he plan m oves t o t he MRP and CRP m odules, however, purchasing and product ion const raint s ent er t he pict ure, and t hese m odules m ay discover t hat t he necessary resources can't be in place at t he required t im e. I f t his happens, hum an planners exam ine t he problem and look for ways t o solve it . For exam ple, t hey m ay be able t o expedit e som e purchasing, add a shift at one or m ore plant s, or out source som e of t he product ion. I f t he planners are unable t o relieve t he const raint s t hat keep t he plan from working, t hey t ypically relax t he original requirem ent s by pushing out som e due dat es and t hen run t he syst em again. This descript ion j ust scrat ches t he surface of cont em porary ERP syst em s. The am ount of work perform ed by t hese syst em s in scheduling t he act ivit ies of a

m anufact uring com pany is st aggering in bot h it s volum e and com plexit y, and it 's safe t o say t hat m uch of m odern m anufact uring would be im possible wit h out t he aid of t hese powerhouse syst em s. There are, however, som e lim it at ions of ERP t hat affect t he qualit y of it s plans. I n part icular, t he fact t hat ERP relies ent irely on back scheduling m eans t hat t he syst em schedules every act ivit y at t he lat est possible t im e. Given t he high cost s of holding finished goods, t hat 's oft en t he best way t o plan product ion. But t here m ay be t im es when earlier product ion would be less cost ly, and an ERP syst em would m iss t hose opport unit ies because it doesn't evaluat e early product ion as an opt ion. Sim ilarly, ERP syst em s assum e t hat you already know what you want t o produce. This is cert ainly a reasonable assum pt ion, but it m eans t hat ERP syst em s aren't m uch help when it com es t o m aking decisions about how t o priorit ize product ion when dem and exceeds supply, how t o find t he m ost cost effect ive m ix of product s for each plant given local dem and and selling prices, and ot her quest ions of t his nat ure. Fort unat ely, t he newer generat ion of APS syst em s can do t hese t hings and m ore, m aking t hem an excellent com plem ent t o ERP's powerful scheduling abilit ies.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 11. Scheduling Supply

Optimizing with APS Advanced planning and scheduling ( APS) syst em s are sim ilar t o ERP syst em s in t hat t hey have separat e m odules for planning procurem ent , product ion, and dist ribut ion, but t he way t hose m odules int eract t o produce a m ast er schedule is different . Rat her t han t aking a dem and forecast as an input , m ost APS packages include a dem and planning m odule t o generat e t hat forecast for you ( Figure 11.4) . The dem and planning m odule passes it s forecast t o a m ast er planning m odule, which calls on t he services of t hree subordinat e m odules t o work up t he best plans for purchasing, product ion, and dist ribut ion.

Figure 11.4. The APS Planning Process

All t hree of t hese specialized planners work on t he problem concurrent ly, feeding t ent at ive plans back t o t he m ast er planner as t hose plans t ake shape. The m ast er planner com bines t hat feedback t o reduce t he set of possible plans, t hen request s a revised set of plans from it s subordinat es. This it erat ive

process cont inues unt il t he m ast er planner ident ifies t he m ost cost - effect ive plan for m eet ing expect ed dem and. Once t he hum an planners approve t his m ast er plan, t he m odules responsible for m at erials, product ion, and dist ribut ion pass t heir plans on t o anot her set of m odules ( shown in Figure 6.3 ) t hat produce det ailed schedules for purchasing, product ion, and dist ribut ion oper at ions. A powerful feat ure of t he APS planning m odel is t hat changes can propagat e in bot h direct ions. As wit h ERP, a change t o t he m ast er plan can cascade down t o lower- level m odules, causing t hem t o alt er t heir plans. But unlike ERP, changes in t he lower- level plans are com m unicat ed upward t o t he m ast er plan, causing it t o m odify it s own plan. This bidirect ional flow can save a great deal of effort and guesswork. For exam ple, it allows a purchasing m anager t o m odify t he purchasing plan direct ly and propagat e t he effect s upward, which is m uch m ore efficient t han having t he m ast er planner run t he syst em repeat edly in order t o produce t he desired change from t he t op down. This capabilit y also m eans t hat APS syst em s can be used t o explore what - if scenarios t o discover t he pot ent ial effect s of m at erial short ages, st rikes, and ot her disrupt ions t o planned oper at ions. The reason APS syst em s are able t o find t he m ost cost - effect ive product ion plan is t hat t hey use m at hem at ical m odels of t he sort described in Chapt er 5 t o calculat e opt im al solut ions. Because t hese m odels are able t o handle t housands of param et ers, APS syst em s can t ake int o account a huge num ber of const raint s on product ion, including t he cost and availabilit y of m at erials, m achinery, labor, and ot her key resources. For exam ple, you could require t hat product ion be lim it ed t o cert ain plant s, t hat 97% of cust om er orders be delivered on t im e, and t hat no overt im e be used, t hen have t he m odel find t he plan t hat sat isfies t hose const raint s at t he lowest possible cost . APS syst em s also use rules t o det erm ine preferences am ong plant s when m ore t han one can handle a product ion run, apply flexible crit eria when choosing am ong t ransport at ion m odes and carriers, and m ake count less ot her decisions based on business rules provided by hum an planners. Anot her at t ract ive feat ure of APS syst em s is t hat t hey respond int elligent ly t o sit uat ions in which t here aren't enough m at erials, product ion capacit y, or dist ribut ion opt ions t o handle t he required load. APS allows you t o aut om at ically priorit ize orders based on t heir size, t heir profit abilit y, t he im port ance of t he cust om er, penalt ies for lat e delivery, and sim ilar considerat ions. APS can also find t he m ost profit able m ix of product s for any given plant , decide when t o out source product ion and dist ribut ion, and m ake ot her decisions t hat go beyond t he basic scheduling of operat ions. Gaining t he advant ages of APS doesn't require giving up your exist ing ERP syst em . As described in Chapt er 6, t he t wo syst em s are rout inely used in com binat ion wit h each ot her, part icularly in planning t he operat ions of m ult iple plant s wit hin t he sam e supply chain. To get t he best of bot h worlds, use APS t o work out t he opt im al solut ion for t he port ion of t he supply chain you are planning, t hen pass t hat high- level plan t o t he ERP syst em s running in each plant . That t echnique let s t he APS syst em use business logic t o choose t he best dat es for t he ERP runs, leaving t he ERP syst em s t o flesh out local plans based on t hese dat es. Once t he local ERP syst em s have generat ed t heir plans, t he operat ional m odules of t he ERP syst em support t he daily act ivit ies of each plant . I n order t o use APS in com binat ion wit h ERP, you need t o set up dat a linkages

so t hat t he syst em s can int eract wit h each ot her ( Figure 11.5) . First , t he m at erial and product ion planning m odules of t he APS syst em m ust have access t o t he bills of m at erials and operat ions m aint ained by t he ERP syst em s in order t o ident ify all t he com ponent m at erials and t asks. Second, t he m ast er planning m odule on t he APS side needs a way t o pass dat es t o t he MPS m odules t o give t hem t arget s for t heir local planning. Third, t he order m anagem ent m odules of t he ERP syst em s should t o be able t o access t he ATP ( available- t o- prom ise) services of t he APS syst em in order t o uses it s advanced ATP capabilit ies.

Figure 11.5. Linking APS to ERP

I n years past , set t ing up t hese dat a linkages could be a m aj or undert aking because of t he closed nat ure of t he syst em s. However, recent effort s t o open up t hese syst em s and m ake t heir dat a available in st andard form at s have m ade int egrat ion subst ant ially easier. The current t rend of incorporat ing APS funct ionalit y int o st andard ERP packages should event ually m ake t he linkages aut om at ic.

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Chapter 11. Scheduling Supply

Validating with Simulators Alt hough APS syst em s use m ore sophist icat ed m odels of t he supply chain t han ERP syst em s do, t he APS m odels are st ill subj ect t o som e im port ant rest rict ions. The abilit y of APS t o generat e opt im ized solut ions is due prim arily t o t he use of linear program m ing and relat ed m at hem at ical t echniques. As described in Chapt er 5, linear program m ing requires t hat all t he relat ions in t he m odel be linear in form —t he best - behaved " rogue" in Panel A of t he rogues gallery of relat ions ( see Figure 4.4 ) . I n t he ext ension of linear program m ing known as m ixed- int eger program m ing, t his assum pt ion is relaxed t o allow som e relat ions t o t ake on t he st epwise pat t ern shown in Panel D of Figure 4.4 , but t he ot her kinds of relat ions aren't perm it t ed. This rest rict ion doesn't m ean t hat you can't t rust t he calculat ions m ade by APS syst em s; it j ust m eans t hat you need t o bear t he lim it at ions in m ind when you look at t he result s. What t he APS syst em act ually does is approxim at e curved relat ions using t he closest linear funct ions. I f t he curve is reasonably close t o a st raight line over t he range of values used in a planning session, a linear approxim at ion will have very lit t le im pact on t he result s. For relat ions t hat are highly nonlinear, it m ay be possible t o break a relat ion down int o sim pler com ponent s ( see Figure 8.3 for an exam ple of how t his works) , or t o approxim at e it by com bining a sequence of linear segm ent s. So t he presence of nonlinear relat ions in a supply chain isn't a show st opper; it j ust m eans t hat t he m odelers have t o be aware of t hese relat ions and handle t hem appr opr iat ely . Anot her assum pt ion underlying linear program m ing and it s kin is t hat all param et er values are fixed and known wit h cert aint y. This is hardly a realist ic assum pt ion for supply chains, which are charact erized by uncert aint y at every st age. Here again, however, violat ions of t his assum pt ion don't necessarily invalidat e t he result s of an opt im izer run; t hey j ust require caut ion in int erpret ing t he out put . I f you have reason t o believe t hat one or m ore of your key param et ers is changing over t im e, or t hat t here is enough random variat ion t o m ake t he result s unreliable, you can com pensat e for t his by m aking m ult iple runs using different values t o det erm ine t he effect s of variabilit y in each param et er. This can be a slow process because it requires running t he m odel m any t im es, but it does provide a way of prot ect ing against violat ions of t he const ancy assum pt ion.

I n short , alt hough t he m odels used by APS are superior t o t hose of ERP, t hey are st ill lim it ed in im port ant ways. Fort unat ely, sim ulat ion m odels are ent irely free of t hese rest rict ions; t hey can represent even t he m ost com plex, nonlinear relat ions, and t hey can accom m odat e any degree or t ype of variabilit y in param et er values. For exam ple, a sim ulat or can explore t he effect s of allowing price, dem and, supply, and ot her key param et ers t o change over t im e, including random variat ions in t hese param et ers from one m om ent t o t he next . Because sim ulat ors incorporat e variabilit y by running a series of Mont e Carlo t rials, t hey produce dist ribut ions of expect ed values for each out put rat her t han j ust a single num ber. I n effect , all t he known sources of variabilit y are t aken int o account by t he m odel, and t he result s can be t rust ed t o hold across all possible v ar iat ions. The reason dist ribut ions are so im port ant is t hat variabilit y in supply chains t ranslat es direct ly int o risk, and one of t he goals of planning is t o reduce risk. To see how a sim ulat or can help m anage risk, suppose your com pany is bidding on a m ult im illion- dollar product ion run of a cust om product . Your APS syst em works out t he opt im al product ion plan, and your ERP syst em produces a det ailed schedule t hat says t he run can be com plet ed in 100 days. But neit her of t hese syst em s has t aken variabilit y int o account , so you run a sim ulat ion of t he product ion process t o check t he effect s of variabilit y. The result is t he dist ribut ion of com plet ion dat es shown in Figure 11.6. This dist ribut ion reveals t hat , while t he com plet ion dat e produced by t he ERP syst em is t he single m ost likely out com e, your com pany act ually has only a 50–50 chance of com plet ing t he run by t hat dat e. This result not only t ells you t hat you should pad t he dat e t o reduce t he risk of m issing your deadline, it also t ells you exact ly how m uch padding you need. I f you want a 97% chance of com plet ing t he j ob on t im e, for exam ple, you should prom ise com plet ion in 140 days rat her t han 100. Then see if you can negot iat e a bonus for early com plet ion.

Figure 11.6. Uncertainty of a Completion Date

I n addit ion t o helping you m anage risk, sim ulat ions offer ot her im port ant benefit s. For one t hing, sim ulat ion m odels generally st ick m uch closer t o t he underlying concept ual m odel t han do t he m ore abst ract m odels used in ERP and APS syst em s. Furt herm ore, sim ulat ors include graphical anim at ion t ools t hat display t he concept ual m odel on a com put er screen during bot h design and execut ion. What t his m eans t o you as a m anager is t hat your business m odel is direct ly visible t o you. You can see how your facilit ies are laid out geographically, wat ch t he m at erials flow am ong t hem , ident ify build- ups and bot t lenecks, and m ake changes right on t he screen t o explore different ways t o im prove t he chain. Given t hese advant ages, sim ulat ors are excellent t ools for underst anding how t he supply chain works, playing what - if wit h different configurat ions, and discovering t he effect s of business policies governing fulfillm ent , replenishm ent , and ot her operat ions. Despit e t hese benefit s, sim ulat ion is not a replacem ent for eit her APS or ERP. Alt hough sim ulat ors can im prove a supply plan using t he t echniques of hillclim bing described in Chapt er 5, t hey lack t he abilit y t o seek out opt im al solut ions t he way APS syst em s do. Sim ulat ors also lack t he abilit y t o generat e t he det ailed schedules of ERP, and t hey offer none of ERP's support for day- t oday operat ions. As wit h ot her m odeling sit uat ions described in t his book, it isn't a m at t er of choosing t he best t ool, but of using t he best m ix of t ools for a part icular j ob. I n t he case of supply planning, one of t he m ost effect ive st rat egies is t o use a com binat ion of ERP, APS, and sim ulat ion syst em s, as shown in Figure 11.7. I n t his approach, your planners use t he APS syst em t o develop an opt im al plan, t hen use a sim ulat or t o fine- t une t hat plan t o handle t he effect s of variabilit y, non linear relat ions, and ot her fact ors beyond t he scope of APS. They t hen pass t his t uned version of t he m ast er plan on t o t he support ing set of ERP syst em s for det ailed scheduling and operat ions. I t 's not t he quickest or cheapest way t o build a schedule, but t he cost of t his com bined st rat egy can usually be j ust ified given t he huge im pact of supply chain failures on bot h operat ing capit al and corporat e valuat ion.

Figure 11.7. Using a Simulator with APS and ERP

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Chapter 11. Scheduling Supply

Integrating Schedules The t echniques discussed in t he preceding sect ions are designed t o help a single com pany plan it s product ion operat ions, int egrat ing plans across m ult iple facilit ies as necessary t o produce a coordinat ed flow of goods. There rem ains t he problem of int egrat ing product ion plans across m ult iple com panies in t he supply chain, sm oot hing t he flow of goods bet w een as well as wit hin com panies. This problem is direct ly analogous t o t he one exam ined at t he end of t he preceding chapt er in t he discussion of shared forecast ing: When com panies plan t heir product ion operat ions independent ly, t hey engage in a great deal of redundant effort , and t he likelihood t hat t heir separat e plans will m esh correct ly when t hey are execut ed is effect ively zero. Figure 11.8 brings t he problem int o sharper focus by illust rat ing t he plans each com pany generat es for what it will buy, m ake, and sell in t he com ing m ont hs. Because t wo of t hese t hree act ivit ies involve int eract ing wit h t rading part ners, each com pany's plans include im plicit predict ions about what t hose ot her com panies will be buying and selling during t hose sam e m ont hs. I n effect , each com pany m akes assum pt ions about t he plans of t he com panies it deals wit h and em beds t hose assum pt ions int o it s own plans, which are, of course, t he subj ect of t he ot her com panies' assum pt ions. This is hardly an efficient way t o do business, and it seriously im pairs t he effect iveness of t he chain as a whole. I f Com pany A plans t o buy 10,000 unit s of a product from Com pany B, but B only plans t o sell 4,000 of it s lim it ed product ion run t o A, t hese com panies are going t o run int o serious problem s when t hey t ry t o execut e t heir plans. I n t he old world of com pet it ive purchasing t his was j ust how business was done, but in t he new world of chain- based com pet it ion, it 's a nonst art er.

Figure 11.8. Independent Planning

Supply chain breakdowns of t his sort are expensive for all concerned, but t he expense runs deeper t han t he obvious cost s of lost sales and unsold invent ory. As described in Chapt er 8, com panies all across t he supply chain m aint ain safet y st ock t o reduce t he likelihood of supply failures. This safet y st ock is basically dead invent ory. I t doesn't m ove, it cont ribut es not hing t o t he product ion process, and it adds no value t o t he final product . I t j ust sit s t here t aking up space, buffering a com pany against t he uncert aint ies of supply and dem and. Worse yet , it 's redundant buffering because bot h part ies at each link hold safet y st ock t o hedge against t he sam e supply risk. The supplier is holding ext ra st ock of it s finished goods t o cover unexpect ed dem and on t he part of t he cust om er, and t he cust om er is holding ext ra st ock of t hose sam e goods t o cover unexpect ed short ages on t he part of t he supplier. This redundant buffering is an exam ple of how st andard pract ices can lead t o lose- lose sit uat ions bet ween t rading part ners. Figure 11.9 illust rat es t he sit uat ion in t erm s of t he t radeoff diagram int roduced in Chapt er 3; in t his case, t he lose- lose region lies above t he line rat her t han below it because t he diagram is based on cost rat her t han profit . I n effect , t he insurance represent ed by safet y st ock is being paid for t wice over, which inevit ably raises t he t ot al cost of t he delivered goods. I f t rading part ners did not hing m ore t han agree on t he t ot al level of risk and divide t he necessary buffer bet ween t hem , t hey could at least get t his aspect of t heir relat ionship back t o t he win- lose line. This m ay not be possible in an open- m arket sit uat ion, in which m any cust om ers are buying t he sam e goods from m any sellers, but it is a nat ural and st raight forward way t o pull cost out of a supply chain t hat is being planned and m anaged collaborat ively. This cost reduct ion can be achieved even if t here is no reduct ion of uncert aint y in t he quant it y of goods t hat will flow across t he link.

Figure 11.9. Tradeoffs in Safety Stock Costs

Alt hough reducing redundant buffering is a good first st ep, t rading part ners can achieve m uch great er savings if t hey work t oget her t o reduce uncert aint y rat her t han j ust doing a bet t er j ob of coping wit h it . Because all of t he dem and in quest ion is dependent dem and, it can be predict ed wit h high levels of confidence once independent dem and is known. The level of independent dem and can be predict ed t hrough collaborat ive forecast ing, as described in Chapt er 10 , and it can be com m unicat ed in real t im e by t ransm it t ing consum er buying event s upst ream , as described in Chapt er 3. A com binat ion of t hese t wo t echniques, in which j oint forecast s are cont inuously updat ed based on em erging buying pat t erns, can resolve independent dem and t o a fairly narrow range of values, allowing planners t o calculat e dependent dem and at each link. I f t his solut ion sounds easy, it 's not ; j oint planning across a supply can be a t im e- consum ing, frust rat ing, and error- prone process. But t he pot ent ial savings from elim inat ing excess safet y st ock are t rem endous, and t he effort of collaborat ing on supply planning can give a t rading relat ionship a solid push int o t he win- win region, as shown in Figure 11.9. This is anot her exam ple of subst it ut ing inform at ion for invent ory; collect ing and com m unicat ing t he inform at ion has a cost , but t hat cost is far less t han t he cost of holding t he invent ory. Moreover, t he benefit s of collaborat ive planning don't have t o be t aken on fait h. There are st andard t echniques for calculat ing t hese savings, so it is relat ively easy t o j ust ify t he cost s of j oint planning based on near- t erm savings. I n one respect , collaborat ive supply planning is easier t han you m ight expect because it uses t he sam e t ools and t echniques as int ernal planning. I n part icular, APS syst em s provide an excellent plat form for int egrat ing plans across com panies, and supply chain sim ulat ors give t rading part ners t he abilit y t o build shared m odels of how t heir supply chain works t oday and how t hey

could work bet t er in t he fut ure. One of t he benefit s of building shared m odels is t hat you can det erm ine t he m ost cost effect ive locat ion for what ever safet y st ock needs t o be m aint ained in t he chain. The asym m et ric t radeoff curve in Figure 11.9 illust rat es a sit uat ion in which it is cheaper for a supplier t o hold t he st ock t han for t he cust om er t o hold it . I n t he m ost basic form of j oint planning, each pair of adj acent t rading part ners produces a com m on plan for t he goods t hat will flow bet ween t he t wo com panies. This link- by- link planning is a good st art , but it requires a great deal of redundant effort . I t can also lead t o waves of change cascading up and down t he chain, keeping t he chain const ant ly off balance and out of synch. For exam ple, if a com pany downst ream needed t o increase it s planned orders for a part icular week, it would work out t hat change wit h it s im m ediat e suppliers, who would t hen have t o revise t heir plans and get t oget her wit h t heir own suppliers, and so on. I n t he m eant im e, an upst ream com pany m ight have t o reduce it s planned product ion for t hat sam e week due t o problem s at one of it s plant s, and it s revisions would cascade down t he chain. These waves of change hit t ing each ot her as t hey m ove up and down t he change can cause havoc in t he planning process. A m uch bet t er solut ion is t o expand t he j oint planning effort t o include m ult iple links in t he chain, as shown in Figure 11.10. The crit ical inform at ion t hat all part ies need t o agree on is what m at erials will m ove across t he links on what dat es. All ot her planning fact ors—individual order quant it ies, int ernal product ion schedules, invent ory safet y levels, and t he like—are subordinat e t o t he overall inform at ion about supply m ovem ent and can be planned locally. I f t here is a change in t he planned m ovem ent s at any st age, such as reduced requirem ent s downst ream or proj ect ed short ages upst ream , all t rading part ners learn of t hese changes at once and can begin t o plan around t hem im m ediat ely .

Figure 11.10. Collaborative Planning

At present , collaborat ive planning across ownership boundaries is t he except ion rat her t han t he rule. There is a growing recognit ion am ong supply chain planners t hat t his is t he next st ep in supply chain int egrat ion, but t he challenges are form idable. At t he t echnical level, post ing and updat ing shared plans requires a com m on com m unicat ion m edium and st andard prot ocols for exchanging product ion dat a. The I nt ernet provides t he necessary m edium , and st andards based on XML are beginning t o em erge. But t he challenges aren't

lim it ed t o t echnical issues; t he m ore serious obst acle is t he problem of inform at ion sharing. Just as wit h collaborat ive forecast ing, collaborat ive planning requires t he exchange of highly confident ial inform at ion, and sharing t hat inform at ion wit h suppliers and cust om ers isn't easy. Given it s com pet it ive advant ages, however, j oint product ion planning is inevit able. Trading part ners t hat m anage t o overcom e t he obst acles t o t his pract ice sooner rat her t han lat er will gain a first - m over advant age in t he new com pet it ion bet ween supply chains. Un le ss you h a ve a lot of e x pe r ie n ce w it h in for m a t ion t e ch n ology, t h e a r r a y of soft w a r e a va ila ble for pla n n in g a su pply ch a in ca n se e m ove r w h e lm in g. H ow e ve r , you don 't h a ve t o h a ve it a ll in pla ce r igh t a w a y; you ca n st a r t fr om w h e r e you a r e a n d gr a du a lly bu ild u p you r cor por a t e t oolk it u n t il you fin d t h a t t h e r igh t t ool a lw a ys fa lls t o h a n d. Th e st a r t in g poin t is u n de r st a n din g t h e r a n ge of t ools a va ila ble a n d t h e pr ope r u se s of e a ch . As vit a l a s you r ERP syst e m m a y be t o you r com pa n y, you a lso n e e d t h e a bilit y t o bu ild r e a list ic m ode ls of you r ch a in , u sin g APS syst e m s a n d sim u la t or s t o pla n t h e m ost pr ofit a ble flow of goods a cr oss t h e ch a in .

