Introduction to Supply Chain Resilience: Management, Modelling, Technology 3030704890, 9783030704896

Citation preview

Classroom Companion: Business

Dmitry Ivanov

Introduction to Supply Chain Resilience Management, Modelling, Technology

Classroom Companion: Business

The Classroom Companion series in Business features foundational and introductory books aimed at students to learn the core concepts, fundamental methods, theories and tools of the subject. The books offer a firm foundation for students preparing to move towards advanced learning. Each book follows a clear didactic structure and presents easy adoption opportunities for lecturers.

More information about this series at http://www.­springer.­com/series/16374

Dmitry Ivanov

Introduction to Supply Chain Resilience Management, Modelling, Technology

Dmitry Ivanov Department of Business and Economics Berlin School of Economics and Law Berlin, Germany

ISSN 2662-2866     ISSN 2662-2874 (electronic) Classroom Companion: Business ISBN 978-3-030-70489-6    ISBN 978-3-030-70490-2 (eBook) https://doi.org/10.1007/978-3-030-70490-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

V

Preface Resilience is of vital importance for design and management of viable value creation networks. The design and management of not only an efficient but a resilient supply chain capable of operations and demand fulfilment continuity despite severe disruptions is imperative and has been highlighted in literature and practice alike for the last two decades. However, the COVID-19 pandemic has unveiled the lack of resilience in many supply chains, as complex networks failed from disruptions at local nodes, their propagation (i.e., the ripple effect), and the resulting missing connectivity. Supply chain resilience theory explains how complex networks can maintain connectivity, survive, and recover during and after disruptions and severe crises. At the same time, since supply chains are a backbone of economy, providing markets and society with goods and services (e.g., food, communication, and mobility) in a non-­interrupted manner despite disruptions and crises is directly connected to supply chain resilience. If supply chains are not resilient, if they disconnect and collapse when facing disruptions and severe crises, then we all will be at risk of shortages of critical products and services needed for our life. Major local and global disruptions (e.g., natural and man-made catastrophes, strikes, financial crises, and epidemic outbreaks) have significant adverse effects on corporate performance, particularly for businesses with complex, global supply chains. Managing disruptions and their associated effects is therefore a key focus for firms posing resilience as a central determinant in supply chain management. Supply chain resilience is a firm’s capability to withstand, adapt, and recover from disruptions to meet customer demands, maintain some target performance and operations continuity in a vulnerable environment. Resilience reflects the supply chain’s systemic ability to absorb negative external disruptions and restore normal operations. The fundamentals of supply chain resilience theory have been developed in response to more and more frequent natural and man-made disasters in the first two decades of the twenty-first century. This knowledge has helped many firms to cope with severe supply chain disruptions. On a larger scale, the importance of supply chain resilience became highly evident during the COVID-19 pandemic that has changed the operational conditions of many firms and supply chains on an unprecedented scale. During the pandemic, companies have extensively dealt with the concept of supply chain resilience as one of the central supply chain management perspectives. Moreover, the COVID-19 pandemic has unveiled a new setting for supply chain resilience, that is, the supply chain crisis and the associated notions of supply chain viability and survivability, which goes beyond an event-driven disruption context of resilience. Despite the increasing interest in and importance of supply chain resilience, there is no concise and generally understandable source which could be taken as an introduction to supply chain resilience. This book offers an introduction to supply chain resilience in a concise but comprehensive form, covering management,

VI

Preface

­ odeling, and technology perspectives. It is also designed as a supplementary satm ellite book for the textbook Global Supply Chain and Operations Management (Ivanov et al. 2021), advancing the chapters devoted to supply chain risks and resilience. In this book, we offer an introduction to major concepts and principles of supply chain resilience. The book aims at delineating major features of supply chain resilience and explaining methodologies to mitigate supply chain disruptions and recover. Throughout the book, numerous practical examples and short case studies are provided to illustrate theoretical concepts. It also reviews and explains novel frameworks and concepts related to viable supply chains and the ripple effect. Without relying heavily on mathematical derivations, the book offers a structured presentation and explanation of major concepts and methods to build/improve supply chain resilience and tackle disruption risks in a simple, predictable format to make it easy to understand for students and professionals with both management and engineering backgrounds. Thus far, the book conceptualizes supply chain resilience and the related concepts such as disruption management, ripple effect, and viability. Graduate/PhD students and supply chain professionals alike would benefit from a structured and didactically oriented concise presentation of the concepts, principles, and methods to manage supply chain resilience. Providing graduate students and supply chain risk managers with working knowledge on contemporary theory of supply chain resilience, this book contributes to building more resilient supply chains to ensure survivability of companies and non-interrupted flows of products and services to markets. The author wishes to thank Dr. Christian Rauscher, Executive Editor Business/ OR/MIS at Springer Nature; Mrs. Jialin Yin and Mrs. Faith Su, Associate Editors at Springer Nature; and the entire Springer production team for their assistance and guidance in successfully completing this book. We also thank all colleagues working in supply chain resilience area for publishing their works, sharing the knowledge, and helping build more resilient supply chain networks with the help of their research results. Last but not least, I cordially thank my family who supported me enormously during our work on the book. Dmitry Ivanov

Berlin, Germany January 2021

VII

Abbreviations BCT

business continuation time

BDA

big data analytics

BI

business intelligence

BIBO bounded-in-bounded-out BIV

business impact value

BN

Bayesian network

CPT

conditional probability table

DAG

directed acyclic graph

DC

distributions center

DP

daily profit

DSS

decision-support system

ERP

enterprise resource planning

HILF high-impact-low-frequency ISN

intertwined supply network

KPI

key performance indicator

LIHF

low-impact high frequency

OEM

original equipment manufacturer

PML

possible maximum loss

RBIT

residual business interruption time

RFID

radio-frequency identification

SE SIR

supplier exposure supplier importance ratio

T&T

tracking and tracing

TTR time-to-recover TTS time-to-survive VSC

viable supply chain

VSM

viable system model

IX

Contents 1

Supply Chain Risks, Disruptions, and Ripple Effect . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Uncertainty and Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1.1 Definition of Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1.2 Definition of Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 Disruption Risks in Supply Chains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.2.1 Supply Chain Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.2.2 Definition and Classification of Disruptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.3 Ripple Effect in Supply Chains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.3.1 Definition of the Ripple Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.3.2 Reasons and Countermeasures for the Ripple Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.3.3 Disruption Tails and Overlays: When Ripple Effect and Bullwhip Effects Intersect . . . . 18 1.4 Super Disruptions and Supply Chain Crises: Example of the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.5 Questions and Discussion Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  23 2

Managing Supply Chain Resilience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.1 Historical Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.2 Strategic Understanding of Supply Chain Resilience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.3 Supply Chain Resilience Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.4 Resilience Capabilities and Recovery Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.5 Framework of Resilience Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 2.5.1 Absorptive Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.5.2 Adaptive Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 2.5.3 Restorative Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 2.6 Costs and Value of Supply Chain Resilience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 2.6.1 LCN (Low-Certainty-Need) Supply Chain Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.6.2 Lean Resilience: The AURA (Active Usage of Resilience Assets) Framework . . . . . . . . . . . 50 2.7 Supply Chain Resilience During a Global Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 2.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3

Modeling Supply Chain Resilience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

3.1 Modeling Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.2 End-to-End Visibility, Digital Technology, and Resilience . . . . . . . . . . . . . . . . . . . . . . . . . 67 3.3 Optimization: Recovery Model of a Multi-stage Supply Chain . . . . . . . . . . . . . . . . . . . . 71 3.3.1 Problem Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.3.2 Mathematical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 3.4 Simulation: Ripple Effect Prediction During the COVID-19 Pandemic . . . . . . . . . . . . . 79 3.4.1 Problem Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 3.4.2 Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 3.4.3 Managerial Insights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

X

Contents

4

Measuring Supply Chain Resilience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

4.1 Measures of Supply Chain Resilience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 4.2 Complexity Theory: Entropy-Based Assessment of Supply Chain Adaptability . . . . 101 4.2.1 Definition of Supply Chain Adaptability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 4.2.2 Quantitative Estimation of Supply Chain Adaptability: Basic Computation . . . . . . . . . . 102 4.2.3 Quantitative Assessment of Supply Chain Adaptability: An Extension . . . . . . . . . . . . . . . 105 4.3 Measuring Supply Chain Resilience Using Bayesian Networks . . . . . . . . . . . . . . . . . . . 107 4.3.1 Problem Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4.3.2 Methodology of Bayesian Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4.3.3 Resilience Metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 4.4 Ripple Effect Exposure Quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 4.5 Network Design Characteristics and Their Relations to Supply Chain Resilience . . . 121 4.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 5