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Part IV. Planning

Chapter 12. Improving Performance Th e t e ch n iqu e s of de m a n d for e ca st in g a n d su pply pla n n in g offe r m a n y oppor t u n it ie s for im pr ovin g t h e pe r for m a n ce of a su pply ch a in , bu t t h ose im pr ove m e n t s don 't com e a u t om a t ica lly. Th e e sse n t ia l fou n da t ion of a n y im pr ove m e n t e ffor t is a cle a r a n d con sist e n t se t of bu sin e ss obj e ct ive s. On ce you k n ow w h e r e you w a n t t o go, you ca n figu r e ou t h ow t o ge t t h e r e , t h e n ch oose t h e be st w a ys t o m e a su r e you r pr ogr e ss. H ow e ve r , obj e ct ive s on ly w or k if t h e y a ll pu ll t h e com pa n y in t h e sa m e dir e ct ion , so obj e ct ive s h a ve t o be ca r e fu lly a lign e d w it h e a ch ot h e r . Fin a lly, a lt h ou gh m ost e ffor t s focu s on ope r a t ion a l im pr ove m e n t s, for e ca st in g a n d pla n n in g a r e t h e m se lve s le a r n e d ca pa bilit ie s t h a t you n e e d t o m on it or a n d im pr ove ove r t im e .

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 12. Improving Performance

Setting Objectives Chapt er 9 provided a fram ework for underst anding t he wide range of m easures available for m onit oring t he perform ance of supply chains and organizing t hem int o m easures of t im e, cost , efficiency, and effect iveness. Alt hough you m ight learn som et hing useful from any of t hese m easures, t heir real value com es from helping you t rack your progress t oward specific obj ect ives. Sim ply put , you need a clear and coherent set of business obj ect ives t o guide your at t em pt s t o im prove your supply chain. Are you t rying t o reduce cost s? I ncrease cust om er sat isfact ion? Get your product s t o m arket fast er? I ncrease your m arket share? All of t he above? I f you don't know what you want t o accom plish, no am ount of m easurem ent will solve your problem . Once you have set your obj ect ives, m easuring progress t oward t hese obj ect ives is st raight forward ( Figure 12.1) . The first st ep is t o choose an appropriat e set of m easures for t racking your progress t oward each obj ect ive. For each m easure you select , you need t o t ake a baseline reading t o det erm ine your current perform ance, set a t arget level for your fut ure perform ance, and t hen t ake periodic readings t o m onit or progress t oward t hat t arget . I f you want t o im prove t he efficiency of your fulfillm ent process, for exam ple, you m ay decide t o m easure fulfillm ent lead t im e, order processing cost , and t he num ber of orders per cust om er service represent at ive. This would at t ack t he problem from t hree different perspect ives: t im e, cost , and efficiency. Each of t hese m easures would go t hrough t he cycle shown in Figure 12.1.

Figure 12.1. Measuring Performance

As t his exam ple illust rat es, using m ult iple m easures for each obj ect ive can help ensure t hat your com pany is act ually im proving it s perform ance rat her t han j ust m aking it s num bers. Reducing fulfillm ent lead t im e is good, but if t he cost per order goes up as well t hen t here m ay not be a t rue increase in efficiency. Sim ilarly, j ust m easuring t he product ivit y of cust om er service reps m ay not provide t he full pict ure because it could be possible t o hit t he t arget for t his m easure by laying off a few reps and dist ribut ing t he load over t he rem aining ones, t hereby slowing down t he fulfillm ent process. For t hat m at t er, even t hree m easures m ight not be enough in t his case; it m ight be possible t o hit all t hree t arget s j ust by pushing everyone t o work fast er, causing errors t hat cost m ore t han t he savings from t he im provem ent s. I f t hat 's a legit im at e concern, you'd probably want t o round out t he set wit h a m easure of order accuracy or cust om er sat isfact ion. This exam ple also illust rat es t he im port ance of looking for pat t erns in t he way relat ed m easures change over t im e. As anot her exam ple, suppose you are t rying t o enhance your com pet it iveness by im proving your cust om er service level ( CSL) . I f your m easures of CSL go up but your m easures of cust om er sat isfact ion don't go up wit h t hem , t hat t ells you t hat som et hing is wrong wit h eit her t he obj ect ive or t he m easures. I t m ay be t hat your cust om ers are act ually unhappy about som et hing ot her t han t he service level, or it could be t hat you are using t he wrong m easures for CSL. But t he unexpect ed pat t ern indicat es t hat som et hing isn't quit e right , and you need t o find out what it is. The fact t hat m ult iple m easures m ay be required for each obj ect ive underscores t he im port ance of t ackling a reasonable num ber of obj ect ives at any one t im e. Research has shown t hat m ost com panies set t oo m any obj ect ives and t ake t oo few m easurem ent s, producing conflict about t he com pany's direct ion and confusion about it s progress. According t o t he result s of one st udy, indust ry leaders in supply chain perform ance usually focus t heir effort s on t hree t o five key areas, defining and t racking several m easures of each. Anot her int erest ing charact erist ic of t hese leading com panies is t hat t hey favor m easures of effect iveness over m easures of efficiency. For exam ple, 85% m easure on- t im e delivery, whereas only 75% m easure supply chain cost s and barely half ( 53% ) m easure invent ory t urns. Anot her key t o successful im provem ent is set t ing a realist ic, achievable t arget for each m easure. Suppose you decide t o adopt a perfect - order m easure and

find t hat your current rat e is 82% . Your goal m ay be t o bring t his up t o 97% , but achieving t hat level of im provem ent in a reasonable t im e fram e probably isn't realist ic. A bet t er approach would be t o shoot for som et hing like 90% aft er a year, 95% aft er anot her year, and 97% aft er a t hird. That way you have a series of cont rolled successes t o build on rat her t han t aking a single, wild shot at your ult im at e goal. There are t hree com m on ways of set t ing t arget s for obj ect ives: going for a percent age im provem ent over t he current perform ance, benchm arking yourself against t he com pet it ion, and using form al m odels t o discover opport unit ies for im provem ent . Target ing percent age im provem ent s is by far t he m ost com m on of t he t hree, probably because it 's t he easiest , but t here are im port ant advant ages t o using t he ot her t wo t echniques in addit ion. For exam ple, if com pet it ive benchm arks reveal t hat you are am ong t he best - in- class com panies on a part icular m easure, an at t em pt t o increase t hat m easure by a large percent age is very likely t o fail, and it m ight well t ake your best - in- class perform ance down wit h it . Anot her at t ract ive aspect of indust ry benchm arks is t hat t hey reveal t he spread am ong t he com pet it ion, and larger spreads generally t ranslat e int o bigger opport unit ies. Supply chain benchm arks, in cont rast t o som e ot her operat ional areas, oft en reveal subst ant ial discrepancies in perform ance. Figure 12.2 illust rat es a few result s from one survey t hat com pared com panies ranked as having " good" t o " excellent " supply chain perform ance wit h t hose ranked as " poor." The com panies did not differ by a few percent age point s; t he com panies at t he lower end of t he scale t ook alm ost 50% longer t o fill t heir orders and cycle t heir cash, held t heir invent ory nearly t wice as long, and had t wice as m any lat e deliveries. These gaps are huge, and t hey t ranslat e int o a t rem endous financial and com pet it ive advant age for t he superior com panies.

Figure 12.2. Some Typical Benchmarks

The least com m on t echnique for set t ing t arget s is using form al m odels, which is unfort unat e because t his approach can be t he m ost revealing of t he t hree. I f you use an APS syst em or a sim ulat or t o m odel your supply chain and search

for opt im al solut ions, you m ay find t hat you have t he pot ent ial t o achieve breakt hrough perform ance in an unsuspect ed area. Bet t er st ill, rat her t han j ust giving you general feedback on how well you are doing against t he com pet it ion, t he m odel will show you exact ly what you need t o do t o achieve t he breakt hrough. For exam ple, it m ight reveal t hat out sourcing all your deliveries t o Federal Express would double your perform ance on several key m easures while also cut t ing your capit al cost s, even t hough individual deliveries would be m ore expensive t han t hey are now. Of course, t he m odel is only as good as t he assum pt ions t hat go int o it , but you can t est t he m odel by t rying out t he new idea on a sm all scale and refining t he assum pt ions based on t he result s.

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Chapter 12. Improving Performance

Avoiding Conflicts Once you have chosen your m easures and set t heir t arget s, t he act ual values you record will give you cont inuous feedback on how you are doing on your obj ect ives. I n principle, as long as each m easure is m oving in t he desired direct ion, you should be seeing st eady im provem ent in your supply chain. I n pract ice, however, obj ect ives oft en conflict wit h each ot her, so t hat progress t oward one obj ect ive t akes you furt her away from anot her obj ect ive. This problem of conflict ing obj ect ives can be part icularly hard t o det ect in supply chains because different groups wit hin t he com pany m ay set t heir own obj ect ives wit hout ever realizing t hat t hey are creat ing conflict s. However, if you fail t o det ect and elim inat e t hese conflict s, your com pany will work against it self, exert ing great er effort but reducing it s abilit y t o m ake any real progress. Figure 12.3 shows a sim ple yet com m on exam ple of t his kind of conflict . A m anufact uring group is pursuing an obj ect ive t o increase invent ory t urns from 14 t o 18, so it is t rying t o reduce all t hree of it s invent ories. Meanwhile, t he purchasing group is t rying t o m eet an obj ect ive t o reduce order cost s by 10% by placing larger orders, which drives t he raw m at erials invent ory up rat her t han down. At t he sam e t im e, t he sales force is frant ically shoving product out t he door t o earn bonuses for hit t ing t heir quart erly quot as, and it needs m ore finished goods so it can offer cust om ers bet t er select ion and fast er delivery. Rat her t han guide t he com pany on a st eady course of im provem ent , t hese incom pat ible obj ect ives creat e a chronic t ension t hat pulls t he com pany in different direct ions. The com pany want s t o run, but , like Frankenst ein's m onst er, m ost of it s energy goes int o fight ing it s own m ovem ent s, and t he best it can do is st agger forward.

Figure 12.3. Conflicts Among Objectives

Clearly, t he only way t o m ake any real progress is t o align obj ect ives across all t he groups involved in m anaging t he chain. Unfort unat ely, effect ive supply chain m anagem ent involves alm ost every group in t he com pany, and t he longst anding m ot ivat ions and pract ices of t hese groups m akes alignm ent a difficult process. I n m any cases, it isn't even clear how t o t ranslat e obj ect ives int o com m on unit s. How can sales incent ives, which are revenue based, be aligned wit h product ion obj ect ives, which are based on cost , qualit y, product ivit y, and ot her m easures? And if aligning obj ect ives wit hin a single com pany is t his hard, how can a group of independent ly owned com panies hope t o set and achieve shared obj ect ives across a supply chain? One approach t o solving t his problem is t o find a single, com m on obj ect ive and m ap all ot her obj ect ives back t o it . The obvious candidat e for t his com m on obj ect ive is profit ; if achieving an obj ect ive reduces profit s rat her t han increasing t hem , t hen it m ay not be such a good obj ect ive. Of course, som e obj ect ives m ay reduce short - t erm profit s in order t o increase profit s in t he long run, but t here are st andard business form ulas t o handle t hat sit uat ion by t aking int o account t he t im e value of m oney. I n fact , it 's helpful t o t hink of obj ect ives as being on t hree levels, corresponding t o t he t hree levels of m anagem ent used t o organize t his book: operat ions, planning, and design ( Figure 12.4) . To j ust ify operat ional obj ect ives, you only have t o show an increase in sales or a decrease in cost s. For planning obj ect ives, you m ight com pare t he net present value of fut ure profit s against t he m ore im m ediat e cost s t o dem onst rat e t he expect ed real profit from m eet ing t he obj ect ive. Sim ilarly, you would j ust ify capit al expendit ures t o im prove t he design of t he chain by calculat ing t he ret urn on t he invest m ent in t hose asset s. Once you have m ade t hese adj ust m ent s for t he t im e value of m oney, you should be able t o m ap all obj ect ives int o t he com m on currency of profit , m aking it easy t o com pare t heir relat ive m erit s and ensure t hat t hey are all in alignm ent wit h profit abilit y.

Figure 12.4. Mapping Objectives to Profit

Mapping obj ect ives t o profit is sim ple in principle, but in pract ice it can quickly becom e so com plex t hat t he only way t o underst and t he j oint im pact of obj ect ives on profit is t o m odel t he chain wit h t hese obj ect ives in place and wat ch what happens. To get a feel for how quickly t he conflict s arise, consider t he sim ple concept ual m odel of revenue and expense shown in Figure 12.5. This business syst em has four input s, all under your cont rol, and a single out put , profit . As indicat ed by t he t wo kinds of connect ing lines on t he right side of t he figure, profit goes up wit h increases in revenue, and it goes down wit h increases in expenses. Revenue, in t urn, can be increased by raising eit her unit prices or sales volum e, and expense can be decreased by reducing t he cost s of eit her capacit y or m at erials. So far so good; it 's perfect ly clear which way t o t urn each knob t o increase profit s.

Figure 12.5. A Basic Revenue and Expense Model

Well, alm ost —t here is a slight conflict in t hat increasing t he price beyond a cert ain point discourages buyers and reduces t he sales volum e, as indicat ed by t he negat ive link bet ween price and volum e. This m eans price has conflict ing effect s on revenue: at low prices, increasing t he price increases revenue, and at higher prices, increasing t he price decreases revenue. The point where t he profit s reach a peak would, of course, be a good price t o act ually put on t he product . But what is t hat price? I t depends on t he part iculars of t he m odel: t he values of t he param et ers, t he shapes of t he individual relat ions, and so on. At least in t his sim ple m odel, reducing t he cost of m at erials will always increase your profit , but t here is no sim ple rule t hat t ells you how raising or lowering t he price will affect profit . This is not a part icularly deep insight ; every m anager knows t hat price involves a t radeoff bet ween profit per unit and t he num ber of unit s sold. But t he m odel get s a bit m ore int erest ing wit h t he addit ion of a few obj ect ives. Figure 12.6 shows t he sam e basic m odel of profit wit h t hree com m on m easures of supply chain perform ance: lead t im e, fill rat e, and invent ory. I t 's obvious how t hese m easures will appear in supply chain obj ect ives; nearly every com pany would like t o reduce it s lead t im es, im prove it s fill rat es, and bring down it s invent ory levels. But are t hese com pat ible obj ect ives?

Figure 12.6. Aligning Measures to Profit

Consider t he effect s of invent ory levels. As you can see from t he connect ions on t he left side of t he diagram , high levels of invent ory increase bot h capacit y cost and m at erial cost , so reducing invent ory would definit ely reduce expenses. But having m ore invent ory perm it s higher fill rat es and short er lead t im es, so t here's a conflict am ong t he obj ect ives right t here. Of course, t here are ot her ways t o im prove lead t im es and fill rat es. For exam ple, you can reduce lead t im es by pushing invent ory out closer t o cust om ers, but t hat drives up capacit y cost s because it requires m ore st orage facilit ies. And so it goes. I n short , t here is sim ply no way t o ant icipat e t he effect s of changing any of t hese m easures wit hout a det ailed underst anding of t he syst em as a whole. Even t hough it 's " obvious" which way t o t urn t he knobs, any change you m ake could end up hurt ing profit s rat her t han helping t hem . There is a huge lesson t o be learned from t his t iny m odel: There are no sim ple form ulas for im proving your supply chain. Reducing lead t im es, im proving fill rat es, and increasing t urns m ay produce dram at ic im provem ent s in perform ance, but t hey can also do m ore harm t han good. Each of t hese m easures has an opt im al set t ing, and t hose set t ings int eract in com plex ways. The only way t o reliably im prove your supply chain is t o m odel it and let t he m odel seek out t he set t ings t hat will produce t he m ost profit . Then you can use t hose set t ings as your t arget s for each m easure rat her t han picking arbit rary t arget s or j ust pushing as hard as you can in what seem s t o be t he right direct ion. I f t he m odel says t hat you should increase invent ory t urns from 10 t o 15, t hen st op at 15; cranking t hem up t o 20 m ay be as bad for your com pany as st aying at 10. This can be a difficult lesson t o absorb. Throughout t he hist ory of business, m anagers have m ade subj ect ive j udgm ent s about what would im prove t he perform ance of t heir groups, t hen worked t o do as well as possible on t heir chosen m easures. That t im e- honored form ula for success has been shat t ered in t he past few decades wit h t he advent of inform at ion t echnology. Mat hem at ical and sim ulat ion m odels now reveal t he t rue com plexit y of business syst em s, m aking visible t he int ricat e int erdependencies am ong obj ect ives and m easures t hat once seem ed t o st and on t heir own m erit s. Today, t he pat h t o excellence lies not in im proving individual m easures of perform ance such as t urns and fill

rat es, but using form al m odels t o find t he best balance am ong t hese m easures.

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Chapter 12. Improving Performance

Aligning Incentives I n sum , im proving t he perform ance of your supply chain involves using form al m odels t o find t he perform ance levels t hat m axim ize profit , set t ing obj ect ives for m oving t oward t hese levels, and t aking syst em at ic m easurem ent s t o t rack your progress. This pict ure of success is nearly com plet e, but t here is st ill a large piece m issing: m ot ivat ing your people t o achieve t he obj ect ives. As any experienced m anager knows, it isn't enough t o set obj ect ives and exhort people t o st rive t oward t hem . You have t o provide incent ives t hat reward people for m aking t he right kinds of choices, and t he incent ives have t o be powerful enough t o produce significant changes in behavior. To dat e, incent ives have not been handled very well in supply chain m anagem ent . Em ployees oft en receive incent ives t hat are at cross- purposes wit h corporat e obj ect ives, and t heir incent ives are rarely t ied t o supply chain perform ance. One recent st udy produced som e dism al st at ist ics: Only 25% of com panies in t he Unit ed St at es use incent ives based on supply chain perform ance; nearly all of t hese incent ives are based on int ernal perform ance m easures and are not t ied t o t he perform ance of t he chain as a whole; t he m aj orit y of com panies choose t he wrong m easures for t heir incent ives; and t heir incent ives are rarely aligned in a way t hat encourages consist ent behavior. The use of incent ives is clearly due for som e profound changes. Here's an exam ple of how deep t he changes m ay have t o go. I nst ead of basing sales com m issions on t ot al sales, why not base t hem on cont ribut ion t o profit ? I f all your product s are equally profit able, it will work out t o be t he sam e t hing. But if, like m ost com panies, you m ake nearly all of your profit on 20% of your product s ( see Chapt er 13 ) , why not encourage t he sales t eam t o sell t he product s t hat act ually m ake m oney for you? Not only would t his im prove t he bot t om line, it could help m ot ivat e your sales force t o support profit - orient ed init iat ives t hat t hey m ight ot herwise resist , such as raising prices on product s t hat don't cover t heir cost s, or keeping invent ories of finished goods wit hin reasonable bounds. Anyt hing t hey can do t o increase t he profit abilit y of t he product s t hey sell is m oney in t heir own pocket s. Once you accept profit as t he com m on denom inat or across obj ect ives, int erest ing opport unit ies open up for ret hinking policies you m ay not even realize you had. Case in point : What 's t he policy governing t he sequence wit h which you process incom ing orders? Unless you are a highly unusual com pany,

you have an unst at ed, im plicit policy of processing t hem in t he order in which t hey arrive. A recent st udy found t hat m aking one sm all change—servicing orders according t o t heir profit pot ent ial rat her t han t heir arrival dat e—could increase average profit s by 18% per year. Most m anagers would be t hrilled t o get t hat kind of j um p in profit from such a sim ple change, but t he idea would sim ply never occur t o t hem unless t hey were already exam ining every policy for it s cont ribut ion t o profit . I ncent ive alignm ent is a com plex discipline wit h robust m at hem at ical foundat ions, but t he business m essage is sim ple: You have t o m ake sure t hat everyone's personal win is consist ent wit h your obj ect ives as a com pany. The good news here is t hat incent ives really do work; wit h rare except ions, t he people in your com pany will behave in ways t hat m axim ize t heir personal rewards, however t hose rewards are defined. I f you align your com pany's incent ives so t hey all point in t he sam e direct ion, you will creat e a powerful force t hat can drive t he com pany t o unprecedent ed levels of perform ance. I f you allow t hese incent ives t o point in different direct ions, all t hat energy will work against it self and t he opport unit y for st ellar perform ance will be lost . Wit h t he addit ion of t his m issing piece—alignm ent of incent ives across t he organizat ion—t he pict ure is com plet e; get t ing t he m axim um perform ance out of your com pany requires four dist inct st eps ( Figure 12.7) . First , use business m odels t o ident ify t he com binat ion of perform ance t arget s t hat m axim izes profit s. Second, set achievable obj ect ives t hat bring your com pany closer t o it s ideal configurat ion. Third, m ot ivat e your people t o st rive for t hese obj ect ives by m aking t heir incent ives cont ingent on hit t ing t he t arget s. Fourt h, set up a syst em at ic program of m easurem ent t o t rack your progress on each obj ect ive. As shown in t he illust rat ion, t he result s of t hese m easurem ent s provide t he feedback necessary t o guide t he ent ire program : They det erm ine t he sizes of incent ive awards, t hey indicat e how m uch progress you are m aking t oward achieving your t arget s, and t hey provide vit al feedback on t he business m odel so t hat you can im prove it over t im e.

Figure 12.7. Improving Performance

To im prove t he perform ance of a supply chain, t he process shown in Figur e 12.7 has t o be applied not j ust t o your own com pany but t o t he chain as a whole. One of t he key insight s of m odern supply chain m anagem ent is t hat im provem ent s in a single link of t he chain oft en harm t he ot her links in ways t hat cancel out local benefit s. The indust ry program s described in Chapt er 3 illust rat e t his principle nicely in t hat t hey usually solve supply chain problem s by displacing t hose problem s ont o ot her m em bers of t he chain. I n order for t he chain t o im prove as a whole, it s m em bers m ust be willing t o sacrifice such local advant ages for t he great er good of being part of a successful chain. On t he surface, t his m ight seem t o call for som et hing akin t o corporat e alt ruism , but it doesn't ; t here are ways t o m ake it pay for everyone t o play, and finding t hose ways is t he key t o building a com pet it ive chain. This is where t he idea of m apping all obj ect ives ont o profit really com es int o it s own. Sim ply put , m aking a profit m ay be t he only obj ect ive t hat all t he com panies in a chain have in com m on. I f t heir individual profit s can be aligned wit h t he t ot al profit s of t he chain, t hen it is in t he int erest s of each com pany t o work for t he good of t he chain. I f t heir profit s aren't aligned, t hen t hey will inevit ably pull in different direct ions and reduce t he perform ance of t he chain. This is not t o suggest t hat t he com pet it ive elem ent of t rade relat ionships can be elim inat ed alt oget her; t he discussion of gam e t heory in Chapt er 3 m ade t hat clear enough. Rat her, t he goal should be t o neut ralize t he com pet it ive elem ent by m oving relat ionships int o t he win- win region and allocat ing t he winnings equit ably. I t 's harder t o visualize t his t radeoff funct ion when m ult iple com panies are involved because t he t wo- dim ensional graph shown in Figur e 3.10 would have t o be expanded int o m any dim ensions, wit h a separat e axis for each com pany. But you don't have t o visualize t he result ; t hese diagram s are useful for explaining t he concept , but t hey aren't act ually used in pract ice. What you need t o do is build a shared m odel of t he chain, opt im ize t hat m odel

t o m axim ize t he t ot al profit across t he chain, and t hen negot iat e t he allocat ion of t his profit .