Supply Chain Viability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

System-Theoretic Foundations of Supply Chain Resilience and Viability: Multi-­ Structural Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 5.2 Viable Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 5.2.1 Supply Chain Viability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 5.2.2 Viable Supply Chain Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 5.3 Intertwined Supply Networks and Their Viability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 5.4 Viability and Adaptation of Supply Chains: The Climate Change Challenge . . . . . . . 141 5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

5.1

Supplementary Information Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

XI

Author Bio Dmitry Ivanov

is professor of supply chain and operations management at Berlin School of Economics and Law (HWR Berlin), deputy director and executive board member of the Institute for Logistics (IfL) at HWR Berlin, and faculty director of the master’s degree program on global supply chain and operations management at HWR Berlin. His research explores structural dynamics and control in complex networks, with applications to supply chain resilience, scheduling in Industry 4.0 systems, supply chain simulation, risk analytics, and digital supply chain twins. He is co-­ author of structural dynamics control methods for supply chain management. His research coined several seminal academic and practical directions such as the ripple effect in supply chains and supply chain viability. He applies mathematical programming, simulation, control, and fuzzy theoretic methods. Based upon triangle “process-model-technology,” he investigates the dynamics of complex networks in production, logistics, and supply chains. Most of his research stems from real practical context and focuses on the interface of supply chain management, operations research, industrial engineering, and digital technology. He has been teaching courses in operations management, supply chain management, logistics, management information systems, and strategic management at undergraduate, master’s, PhD, and executive MBA levels at different universities worldwide in English, German, and Russian for more than 20 years. He has given guest lectures and webinars, presented scholarly papers and has been a visiting professor at numerous universities in Asia, Europe, and North America. He enjoys being part of students’ learning experience and creating a safe and active learning environment. He sees his job as an educator to equip future industry leaders with working knowledge and skills in management and technology that would shape more resilient, adaptable, and sustainable supply chains and operations. His academic background includes industrial engineering and management, operations research, and applied control theory. He studied industrial engineering and production management in St. Petersburg and Chemnitz and graduated with honors. He earned his PhD (Dr.rer.pol.), Doctor of Science (ScD), and Habilitation (Dr. habil.) degrees in 2006 (TU Chemnitz), 2008 (FINEC St. Petersburg), and 2011 (TU Chemnitz), respectively. Prior to becoming an academic, he was mainly engaged in industry and consulting, especially for process optimization in manufacturing and logistics and ERP systems. His practical expertise includes numerous projects on the application of operations research and process optimization methods to operations design, logistics, scheduling, and supply chain management. Prior to joining the Berlin School of Economics and Law, he was professor and interim chair of operations management at University of Hamburg. Professor Ivanov’s research makes impact. His papers belong to the most cited worldwide in the areas of supply chain resilience and digital supply chain. For example, he has been the most cited author of premier journals International Journal of Production Research and Transportation Research: Part E in 2020.  His