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 12. Improving Performance

Improving Planning Alt hough t he exam ples in t he preceding sect ions focused on obj ect ives for operat ional perform ance, t his is only one of t he t hree levels of obj ect ives shown in Figure 12.4. The operat ional level usually get s t he m ost at t ent ion when it com es t o perform ance because t he payoffs from im provem ent are im m ediat e, but t here is a danger of focusing so heavily on operat ions t hat you m iss even bet t er opport unit ies in t he design and planning of your chain. Yes, you can increase your cust om er service level by holding invent ory closer t o your cust om ers, but t hat requires higher invent ory levels, so it j ust t rades one operat ional obj ect ive off against anot her. On t he ot her hand, if you can increase your abilit y t o forecast your cust om ers' requirem ent s and schedule supplies t o arrive j ust as t hey are needed, you m ay be able t o im prove cust om er service while r educing t ot al invent ory. That would be a win on bot h m easures. Forecast ing provides an excellent case in point . As anyone in t he business will be quick t o t ell you, t he first rule of forecast ing is t hat forecast s are always wrong. No m at t er how well you predict t he syst em at ic com ponent s of dem and, t here is always a random com ponent t hat can't be predict ed. Given t his built - in lim it at ion, get t ing bet t er at forecast ing doesn't m ean elim inat ing error alt oget her. Rat her, t he goal is t o m ake t he residual error as sm all as possible while elim inat ing any bias t oward under- or overpredict ing dem and. Meet ing t hat goal requires t wo different m easures, one t o m onit or t he m agnit ude of forecast ing errors and anot her t o m onit or t heir bias. Bot h of t hese m easures are calculat ed from a set of com parable predict ions, such as t he set of forecast s for a part icular product across different sales t errit ories. Forecast ing expert s have several good st at ist ics for analyzing t he m agnit ude of errors, but for m anagem ent purposes your best bet is probably t he m e a n a bsolu t e pe r ce n t a ge e r r or ( M APE) . Sim ply put , t he MAPE t ells you how m any percent age point s your forecast s t end t o be off t he m ark, regardless of whet her t hey are t oo high or t oo low. By m onit oring t he MAPE over t im e, you can see whet her you are m aking any progress in reducing t he size of errors, or m ake sure t hat a reliable forecast ing procedure doesn't go bad on you. I n Figure 12.8, Product A has a m oderat e but st able forecast error, whereas Product B has a lower error on average but now seem s t o be get t ing out of cont rol. As t his exam ple illust rat es, one of t he advant ages of using a m easure based on percent ages is t hat it t ranslat es error m agnit udes int o st andard unit s,

canceling out any differences due t o t he act ual volum e of sales.

Figure 12.8. Measuring the Magnitude of Forecast Errors

For m onit oring t he bias of forecast ing errors, t he t r a ck in g sign a l is a good choice for m anagers because, like t he MAPE, it expresses bias in st andard unit s t hat don't depend on sales volum es. When t here is no bias, t he t racking signal is zero. A posit ive signal m eans t hat m ost errors occurred because dem and exceeded t he forecast , whereas a negat ive signal m eans t hat dem and fell below t he forecast . Figure 12.9 shows t hat Product A, which is st able in t erm s of error m agnit ude, is not doing so well on t he MAPE m easure because it 's showing an increasing bias t oward predict ing m ore dem and t han is act ually being realized. Product B, which shows increasingly large errors, does all right on t he MAPE because it st ill shows no t endency t o under- or overshoot t he m ar k .

Figure 12.9. Measuring Bias in Forecast Errors

The graphs in Figures 12.8 and 12.9 illust rat e how forecast ing errors are m onit ored, but it isn't act ually necessary t o draw t hese plot s in pract ice. I nst ead, forecast ers set t hresholds on t hese m easures, as indicat ed by t he shaded areas in t he diagram s, and let t heir forecast ing syst em s call t heir at t ent ion t o forecast s t hat go out of bounds. Using t hese aut om at ed t hresholds m akes it easy t o t rack progress t oward obj ect ives for im proving t he forecast ing process—you j ust set t he t hreshold t o t he desired level and t he soft ware will let you know whenever you exceed it . The t hresholds shown in Figures 12.8 and 12.9 are reasonably t ypical for volum e product s; it 's good pract ice t o keep t he MAPE down in single digit s, and a t racking signal t hat is m ore t han four t o six point s away from zero is cause for concern. What do you do when a m easure of forecast error exceeds one of t hese t hresholds? The answer depends on t he forecast ing t echnique you are using. I f t he m agnit ude goes out of bounds and you're basing your forecast s on m arket research, you m ay be able t o im prove t he reliabilit y of your dat a by increasing your sam ple sizes. I f you are using t he Delphi m et hod and get t ing a consist ent bias t oward overforecast ing, you should t alk t o t he m em bers of t he forecast ing t eam about where t heir opt im ism is com ing from . I f you are using t im e series t echniques, t hen a breakdown in eit her m agnit ude or bias is t elling you it 's t im e t o consider using a m ore powerful m odel. The breakdown of a forecast ing m et hod isn't always bad news. Suppose you've been successfully forecast ing dem and for a product based on running averages for a num ber of years, but now t he error com ponent is get t ing larger each m ont h and t he t racking signal is up around 8. That j ust m eans t hat t he dem and for t hat product used t o be st at ic but is now increasing, and you need t o add a t rend com ponent t o your forecast ing m odel t o accom m odat e t he increase. Having t o adj ust a forecast ing t echnique because sales are t aking off is t he kind of problem m ost m anagers would love t o have. I n cont rast t o t hese well- est ablished t echniques for m onit oring forecast errors,

m uch less at t ent ion has been paid t o m easuring scheduling errors. However, t he sam e principles apply, and t he best approach is t o syst em at ically m easure bot h t he m agnit ude and t he bias of scheduling errors, set t ing t hresholds on bot h t o t rigger alarm s when eit her kind of error grows t oo large. The m ost com m on problem is t hat schedules are rout inely m issed, som et im es wit h such regularit y t hat planners—and t heir m anagers—becom e fat alist ic about it . But fat alism is t he wrong response t o t his sit uat ion. I f t here is a consist ent bias in t he scheduling process, it can and should be correct ed, if only by adding a correct ion t o what ever dat es t he planning process com es up wit h. This is a com m on pract ice, but it usually t akes t he form of covert " fudge fact ors" t hat planners at t em pt t o hide from m anagem ent . A m uch bet t er approach is t o t reat com plet ion dat es as " forecast s" of fut ure event s t hat are inherent ly uncert ain, and t o develop syst em at ic, public t echniques for t ranslat ing t he out put of scheduling syst em s int o achievable forecast s of act ual com plet ion dat es. Forecast ing and scheduling are usually t reat ed as separat e act ivit ies and are carried out by different groups, but t here is a nat ural cont inuit y bet ween t he t wo in which t he forecast ing process flows sm oot hly int o t he scheduling process. I nst ead of forecast ing dem and for a given t im e fram e, t hen building schedules t o m eet t hat dem and, a m ore effect ive t echnique is t o do a rolling forecast t hat cont inuously inform s t he schedules t hat depend on it . This way, uncert aint y can be rem oved from t he forecast for each period as t hat period approaches, allowing you t o fine- t une product ion runs as you get closer t o t heir st art dat es. Alt hough it 's not com m on pract ice, anot her helpful t echnique is t o use t he analysis of forecast ing errors t o im prove t he qualit y of t he scheduling process. I f you know t hat t here is a growing bias t oward overest im at ing dem and for a part icular line of product s, for exam ple, it cert ainly m akes sense t o pare back scheduled product ion accordingly. More im port ant , t he m agnit ude of t he forecast ing error provides dat a for risk m anagem ent in scheduling product ion. I f you know t hat your forecast s for a part icular product are t ypically off by 20% , you need t o have subst ant ially m ore safet y st ock and reserve capacit y t han you do for a product wit h a 5% forecast error. I m pr ovin g t h e pe r for m a n ce of a su pply ch a in is n o sm a ll u n de r t a k in g, bu t t h e n e w com pe t it ion be t w e e n ch a in s m e a n s t h a t it 's a pr oble m you h a ve t o solve . Th e m ost im por t a n t poin t t o t a k e a w a y fr om t h is ch a pt e r is t h a t t h e r e a r e n o sim ple a n sw e r s, n o m a gic for m u la s for pu llin g ou t e x ce ss t im e a n d cost . Effor t s t o r e du ce le a d t im e s, a cce le r a t e t h e flow of in ve n t or y, a n d ot h e r popu la r obj e ct ive s m a y be pa r t of a n ove r a ll solu t ion , bu t t h e y ca n a lso m a k e t h e pr oble m w or se . I f you w a n t a cle a r be a con t o k e e p you on t h e pa t h t o su cce ss, h e r e it is in a sin gle se n t e n ce : Th e on ly su r e w a y t o im pr ove you r su pply ch a in is t o m ode l it a n d le t t h e m ode l se e k ou t t h e se t t in gs t h a t w ill pr odu ce t h e m ost pr ofit . I f t h is is a h a r d m e ssa ge for m ost m a n a ge r s t o a cce pt , u se t h a t fa ct t o you r a dva n t a ge : Em br a ce t h e ide a a n d r u n w it h it w h ile ot h e r s a r e st ill m u llin g it ove r .

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Part V: Design

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Part V. Design

Chapter 13. Mastering Demand Th is ch a pt e r m a r k s t h e t r a n sit ion t o t h e h igh e st le ve l of su pply ch a in m a n a ge m e n t : m a k in g t h e de sign de cision s t h a t u lt im a t e ly de t e r m in e t h e ca pa bilit ie s a n d lim it a t ion s of you r ch a in . Th e fir st st e p in de sign in g a su pply ch a in is u n de r st a n din g t h e pa t t e r n of de m a n d you r ch a in h a s t o se r ve . Th is de m a n d pa t t e r n is for m e d by t h e in t e r se ct ion of cu st om e r r e qu ir e m e n t s, a s de scr ibe d in t h e fir st se ct ion , a n d pr odu ct con st r a in t s, discu sse d in t h e se con d se ct ion . Alt h ou gh de m a n d is u su a lly t a k e n a s a give n , t h e t h ir d se ct ion in t r odu ce s a va r ie t y of t e ch n iqu e s you ca n u se t o im pr ove t h e sh a pe of de m a n d t o be t t e r fit you r ch a in , in clu din g a fe w t e ch n iqu e s t h a t a ct u a lly im pr ove de m a n d by r e du cin g it . Th e la st se ct ion e x a m in e s t h e ph e n om e n on of de m a n d a m plifica t ion a n d r e ve a ls t h a t m ost of t h is a m plifica t ion is ca u se d by st a n da r d pr a ct ice s in su pply ch a in m a n a ge m e n t t h a t a r e e a sily m odifie d t o st a bilize t h e flow of de m a n d.

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Chapter 13. Mastering Demand

Knowing the Customer Wit h cont em porary soft ware, designing a supply chain is vast ly easier t han it used t o be. Rat her t han laboriously calculat ing dist ances, cost s, t im es, order sizes, and ot her quant it ies, planners can now use soft ware t o generat e t hese values aut om at ically, based on a geographical analysis of dem and and supply. Wit h t hese powerful new t ools in hand, supply chain m anagers can focus t heir at t ent ion on t he high- level t asks of analyzing dem and, defining obj ect ives, and ident ifying const raint s. The st art ing point for t his process is perform ing a geographical analysis of dem and. This analysis can be as sim ple as plot t ing cust om er locat ions on a m ap or as com plex as com bining consum er profile dat a wit h populat ion densit y figures st rat ified by incom e and ot her charact erist ics. I f your cust om ers are large com panies and you only have a few dozen of t hem , you can work direct ly wit h individual cust om ers' locat ions. I f your cust om ers num ber in t he t housands, you'll need t o group t hem int o service regions and use t he dat a for t he regions in t he analysis. There are various t echniques for allocat ing cust om ers t o regions, but t he easiest way is t o use post al codes. A com m on rule of t hum b is t o aggregat e cust om ers int o about 150 t o 200 regions, each of which is represent ed by a cent ral service point . This is a m anageable num ber of locat ions for planning purposes, and it doesn't int roduce m ore t han about a 1% error in t he est im at ion of t ot al t ransport at ion cost s. I n addit ion t o analyzing t he volum e and t ype of product s t hat cust om ers buy, it 's im port ant t o exam ine t heir act ual buying pat t erns. The analysis should focus on five m aj or fact ors, as shown in Figure 13.1: t he t ot al volum e of product t hat cust om ers purchase per period, t he frequency of orders, t he lot sizes wit hin each order, t he variet y of product s included in each order, and t he cust om er service level ( CSL) required t o keep t hem happy. To cit e t wo ext rem es, j ust - in- t im e ( JI T) product ion facilit ies m ay require daily deliveries of sm all lot s of very specific product m ixes, and t hey m ay place very t ight const raint s on fill rat es and delivery t im es. At t he ot her end of t he scale, wholesale dist ribut ors m ay place infrequent orders for large quant it ies of product s, require a different m ix on each order, and have m uch less rest rict ive requirem ent s for t im e of delivery. These t wo kinds of cust om ers would place very different dem ands on a supply chain. For exam ple, t he JI T cust om ers would be best served by high- t hroughput dist ribut ion cent ers locat ed close t o t heir plant s, whereas t he dist ribut ors m ight be adequat ely served by a

cent ralized, general- purpose st orage facilit y.

Figure 13.1. Customer Buying Pattern

Analyzing t he cust om er service level ( CSL) requirem ent s of individual cust om ers can help you ident ify opport unit ies for m aj or savings in your supply chain. Som e com panies t ake pride in set t ing a high st andard for CSL and applying t hat st andard across t he board, but providing a higher level of service t han cust om ers act ually need can be wast eful given t he t rem endous expense of m aint aining high CSLs ( see Chapt er 8) . A m ore cost - effect ive approach is t o vary CSL according t o individual cust om ers' needs, elim inat ing t he wast e of " over- serving" cust om ers wit h low requirem ent s while also avoiding unaccept able service for cust om ers wit h high requirem ent s ( Figure 13.2) . I f you want t o be recognized for excellent service, you will probably want t o keep your CSL in t he upper range of t he accept able zone, as shown in t he illust rat ion, but if you com pet e prim arily on price t hen t he " adequat e service" range will help you keep your cost s down.

Figure 13.2. Setting the Customer Service Level

Once you have analyzed your cust om ers wit h regard t o t heir buying pat t erns, t he next st ep is t o look for correlat ions bet ween how t hey buy and where t hey are locat ed. For exam ple, t he com m on pract ice of defining regions based on t he num ber of cust om ers in each area works only if dem and is fairly evenly dist ribut ed across cust om ers. This is oft en t he case when t he cust om ers are end consum ers, but it 's rarely t he case when t hey are com panies. As described in Chapt er 10 ( see Figure 10.5) , t he dist ribut ion of sales volum e across corporat e cust om ers oft en follows t he Paret o pat t ern: The t op 20% of cust om ers account for 80% of sales, and t he bot t om 50% account s for j ust 5% of sales. I f your cust om ers display anyt hing like t his degree of skew, it m ay help t o fact or t he locat ion of your t op cust om ers int o your geographic analysis of dem and. I f your biggest cust om ers are all locat ed in or near large cit ies, for exam ple, t hat could m ean t hat 90% of your dem and is clust ered in a relat ively sm all num ber of locat ions. I f it happens t hat t hose cust om ers are also t he ones t hat require high CSLs, you know right where t o put your regional warehouses. I n analyzing t he buying habit s of your cust om ers, bear in m ind t hat it 's t he broad pat t erns t hat you're aft er, not t he det ailed differences. I t m ay be t hat your cust om er base is sufficient ly hom ogeneous t hat a sim ple breakdown of dem and by region capt ures all t he inform at ion you need. I f so, t hat 's excellent news because it m eans you can engineer t he ent ire chain t o sat isfy a single set of requirem ent s, t he best possible st art ing point for building a world- class supply chain. More likely, your cust om ers will fall int o a relat ively sm all num ber of t ypes based on t heir habit s and requirem ent s. I n t his case, you need

t o design a chain t hat is flexible enough t o m eet t he different set s of needs wit hout incurring unnecessary cost s. I n effect , you m ay need t o design t wo or m ore supply chains t hat can operat e across a com m on set of facilit ies. For exam ple, suppose your analysis reveals t hat your cust om ers fall int o t hree broad segm ent s, which you designat e as Types A, B, and C in keeping wit h t heir corporat e personalit ies. Your Type A cust om ers are JI T plant s t hat buy specific part s kit s and require one- day lead t im es wit h 30- m inut e delivery windows. They have dem anding requirem ent s, but t hey are willing t o collaborat e wit h you on forecast ing and scheduling t o help you m eet t hose requirem ent s. The Type Bs are m ost ly low- volum e plant s t hat purchase a range of non- kit t ed product s wit h t wo- t o t hree- day lead t im es, and t he Type Cs are j ob shops t hat are com fort able wit h one- t o t wo- week lead t im es. I f you design a single supply chain t hat t reat s all t hree groups t he sam e, t he m ost likely out com e is t hat you will be const ant ly expedit ing deliveries for your Type A cust om ers in order t o get t hose deliveries t o m ove fast er t han t he bulk of your goods, and you m ay well find t hat t he cost st ruct ure of your chain m akes you t oo expensive for m any of your Type C cust om ers. But if you set up specialized facilit ies and procedures of t he sort shown in Figure 13.3, you m ay be able t o keep everyone happy at a reasonable cost . I n t his part icular design, Type A cust om ers are served by sm all warehouses adj acent t o t heir plant s, m aking precisely t im ed deliveries in reusable part s- kit cont ainers. Type B cust om ers are served by having a package carrier such as UPS or FedEx deliver shipm ent s from a cent ral warehouse, and Type C cust om ers are served by a convent ional net work of regional dist ribut ion cent ers.

Figure 13.3. Overlapping Supply Chains

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Chapter 13. Mastering Demand

Analyzing the Product I n addit ion t o t he requirem ent s cust om ers place on product s and t heir delivery, t he qualit ies of t he product s t hem selves im pose const raint s on how t hey are packaged, t ransport ed, and st ored. As shown in Figure 13.4, t hese requirem ent s can be underst ood in t erm s of t hree key considerat ions: form , densit y, and risk.

Figure 13.4. Intrinsic Product Qualities

Wit h regard t o form , t he m aj or concern is whet her a product is shipped in bulk or packaged form . Shipping and st oring bulk m at erials is m uch cheaper t han handling packages; for exam ple, it cost s about 77 cent s per t on t o ship sugar from Hawaii t o t he m ainland in bulk, as com pared t o m ore t han $20 per t on shipping it in bags. When m at erials are shipped in bulk form , t he st at e of t he m at erial is a key considerat ion because solids, liquids, and gases differ great ly

in t he way t hey are t ransport ed. For exam ple, som e liquids and gases can m ove t hrough pipelines, providing cheap t ransport at ion per unit shipped wit h const ant , dependable t hroughput . St at e is im port ant for packaged goods as well because liquids and gases generally require relat ively expensive packaging such as t anks, barrels, bot t les, or cans, and econom ic or environm ent al concerns m ay dict at e t hat t hese cont ainers be ret urned for reuse. Densit y, expressed as t he rat io of weight t o volum e, is also an im port ant considerat ion in supply chain design. Low- densit y product s are m ore expensive t o ship because vehicles and cont ainers " cube out " before t hey " weigh out ," filling t he available volum e before t hey reach t heir full hauling capacit y. When low densit y is t he result of t he way a product is const ruct ed, as it is wit h lam ps and lawnm owers, effect ive densit y is oft en increased by shipping product s in a part ially assem bled st at e. A propert y closely relat ed t o densit y is t he product 's value- t o- weight rat io; as t his rat io increases, t he relat ive cost of t ransport at ion drops and m ore opt ions becom e econom ically feasible. When carbon t ravels in t he form of coal, it usually m oves by slow freight and goes no fart her t han it has t o. When carbon t ravels as diam onds, it goes by plane and circles t he globe. A variet y of qualit ies relat ed t o risk can require special handling, packaging, t ransport at ion, and st orage. Fragile it em s require addit ional packaging t o prevent breakage during t ransport and st orage. Perishable product s risk spoilage, placing const raint s on t he lengt h of t im e t hey can be in t ransit or st orage, and som e need const ant refrigerat ion t o preserve t heir freshness. Hazardous product s, such as explosives and flam m able gases, pose a m ore serious t ype of risk and usually require special handling t o com ply wit h governm ent regulat ions. All of t hese kinds of risk increase t he cost of t ransport at ion, and high- risk product s are oft en shipped separat ely from ot her goods t o isolat e t hese added cost s. I n addit ion t o t hese int rinsic qualit ies, t he design of t he chain has t o t ake int o account whet her t he product s it handles are st andard or cust om . The degree of cust om izat ion can vary from st andard, off- t he- shelf product s t o ones t hat are designed specifically for a single cust om er ( Figure 13.5) . I n general, increased cust om izat ion shift s t he push- pull boundary furt her up t he chain. St andard product s allow t he boundary t o be set right next t o t he consum er, so t hese product s can be m ade t o st ock and pushed all t he way down t he chain in ant icipat ion of dem and. At t he ot her ext rem e, fully cust om ized product s can m ove t he push- pull boundary all t he way up t o suppliers if t he choice of m at erials depends on t he design. Shift ing t his boundary upst ream reduces t he need for invent ory because product s are pulled by im m ediat e dem and rat her t han being pushed down t he chain based on forecast ( see Chapt er 2) , but it also increases t he com plexit y of t he fulfillm ent process and requires m ore flexibilit y in bot h upst ream and downst ream facilit ies.

Figure 13.5. Customization Requirements

Anot her im port ant considerat ion is t he variabilit y in dem and for product s over t im e. Product s wit h st eady, predict able dem and are t he easiest t o handle because t heir requirem ent s are well known and t he chain can be designed around t hose requirem ent s. I f t he dem and varies but does so in a predict able way, t his put s m ore st ress on t he chain but is st ill m anageable. For exam ple, seasonal product s put a heavy load on t heir chains in advance of t heir peak season. I t m ay be possible t o handle t hese peak loads by leveling product ion across t he year, building up invent ory in advance of t he season. This approach lowers t he cost of product ion, but it does so by pushing t he problem down t he chain in t he form of ext ra st orage capacit y t o hold accum ulat ed invent ory. A bet t er approach t o coping wit h seasonable variabilit y is t o use product s wit h different seasons t o count erbalance each ot her, dist ribut ing t he load on t he supply chain as evenly as possible over t he course of a year. This t ends t o happen nat urally in t he apparel indust ry, where sum m er and wint er st yles balance each ot her out over t he course of t he year. Ot her count erbalancing product s m ay be less obvious but can st ill be ident ified. The classic exam ple here is t he plant t hat alt ernat es bet ween snow blowers and lawn m owers, t aking advant age of com m on com ponent s and operat ions t o m inim ize t he cost of t he sem iannual changeovers. The m ost difficult product s t o handle are t hose wit h highly variable dem and t hat can't be predict ed wit h any consist ency. This sit uat ion is m ost com m only encount ered wit h innovat ive product s, which have lit t le or no sales hist ory and whose sales are driven by t rends or fashions. As described in Chapt er 10 ( see Figure 10.8) , such product s have a lifecycle t hat st art s out wit h low dem and and slow growt h, goes t hrough a period of rapid growt h, peaks, and t hen slides int o a gradual decline. As Figure 13.6 illust rat es, t he uncert aint y of t he dem and for such product s also changes syst em at ically over t he sam e t im e fram e: Dem and for newly int roduced product s is highly uncert ain, and t his uncert aint y st art s t o decline only as t he full m arket em braces t he product and t he growt h rat e begins t o fall off. I t is only aft er t he product is well int o it s peak sales period t hat sales becom e reasonably predict able.

Figure 13.6. Demand Uncertainty over Product Life

Given t he high uncert aint y of dem and for new product s, it 's hard t o know how m any t o build or how m uch capacit y t o devot e t o product ion and invent ory. Unt il a product approaches it s sales peak and begins t o exhibit a st able pat t ern, t he supply chain for t hat product needs t o m aint ain high levels of safet y st ock t o handle higher- t han- expect ed sales. I n addit ion, considerable excess capacit y m ust be held in reserve in order t o ram p up product ion quickly in t he event t hat t he product t akes off. Many a com pany has realized it s dream of bringing a killer product t o m arket , only t o find it s overnight success shat t ered by chronic short ages, cost overruns, and qualit y problem s as it s supply chain st ruggles t o cope wit h t he explosion in dem and. But erring in t he ot her direct ion can lead t o equally devast at ing problem s, including excess invent ory, idle plant s, and m assive ret urns. Dealing wit h innovat ive product s is one of t he t oughest problem s in supply chains, and it 's a core concern in t he discussion of supply chain st rat egy in t he next chapt er. I n supply chain design, product s are subj ect t o t he sam e const raint as cust om ers wit h regard t o how m any product s can be planned independent ly. I n general, if you deal wit h m ore t han a couple hundred different product s, you need t o aggregat e t hose product s in order t o keep t he num bers m anageable. For exam ple, a chain of discount st ores wit h 20,000 product s m ight organize t hose product s int o 100 groups averaging 200 product s each. Unlike cust om ers, which are usually grouped by region, product groups should be based on dem and pat t erns, using t he sam e groups t hat were used for aggregat e forecast ing and scheduling ( see Chapt ers 10 and 11 ) . For som e com panies, t he dem and pat t erns t hat dist inguish t heir product groups are sufficient ly well aligned wit h convent ional product fam ilies t hat t hese fam ilies can be used t o aggregat e product s when designing t he chain. However, t his is not a foregone conclusion; product fam ilies are usually based on sim ilarit y of product ion or consum pt ion rat her t han sim ilarit y in t heir dem and pat t erns, and fam ilies oft en m ix product s wit h very different supply chain requirem ent s. For exam ple, m arket ing m ay choose t o group product s in ways t hat encourage cross selling, such as grouping accessories wit h t he product s t hey m at ch—purses wit h shoes, say, or bit s wit h drills. Oft en, t hese

accessories have very different supply chains from t he m at ching product s and should not be grouped t oget her in designing t he chain. I n deciding how t o aggregat e product s for design purposes, it is im port ant —as always—t o t ake t he sales volum e of product s int o account . I f a Paret o Analysis reveals t hat 20% of your product line account s for 80% of your sales, you should be able t o t ake advant age of t hat fact in designing your supply chain. For exam ple, you m ight be able t o handle fast m overs separat ely, gaining som e econom ies of scale by shipping t hem only in full pallet s or full t ruckloads. Alt ernat ively, you m ight be able t o convert t he ot her 80% of your product s t o a cent ralized warehousing syst em , allowing t hem t o bypass your regional dist ribut ion cent ers. That would slash t he num ber of SKUs you have t o t rack and queue at t he regional dist ribut ion cent ers ( DCs) t o a fift h of what it would ot herwise be, allowing you t o st ream line operat ions at your DCs while get t ing rid of invent ories t hat don't t urn over rapidly. Even if t he cent ralized syst em required t he use of fast er shipping m odes for som e orders, t he cost savings of skipping t he DCs could yield a subst ant ial net savings.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 13. Mastering Demand

Shaping Demand The discussion of dem and up t o t his point has t aken a som ewhat react ive point of view, st ressing t he im port ance of underst anding t he nat ure of dem and and designing t he chain accordingly. But it is possible t o t ake a m ore proact ive st ance t oward dem and, act ively shaping it t o suit your purposes rat her t han working wit hin t he const raint s it im poses. The obvious exam ple of t his is using m arket ing t echniques t o increase dem and, but t hat 's not t he only approach t o shaping dem and, and it m ay not even be t he best one. I n fact , som e of t he m ost effect ive t echniques for im proving dem and act ually involve r educing it , at least in t he short run. One of t he m ost im port ant t hings you can do t o im prove t he shape of dem and is m ake sure t hat you are serving t he right cust om ers. No m at t er how well you design your supply chain, it can't m eet t he needs of every kind of cust om er. I f your prim ary obj ect ive is t o pull t im e and cost out of your chain, t hen you are going t o have a hard t im e m eet ing t he needs of cust om ers t hat require fast delivery and perfect fulfillm ent in response t o unpredict able orders. I f you t ry, you could find yourself expedit ing m ost of t heir orders, serving t hem at a net loss, and disrupt ing t he rest of your supply flow in t he process. Sim ilarly, if you opt for a st rat egy based on flexible service and cust om product s, t here's no point in t rying t o serve cust om ers t hat buy a const ant st ream of st andard product s. You will never be able t o m at ch t he prices of t he low- cost provider and st ill m ake m oney on t hese cust om ers. The relent less pursuit of revenue oft en blinds com panies t o t he harm t hat com es from serving cust om ers at a loss. I ndeed, m ost don't even know which cust om ers are producing t heir profit s. I f you ran a Paret o Analysis on your own cust om er base using profit s rat her t han sales as t he m easure, would it surprise you t o learn t hat j ust 20% of your cust om ers produced 80% of your profit s? I f so, get ready for a shock; t he sit uat ion m ay be m uch worse t han t his. Unlike sales, profit s can go int o t he negat ive range, allowing t he skewing t o be even m ore ext rem e. One firm discovered t hat t he t op 20% of it s cust om ers account ed not for 80% but for 225% of it s profit s. The next 60% of it s cust om ers hovered around t he breakeven point , and t he bot t om 20% act ually r educed profit s by a st unning 125% . For t his com pany, a supply chain design t hat led t hese unprofit able cust om ers t o t ake t heir business elsewhere would m ake a huge cont ribut ion t o t he bot t om line, even if it didn't im prove t he perform ance of t he chain at all.

The idea of t urning away cust om ers m ay sound like heresy, but if it produces dram at ic increases in profit s it m ay be t he only rat ional choice. Of course, it would be bad form j ust t o call up cert ain cust om ers and t ell t hem you no longer wish t o do business wit h t hem , but t here are m arket m echanism s t hat can achieve t he sam e end and m ay produce an even bet t er result . I f you can ident ify what is causing you t o lose m oney wit h som e of your cust om ers, you m ay be able t o change eit her your cost s or your prices in a way t hat m akes t hese cust om ers profit able. Oft en as not , it is t he cust om ers who place t he great est dem ands on perform ance who also dem and t he biggest concessions on price. One way t o count er t his punit ive behavior is t o set up a t iered pricing st ruct ure based on service levels, t hen refuse t o discount t he prem ier service. This approach leaves your cust om ers wit h com plet e freedom of choice: They can allow you t o m ake a profit , or t hey can inflict t heir business on one of your com pet it or s. Elim inat ing profit sinks from your cust om er base is only half a solut ion; t he ot her half is avoiding such cust om ers in t he fut ure. The sim plest way t o do t his is t o be sure your m arket ing and sales m essages at t ract t he right kinds of cust om ers, and t hat m eans being clear about your dist inct ive com pet ence. I f you follow t he t im e- honored pract ice of prom ising cust om ers everyt hing—t he fast est service and t he best product s at t he lowest price—t hen your cust om ers are right t o expect you t o deliver on your prom ises no m at t er what it cost s you. I f you t ell t hem honest ly t he ways in which you excel, t hey'll choose you for t he right reasons and you'll bot h com e out ahead. The idea of being select ive about your cust om ers will st rike som e m anagers as a radical not ion, but it 's no m ore radical t han being select ive about your suppliers. As described in Chapt er 12 , t he key t o get t ing sust ained cooperat ion across t he supply chain is t o align everyone's incent ives. I f you bring in cust om ers whose needs and expect at ions don't m at ch t he capabilit ies wit h your supply chain, aligning t hose incent ives becom es ext rem ely difficult . Unfort unat ely, t he pract ice of selling t o anyone who will buy is deeply ent renched, so it t akes careful alignm ent of incent ives wit hin your own organizat ion t o alt er t his behavior. Basing sales com m issions on profit rat her t han revenue, as suggest ed in Chapt er 12 , m ight be a good place t o st art . Anot her idea is t o reward t he m arket ing group based on t he qualit y rat her t han t he quant it y of leads—and m aking t he alignm ent of cust om er requirem ent s essent ial t o t he definit ion of a qualified lead. Just as serving t he right cust om ers is vit al t o shaping dem and, being select ive about t he product s you sell is also crit ical. I n years past , decisions regarding new product s were usually m ade wit hout regard t o supply chain const raint s, since delivering t he goods was a relat ively low- level funct ion. I n t he new chainbased com pet it ion, selling product s t hat don't fit t he supply chain is a hard decision t o j ust ify. I t 's not j ust a problem wit h m oving t hose part icular product s t hrough t he chain cost - effect ively; t he deeper problem is t hat t heir requirem ent s can keep t he ent ire chain from reaching peak perform ance. I f you have t o m aint ain special equipm ent at all your facilit ies j ust t o handle a sm all, low- m argin segm ent of your product line, t his would be a good t im e t o consider divest ing t hat line. I f your goal is t o com pet e wit h ot her chains based on cost , t hen you m ay want t o ret hink som e of your m ore innovat ive product s, which require excess safet y st ock and capacit y. As wit h culling out unprofit able cust om ers, dropping product s t hat don't fit your supply chain is only half a solut ion; t he ot her half lies in m aking sure t hat all new product s are well suit ed t o your chain. I f your goals include m aking

your chain m ore flexible and responsive t o changing needs, t hen you should t ake advant age of your abilit y t o accom m odat e variable dem and and seek out innovat ive product s t hat ot her chains can't handle cost - effect ively. Bet t er st ill, use supply chain considerat ions t o help shape t he design of new product s, allowing innovat ion not only in t he product it self but in how you bring it t o m arket . The sect ions in Chapt er 15 dealing wit h design for supply and t he use of post ponem ent should give you som e good ideas about how t o do t his. The current em phasis on im proving t he efficiency of supply chains can obscure t he fact t hat t he biggest opport unit ies st ill lie in innovat ion. As shown in Figur e 13.7 , m ost of t he dollars devot ed t o im proving t he supply chain are spent on aut om at ing operat ions, a relat ively safe invest m ent wit h an ROI t hat is easy t o est im at e. The fewest dollars are spent at t he design level, where t here is t he m ost opport unit y for innovat ion. The irony here is t hat innovat ion offers a far bigger pot ent ial for ret urn. I f you want t o gain a few point s of m arket share, by all m eans shave a few point s off your cost s. But if you want t o dom inat e your m arket , you need t o do som et hing t hat t he com pet it ion can't m at ch j ust by increasing efficiency.

Figure 13.7. The ROI Irony

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 13. Mastering Demand

Stabilizing Demand I n addit ion t o focusing on cust om ers and product s t hat fit your supply chain, you can also shape dem and by st abilizing it . As described in Chapt er 2, variabilit y is one of t he m ost cost ly problem s in supply chains, part icularly when it am plifies as it flows up t he chain. Anyt hing you can do t o st abilize t he flow of dem and across t he chain will im prove your perform ance and give you a subst ant ial advant age over chains t hat have t o cope wit h higher levels of v ar iabilit y . The biggest source of variabilit y in supply chains is a phenom enon called de m a n d lu m pin g, in which a st eady flow of dem and is divided up int o arbit rary chunks t hat appear as sudden surges in dem and. I n Figure 13.8, a ret ailer sells product s at a const ant daily rat e, but doesn't replenish it s st ock unt il it hit s a fixed reorder point . When it does reorder, it rounds up it s requirem ent s t o t he next level of packaging t o avoid handling individual it em s, t hen rounds up a lit t le furt her if it 's close t o t he next quant it y break in t he dist ribut or's discount schedule. The dist ribut or follows a sim ilar policy, but it wait s longer and buys in larger quant it ies in order t o get bet t er prices. When it finally does place an order, t he quant it y is so large t hat it exhaust s t he producer's invent ory of finished goods and t riggers anot her product ion run.

Figure 13.8. Demand Lumping

As t his exam ple illust rat es, lum ping dist ort s t he dem and signal in t wo ways. First , it t hrows off t he t im ing, delaying t he dem and signal as it m oves upst ream . I f all t he producer sees is t his incom ing dem and signal, it doesn't even know t hat it s product is selling unt il aft er nine weeks of sales. Second, it am plifies t he apparent signal. When t he producer finally does receive inform at ion about dem and, it com es in such a large order t hat t he producer m ay ram p up product ion t o handle t he surge. I f t he product cont inues t o sell at a st eady rat e, t he chain will gradually st abilize. But even t he sm allest variat ion in sales will cont inue t o am plify up t he chain, producing t he infam ous " bullwhip effect " t hat has such a devast at ing im pact on upst ream suppliers ( see Chapt er 2) . As t his exam ple illust rat es, dem and lum ping is usually a by- product of such rout ine pract ices as quant it y discount s, econom ic replenishm ent policies, volum e packaging, and bat ch product ion runs. These are all sound business pract ices, having been developed t o t ake advant age of econom ies of scale. The fact t hat t hese pract ices also creat e havoc in supply chains is a highly count erint uit ive but deeply im port ant insight . I t seem s t hat t here is a fundam ent al t ension bet ween econom ies of scale and t he sm oot h flow of dem and up t he supply chain. There are ot her causes of dem and lum ping t hat aren't relat ed t o econom ies of scale. One is for w a r d bu yin g, in which cust om ers purchase supplies before t hey are needed in order t o t ake advant age of favorable prices. These prices m ay be t he result of nat ural fluct uat ions in t he m arket , but t hey are usually caused by prom ot ions on t he part of suppliers. Anot her culprit is hoarding, in which cust om ers buy m ore t han t hey need in order t o prot ect t hem selves against current or expect ed short ages. Hoarding can have part icularly nast y effect s on dem and because it cont ains a posit ive feedback loop: Hoarding increases scarcit y, which furt her increases hoarding, and so on. I n som e sit uat ions, such as chip short ages in t he elect ronics indust ry, t his selfam plificat ion can escalat e a relat ively m inor short fall int o a worldwide crisis. Does t his m ean t hat you have t o give up all your est ablished business pract ices in order t o st abilize dem and? No, but you do need t o m odify t hose pract ices t o reduce t he incent ive t o lum p dem and. For exam ple, t ry basing quant it y discount s on t ot al volum e rat her t han t he size of individual orders. This st ill encourages cust om ers t o buy in quant it y, but it elim inat es t he incent ive t o

inflat e each order. The result will likely be a larger num ber of sm aller orders, which will reduce your econom ies of scale in order processing. However, t hat problem can be solved by st ream lining order m anagem ent , as t he indust ry program s described in Chapt er 3 am ply dem onst rat e. Here are t wo m ore exam ples of how you can m odify com m on pract ices t o reduce t he problem of lum ping. I nst ead of basing prom ot ional prices on t he quant it y purchased by your cust om ers, base t hem on t he quant it y t hey sell t o t heir cust om ers. Using t his se ll- t h r ou gh am ount reduces forward buying and helps ensure t hat prom ot ions act ually m ove product down t he chain rat her t han j ust pushing it t o t he next link. Sim ilarly, you can reduce hoarding wit h a t u r n - a n d- e a r n syst em , in which cust om ers can only purchase scarce product s in proport ion t o t heir out going sales. This discourages cust om ers from " gam ing" t he syst em , inflat ing t heir orders in hopes of increasing t heir allocat ions. One of t he m ost effect ive t echniques is t o use prom ot ions t o st abilize dem and rat her t han inflat e it . Figure 13.9 shows a forecast for consum er dem and wit h a serious dip over a period of about six weeks. Producers would norm ally respond t o t his kind of dip eit her by scaling back product ion during t he slum p or by holding product ion st eady and st ockpiling invent ory. Bot h alt ernat ives have t heir cost s and benefit s, but t here is a t hird choice t hat m ay be preferable t o bot h: Run a prom ot ion during t he slum p, raising dem and enough t o consum e t he out put of a st able product ion schedule. Even if m ost of t his increased dem and is due t o forward buying, t hat 's okay because, in t his case, t he forward buying is working t o st abilize dem and rat her t han dist ort it .

Figure 13.9. Smoothing Demand with Promotions

M a st e r in g de m a n d r a t h e r t h a n j u st m a n a gin g it is a pow e r fu l w e a pon in ch a in - ba se d com pe t it ion , bu t it 's n ot a n e a sy on e t o w ie ld. I n a ddit ion t o k n ow in g you r cu st om e r s a n d u n de r st a n din g h ow you r pr odu ct s fit t h e ir n e e ds, you h a ve t o be w illin g t o m a k e h a r d ch oice s a bou t w h ich cu st om e r s a n d pr odu ct s a r e r igh t for you r ch a in , a n d you h a ve t o

m odify bu sin e ss pr a ct ice s t h a t a r e de e ply in gr a in e d in you r cor por a t e cu lt u r e . Bu t m a st e r y is n e ve r a ch ie ve d in st a n t ly, a n d t h e se ch a n ge s don 't h a ve t o com e a ll a t on ce . I f you ca n sim ply sh ift you r t h in k in g a bou t de m a n d fr om a con ve n t ion a l, r e a ct ive st a n ce t o a m or e pr oa ct ive poin t of vie w , u n de r st a n din g t h a t you ca n sh a pe de m a n d t o fit t h e com pe t it ive a dva n t a ge s of you r su pply ch a in , you 'r e a lr e a dy a st e p a h e a d of m ost m a n a ge r s.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Part V. Design

Chapter 14. Designing the Chain Th e fir st st e p in de sign in g a su pply ch a in is t o for m u la t e a st r a t e gy for t h e ch a in . Th e m ost cr it ica l e le m e n t of t h is st r a t e gy, de scr ibe d in t h e fir st se ct ion , is de cidin g h ow t o m a k e t h e t r a de off be t w e e n fle x ibilit y a n d e fficie n cy. On ce you r st r a t e gy is in pla ce , t h e n e x t st e p is t o a n a lyze you r e x ist in g su pply ch a in a n d ide n t ify opt ion s for im pr ovin g it , a s de scr ibe d in t h e se con d se ct ion . Th e t h ir d st e p is t o u se m a t h e m a t ica l a n d sim u la t ion m ode ls t o e va lu a t e t h e opt ion s you 've ide n t ifie d a n d pr odu ce t h e de sign t h a t be st sa t isfie s you r obj e ct ive s. Th is la st st e p is h igh ly a u t om a t e d, bu t don 't le t t h a t fool you —t h e m ost pow e r fu l t ool for su pply ch a in de sign is st ill you r ow n in sigh t s in t o t h e n a t u r e of you r bu sin e ss, a n d it 's vit a l t h a t you be a ct ive ly in volve d t h r ou gh ou t t h e pr oce ss.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 14. Designing the Chain

Choosing a Strategy I n t he new, chain- based com pet it ion, success depends on form ulat ing and execut ing a clear st rat egy for your chain. This is not yet a com m on insight ; t he very idea t hat supply chains require a st rat egy would com e as a surprise t o m any m anagers. This is perhaps underst andable; in years past , when logist ics was viewed as a support funct ion, m anaging t he chain was prim arily a m at t er of finding t he best way t o m ove what ever t he com pany chose t o sell. But in t he new com pet it ion, t he priorit ies are reversed: I f you can't creat e and deliver product s in a t im ely, cost - effect ive m anner, it doesn't m at t er m uch how well you design and m arket t hem . This reversal places supply chain decisions at t he very heart of corporat e st rat egy. Supply chains have leapt from t he backroom t o t he boardroom so quickly t hat m ost com panies are j ust now beginning t o form ulat e a st rat egy. Think back t o t he survey I m ent ioned in Chapt er 1: Ninet y- one percent of execut ives in m anufact uring com panies ranked supply chain m anagem ent as vit al t o t heir success, yet 59% of t hose execut ives st at ed t hat t heir com panies had no st rat egy for im proving t heir supply chains. The survey didn't ask how m any execut ives in com panies t hat did have a st rat egy believed it was a good one, or how m any felt t hey were im plem ent ing t heir st rat egy successfully. However, it did find t hat only 2% of t he execut ives regarded t heir supply chains as " excellent ," so t here can't be very m any who were sat isfied on eit her count . This widespread lack of st rat egy is what m akes supply chain m anagem ent such a hot bed of act ivit y t oday. The st akes are high and t he bar is low, so t he business opport unit y is huge. Sim ply put , you don't need t o form ulat e t he perfect st rat egy t o win t his gam e. I f you can j ust put t oget her a reasonably good st rat egy and im plem ent it consist ent ly, you will be well ahead of t he com pet it ion. Moreover, form ulat ing a supply chain st rat egy is not a part icularly challenging t ask. The key issues can be described in a few paragraphs. Many considerat ions go int o form ulat ing a supply chain st rat egy, but one concern dom inat es t he rest —t he t radeoff bet ween flexibilit y and efficiency. Most m anagers, if asked whet her t hey want ed t heir chain t o be flexible or efficient , would answer " bot h" wit hout a second t hought . Unfort unat ely, t he deep t radeoff bet ween t he t wo m akes having bot h an unrealist ic goal.

I ncreasing flexibilit y generally requires a com pany t o increase safet y st ock and m aint ain reserve capacit y t o m eet unexpect ed dem and, and increasing efficiency requires driving bot h of t hese reserves as low as possible. You can st rike any balance you want bet ween t he t wo, but you can't elim inat e t he t r adeoff. However, t he t radeoff bet ween efficiency and flexibilit y isn't absolut e. The sit uat ion is com parable t o t he t radeoffs bet ween t rading part ners discussed in Chapt er 3, and applying a t radeoff diagram produces a sim ilar curve. As Figur e 14.1 illust rat es, t here are int erm ediat e " win- win" posit ions t hat allow t he t wo qualit ies t o be com bined t o som e degree. But t here is also an upper bound, called t he e fficie n t fr on t ie r , t hat const raint s t he t ot al of t he t wo. As new pract ices im prove t he capabilit ies of supply chains, t his front ier is pushed out ward, reducing t he need t o com prom ise bet ween flexibilit y and efficiency. However, you are always const rained by t he current front ier, and you have t o choose t he m ost advant ageous point along t hat front ier.