XII

Author Bio

research record includes over 350 publications, with over 100 papers in prestigious academic journals, three editions of the leading textbook Global Supply Chain and Operations Management, the research books Structural Dynamics and Resilience in Supply Chain Risk Management and Scheduling in Industry 4.0 and Cloud Manufacturing, and Handbook of Ripple Effects in the Supply Chain. Professor Ivanov’s research has been published in various academic journals, including Annals of Operations Research, Annual Reviews in Control, Central European Journal of Operations Research, Computers and Industrial Engineering, European Journal of Operational Research, Expert Systems with Applications, IEEE Transactions on Engineering Management, IISE Transactions, International Journal of Information Management, International Journal of Integrated Supply Management, International Journal of Inventory Research, International Journal of Physical Distribution & Logistics Management, International Journal of Production Research, International Journal of Production Economics, International Journal of Technology Management, International Journal of Systems Science, International Transactions in Operational Research, Journal of Scheduling, Omega, Production Planning and Control, and Transportation Research: Part E. He is a recipient of German Chancellor Scholarship Award (2005–2006), Best Paper Awards of International Journal of Production Research (2018,2019, 2020), and Commended Paper Award at International Conference LogDynamics (2018). He is listed in WiWo rankings 2018 and 2020 “The Best Researchers in Business and Management” in categories TOP 100 and Long-Term Stars. His research projects have been supported by funding by the European Commission (Horizon 2020), DFG (German Research Foundation), DAAD, Alexander von Humboldt-­ Foundation, as well industrial companies. Dr. Ivanov is profiled in supply chain and operations management, operations research and industrial engineering. He delivered invited plenary, keynote, and panel talks at the conferences of INFORMS, IFPR, DSI, IFAC, and IFIP. He is passionate about bridging the knowledge of different disciplines and applying it to solution of practically relevant problems. He is leading working groups, tracks, and sessions on the digital supply chain, supply chain risk management, and resilience in global research communities. He is editor of the International Journal of Integrated Supply Management and an associate editor of the International Journal of Production Research, International Transactions in Operational Research, and International Journal of Systems Science. He is an editorial board member as well as associate and guest-editor in different journals, including  Annals of Operations Research, Annual Reviews in Control, International Journal of Production Economics, International Journal of Production Research, International Transactions in Operational Research, International Journal of Integrated Supply Management, International Journal of Information Management, International Journal of Inventory Research, International Journal of Physical Distribution and Logistics Management, International Journal of Systems Science, and Production Journal. He is chairman of IFAC TC 5.2 “Manufacturing Modelling for Management and Control” and co-chairman of the IFAC TC 5.2 Working group “Supply Network ­Engineering”. He has been member of numerous associations, including CSCMP, DSI, GOR, INFORMS, POMS, and VHB. He has been general conference chair

XIII Author Bio

of IFAC MIM 2019 – one of the worldwide largest conferences in manufacturing, industrial engineering, operations, and supply chain management (750 participants). He regularly presents his research results and has been chairman, IPC, and advisory board member of over 50 international conferences in supply chain and operations management, industrial engineering, and control and information sciences, where he has organized numerous tracks and sessions (including EURO, INFORMS, IFORS, DSI, POMS, OR, IFAC World Congress, IFAC MIM, IFAC INCOM, IFAC ISM, IFIP PRO-VE, and LDIC, to name a few).

1

Supply Chain Risks, Disruptions, and Ripple Effect Contents 1.1

Uncertainty and Risk – 3

1.1.1 1.1.2

Definition of Uncertainty – 3 Definition of Risk – 7

1.2

 isruption Risks in Supply D Chains – 8

1.2.1 1.2.2

Supply Chain Risks – 8 Definition and Classification of Disruptions – 11

1.3

 ipple Effect in Supply R Chains – 14

1.3.1

 efinition of the Ripple D Effect – 14 Reasons and Countermeasures for the Ripple Effect – 17 Disruption Tails and Overlays: When Ripple Effect and Bullwhip Effects Intersect – 18

1.3.2 1.3.3

Supplementary Information The online version of this chapter (https://doi.org/10.1007/978-3-030-70490-2_1) contains supplementary material, which is available to authorized users. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 D. Ivanov, Introduction to Supply Chain Resilience, Classroom Companion: Business, https://doi.org/10.1007/978-3-030-70490-2_1

1

1.4

 uper Disruptions and Supply S Chain Crises: Example of the COVID-19 Pandemic – 20

1.5

Questions and Discussion Points – 22 References – 23

1

3 1.1 · Uncertainty and Risk

..      Fig. 1.1  Supply chain resilience function

Disruptive Events

Withstand

Recover

Supply Chain Resilience

nnLearning Objectives In this chapter, we introduce major concepts related to uncertainty and risks in supply chains. Uncertainty, risks, and the resulting disruptive events are major vulnerability drivers in the supply chain which can be balanced and recovered by resilience capabilities (which we will learn in 7 Chap. 2) if utilized properly (. Fig. 1.1). To this end, our learning objectives for this chapter are as follows: 55 Understand the notions of uncertainty and risks 55 Classify supply chain disruptions 55 Explain the ripple effect in the supply chain 55 Analyze disruption overlays and disruption tails 55 Explain super-disruptions to supply chains using example of the COVID-19 pandemic  

1.1

 

Uncertainty and Risk

1.1.1

Definition of Uncertainty

The major concern of supply chain resilience is to enable continuity of firm’s operations in the presence of uncertainties and disruptions. Decision-making under uncertainty belongs to the most important areas of resilient supply chain management (Klibi et al. 2010; Sodhi and Tang 2012; Ivanov 2018a; Sawik 2020). Definition Uncertainty is a system property characterizing the incompleteness of our knowledge about the system, its environment, and the conditions of its development.