Figure 14.1. The Efficient Frontier

The m ost im port ant considerat ion in deciding where t o place your com pany along t his t radeoff curve is your corporat e posit ion in g st r a t e gy . I n t he m anufact uring sect or, posit ioning is based prim arily on t hree qualit ies: product , price, and service. The goal of your com pany should be t o st ake out a defensible posit ion in t he m arket based on som e com binat ion of t hese qualit ies. I f a dom inant com pany in your indust ry were firm ly ent renched as t he low- cost provider, for exam ple, you would probably want t o different iat e yourself based on t he qualit y of your product s or services. St aking out a posit ion for your com pany involves yet anot her t radeoff am ong com pet ing qualit ies. There has been a t endency in recent years t o im agine t hat it 's possible t o be t he best on all t hree qualit ies, as expressed in t he m ant ra

" fast er, bet t er, cheaper," but t he business realit y is t hat t hese qualit ies inevit ably t rade off against each ot her. The best product cost s m ore t o build, and t he best service cost s m ore t o deliver; realist ically, you j ust can't provide eit her of t hese in com binat ion wit h t he lowest prices and st ill hope t o m ake a profit . Adopt ing a st rat egy t hat calls for being t he best in all t hree qualit ies is t he sam e as having no st rat egy at all. Your choice of a posit ioning st rat egy places st rong const raint s on t he way you m ake t he t radeoff bet ween efficiency and flexibilit y in your supply chain (Figur e 14.2 ) . I f you want t o be t he low- price leader, your only viable opt ion is t o build t he m ost efficient , econom ical chain possible; if you don't , you will inevit ably lose your posit ion t o a com pany t hat can squeeze m ore cost out of it s chain. I f you st ake your reput at ion on t he qualit y of your service, you need a highly flexible chain t hat can deliver your product s quickly and reliably even under t he m ost uncert ain condit ions. I f you t ake t he m iddle posit ion and em phasize t he qualit y of your product s, your choice depends on t he nat ure of t hose product s; if t hey are innovat ive, you need a m ore flexible chain t o cope wit h uncert ain dem and t han if t hey were m at ure product s wit h st able sales.

Figure 14.2. The Influence of Corporate Positioning

I t 's difficult t o set and m aint ain a single, clear st rat egy for your supply chain, and you shouldn't com plicat e t he st rat egy if you can possibly avoid it . However, you m ay not have a choice: I f you have a m ix of cust om ers and product s t hat j ust won't fit a single st rat egy and you aren't in a posit ion t o divest yourself of t he m isfit s, you m ay have t o im plem ent t wo or m ore st rat egies wit hin your supply chain. An exam ple in Chapt er 13 ( see Figur e 13.3 ) showed how you could serve t hree groups of cust om ers wit h incom pat ible requirem ent s by defining t hree different pat hs t hrough t he sam e supply chain, each of which reflect s a different st rat egy for delivering t he goods. A sim ilar syst em of overlapping supply chains m ight be used in t he case of incom pat ible product fam ilies. Som e very successful com panies use m ult iple supply chain st rat egies. Wal- Mart m oves it s goods using a m ixt ure of dist ribut ion cent ers, cross- docks, and direct deliveries, depending on t he product , and it sells t hose goods using a com binat ion of convent ional, VMI ( vendor m anaged invent ory) , and consignm ent supplier relat ionships. But t he fact t hat Wal- Mart can handle all

t hese com binat ions doesn't m ean t hat you can, and t rying t o com bine m ult iple st rat egies right away m ay prevent you from t ruly excelling at any one of t hem . Wal- Mart developed it s overlapping st rat egies only aft er it achieved dom inance wit h a single st rat egy founded on t he relent less pursuit of efficiency. You will likely have m ore success if you, t oo, st art out wit h a single, clear st rat egy and m ast er it before you com plicat e it wit h supplem ent ary t echniques.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 14. Designing the Chain

Exploring Your Options Once you have decided on a core st rat egy, you need t o set t he scope of t he design effort . I f your com pany is vert ically int egrat ed, so t hat m uch of t he supply chain is under your direct cont rol, t he scope m ay nat urally run from your im m ediat e suppliers t o your im m ediat e cust om ers. I f not , you will probably need t o include your cust om ers' cust om ers and your suppliers' suppliers t o achieve m aj or im provem ent s. The m ore of t he chain you can int egrat e under t he new design, t he great er t he opport unit y t o build a com pet it ive supply chain. But be careful not t o overext end yourself; t here are dim inishing ret urns as you add m ore com panies t o t he design effort , and t he overhead of t rying t o m anage t oo m any relat ionships can easily swam p t he benefit s of expanded int egrat ion. One t est of reasonableness for t he design scope is t he degree of branching. Recall from Chapt er 2 t hat suppliers are oft en organized int o t iers ( see Figur e 2.10 ) . I f you only deal wit h a handful of Tier 1 suppliers, t hey need t o be included in t he design. I f t heir collect ive supplier base also happens t o be sm all, it m ay m ake sense t o bring in t he Tier 2 suppliers as well. But at som e point , t he fan- out of suppliers will becom e t oo ext rem e, so t hat ext ending t he design t o include an addit ional t ier would seriously inflat e t he num ber of part ies involved ( Figure 14.3) . This link in t he chain is oft en t he sam e point at which supplies becom e generic com m odit ies t hat are purchased prim arily on price, so t here isn't m uch t o be gained by including t hese suppliers in any case. Sim ilar logic applies on t he cust om er side.

Figure 14.3. Scoping the Design

Once you have set a rough scope for t he design, you need t o decide which com panies wit hin t hat scope you'd like t o have as part ners in your effort s t o im prove t he chain. I f t he design only spans a handful of com panies, it m ay m ake sense t o get t hem all involved. But t hat 's rarely feasible, and in m ost cases t he collaborat ion will be lim it ed t o t he com panies t hat exert t he m ost influence over t he perform ance of t he chain. Supply chain m anagers generally know who t he key players are, but t here m ay st ill be som e difficult choices. Here are a few t hings you m ight want t o look for: 1 . Volu m e of bu sin e ss— The m ost obvious candidat es are your largest cust om ers and suppliers, since t hey account for t he m aj orit y of your business and t herefore represent t he largest opport unit ies for im pr ov em ent . 2 . Va lu e a dde d— The m ore a supplier or cust om er cont ribut es t o t he qualit y of your product s or t he ease wit h which t hey flow t hrough t he chain t o t he end consum er, t he m ore im port ant it is t o engage t hem in t he design process. 3 . I n t e r de pe n de n ce— A sm all cust om er t hat depends on you for cust om supplies will be m uch m ore likely t o cont ribut e t o a successful design t han a large one t hat buys int erchangeable part s from you. Sim ilar logic applies t o t he supply side. 4 . Com m on st r a t e gy— I f your st rat egy is based on efficiency, bring in com panies wit h a proven abilit y t o operat e a lean chain. I f your st rat egy is based on flexibilit y, focus on t he com panies t hat t hrive on innovat ion. 5 . W illin gn e ss t o pa r t n e r— There's not m uch point in working up an int egrat ed design if your t rading part ners aren't predisposed t o m aking t he necessary invest m ent t o im prove t he chain. Working t hrough t he decisions about who would m ake t he best part ners in designing a bet t er supply chain offers a good opport unit y t o ret hink your own role wit hin t he chain. I t m ay be t he legacy of vert ical int egrat ion, or it could be

t he result of m ergers and acquisit ions, but m any com panies cont inue t o perform supply chain funct ions t hat ot hers could do m uch m ore cost effect ively. As t hese funct ions becom e increasingly specialized, it becom es m ore and m ore im port ant for each com pany t o focus on it s core com pet ence, reserving for it self only t hose funct ions at which it t ruly excels. The hardest part of ident ifying your core com pet ence is adm it t ing t hat you aren't t he best at everyt hing you do. Rat her t han st ruggling t o define your core com pet ence in t he abst ract , as m any com panies do, let t he design process provide som e solid dat a on your st rengt hs and weaknesses. The t echnique is sim ple: Just include out sourcing opt ions for all but t he m ost cent ral funct ions, and let t hose opt ions " com pet e" against your in- house abilit ies t o see which ones produce t he best chain. I f t he best designs all out source a part icular funct ion, t hen t hat funct ion probably isn't part of your core com pet ence. The st art ing point for a new design is a working m odel of t he supply chain as it exist s t oday. You can delegat e t his t ask t o professional m odelers, but in m y experience you'll get m uch bet t er result s if you assem ble a t eam of operat ional m anagers t o sket ch out a concept ual m odel first , preferably wit h t he aid of an experienced facilit at or. Alt hough soft ware t ools m ay be useful during t hese m odeling sessions, t he m ost powerful t ool is a very large whit eboard. The goal of t his effort is t o develop a shared underst anding of how t he chain act ually works in it s present form , and t hat usually happens t he fast est when all t he m anagers involved are able t o look at and work on t he sam e diagram s. Why should m anagers build t he first m odel? For st art ers, m ost of t he knowledge about how t he chain works is in t heir heads, and having t hem build a m odel t oget her is a fast , efficient way t o capt ure t hat knowledge. I t 's also a good way t o discover discrepancies in t heir viewpoint s; if you ask 10 m anagers how t heir supply chains work, you will usually get at least 10 different answers, and t he sooner t hose differences are resolved t he bet t er. The t echnique also get s m anagers from different organizat ions working t oget her as a t eam , allowing t hem t o influence t he fut ure design right from t he out set while also building t he relat ionships t hat will m ake organizat ional change possible. Finally, having operat ional m anagers build t he m odel alm ost always reveals business opport unit ies t hat would never occur t o t echnical m odelers. A proven approach t o building t he concept ual m odel is t o use a com binat ion of sim ple diagram s and narrat ives. Figure 14.4 offers a highly sim plified exam ple of t he kind of diagram t hat em erges from t hese sessions. The exam ple is for a hypot het ical lock m aker, Am lock, t hat operat es four plant s and t wo dist ribut ion cent ers ( DCs) . The arrows indicat e t he basic flow of m at erials, but t hey don't t ell t he whole st ory; t hat 's where t he narrat ives com e in. The basic com ponent s for a keyed lock are fabricat ed in Hunt sville, aft er which t he part s for t he locking m echanism are sent t o Dayt on and t he rem ainder are shipped t o one of t he t wo Mexican plant s for prim ary assem bly. The assem bled unit s and t he keyed cylinders are ret urned t o Hunt sville, where t hey undergo final assem bly and are shipped out t o t he DCs. Keyless locks use a subset of t he chain; t hey bypass Dayt on alt oget her, and som e of t hem are fully assem bled in t he Mexican plant s and shipped direct ly t o t he DCs.

Figure 14.4. A Simple Conceptual Model

Once t he m anagers in t he m odeling session agree on how t he current chain works, it 's t im e for t hem t o look for opport unit ies t o im prove t he chain. I f t he chain needs addit ional capacit y, how should t hat be achieved? Can exist ing plant s be expanded, or would it be bet t er t o close som e of t hem and build new ones? Would it m ake sense t o m ove som e of t he operat ions from one plant t o anot her? Are t here ways of reducing t he dist ance m at erials have t o t ravel as t hey m ove t hrough t he chain? The Am lock exam ple offers opport unit ies t o explore all t hese opt ions and m ore. I f t he com pany needs m ore capacit y, it m ight consider expanding t he Mexican plant s, adding a t hird plant , or shut t ing t hem bot h down in favor of a larger, m ore efficient facilit y. As for m oving operat ions, it m ight be possible t o m ove t he keying process int o Hunt sville and close t he Dayt on plant . Anot her opt ion would be t o t ransfer final assem bly t o Mexico, elim inat ing t he need for keyed locks t o m ake t he long t rip back t o Hunt sville before going t o t he DCs. The purpose of exploring t hese alt ernat ives is not t o m ake decisions, but t o choose t he opt ions t hat should be evaluat ed in t he form al m odel. This can be a hard discipline t o m aint ain because m anagers oft en becom e ent husiast ic about t heir ideas for im provem ent and want t o see t hem becom e part of t he final design. For exam ple, t he Am lock m anagers m ight becom e so enam ored of t he idea of m oving final assem bly t o Mexico t hat t hey st op looking for ot her opt ions. This m ay be an excellent idea, but t here isn't nearly enough inform at ion in t he concept ual m odel t o m ake decisions about t he act ual cost s and benefit s of any one change, nor is t here any way t o look at t he various com binat ions of ideas t o see which configurat ion would work t he best . That 's what t he form al m odels are for. Once t he m anagers have com plet ed t heir concept ual m odel and generat ed a list of opt ions t hey'd like t o evaluat e, t echnical m odelers t ranslat e t heir result s int o a m at hem at ical or sim ulat ion m odel. The first st ep in t his t ranslat ion process is t o assem ble det ailed inform at ion about all t he elem ent s of t he concept ual m odel, including t he suppliers, cust om ers, product s, operat ions, facilit ies, and t ransport at ion links. Figure 14.5 illust rat es som e of t he inform at ion t hat would t ypically be required, but t he act ual dat a depends on

bot h t he charact erist ics of t he supply chain and t he kind of m odel used. For exam ple, if operat ions have t he sam e cost and durat ion at all facilit ies, t hese propert ies can be at t ached t o t he operat ions t hem selves. Ot herwise, t he m odelers will need separat e num bers for each facilit y, as shown in t he t able.

Figure 14.5. Inputs for a Formal Model

I n addit ion t o dat a about exist ing elem ent s of t he supply chain, t he m odelers need det ailed inform at ion about t he opt ions t hey are t o evaluat e: They need t o know t he cost s involved in changing t he capacit y of each facilit y, t he upper and lower bounds of capacit y for each, t he cost of closing exist ing facilit ies or building new ones, and so on. I f you want t o explore your opt ions for m oving new product s t hrough t he chain, t he m odelers will need dem and forecast s, bills of m at erials, planned product ion sit es, and sim ilar inform at ion on each new product you are considering.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 14. Designing the Chain

Designing the Chain The st rat egy is set , t he current chain is diagram m ed and described, and t he opt ions are on t he t able: I t 's t im e t o build a form al m odel of t he chain. I f you haven't already done so, you now have t o m ake t he choice bet ween a m at hem at ical opt im izer and a sim ulat or. A sim ulat or m ay be a nat ural next st ep if your chain is com plex and you are st ill t rying t o get a handle on how it all works. But if you're ready t o m ove t oward decisions, an opt im izer will give you m ore help in choosing t he best possible configurat ion. I n t he following discussion, I assum e t hat you are using an opt im izer, t hen I add som e rem arks on t he use of sim ulat ors at t he end of t he sect ion. The goal is not t o explain how t o use eit her of t hese syst em s—your m odeling t eam will t ake care of t hat —but t o give you enough insight int o t he process t hat you can give t he m odelers t he guidance t hey require and underst and t he result s t hey bring back t o you. Opt im izers com e in a variet y of form s. They are t he core t echnology underlying advanced planning and scheduling ( APS) syst em s, t hey are com m on in st andalone supply- chain design t ools, and t hey can be purchased—or even downloaded for free on t he Web—as plug- in m odules for ot her m odeling syst em s. I f you have Microsoft Excel, you already have one on your deskt op; j ust choose Tools and t hen Solver from t he m ain m enu and you'll be present ed wit h a sm all but powerful opt im izer. I f you are curious about how opt im izers work, experim ent ing wit h Excel's solver is an excellent way t o becom e fam iliar wit h t he t echnology. Basically, an opt im izer is a syst em wit h a large num ber of input s and a single out put —t he best design for your supply chain given t he input s ( Figure 14.6) . All but one of t he input s t ake t he form of con st r a in t s, which is opt im izer j argon for m at hem at ical expressions t hat describe t he current chain and your opt ions for m odifying it . The ot her input is t he obj e ct ive fu n ct ion , a form ula t he m odelers const ruct t o reflect your obj ect ives for t he design. The opt im izer t akes t he const raint s as input s and uses a variant of linear program m ing t o find t he design t hat best sat isfies t he obj ect ives, producing t he winning design as it s out put . Your role in all t his is t o give t he m odelers t he inform at ion t hey need t o prepare t he const raint s and t he obj ect ive funct ion, t hen review t he result ing design wit h t hem .

Figure 14.6. Using an Optimizer

Alt hough t he const raint s are all expressed in t he sam e m at hem at ical form , it 's helpful t o t hink of t hem as falling int o four cat egories, as shown in Figure 14.6. The dem and const raint s are forecast s of how m uch product has t o be delivered in each geographical region, as described in Chapt er 13 . The resource const raint s provide det ailed inform at ion about t he product s, facilit ies, and ot her elem ent s of your current supply chain, as list ed in Figure 14.5. The opt ions represent t he alt ernat ives you'd like t he opt im izer t o explore, such as changing t he capacit ies of som e plant s or opening new plant s. The rest rict ions express what ever lim it at ions you want t o place on t he design, including t he required cust om er service level, t he level of reserve capacit y you'd like t o m aint ain, and t he am ount of m oney available for capit al im provem ent s. The obj ect ive funct ion represent s t he quant it y you want t o opt im ize in t he design. I f you have t he m odelers use t ot al cost for t he obj ect ive funct ion, t he opt im izer will analyze all possible configurat ions t o find t he design t hat m eet s t he expect ed dem and at t he lowest cost . I f you ask t o have t he m odel opt im ize t he order fill rat e, t he design you get back will have t he highest fill rat e you can achieve, regardless of t he cost . These exam ples raise an im port ant quest ion: What if you want t he design t o opt im ize t wo or m ore quant it ies, such as cost an d fill rat e? The short answer is t hat you can't do it because t hese t wo quant it ies t rade off against each ot her—increasing t he fill rat e raises t he cost , and cut t ing cost s m ay reduce fill rat es. The m ost you can ask for is a good balance bet ween t he t wo, given t heir relat ive im port ance t o you, and t here are opt im izat ion t echniques t o help you balance t wo or m ore obj ect ives against each ot her. A sim pler solut ion is t o express one of t he t wo quant it ies as a const raint , leaving t he m odel free t o opt im ize t he ot her quant it y. I n t he current exam ple, t he usual procedure is t o t reat t he m inim um fill rat e as a const raint and let t he m odel opt im ize against cost . I f it t urns out t hat achieving a fill rat e of, say, 97% is t oo expensive under t he best of condit ions, you sim ply ask t he m odelers t o find out how m uch you could save by lowering it a point or t wo. Cost is a com m on choice for t he obj ect ive funct ion because, as described in Chapt er 12 , it nat urally aligns ot her business obj ect ives. However, using cost does t end t o favor im m ediat e, operat ional benefit s over longer- t erm im provem ent s, and it com plet ely ignores t he im pact of t he design on revenue.

I n m y view, t he ideal obj ect ive funct ion is t he " com m on denom inat or" shown on t he right side of Figure 12.4: t he proj ect ed profit over a specific period of t im e, including adj ust m ent s for t he t im e value of m oney. However, using profit rat her t han cost requires t he m odel t o deal wit h addit ional fact ors such as pricing, discount ing, and non- product ion cost s, so using profit as t he obj ect ive funct ion is not yet a com m on pract ice. Designing a supply chain isn't a one- shot deal; it usually t akes several passes j ust t o get all t he const raint s sort ed out . The nat ural procedure is for m odelers t o run t he opt im izer on t he init ial set of dat a, review t he result s wit h you, t hen change t he input s based on t hat review. This refinem ent process offers a good opport unit y t o ask all t hose int erest ing " what if" quest ions. What would it cost t o bum p t he cust om er service level up t hree point s? What would happen if you doubled t he budget for new const ruct ion? How different would t he design be if you gave t he opt im izer t he opt ion of shut t ing down t hree of your older plant s and out sourcing t heir product ion? The opt im izer can answer all t hese quest ions and m ore. You should also perform " what if" experim ent s t o see how t he design holds up under variat ions in dem and and supply. Keep in m ind t hat opt im izers t reat all const raint s as fixed values, and t hat 's not a realist ic assum pt ion. I n order t o underst and t he effect s of variabilit y in dem and and supply, you have t o t est t he design over a range of values for each and see how it perform s. I f your dem and pat t erns are fairly st able and your st rat egy is t o build a lean chain, it m ay only t ake a few of t hese " what if" experim ent s t o be confident t hat your design is robust across reasonable variabilit y. I f you are designing a flexible chain specifically t o cope wit h high variabilit y, however, you m ay want t o t ake t he addit ional st ep of sim ulat ing t he design. The lim it at ions of m at hem at ical opt im izers are neat ly com plem ent ed by t he st rengt hs of sim ulat ion t ools. As described in Chapt er 5, sim ulat ors can assign dist ribut ions of possible values t o param et ers rat her t han assum ing a single, fixed value for each. The sim ulat ions run m any t im es, picking a num ber at random from t he appropriat e dist ribut ion each t im e t hey need a value for a param et er. I n t his way, sim ulat ors provide a det ailed analysis of t he effect s of variabilit y, yielding a clear indicat ion of whet her a design is robust across t ypical variat ions in dem and, supply, capacit y, and ot her param et ers. The ot her im port ant advant age of sim ulat ors is t hat t hey aren't lim it ed t o linear relat ions. For exam ple, if you are concerned about t he fact t hat price breaks and ot her effect s of quant it y are int roducing nonlinear relat ions, a sim ulat ion m odel can t ell you whet her t hese nonlinear relat ions are affect ing your result s. I f sim ulat ions are so m uch bet t er in t his regard, why not use t hem in place of opt im izers? Precisely because t hey lack t he abilit y t o seek out opt im al solut ions. That 's why sim ulat ions are a com plem ent t o opt im izers rat her t han a replacem ent . A good way t o com bine t he t wo t ypes is t o use an opt im izer t o generat e one or m ore candidat e m odels, t hen use a sim ulat or t o st ress- t est t hese m odels under condit ions of variabilit y and nonlinearit y ( Figure 14.7) . This approach also m akes t he best use of t he hill- clim bing abilit y of sim ulat ion m odels ( see Chapt er 5) ; once you put a sim ulat ion m odel in t he region of t he desired solut ion, it can fine- t une t he values of one or m ore key param et ers under it s m ore realist ic assum pt ions. I n short , using a sim ulat or in conj unct ion wit h an opt im izer gives you t he best of bot h t ools: The opt im izer does t he heavy lift ing of sort ing t hrough m illions of possible designs, and t he sim ulat or does t he finesse work of validat ing and refining t he best candidat es.

Figure 14.7. Refining the Design

The m om ent of t rut h com es when t he opt im ized design is com plet e and ready for com parison wit h t he current supply chain. Convert ing an operat ing supply chain over t o a new design is an expensive, disrupt ive, and risky proposit ion for all concerned, so t he expect ed ROI from m aking t his conversion has t o be subst ant ial t o j ust ify t he change. The invest m ent side of t his calculat ion includes t he init ial cost s of conversion—t he acquisit ion of new facilit ies and equipm ent , t he cost s of educat ion and t raining, and ot her expenses—t oget her wit h t he cont inuing cost s of j oint ly m anaging t he new chain as an int egrat ed syst em . The ret urn includes t he t ot al savings in operat ing cost s t oget her wit h what ever increased sales are expect ed t o result . The basis for calculat ing t he expect ed ret urn on t his invest m ent is not t he perform ance of t he current supply chain as it operat es in t oday's m arket , but t he proj ect ed figures for t hat chain as it will cont inue int o t he fut ure, including t he effect s of all t he int rinsic and ext rinsic fact ors reviewed in Chapt er 10 ( see Figure 10.7) . This is an im port ant dist inct ion t hat m ay well cast t he deciding vot e in t he decision t o m ake t he change. An ROI based on t he assum pt ion of a st at ic m arket wit h st able com pet it ion m ight indicat e t hat t here is lit t le t o be gained by changing your current pract ices. But if t he m arket is dem anding increased perform ance at lower cost , or if your com pet it ors are already int egrat ing t heir supply chains and upping t he ant e for t he ent ire m arket , a realist ic forecast could show t hat your sales will plum m et if you don't im prove your chain. I n t hat case, what m ight init ially look like a breakeven proposit ion m ay act ually be a m ake- or- break decision. Th e a bilit y of su pply- ch a in de sign syst e m s t o fin d opt im a l solu t ion s give s you a pow e r fu l t ool for im pr ovin g you r ch a in , bu t r e m e m be r t h a t t h e m ost pow e r fu l t ool of a ll is st ill you r ow n e x pe r ie n ce a s a m a n a ge r . Opt im ize r s ca n on ly e va lu a t e t h e opt ion s you give t h e m , so t h e qu a lit y of t h e de sign you ge t is a dir e ct r e fle ct ion of t h e qu a lit y of you r ow n t h in k in g a bou t you r bu sin e ss. Th a t 's w h y it 's cr it ica l t h a t you a n d you r fe llow m a n a ge r s dr ive t h e de sign pr oce ss, pu sh in g t h e m ode le r s t o t r y t h in gs t h e y w ou ld n e ve r t h in k of ba se d on you r cu r r e n t pr a ct ice s. Th a t 's a lso w h y you n e e d t o be u p t o spe e d on t h e cu r r e n t be st pr a ct ice s in t h e in du st r y, a s t h e se a r e t h e t e ch n iqu e s t h a t a llow you t o k e e p pu sh in g t h e lim it s of w h a t you ca n a ch ie ve in you r de sign . Th e n e x t a n d fin a l ch a pt e r e x plor e s fou r of t h e m ost e x cit in g de ve lopm e n t s on t h e fr on t ie r of su pply ch a in m a n a ge m e n t .

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Part V. Design

Chapter 15. Maximizing Performance Ch a pt e r 1 4 de scr ibe d h ow t h e st r a t e gic t r a de off be t w e e n e fficie n cy a n d fle x ibilit y is con st r a in e d by t h e e fficie n t fr on t ie r , a n d it offe r e d a w a y t o ide n t ify t h e be st ope r a t in g poin t on t h is fr on t ie r give n a pa r t icu la r su pply ch a in st r a t e gy. Bu t t h e e fficie n t fr on t ie r is con st a n t ly be in g pu sh e d for w a r d by n e w t e ch n iqu e s a n d t e ch n ologie s ( Figu r e 1 5 .1 ) , so oppor t u n it ie s for com pe t it ive a dva n t a ge a r e con st a n t ly in cr e a sin g. Th is fin a l ch a pt e r look s a t fou r w a ys in w h ich su pply ch a in le a de r s a r e a dva n cin g t h e e fficie n t fr on t ie r t oda y: a cce le r a t in g t h e m ove m e n t of in ve n t or y a cr oss t h e ch a in , poolin g r isk by for m in g " vir t u a l in ve n t or ie s" a cr oss m u lt iple sit e s, de sign in g pr odu ct s spe cifica lly t o su it t h e su pply ch a in , a n d post pon in g t h e diffe r e n t ia t ion a m on g pr odu ct s a s la t e in t h e ch a in a s possible .

Figure 15.1. Advancing the Frontier

Te a m - Fly Top

Te a m - Fly

Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 15. Maximizing Performance

Increasing Velocity One of t he sim plest ways t o advance t he efficient front ier is t o accelerat e t he flow of goods across your chain. Accelerat ion im proves efficiency because invent ory doesn't st ay in t he chain as long, which brings down t he cost s of holding t hat invent ory. At t he sam e t im e, increasing t he velocit y of invent ory enhances flexibilit y because it reduces t he t im e required t o change what 's in t he pipeline in response t o changing dem and. I f it t akes six weeks for your invent ory t o go from product ion t o consum er, t hen your product m ix will always lag behind dem and by a couple of m ont hs. On t he ot her hand, if it t akes six days t o go from product ion t o consum er, you can change t hat m ix in a week. An obvious way t o increase velocit y is t o swit ch t o a fast er m ode of t ransport at ion. I f you are sending goods overseas by ship and have t he opt ion of using airfreight , you m ay be able t o realize a net benefit from m aking t he swit ch. But t he cost of fast er t ransport at ion is oft en subst ant ial, so it 's hard t o push t he front ier very far j ust by increasing t ransport at ion speed. To produce a net advant age, t he increased t ransport at ion cost s have t o be m ore t han offset by t he financial benefit s of decreased holding cost s, im proved sales, reduced writ e- downs, or som e com binat ion of t hese fact ors. A m uch m ore effect ive way t o increase velocit y is t o im prove t he way t he chain handles goods t hat aren't in m ot ion. Despit e all t he effort s t o im prove t he efficiency of supply chains over t he past few decades, invent ory st ill spends t he m aj orit y of it s t im e sit t ing around wait ing for som et hing t o happen. As not ed in Chapt er 9, a st udy of t he Brit ish aut o indust ry found t hat st eel com ponent s spent 97% of t heir t im e idle. Your invent ory m ay not be quit e t hat sluggish, but if you gat her t he dat a and calculat e t he result s for a few of your own com ponent s you m ay find t hat you're not m uch bet t er off. I n short , t he bet t er way t o increase t he velocit y of invent ory is not t o m ove it fast er when it does m ove, but t o get it t o spend m ore of it s t im e in m ot ion. Achieving t hat goal is m uch harder t han j ust changing t he t ransport at ion m ode; you have t o conduct syst em at ic st udies of how invent ory m oves across t he chain, exam ine each place it st ops, and look for ways t o get it m oving again. To achieve significant ly higher velocit ies, you m ay need t o reengineer your supply chain operat ions, applying t he t echniques of JI T, lean product ion, and relat ed disciplines.

You can get a quick sense of where slowdowns occur j ust by looking at t he size of t he queues of raw m at erials t hat build up wit hin facilit ies, bot h at t he receiving docks and in front of individual workst at ions. I n Figure 15.2, t he large queue at Facilit y F indicat es t hat t his facilit y is a bot t leneck, and t he em pt y queue at Facilit y I indicat es t hat it is being held up by F. When you find a bot t leneck, your first choice should be t o find a way t o increase t he t hroughput for t hat operat ion, eit her by adding capacit y or by im proving t he operat ion it self. I f you can't find a way t o fix t he bot t leneck, t hen t ry scaling back t he upst ream operat ions t hat feed int o t he bot t leneck ( Facilit y C in t he exam ple) . I t m ay seem count erint uit ive t o accelerat e t he flow of invent ory by slowing som e operat ions down, but t hat 's precisely t he effect you will achieve. By not pulling invent ory int o t he chain unt il it has a clear pat h, you're m aking sure t hat invent ory m oves fast er once it does ent er t he chain.

Figure 15.2. Looking for Queues

For a m ore revealing view of how invent ory spends it s t im e, have som eone record t he t im e invent ory spends in each locat ion wit hin t he chain and plot t he result s as a t im e- in- process chart of t he sort shown in Figure 9.11. Bet t er st ill, t ake t his approach one st ep furt her by m aking a dist inct ion bet ween act ivit ies t hat add value and t hose t hat don't . The only act ivit ies t hat add value are t hose t hat change t he product in a way t hat increases it s ut ilit y t o t he cust om er, usually by changing eit her it s form or it s locat ion t o bring it closer t o t he needs of t he cust om er. The st udy t hat showed invent ory in t he Brit ish aut o indust ry sit t ing idle 97% of t he t im e also revealed t hat when t he invent ory w as m oving, less t han a t hird of t he t im e was spent adding value. As bad as t his m ay seem , it can get worse because som e act ivit ies can act ually reduce value. I n t he Am lock exam ple of Chapt er 14 , t he t im e locks spend in t ransit t o t he Mexican plant s act ually reduces t heir value because it t akes t hem fart her away from t he consum ers who will event ually buy t hem . Tracing t he m ovem ent of t ens of t housands of product s t hrough your supply chain t o ident ify bot t lenecks and unproduct ive operat ions can be a daunt ing t ask, but t his is an area where t echnology can great ly ease t he burden. As described in Chapt er 6, t racking syst em s, supply- chain visibilit y syst em s, and

event - m anagem ent soft ware can rem ove m ost of t he drudgery from t his effort and aut om at ically alert you t o any slowdowns in t he chain. I dent ificat ion t echnologies such as barcodes and radio frequency ( RF) t ags can go even furt her t o m ake t his an effort less act ivit y by aut om at ing t he ent ire process. For exam ple, clot hing m anufact urers now have t he abilit y t o print RF t ransm it t ers t he size of a grain of salt direct ly ont o t he t ags sewn int o t heir garm ent s, an innovat ion t hat would allow t hem t o t rack every art icle of clot hing from it s offshore m anufact urer t o it s point of sale. I n t his case, however, t he t echnology m ay be a bit t oo advanced for t he m arket ; Benet t on recent ly announced t hat , despit e earlier report s, it was not going t o put t hese t ags in it s clot hing. The com pany's cust om ers were dist urbed by t he idea t hat t he com pany could t rack t heir m ovem ent s when t hey were wearing t he clot hes, despit e Benet t on's prom ise t o disable t he t ags at t he point of sale. Alt hough t he at t em pt t o increase velocit y is prim arily direct ed at t he flow of invent ory, t here are advant ages t o be gained from accelerat ing t he flow of dem and and cash as well. The fast er dem and m oves up t he supply chain, t he m ore quickly upst ream suppliers can respond t o changes in t hat dem and. I n addit ion, accelerat ing t he dem and signal is one of t he m ost effect ive ways of elim inat ing dem and am plificat ion, which is t he source of m uch disrupt ion in supply chains. Finally, accelerat ing t he flow of cash reduces t he t ot al cost of debt across t he chain, furt her im proving efficiency wit hout im pairing flexibilit y. A case in point is Cisco Syst em s, which uses inst ant paym ent in it s supply chain t o help suppliers offset t he cost s of rapid delivery. The m oral: When you t hink about how you can increase t he velocit y of your supply chain, consider t he flow of dem and and cash as well as t he flow of supply.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 15. Maximizing Performance

Pooling Risk The second t echnique for advancing t he efficient front ier is r isk poolin g. The idea behind risk pooling is t o com bine t he m anagem ent of invent ories t hat would ot herwise be cont rolled separat ely so t hat variabilit y in dem and can be handled wit h less safet y st ock. To see how t his works, t ake a look at Figure 15.3, which com pares t he invent ory levels required t o m eet a 97% cust om er service level ( CSL) wit h eit her t hree regional invent ories or a single, cent ralized invent ory. Wit h regional invent ories, each region has t o have 150 unit s of a product on hand in order t o m eet t he t arget CSL, for a t ot al of 450 unit s. Wit h a cent ralized invent ory, only 300 unit s are required.

Figure 15.3. Risk Pooling

What account s for t he difference? The proper explanat ion would require an excursion int o st at ist ics, but t he sim ple answer is t hat local variat ions in dem and t end t o cancel each ot her out . I n Figure 15.3, random variabilit y m ight

cause dem and t o be high in t he first region, average in t he second, and low in t he t hird, or it m ight lead t o som e ot her com binat ion of dem and levels, but it 's relat ively unlikely t hat it will happen t o be high in all t hree regions at t he sam e t im e. I f t he invent ories for t he t hree regions are pooled, t hen t he sam e safet y st ock can cover t he risk of high dem and in any of t hree regions, reducing t he t ot al invent ory requirem ent s. Depending on condit ions, it m ay be possible t o reduce invent ories—and, hence, holding cost s—by 25% t o 35% using risk pooling. Of course, a decision t o cent ralize invent ories involves m ore t han j ust t he requirem ent s for safet y st ock. I t m ight not be possible t o hit your t arget CSL wit hout holding st ock close t o your cust om ers, or t he cost s of using fast er t ransport at ion m ight be great er t han t he savings due t o holding less invent ory. But risk pooling doesn't require t hat invent ories act ually be locat ed in t he sam e place. All it requires is t hat t hey be m anaged as a com m on pool. This can be done t hrough a variet y of t echniques, including echelon invent ory, m ult isourcing, t ransshipm ent , and direct shipm ent , as defined in t he following par agr aphs. As described in Chapt er 2, m any dist ribut ion net works have m ult iple levels or echelons. For exam ple, product s m ight m ove from a single, cent ral warehouse t hrough several regional dist ribut ion cent ers and t hen t o a large num ber of widely dist ribut ed st ores ( Figure 15.4) . While it is possible for each of t hese facilit ies t o m anage it s invent ory independent ly, t his is rarely done because it is far m ore efficient t o m anage t hem collect ively as an e ch e lon in ve n t or y. I n t his approach, t he set of facilit ies leading from t he cent ral facilit y t hrough t he ret ail st ore is t reat ed as a com m on risk pool, wit h t he cent ral st ock providing backup t o t he regional st ock, which provides backup t o t he st ore st ock in t urn. So long as t he delays in get t ing st ock from t he next level up t he chain are t olerable t o t he cust om er, t he t ot al invent ory in an echelon syst em can be great ly reduced t hrough risk pooling.

Figure 15.4. An Echelon Inventory System

As shown in Figure 15.4, t he m ost com m on arrangem ent in an echelon dist ribut ion syst em is for each facilit y t o be served by a single upst ream facilit y. This st ruct ure sim plifies t he adm inist rat ion of t he syst em , but it lim it s t he savings t hat can be realized t hrough risk pooling. I f each facilit y can receive goods from t wo or m ore upst ream facilit ies, t hen t he invent ories of

t hose facilit ies aut om at ically form a risk pool t hat reduces t he need for safet y st ock. Figure 15.5 illust rat es t his by showing a variat ion of t he cent ralizat ion approach illust rat ed in Figure 15.3. I nst ead of serving all t hree regions from a single cent ral warehouse, each region is served by it s own warehouse, but t he warehouses backst op each ot her in t he event of a st ockout . This allows t he t ot al invent ory t o be reduced t o 300 unit s, as wit h t he cent ralized invent ory, but it ret ains t he cust om er proxim it y of t he regional facilit ies.

Figure 15.5. Pooling Risk Across Regions

Shipping from m ore dist ant facilit ies t akes longer and is m ore expensive, so it m ay not be econom ically feasible t o support all possible links bet ween warehouses and regions. The solut ion t o t his problem is t o divide t he facilit ies int o separat e but overlapping risk pools, as shown in Figure 15.6. Most of t he benefit s of risk pooling are achieved wit h t he first few m em bers in each pool, so overlapping pools can keep t he num ber of links t o a reasonable level while st ill achieving significant reduct ions in invent ory. The st rat egy works even if shipping product s from m ore dist ant facilit ies result s in a net loss on any given sale. Because such event s are relat ively infrequent , t hey are m ore t han paid for by t he savings in holding cost s.

Figure 15.6. Overlapping Risk Pools

The benefit s of risk pooling can also be achieved t hrough t r a n ssh ipm e n t , in which t he facilit ies at a given level of t he chain exchange invent ory am ong t hem selves. This t echnique is m ore expensive t han m ult i- sourcing because product s t ravel fart her on average, but som et im es it 's t he only opt ion. This is clearly t he case at t he ret ail level because t here are no downst ream facilit ies t o receive m erged shipm ent s, and t his is where t ransshipm ent is m ost oft en pract iced. St ores are norm ally arranged int o overlapping risk pools based on proxim it y, as shown in Figure 15.6, and given elect ronic access t o each ot her's invent ory. This allows t he st ores t o deal wit h st ockout s by assuring cust om ers t hat t he product t hey want is in st ock and will be available wit hin a short period, t ypically t he following day. Yet anot her way t o achieve risk pooling is t hrough dir e ct sh ipm e n t , in which one or m ore links of a supply chain are bypassed alt oget her. I n Figure 15.4, for exam ple, a large order m ight be shipped direct ly from t he cent ral warehouse t o a ret ail out let , skipping t he regional warehouse. As t his exam ple suggest s, you can t hink of direct shipm ent as a variant of echelon invent ory in t he sense t hat upst ream facilit ies backst op t he invent ory of down st ream facilit ies. The advant age of direct shipm ent is t hat it avoids all t he cost of m oving t hrough t he int erm ediat e facilit ies, including t he t im e and expense of unloading, st oring, ret rieving, and reloading m erchandise. Direct shipm ent is oft en used t o handle large orders t hat hit t ransport at ion breakpoint s, such as full t ruckload ( FTL) deliveries, regardless of whet her a closer facilit y has t he goods in st ock. Not only does t his pract ice elim inat e local handling cost s, it also reduces t ot al shipping cost s by t aking advant age of FTL rat es. A furt her advant age of t his pract ice is t hat because downst ream st orage facilit ies don't have t o handle large orders, t hey can reduce bot h t heir cycle st ock and t heir safet y st ock wit hout reducing service levels. Risk pooling is an excellent t ool for advancing t he efficient front ier because pooling can be m apped ont o any com binat ion of efficiency and flexibilit y, depending on your st rat egy. I f you are seeking t he m ost efficient chain, you

can t rim invent ory levels by as m uch as a t hird wit hout com prom ising your service level. I f your st rat egy is based on flexibilit y, you can respond t o great er fluct uat ions in dem and wit hout increasing invent ories. I f you are looking t o st rike a balance, you can im prove bot h qualit ies in what ever m ix you prefer. Powerful as it is, risk pooling is not a panacea, and t he leverage you can gain from it depends on t he nat ure of dem and. I f t he dem and for a product is highly st able, t hen you don't need m uch safet y st ock t o begin wit h, so t here is less t o be gained by reducing it . More subt ly, if dem and in t he various regions t ends t o rise and fall t oget her, t hen risk pooling won't be very effect ive because short ages in one region will m ost likely be accom panied by short ages in ot her regions. This is not t o say t hat risk pooling can't be used under t hese condit ions. Rat her, t he point is t hat t he t echnique produces t he great est benefit s when dem and is uncert ain and relat ively independent across regions. Anot her caut ion regarding t his t echnique is t hat it can be m uch harder t o m anage t han a st andard echelon dist ribut ion syst em . I nst ead of always receiving goods from a single upst ream facilit y, a facilit y can pull goods from m ult iple facilit ies in t he echelon above it , skip t hat echelon alt oget her and source from furt her upst ream , or t ap peer facilit ies wit hin it s own echelon. To com plicat e m at t ers furt her, each of t hese alt ernat ives m ay be lim it ed t o risk pools whose m em bership varies wit h every facilit y ( see Figure 15.6) . Properly m anaged, t hese sourcing alt ernat ives can push t he efficient front ier well beyond it s current bounds. I m properly m anaged, t hey can leave a com pany floundering in t he hint erland, far away from t he front ier.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 15. Maximizing Performance

Designing for Supply I n t he 1980s t here was a m aj or effort am ong m anufact uring com panies t o design product s t hat were easier t o build. This effort , known bot h as design for m anufact ur ing and concurrent engineering, was a significant depart ure from past pract ices, in which engineers designed a product and t hen handed it over t o m anufact uring t o figure out how t o build it . By t aking m anufact uring requirem ent s int o account during t he design process, com panies t hat adopt ed t his approach were able t o sim plify product ion, reduce cost s, and enhance qualit y . Today, design for m anufact uring is being pushed out side t he four walls of t he fact ory and applied t o ent ire supply chains. The new m ovem ent —called d e sig n for su pply—t akes int o account t he ent ire sequence of operat ions and m ovem ent s necessary t o convert raw m at erials int o usable product s. Many of t he t echniques are t aken direct ly from design for m anufact uring, t he only change being a shift in focus from a single com pany t o a coalit ion of com panies. Ot her t echniques provide solut ions t hat are unique t o t he problem s of supply chains. Two of t he m ost basic t echniques are sim plificat ion and com m onalit y. The goal of sim plificat ion is t o reduce t he num ber of alt ernat ive assem blies by elim inat ing unnecessary opt ions, even if t hat increases t he cost of com ponent s som ewhat . For exam ple, it m ay cost a bit m ore t o build a power supply t hat works on eit her 110 or 220 volt s, but t hat one sm all change can reduce by half t he num ber of different product s t hat have t o be produced, shipped, and st ocked. Sim ilarly, t he goal of com m onalit y is t o reduce t he num ber of sim ilar com ponent s by reducing t he choices available t o designers. I nst ead of allowing each designer an unlim it ed choice of nut s and bolt s, for exam ple, a chain m ight st andardize on a sm all set of choices and require designers t o work wit h t hese. This m ay cause a few product s t o have larger fast eners t han t hey need, but it also st ream lines product ion by reducing t he variet y of m at erials, and it reduces purchasing cost s by com bining invent ories t hat would ot herwise be handled separ at ely . A m ore am bit ious t echnique is t he use of m odularit y in product design. Rat her t han designing each new product from scrat ch, engineers design product s as assem blies of pluggable com ponent s, using exist ing com ponent s wherever possible. As wit h sim plificat ion and com m onalit y, t his t echnique m ay increase

t he cost of an individual product som ewhat because it requires int erfaces t hat wouldn't be needed for a product using dedicat ed com ponent s. But t hese cost s can be m ore t han offset by t he savings from reusing t he sam e com ponent s across m any different product s. Modularit y can also increase cust om er opt ions by allowing m any configurat ions t o be assem bled from a relat ively sm all set of com ponent s. Anot her advant age of m odularizat ion is t hat m anufact urers can produce t he m odules of a product sim ult aneously rat her t han building t he ent ire product sequent ially. This parallel product ion perm it s short er lead t im es, im proving cust om er service while reducing holding cost s. Parallel product ion also perm it s great er flexibilit y in t he choice of product ion sit es by giving m anufact urers t he opt ion of using specialized facilit ies for t he various com ponent s. I f designers t ake m odularizat ion far enough, m anufact urers can creat e a large num ber of product s from a m inim um num ber of com ponent s. They can t hen produce and ship t hese com ponent s in high volum es, bringing t heir cost s down int o t he com m odit y range, yet deliver a final product t hat is highly cust om ized t o t he needs of it s ult im at e consum er. This is what happened in t he PC indust ry, and it 's t he reason t hat Dell can sell cust om - built com put ers at com m odit y prices. Anot her im port ant t echnique is designing product s for convenient packaging. As described in Chapt er 13 , low- densit y product s are inordinat ely expensive t o ship because t hey cause vehicles t o fill up before t hey reach t heir m axim um carrying weight . I ncreasingly, t hese product s are being designed in a m odular fashion t hat allows final assem bly t o be post poned unt il lat e in t he chain. A part icularly st riking exam ple of t his is ready- t o- assem ble ( RTA) furnit ure such as desks and shelves, which requires t he final assem bly t o be perform ed by t he consum er. This innovat ion has reduced t he cost of t ransport at ion t o t he point where RTA furnit ure is rout inely shipped around t he world. I n t he case of ret ail product s, anot her t echnique used in design for supply is m aking sure t hat t he product will display well in st ores. For exam ple, elect ronic gam es are oft en designed t o be operat ed wit hin t heir packages so t hat consum ers can t ry t hem before t hey buy t hem . Anot her exam ple is t he effect t hat Wal- Mart has had on packaging dim ensions: The st ore has such a st rong preference for goods t hat fit on it s 14- inch shelves t hat m any suppliers have redesigned t heir product s t o fit in packages m easuring 14 inches on a side. Even when t here is no prim ary packaging, display charact erist ics have a big im pact on design. For exam ple, large plast ic it em s such as cans, st orage cont ainers, and lawn furnit ure are now designed so t hat t hey nest inside each ot her when st acked, reducing t he precious ret ail space required t o display t hese low value- t o- volum e product s. One furt her t echnique is t o engage suppliers in t he design of a product . I n years past , suppliers had lit t le or no input in t he design of subassem blies t hey would produce. Their cust om ers sim ply passed t hem a design, and t hey had t o build t o it s specificat ions, even if it was a poor design. Today t here is m uch m ore collaborat ion in design, wit h suppliers being consult ed on feat ures, const ruct ion t echniques, and cost ing in an effort t o im prove t he final product . Chrysler's SCORE program , described in Chapt er 1, epit om ized t he spect acular result s t han can be obt ained by t urning adversarial supplier relat ionships int o t rue design part nerships. The design- for- supply init iat ive is significant in a num ber of respect s, one of

which is t he im plied shift in t he relat ive im port ance of m anufact uring and supply chain m anagem ent . Hist orically, t ransport at ion and logist ics funct ions have been subordinat e t o m anufact uring. I n essence, m anufact uring built what it want ed t o build when it want ed t o build it , and it was up t o logist ics t o supply t hem wit h t he necessary m at erials and t ake t he finished goods off t heir hands. Today t he roles are being reversed, and m anufact uring is being viewed as one com ponent of a m uch larger m achine, t he supply chain. I n t he new order of t hings, t he needs and desires of t he m anufact uring group are oft en subordinat ed t o t he requirem ent s of t he supply chain as a whole. This reversal of roles is a nat ural consequence of t he new com pet it ion bet ween supply chains, but it 's a consequence t hat few com panies have int ernalized t o dat e. The m ain obst acle t o design for supply isn't t he dem ands of t he t echnique it self, but t he difficult y m any product ion m anagers have in adj ust ing t o t he changing priorit ies. This is one of t he reasons t hat any at t em pt t o redesign t he supply chain has t o be em braced, support ed, and act ively m anaged by t he m ost senior execut ives wit hin t he com pany.

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Supply Chains: A Manager's Guide By David A. Taylor, Ph.D. Table of Cont ent s

Chapter 15. Maximizing Performance

Postponing Differentiation The m ost excit ing innovat ion in t he m ovem ent t oward design for supply is a t echnique variously called post pon e m e n t , de la ye d diffe r e n t ia t ion , or, less com m only, freeze- point delay. The basic idea is t o build product s in generic form at t he plant , ship t hem t o dist ribut ion cent ers close t o t heir dest inat ions, t hen perform t he final operat ions t hat result in a specific product ( Figure 15.7) . This t echnique pushes t he efficient front ier because it offers great er econom ies of scale in bot h product ion and t ransport at ion, yet also increases a com pany's flexibilit y t o respond t o changing dem and.

Figure 15.7. The Postponement Technique

The classic success st ory for post ponem ent is Hewlet t - Packard's DeskJet line of print ers. HP was enj oying rising sales of t hese print ers, and product ion in it s Vancouver plant was ram ping up precisely on plan t o m eet t he growing dem and. The problem was t hat t he print ers had t o be configured different ly for different nat ional m arket s, and t he com pany found it self const ant ly out of st ock for som e configurat ions and overst ocked on ot hers. A single plant served t he ent ire world and t he print ers were shipped by boat , so t here was no way HP could adj ust it s m ix of configurat ions fast enough t o solve t he problem . I nst ead, t he com pany redesigned t he print ers t o allow t he count ry configurat ion t o be done at it s dist ribut ion cent ers. This change allowed HP t o

produce generic print ers in high volum es, ship t hem in large quant it ies, and post pone t he final configurat ion unt il t he print ers were very close t o t heir t arget m arket s. The HP st ory illust rat es several advant ages of t he post ponem ent t echnique. First , it allows product s t o be specialized t o different m arket s wit hout com prom ising econom ies of scale in product ion and t ransport at ion. This advant age is vit ally im port ant ; in t oday's consum er- orient ed m arket s, m anufact urers m ust offer product s in ever- increasing variet y, and t hat 's undercut t ing t he econom ies of scale associat ed wit h large product ion runs. Post ponem ent offers a way out of t his dilem m a by allowing t he specializat ion t o be done close t o t he cust om er. The plant regains econom ies of scale by producing large bat ches of generic product s, and cust om ers cont inue t o get t he variet y t hey want . Anot her benefit of post ponem ent is t hat it t akes advant age of risk pooling t o reduce invent ory requirem ent s. As described earlier in t he chapt er, risk pooling reduces t he need for safet y st ock by pulling m ult iple invent ories t oget her, allowing local variat ions in dem and t o cancel each ot her out . Post ponem ent achieves t he sam e effect by effect ively com bining t he invent ories for an ent ire fam ily of product s int o a single pool, significant ly reducing t he t ot al invent ory t hat m ust be held in each region. A closely relat ed benefit is t hat product ion can be based on aggregat e forecast s, which are always m ore accurat e t han det ailed forecast s ( see Chapt er 10 ) . I n effect , t he post ponem ent t echnique allows t he variat ions of a generic product t o be pulled t hrough t he chain by im m ediat e dem and rat her t han being pushed down t he chain on t he basis of uncert ain, it em - level forecast s. At t he sam e t im e, post ponem ent offers an econom ical way t o increase t he level of cust om izat ion by allowing m inor variat ions t o be det erm ined all t he way out t o t he point of sale. Post ponem ent is a form of design for supply, and it can rarely be accom plished wit hout t he use of som e of t he m ore basic t echniques described in t he preceding sect ion. Most im port ant , it requires product s t o be designed and const ruct ed in a m odular way, m aking it easy for downst ream facilit ies t o assem ble t he final configurat ion. I n t he case of com put er peripherals, for exam ple, it m ay be necessary t o redesign t he power supply as a plug- in m odule rat her t han building it int o t he chassis, or t o m ove t he logic t hat differs for PC and Mac peripherals off t he m ot herboard and int o an ext ernal connect or. Like design for supply in general, t he post ponem ent t echnique can require som e difficult organizat ional changes. One of t he obst acles t o HP's post ponem ent plan was t he resist ance of it s dist ribut ion cent ers t o get t ing involved in final assem bly, a very different act ivit y from t he st orage and handling operat ions t hey were accust om ed t o perform ing. I f t he dist ribut ion cent ers lack t he space, equipm ent , or skills necessary t o perform t he final assem bly, convert ing t o post ponem ent can lead t o increases in defect s, delays, and ot her product ion problem s. I f t ransform ing dist ribut ion cent ers int o final assem bly plant s isn't a viable opt ion, post ponem ent can st ill be applied wit hin t he m ain plant . I n t his case t he t echnique is based purely on t im e, and doesn't yield any savings downst ream from t he plant . However, t he increased flexibilit y t hat com es from delaying different iat ion m ay st ill j ust ify t he change. I n a classic exam ple, Benet t on changed t he sequence of operat ions involved in m aking it s sweat ers,

dying t he final sweat er rat her t han dying t he wool prior t o weaving. Alt hough t his change increased t he cost of product ion by 10% , it produced a net benefit because it allowed t he com pany t o respond m uch m ore quickly t o em erging preferences am ong colors. Conversely, if t he final configurat ion process is quit e sim ple, or if ret ailers have special skills, it 's possible t o ext end post ponem ent out t o t he point of purchase ( Figure 15.8) . This is oft en t he case wit h consum er elect ronics, which m ay have t o be configured wit h special cables or adapt ers, but it can also be seen in t he sale of bicycles, which are oft en specially configured for individual consum ers. I t is even possible for final configurat ion t o occur in t he consum er's own hom e, as in t he case of hom e ent ert ainm ent syst em s. But t he m odularizat ion needs t o be very good for t his opt ion t o be viable, as anyone who has st ruggled t o sort out t he funct ions of seven different rem ot es will readily t ell you.

Figure 15.8. Options for Postponement

Post ponem ent offers m any pot ent ial benefit s, but it 's not wit hout it s cost s. I n addit ion t o t he qualit y and organizat ional problem s t hat can arise when assem bly operat ions are pushed down t he supply chain, t he cost of perform ing t hese operat ions downst ream is alm ost always higher t han if t hey were perform ed at t he fact ory. I n addit ion, t he m odularizat ion of t he product and t he resequencing of operat ions m ay t hem selves increase t he cost of product ion, regardless of where final assem bly t akes place. These cost s all have t o be offset before t he t echnique can produce any net savings. Post ponem ent works best when t wo condit ions are m et : A large variet y of configurat ions can be derived from a com m on base product , and t he dem and across t hese configurat ions is hard t o predict . This is quit e com m only t he case wit h innovat ive product s such as clot hing and elect ronic consum er goods, which usually com e in a variet y of st yles, sizes, and colors, and are subj ect t o fads and fashions. But , as t he DeskJet st ory indicat es, even such relat ively m undane product s as print ers can exhibit enough variabilit y in dem and t o m ake t he t echnique advant ageous.

The m ost effect ive approach is t o use post ponem ent select ively across product lines, applying it only where t he advant ages out weigh t he cost . Bet t er st ill is t o apply it select ively w it hin a product line. I n t his variat ion of t he t echnique, you cont inue t o different iat e enough of t he product at t he fact ory t o sat isfy t he m inim al level of dem and you expect for each variat ion, t hen use post ponem ent for t he uncert ain port ion of dem and, where risk pooling works t o your advant age. This approach can offer t he best possible out com e: You keep t he bulk of your product ion in your fact ories where it is cheaper and easier t o m anage, you reduce safet y st ocks in your downst ream facilit ies, and you increase your abilit y t o respond t o unexpect ed dem and at t he point of sale. That com binat ion of benefit s provides an excellent exam ple of pushing t he efficient front ier int o new t errit ory. Ea ch of t h e t e ch n iqu e s de scr ibe d in t h is ch a pt e r —in cr e a sin g ve locit y, poolin g r isk , de sign in g for su pply, a n d post pon in g diffe r e n t ia t ion —ca n give you a sign ifica n t a dva n t a ge ove r you r com pe t it or s, bu t don 't t h in k of t h e m a s in de pe n de n t in it ia t ive s. Ra t h e r , t h in k of t h e m a s con ce pt u a l bu ildin g block s for im pr ovin g you r ch a in , a n d look for w a ys t o com bin e t w o, t h r e e , or e ve n a ll fou r of t h e m t o give you r se lf a s gr e a t a n a dva n t a ge a s possible . Be t t e r st ill, t h in k a bou t h ow t h e se fou r in it ia t ive s pu sh ou t t h e e fficie n t fr on t ie r , t h e n se e k ou t w a ys t o pu sh t h a t fr on t ie r e ve n fu r t h e r . Adopt in g t h e se t e ch n iqu e s ca n pu t you on t h e for e fr on t of su pply ch a in m a n a ge m e n t , bu t in ve n t in g n e w on e s is t h e su r e st w a y t o t u r n you r com pa n y in t o a t r u e le a de r .

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Part V. Design

Notes on Sources Sources indicat ed only by aut hor are list ed in t he Suggest ed Readings following t hese not es.

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Chapter 1 The New Competition 3

The st ory of Siem ens CT is from I ndust ry Week 's profile of it s winners of t he Best Plant s Awards, 2002, which can be found at w w w .indust r y w eek .com / iw inpr int / best plant s.

4

Gillet t e's revam ping of it s chain is described in " 10 Best Supply Chains," Supply Chain Technology News, Oct ober 2002.

4

The definit ive st udy of Chrysler's SCORE program is t he art icle by Jeffrey H. Dyer, " How Chrysler Creat ed an Am erican Keiret su," Harvard Business Rev iew, July–August 1996.

5

Apple's reconst ruct ion of it s supply chain is described by Doug Bart holem ew in " What 's Really Driving Apple's Recovery?" I ndust ry Week , March 15, 1999.

6

The im provem ent s in Am azon.com 's chain are described in " How Am azon Cleared That Hurdle," Business Week , February 4, 2002.

6

The figure for Dell's 5% m argins is from a m ust - read art icle by Miles Cook and Rob Tyndall, " Lessons from t he Leaders," Supply Chain Managem ent Rev iew, Novem ber–Decem ber 2001.

8

Michael Ham m er's charact erizat ion of t he supply chain com es from his book The Agenda: What Every Business Must Do t o Dom inat e t he Decade, New York: Crown, 2001.

8

The problem s wit h Km art 's supply chain are described in t wo art icles: " I T Difficult ies Help Take Km art Down," Com put er w or ld, January 28, 2002; and " Now in Bankrupt cy, Km art St ruggled wit h Supply Chain," I nfor m at ionWeek, January 28, 2002.

9

Nike's problem s wit h it s i2 inst allat ion are described in " Supply Chain Debacle," I nt ernet Week , March 1, 2001.

10 An analysis of Cisco's invent ory writ e- down can be found in Paul Kaihla, " I nside Cisco's $2 Billion Blunder," Business 2.0, March 2002.

11 The Georgia Tech analysis of supply chain problem s is report ed in Vinod R. Singhal and Kevin B. Hendricks, " How Supply Chain Glit ches Torpedo Shareholder Value," Supply Chain Managem ent Review, January–February 2002. 13 The analysis of supply chain cost s as a funct ion of GDP is from Robert Delaney's Annual St at e of Logist ics Report for 2001, " Underst anding I nvent ory—St ay Curious," present ed June 10, 2002, at t he Nat ional Press Club in Washingt on, DC. The report can be found at w w w .cassinfo.com / bob.ht m l. 13 The t wo- t o- one advant age in cost s bet ween average and best - in- class com panies is from Miles Cook and Rob Tyndall, " Lessons from t he Leader s," Supply Chain Managem ent Review, Novem ber–Decem ber 2001. 13 One survey indicat ing t hat t he gap bet ween average and best - in- class com panies wit h regard t o supply chain cost s is increasing is t he KPMG st udy report ed by Derek Slat er in " By t he Num bers," CI O Magazine, February 2000. 14 The exam ple of t he m aj or elect ronics com pany is from Miles Cook and Rob Tyndall, " Lessons from t he Leaders," Supply Chain Managem ent Rev iew, Novem ber–Decem ber 2001. 15 The list of pressures on supply chains is from Nat ional Research Council, Surviving Supply Chain I nt egrat ion: St rat egies for Sm all Manufact urers, Washingt on, DC: Nat ional Academ y Press, 2000, page 28. 15 The survey of execut ives is from George Taninecz, " Forging t he Chain," I ndust ry Week , May 15, 2000. 18 The quot e from t he Nat ional Research Council can be found in t heir Surviving Supply Chain I nt egrat ion: St rat egies for Sm all Manufact urers, Washingt on, DC: Nat ional Academ y Press, 2000, page 24.

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Chapter 2 The Rules of the Game 29 The exam ple of Johnson Cont rols building a seat is described in Robert Handfield and Ernest Nichols, Jr., I nt roduct ion t o Supply Chain Managem ent, Upper Saddle River, NJ: Prent ice Hall, 1999, page 8. 39 The audit of t he m aj or ret ailer t hat needed $200 m illion in safet y st ock is described in Miles Cook and Rob Tyndall, " Lessons from t he Leaders," Supply Chain Managem ent Review, Novem ber–Decem ber 2001.

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Chapter 3 Winning as a Team 46 The shut down of Toyot a's product ion lines is described in Rusht on, Oxley, and Croucher, page 223. 46 The short ages due t o flooding are described in Nat ional Research Council, Surviving Supply Chain I nt egrat ion: St rat egies for Sm all Manufact urers, Washingt on, DC: Nat ional Academ y Press, 2000, page 32. 46 The effect s of t he t errorist at t acks of Sept em ber 11, 2001, including t he cost s of shut t ing down plant s and Ford's m easures t o reduce risks, are described in " Sept . 11 At t acks Reveal Supply- Chain Vulnerabilit ies," ZDNet Tech Updat e, Oct ober 10, 2001. 46 Honda's use of dual suppliers is described in Rusht on, Oxley, and Croucher, page 223. 51 The problem s in adopt ing CPFR are described in Carol Sliwa, " CPFR Clam or Persist s, but Adopt ion Rem ains Slow," Com put er world, June 28, 2002. 51 The st udy of invent ory levels conduct ed at Ohio St at e is report ed in Jam es Gint ner and Bernard LaLonde, " An Hist orical Analysis of I nvent ory Levels: An Explorat ory St udy," Novem ber 2001. The art icle is available at w w w .m anufact ur ing.net. The quot e is from t he sam e source. 53 The st at ist ics on t he scale of Wal- Mart 's operat ions are t aken from Owen Thom as, " Lord of t he Things," Business 2.0, March 2002. 55 The descript ion of how U.S. aut o plant s have displaced invent ory out t o dealerships is from Marshall L. Fisher, " What I s t he Right Supply Chain for Your Product ?" Harvard Business Review , March–April 1997. 67 The survey indicat ing t hat m ost supply chain init iat ives are int ernal t o a single com pany is from Miles Cook and Rob Tyndall, " Lessons from t he Leader s," Supply Chain Managem ent Review, Novem ber–Decem ber 2001.

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Chapter 6 Supply Chain Software 124 The num ber of t ransact ions per day at I ngram Micro is from Christ opher Koch, " Four St rat egies," CI O Magazine, Oct ober 1, 2000.

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Chapter 7 Meeting Demand 143 The figures on t he num ber of pallet s in a warehouse are from Rusht on, Oxley, and Croucher, page 230. 144 The figures on how pickers spend t heir t im e are from Rusht on, Oxley, and Croucher, page 287. 148 The t able on ent ry error rat es is from Rusht on, Oxley, and Croucher, page 331.

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Chapter 8 Maintaining Supply 170 The Forrest er num bers on how m any com panies are using exchanges com es from Miles Cook and Rob Tyndall, " Lessons from t he Leaders," Supply Chain Managem ent Review, Novem ber–Decem ber 2001.

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Chapter 9 Measuring Performance 176 The figures for cash- t o- cash cycle t im es com e from George Taninecz, " Forging t he Chain," I ndust ry Week , May 15, 2000. 185 The num ber of invent ory t urns for Lear com es from David Ross, Com pet ing Through Supply Chain Managem ent : Creat ing Market Winning St rat egies Through Supply Chain Part nerships, Dordrecht , The Net herlands: Kluwer Academ ic Publishers, 1998, page 220. 186 The st udy of t he aut om ot ive indust ry in England can be found in David Taylor and David Brunt , Manufact uring Operat ions and Supply Chain Managem ent : The LEAN Approach, Thom pson Learning, 2001, page 133. ( Not t he sam e David Taylor who wrot e t his m anager's guide.) 191 The source for Cat erpillar sending out 90,000 quest ionnaires per year is Donald V. Fit es, " Make Your Dealers Your Part ners," Harvard Business Rev iew, March–April 1996.

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Chapter 10 Forecasting Demand 208 The concept of t he t ipping point is best explained by Malcolm Gladwell in his com pelling book, The Tipping Point : How Lit t le Things Can Make a Big Difference, New York: Lit t le, Brown and Com pany, 2002. 209 The m odel of consum er choice is described by Paul Orm erod in But t erfly Econom ics: A New General Theory of Social and Econom ic Behavior, New York: Pant heon Books, 1998. 213 The st udies showing t he advant ages of collaborat ive forecast ing are cit ed in David Ross, Com pet ing Through Supply Chain Managem ent : Creat ing Market - Winning St rat egies Through Supply Chain Part nerships, Dordrecht , The Net herlands: Kluwer Academ ic Publishers, 1998, page 218.

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Chapter 12 Improving Performance 237 The st udy on how m any m easures com panies use is cit ed in Miles Cook and Rob Tyndall, " Lessons from t he Leaders," Supply Chain Managem ent Rev iew, Novem ber–Decem ber 2001 239 The benchm ark figures com e from George Taninecz, " Forging t he Chain," I ndust ry Week , May 15, 2000. 246 The observat ions on how ineffect ively incent ives are used are from Miles Cook and Rob Tyndall, " Lessons from t he Leaders," Supply Chain Managem ent Review, Novem ber–Decem ber 2001. 247 The observat ion regarding servicing cust om ers in order of t heir profit pot ent ial com es from David L. Anderson and Allen J. Delat t re, " Predict ions That Will Make You Ret hink Your Supply Chain," Supply Chain Managem ent Review , Sept em ber–Oct ober 2002.

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Chapter 13 Mastering Demand 264 The cost figures for shipping sugar from Hawaii in bulk rat her t han bags are from Sim chi- Levi, Kam insky, and Sim chi- Levi, page 177.

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Chapter 14 Designing the Chain 280 The prim ary reference for t he st rat egic t radeoff bet ween efficiency and flexibilit y is Marshall L. Fisher, " What I s t he Right Supply Chain for Your Pr oduct ?" Harvard Business Review , March–April 1997. Chopra and Meindl explore t he issue on page 33ff.

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Chapter 15 Maximizing Performance 301 The announcem ent of Benet t on's use of RF t ags appeared in t he San Francisco Chronicle, March 12, 2003. 302 The average reduct ions in invent ory due t o risk pooling are from Sim chiLevi, Kam insky, and Sim chi- Levi, page 59.

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Part V. Design

Suggested Readings I nt erm ediat e Level Advanced Level Collect ions of Art icles

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Intermediate Level David Sim chi- Levi, Philip Kam insky, and Edit h Sim chi- Levi, Designing and Managing t he Supply Chain: Concept s, St rat egies, and Case St udies. New York: I rwin McGraw- Hill, 2000. A clear, aut horit at ive t ext writ t en for an execut ive- level course, t his highly regarded book is an excellent next st ep for m anagers who want t o learn m ore about supply chain st rat egy and advanced t echniques. Alan Rusht on, John Oxley, and Phil Croucher, The Handbook of Logist ics and Dist ribut ion Managem ent (2nd Ed.) . London: Kogan Page, 2000. This book offers a det ailed, pragm at ic exam inat ion of t he t act ical and operat ional issues in m anaging a supply chain, support ed by num erous phot ographs and real- world exam ples. Mart in Christ opher, Logist ics and Supply Chain Managem ent : St rat egies for Reducing Cost and I m proving Service (2nd Ed.) Upper Saddle River, NJ: Financial Tim es Prent ice Hall, 1998. A syst em at ic, readable t reat m ent t hat offers valuable insight s int o t he m anagem ent processes necessary t o im plem ent advanced supply chain t echniques.

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Advanced Level Sunil Chopra and Pet er Meindl, Supply Chain Managem ent : St rat egy, Planning, and Operat ion. Upper Saddle River, NJ: Prent ice Hall, 2001. This com prehensive t ext book m ay be slow going for som e m anagers, but it offers a high payoff in t erm s of art iculat e, pract ical guidance on all aspect s of supply chain m anagem ent , and it s excellent organizat ion m akes it easy t o read select ively. Jerem y Shapiro, Modeling t he Supply Chain. Pacific Grove, CA: Duxbury/ Wadswort h Group, 2001. For t hose wit h t he necessary background, t his is t he best available t reat m ent on t he applicat ion of linear program m ing and ot her m at hem at ical t echniques t o supply chain m anagem ent .

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Collections of Articles John A. Woods and Edward J. Marien, The Supply Chain Yearbook , 2001 Edit ion . New York: McGraw- Hill, 2001. Com bines reprint s of m any cont em porary art icles from m anagem ent periodicals wit h an excellent com pendium of associat ions, Web sit es, j ournals, and ot her useful resources. Harvard Business Review on Managing t he Value Chain. Harvard Business Review, 2000. Eight reprint ed art icles on supply chain m anagem ent published in HBR bet ween 1993 and 2000.

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Glossary activity-based costing (ABC) A technique for allocating indirect costs to production activities, making indirect costs more comparable to direct costs and permitting a better assessment of the true cost of creating each product.

advanced planning and scheduling (APS) system A type of software that uses mathematical models and related techniques to find optimal solutions to complex production and supply problems. See also [optimizer] See also [linear programming] advance shipping notice (ASN) A document sent by a supplier to a customer to indicate when an order will be shipped. ASNs are usually transmitted electronically.

aggregate forecast A forecast based on product or customer data that has been grouped by similarity. See also [aggregation] aggregation The practice of grouping similar products or customers to simplify planning and achieve more stable forecasts.

assemble-to-order strategy The practice of building product components in advance of demand, but postponing final assembly until demand is realized. An intermediate strategy between the make-to-stock and make-to-order strategies.

available to promise (ATP) The inventory status of a product that is currently on hand and available for immediate shipment. See also [capable to promise] back scheduling The practice of scheduling activities by working backward from the planned completion date, adding activities to the schedule in the reverse order in which they will be executed. See also [forward scheduling] backhaul A shipment that moves in the opposite direction along a route just taken by a vehicle in making a delivery, allowing it to make use of its hauling capacity on the return trip.

bill of lading A document listing all the goods contained within a shipment and stating the terms governing its transportation. Some bills of lading also serve as title to the goods.

bill of materials (BOM) A listing of the parts and materials that become part of a finished product, organized in a hierarchical structure that reflects their components, subassemblies, or intermediate forms.

bill of operations (BOO) A list of the procedures necessary to produce a finished product from its constituent materials, organized as a hierarchical structure that reflects the sequence in which these procedures must be carried out.

bullwhip effect An alternative name for demand amplification.

capable to promise (CTP) The inventory status of a product that is not immediately on hand but that can be produced within the required fulfillment lead time. See also [available to promise] captive exchange A private electronic exchange that is owned by one or more of the participating organizations and restricted to selected trading partners of the owning organizations. See also [private exchange] See also [public exchange] carrier A company that specializes in transporting goods.

carrying cost The incremental cost of placing orders due to increases in product quantities. So named because the majority of this variable cost is the expense of carrying inventory that is not immediately consumed. Also known as holding cost. See also [order cost] cash-to-cash time A measure of the efficiency with which cash is used in the business. Calculated as the interval between the time a company pays for raw materials and the time it receives payment for the finished goods produced from those

materials.

category management The practice of organizing inventory management, promotions, and related activities around products that consumers view as roughly equivalent in meeting their needs.

collaborative planning, forecasting, and replenishment (CPFR) A multi-industry program that uses the Internet to achieve cooperation across the members of a supply chain to better forecast, plan, and execute the flow of goods.

conceptual model A representation of a real-world system, such as a supply chain, that is constructed out of terms and concepts of the sort listed in this glossary. Conceptual models are expressed as diagrams and descriptions. See also [mathematical model] See also [simulation model] confidence interval A range of numbers within which a predicted value will fall with a specified probability. For example, 9 out of 10 observations will fall within a 90% confidence interval. Confidence intervals are often shown in graphs as small bars above and below the expected value to indicate the range of likely values.

consignment An inventory control practice in which a supplier maintains ownership of inventory on a customer's site until the inventory is sold, monitoring its level and replenishing it as needed.

constraint

In an optimization procedure, a mathematical expression or equation that restricts the range of solutions the method will evaluate. A typical constraint would be an upper bound on capital spending in the design of a supply chain. See also [linear programming] consumer The individual or organization that acquires a product in order to use it for its intended purpose rather than reselling it to someone else. As the terms are used in this book, a consumer is a special type of customer.

continuous replenishment (CR) program An extension of the quick response (QR) program to cover the full range of retail merchandise and to add the techniques of supplier forecasting and vendor-managed inventory.

continuous review An inventory replenishment policy in which a continuous count of inventory is maintained at all times, with orders being placed whenever the count falls below a set threshold. See also [periodic review] cross dock A specialized facility for transferring in-transit inventory between trucks. Typically a long building consisting primarily of receiving docks on one side, shipping docks on the other, and assembly areas between the two. Although nominally a type of storage facility, cross docks do not usually hold goods for more than 24 hours.

cross docking The practice of using cross docks or distribution centers to reallocate shipments across trucks en route from suppliers to customers, allowing each truck to remain full throughout its journey. Products are moved directly from receiving docks to shipping docks, with no intermediate storage.

customer The individual or organization that purchases a product or service in a supply chain transaction. The term is used inconsistently throughout the business literature, leading to unproductive debates over who the "real" customer is. In this book, the term is used to denote a role within a transaction and can be applied to any link in the chain. In this usage, the final customer is the consumer at the end of the supply chain.

customer schedule A special format for an order spanning multiple shipments in which line items are grouped by delivery date.

customer service level (CSL) The target level of product availability for a particular region and product. Service level can be specified in wide a variety of ways, ranging from the maximum distance of inventory from a customer's site to the percent of orders that can be filled from inventory within a specified time.

cycle stock The amount of inventory required to support the operations of a facility, with no reserve to cover unforeseen events. See also [safety stock] cycle time This term is used to denote either (a) the interval between successive repetitions of a cyclical process, as in the cycle time of a machine or assembly line, or (b) the duration of a business process. These conflicting definitions lead to confusion and reduce the value of the term.

days on hand A measure of inventory level, calculated by dividing the quantity on hand by the average daily consumption. Provides the same information as the inventory turnover ratio but in a form more suitable to high-turn

environments.

delayed differentiation A technique in which products with characteristics in common are left in their common form until demand is realized, allowing a better match of production to realized demand. Also called postponement.

Delphi technique A procedure in which forecasts generated by multiple analysts are repeatedly combined and reviewed until a consensus forecast is reached.

demand amplification The tendency for fluctuations in demand to increase as they move up the supply chain. Often referred to as the bullwhip effect in recent literature.

demand lumping A phenomenon in which an otherwise smooth flow of demand up a supply chain is grouped into larger chunks than is necessary to meet operational requirements. Demand lumping is a major contributor to demand amplification. It is known to be caused by batching, forward buying, and hoarding.

dependent demand The demand for a product from customers who are not the end consumers of that product. So named because this demand ultimately depends on consumer demand. See also [independent demand] design for supply The practice of engineering a product in a way that facilitates its flow through the supply chain.

direct shipment A distribution practice in which goods that would normally move by way of a warehouse or distribution center are transported directly from a supplier to a customer.

distribution center (DC) A storage facility in which goods may be staged, sorted, assembled, packaged, and/or stored temporarily as they pass through a particular segment of a supply chain. Distribution centers differ from warehouses primarily in the focus on facilitating distribution rather than holding inventory.

distribution network The set of facilities and lanes that transports finished goods from a production facility to the downstream customers of that facility. A distribution network may be divided into echelons.

dynamic forecasting The practice of revising current forecasts at the end of each period to incorporate the data for that period rather than leaving these forecasts unchanged over successive periods. See also [static forecasting] echelon In a distribution network, a set or layer of facilities functionally equidistant from the production facility that serves them. Comparable to a tier in a procurement network.

echelon inventory When centrally managed, the total inventory distributed across the echelons of a distribution network.

economic order quantity (EOQ) The calculated amount of inventory that should be ordered at one time to minimize the total cost of replenishment, taking into account the opposing effects of order costs and holding costs.

efficient consumer response (ECR) A supply-chain program used in the grocery industry that combines rapid retail replenishment with the techniques of category management and activity-based costing.

efficient frontier A curve describing the most advantageous possible combination of cost and flexibility in a supply chain. This curve is constantly being advanced by best practices in supply chain management.

electronic auction An auction conducted entirely over the Internet, with sellers submitting products to a Web site and buyers using e-mail or Web browsers to place their bids.

electronic catalog A directory of products stored in digital form, usually accessible over the Web, that provides access to product by type and supplier.

electronic data interchange (EDI) A set of protocols for transferring information regarding demand and supply over private electronic networks.

electronic distribution The practice of shipping products in electronic form across the Internet or other electronic medium. Electronic distribution is used for music, documents, software, photographs, tickets, and other products that can be transmitted in digital form.

electronic exchange A digital marketplace, accessible over the Web, that brings together buyers and sellers of a particular type of product and provides them with tools for carrying out transactions.

enterprise resource planning (ERP) system A suite of software that combines tactical-level applications for production and distribution planning with execution systems for order management, inventory control, accounting, and related operations.

external supply chain The portion of a supply chain that spans facilities outside the ownership boundaries of a particular company. See also [internal supply chain] extractor A special kind of supplier that takes raw materials from the earth in either living or inert form. Examples include mines, saw mills, farms, and ranches.

extrinsic factor An influence on demand or some other supply chain characteristic that is beyond a firm's control, such as the state of the economy or the actions of a competitor.

feedback

A physical or information flow from the output of a system into the input side of that system. The appropriate use of feedback is essential for regulating the behavior of a system. See also [positive feedback] See also [negative feedback] finished goods inventory The store of completed products on the output side of a production facility.

forecast horizon The date furthest in the future for which events are predicted in a forecast.

formal model A business model that can be expressed in mathematical or executable form, allowing it to generate numerical predictions from a set of inputs. Of the three types of models discussed in this book, mathematical and simulation models are formal while conceptual models are not.

forward buying The practice of buying supplies before they are needed to take advantage of favorable prices or avoid potential shortages.

forward scheduling The practice of scheduling activities by beginning with the planned start date and adding activities to the schedule in the order in which they will be executed. See also [back scheduling] fulfillment cycle The sequence of events in a supplier organization that manage the three key flows in the fulfillment process: order flow, product flow, and cash flow.

fulfillment lead time The interval between the time an order is placed with a supplier and the time the goods are received by the customer.

full pallet A pallet of goods that contains only a single kind of product. See also [mixed pallet] full truckload (FTL) shipment A shipment of goods that consumes the capacity of a truck, requiring the truck to be dedicated to the shipment. See also [less-than-truckload (LTL) shipment] hill-climbing A technique used to search for a superior configuration of a system such as a supply chain by making a series of small, beneficial changes to the system until no further improvements appear to be possible.

holding cost The incremental cost of placing orders due to increases in product quantities. So named because the majority of this variable cost is the expense of holding inventory that is not immediately consumed. Also known as carrying cost. See also [order cost] independent demand The demand for a product on the part of its end consumers. So named because it is the ultimate source of demand, and doesn't depend on a source of demand further down in the supply chain. See also [dependent demand] inter-modal transportation The practice of using more than one medium of transportation, such as rail and ship, within a single shipment.

internal supply chain The portion of a supply chain that joins the facilities owned by the same company. See also [external supply chain] in-transit inventory Inventory that is currently in a transportation lane between two facilities.

intrinsic factor An influence on demand or some other supply chain characteristic that is within a firm's control, such as the price of a product or the speed of delivery.

inventory turnover ratio A measure of how quickly inventory is used once it arrives at a facility, calculated as the annual sales of a product divided by its average inventory level.

inventory turns Shorthand for inventory turnover ratio.

inventory velocity The speed with which inventory moves through the supply chain. Despite the way the term is commonly used, it does not represent a measure of performance, and companies that seek to increase their inventory velocity continue to rely on such traditional measures as the inventory turnover ratio and days on hand.

item fill rate The percentage of line items, calculated across all orders, for which the full quantity of the requested product is available for immediate shipment.

See also [order fill rate] judgmental techniques The collection of forecasting techniques based on cause-and-effect reasoning rather than statistical analysis. Also known as subjective techniques.

just-in-time (JIT) manufacturing The practice of reducing inventory levels by scheduling materials to arrive just as they are needed in the production process. More broadly, a comprehensive program for improving manufacturing operations to yield higher quality products at reduced expense.

keiretsu The Japanese term for a type of integration in which a manufacturing firm takes partial ownership positions in key suppliers and appoints its own personnel to some management positions.

less-than-truckload (LTL) shipment A shipment of goods that consumes only a fraction of the capacity of a truck, requiring that the truck be shared with other shipments. See also [full truckload (FTL) shipment] level component In time-series analysis, the portion of the forecast demand that is constant and unvarying. See also [trend] See also [seasonal] See also [random components] linear programming (LP) A technique for finding optimal solutions to mathematical models in which all relations between inputs and outputs are linear in form.

make-to-order strategy The practice of making products in response to realized demand rather than making them to stock in advance of demand.

make-to-stock strategy The practice of making products in advance of demand and holding them in finished goods inventory until demand is realized.

mathematical model A representation of a real-world system, such as a supply chain, that is constructed out of mathematical terms and relations. Mathematical models are expressed as formulas and/or procedures for solving equations to predict the behavior of the system. See also [conceptual model] See also [simulation model] mean absolute percentage error (MAPE) A measure of the average deviation between forecast values and their corresponding observed values, regardless of the direction (sign) of those deviations. See also [tracking signal] merge in transit A technique in which separate shipments are combined en route and delivered as a single unit.

mixed pallet A pallet of goods that contains two or more kinds of products. See also [full pallet] mode of transportation The medium by which a vehicle moves products from one facility to another. The primary modes are truck, rail, boat, barge, airplane, and pipeline.

Monte Carlo method The technique of running a simulation model repeatedly using random variables on each run in order to understand behavior of the model across normal variations of business conditions.

moving average The mean value obtained by summing the last N values of a measure and dividing by N, where N is set according to need. Used in forecasting and other applications to obtain a typical value for recent observations of some measure. Increasing the value of N produces more stable values that are less sensitive to recent changes.

negative feedback A form of feedback in which movement of an output of a system in a particular direction is decreased, decelerating that movement. Negative feedback usually leads to stable, bounded outputs that facilitate control of a system. See also [positive feedback] objective function In linear programming, the equation that defines the quantity being optimized, such as total cost or a weighted combination of cost and other performance measures.

on-time delivery A measure of fulfillment effectiveness, calculated as the percentage of orders that arrive at the customer site within the agreed-upon time.

optimization Using a mathematical or procedural technique to explore the space of all possible configurations of a system and identify the configuration that maximizes (or minimizes) a designated output measure. Optimization is

usually carried out using a specialized program called an optimizer. See also [linear programming] optimizer A software program capable of automating the process of optimizing a system using a particular mathematical or procedural technique. See also [optimization] See also [linear programming] order cost The fixed cost of placing an order, regardless of the quantities involved. See also [holding cost] order fill rate The percentage of orders for which the full quantities of all products on the order are available for immediate shipment. See also [item fill rate] packing slip A document enclosed with a shipment that lists the goods included in that shipment together with information about the origin, destination, and means of transport.

parameter A quantity whose value is set prior to performing an analysis that depends on that quantity. Example: Order cost and holding cost are parameters used in the calculation of economic order quantity.

Pareto Analysis A technique for analyzing sales data to determine the extent to which a small number of products accounts for the majority of sales. A common result, often stated as the 80:20 rule, is that 80% of sales come from 20% of the products.

perfect order

A measure of fulfillment effectiveness, calculated as the percentage of orders that ship complete, arrive on time, contain the correct goods, are free of damage, and have accurate paperwork.

periodic review An inventory replenishment policy in which inventory is counted at fixed intervals and orders are placed whenever the current count falls below a set threshold. See also [continuous review] point of sale (POS) system A software application that prices and records the sale of products to customers who are physically on site and take immediate possession of their purchases.

positioning strategy The set of attributes on which a company chooses to differentiate itself from its competition, together with methods for improving those attributes and communicating them to potential customers. In the manufacturing sector, the most common attributes are quality of product, quality of service, and price.

positive feedback A form of feedback in which movement of an output of a system in a particular direction is increased, accelerating that movement. If unchecked by other mechanisms, positive feedback usually leads to exponential growth and "out of control" behavior. See also [negative feedback] postponement An alternate term for delayed differentiation.

primary packaging The level of packaging that immediately encloses a product, such as a bottle,

box, can, or blister pack. See also [secondary packaging] See also [transport packaging] private exchange An electronic exchange with membership rules that exclude parties that would otherwise be qualified to buy and sell the products handled on the exchange. See also [public exchange] procurement network The set of facilities and lanes that transports raw materials to a production facility from the upstream suppliers of that facility. A procurement network may be divided into tiers.

production facility A facility that exists primarily to create products from raw materials, storing materials and products only as necessary to support production operations. See also [storage facility] public exchange An electronic exchange that is open to all qualified buyers and sellers of the products handled on the exchange. See also [private exchange] pull chain A supply chain in which inventory is produced only in response to realized demand at each stage of the chain, with product being "pulled" down the chain by actual orders.

push chain A supply chain in which inventory is produced in advance of demand and "pushed" down the chain toward the consumer.

push-pull boundary

The point in a supply chain in which the driving force switches from pull to push, with pull operating downstream to the consumer and push acting upstream to the extractor.

quick response (QR) A supply chain program on the part of the apparel industry that applied justin-time (JIT) techniques to retail replenishment.

random component In time-series analysis, the variability in demand that remains after the systematic components have been removed. In other words, the aspect of demand that can't be forecast by the model.

raw materials inventory The inventory of incoming materials maintained at a production facility for use in the production process.

relation In systems, a mapping of inputs to outputs that yields one or more outputs for any given input. Relations are usually described by one or more lines in a graph, and they range in form from straight lines (linear relations) to complex curves.

reorder point (ROP) The level or count at which the inventory for a particular product is replenished.

replenishment cycle The sequence of events within a customer organization that manage the three key flows in the replenishment process: order flow, product flow, and

cash flow.

replenishment lead time The interval between the time a company places an order for raw materials and the time it receives those materials.

replenishment policy The set of rules by which a firm decides when to replenish its inventory, how large to make its orders, and how much inventory to maintain on site.

reverse auction An auction in which customers post requests for quotes and suppliers bid against each other to win the business.

risk pooling An inventory management technique in which the safety stock necessary to handle expected fluctuations in supply and demand is reduced by treating two or more physically separate inventories as a single logical inventory.

safety stock The amount of inventory that must be maintained in order to handle fluctuations in supply and demand. See also [cycle stock] seasonal component In time-series analysis, the portion of the forecast demand that varies in a cyclical manner over the course of the year. See also [level] See also [trend] See also [random components] secondary packaging

The level of packaging that groups a standard number of primary packages together for convenience in handling, storage, and sales. The most common form of secondary packaging is the carton.

sell-through The amount of stock acquired by a customer under a promotion that is passed on to that customer's customers during the promotional period. Suppliers may limit the amount of product a customer can buy under a promotion to the sell-through amount in order to reduce forward buying.

ship complete A constraint placed on an order that requires all items in the order to arrive in a single shipment.

shrinkage The reduction in inventory that occurs through pilferage, misplacement, and related forms of attrition.

simulation model A representation of a real-world system, such as a supply chain, that is constructed out of software objects that represent real-world objects. Simulation models are expressed as computer programs that execute the models to observe their expected behavior. See also [conceptual model] See also [mathematical model] static forecasting The practice of generating a forecast and then leaving it unchanged until a new forecast is created. See also [dynamic forecasting] stockout The situation in which there is not enough inventory on hand to fill a

received order.

storage facility A facility that exists primarily to hold goods in anticipation of future demand. Some storage facilities may also perform final assembly and packaging in order to move these operations closer to the end consumer. See also [production facility] subjective techniques The collection of forecasting techniques based on cause-and-effect reasoning rather than statistical analysis. Also known as judgmental techniques.

supplier The organization that provides a product or service in a supply chain transaction. In this book, the term is used as a counterpart to customer, denoting a role within a transaction that can be applied to any link in the chain. In some contexts, the term refers specifically to companies that provide raw materials and is not applied to downstream members of the chain.

supply chain A network of facilities and transportation lanes that transforms raw materials into finished products and delivers those products to consumers.

supply chain management (SCM) The set of activities involved in designing, planning, and executing the flow of demand, supply, and cash across a supply chain.

systematic component In time-series analysis, any component of demand (level, trend, or seasonal) that can be predicted from the model. In other words, everything but the random component.

See also [level] See also [trend] See also [seasonal components] tier In a procurement network, a set or layer of facilities functionally equidistant from the production facility they serve. Comparable to an echelon in a distribution network.

time-series analysis A forecasting technique in which future values of a measure are predicted from a mathematical analysis of historical values of that measure.

tipping point A phenomenon observed in the spread of ideas in which the prevalence of the idea makes a sudden leap from a slow-growth curve to a different, fastgrowth curve. Originally discovered in the study of infectious diseases and subsequently found to apply to product sales, crime waves, and other social activities.

tracking signal A measure of the bias of a forecast to either overestimate or underestimate the observed value. See also [mean absolute percentage error (MAPE)] transport packaging A level of packaging, such as a pallet, that is added to facilitate shipping and storing large quantities of product. See also [primary packaging] See also [secondary packaging] transportation lane A designated pathway for moving goods from one facility to the next within in a supply chain. Lanes are categorized as highways, railways, waterways, air lanes, and pipelines.

transshipment A technique in which goods are shipped laterally within the same echelon of a distribution system, such as between warehouses or between retail stores.

trend component In time-series analysis, the portion of the forecast demand that shows a constant, linear increase over time. See also [level] See also [seasonal] See also [random components] turn-and-earn system A policy in which suppliers limit customer purchases to the quantity of goods they "turn" by shipping them out as finished goods to their own customers. Used to reduce hoarding during periods of limited availability.

vendor-managed inventory (VMI) An inventory control practice in which a supplier monitors and replenishes inventory on a customer's site.

vertical integration The practice of owning facilities across a large segment of a supply chain in order to control as much of the chain as possible. See also [virtual integration] virtual integration A practice in which members of a supply chain collaborate closely with each other in order to gain the benefits of centralized supply chain management while retaining independent ownership and control. See also [vertical integration] warehouse

A storage facility that holds controlled quantities of goods in a particular location within a supply chain. See also [distribution center] Web services A set of technologies that allows software programs to invoke each other's functions using XML and standard Internet protocols.

work-in-process (WIP) inventory Inventory currently being used in a production process or held for use within the production area. Includes all materials that have been removed from raw materials inventory but not yet deposited in finished goods inventory.

XML The extensible markup language for communicating data in a structured format over the Internet.

zero-sum game Any interaction between two parties in which the total gain across the two is fixed, leaving the parties to compete with each other over their relative shares of that gain. Many supply chain relationships that are traditionally viewed as zero-sum interactions are actually much richer than this, including outcomes in which the total gain can be increased or decreased depending on how the parties conduct themselves.

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