4

1

Chapter 1 · Supply Chain Risks, Disruptions, and Ripple Effect

One of the main dangers of uncertainty is the disruption entailing changes in a planned course of events in supply chain operations and (or) a threat of economic performance decrease such as lost sales or stock returns. There are different external and internal and objective and subjective perturbation impacts altering the execution conditions of a supply chain. Uncertainty factors are usually divided into two groups: stochastic factors and non-stochastic factors. The first group can be described via probability models. The factors described as aleatory variables (functions, fields) with known distributions are statistically defined. Aleatory variables with unknown distributions can be of two types: those with known or unknown characteristics (Ivanov and Sokolov 2010, pp. 69–74). Klibi et al. (2010) classify uncertainties and risks in the supply chain as follows: 55 Random uncertainty (demand fluctuation risks) 55 Hazard uncertainty (risk of unusual events with high impact) 55 Deep uncertainty (severe disruption risks) For the formal description of non-stochastic uncertainty, fuzzy description with known membership functions, subjective probabilities for the uncertainty factors, interval description, and combined description of the uncertainty factors are used. In analyzing uncertainty and risks, four aspects are usually encountered. The first is uncertainty itself, the second is risks, the third is disturbances, and the last is the disruptions (. Fig. 1.2). Uncertainty is the general property of a system environment that exists independent of us for any system of a sensible complexity degree. Risk arises from  

Risk

Uncertainty

initiates from uncertainty; can be identified, analyzed, controlled and regulated; can cause a disturbance

exists in any system with a sensible extent of complexity; can be reduced or amplified

Disturbance

Disruption

results from risk; can be prevented and eliminated by means of redundancy and flexibility reserves; can cause a deviation

results from a distrubance; can disrupt the supply chain; can be eliminated by means of adaptation

..      Fig. 1.2  Interrelations of uncertainty, risk, disturbance, and disruption (Ivanov 2018a)

5 1.1 · Uncertainty and Risk

1

uncertainty. Risks can be identified, analyzed, controlled, and regulated. We usually talk about uncertainty factors and the appearance of risks such as the risk of demand fluctuation as a result of the environmental uncertainty. A disturbance (perturbation impact) is the consequence of risks. It may be purposeful (i.e., thefts) and non-purposeful (i.e., demand fluctuations or the occurrence of some events that may necessitate adapting the supply chain). It may cause a deviation (disruption) in the supply chain or not (e.g., a supply chain can be robust and adaptive enough to overcome the disturbance). Operational deviations (e.g., a decrease in fill rate due to some demand fluctuation) or severe disruptions (e.g., supplier unavailability or market disruption) are the results of perturbation influences. They may affect operations, processes, plans, network structures, goals, or strategies. To adjust the supply chain in the case of deviations, adaptation measures need to be taken. Analysis of uncertainty impacts on the system can be related to the categories of stability, robustness, and resilience (. Table 1.1). In . Table 1.1, we illustrate major commonalities and differences between stability, robustness, and resilience for supply chain management.  

 

Definition Stability refers to process control level (e.g., stability of an inventory control policy (Disney and Towill 2002)) and is considered the ability to return to a pre-disturbance state within some pre-determined bounds (i.e., BIBO-stability: bounded input, bounded output stability (Ivanov and Sokolov 2013; Demirel et al. 2019)). We talk about stability when short, limited scope shocks cause disturbances in specific functions of the supply chain (e.g., in an inventory control policy, manifesting in a pendulum-like disturbance effect with a relatively rapid and predictable return to full normalcy). Robustness is the ability to withstand a disruption (or a series of disruptions) to maintain the planned performance (Nair and Vidal 2011; Simchi-Levi et  al. 2018) and is considered as a proactive strategy to cope with disruptions (Rosenhead et al. 1972, Bode and Macdonald 2017; Tan et al. 2019). Thus far, robustness is the ability of a system to not be disrupted in the first place, i.e., to withstand disruptions without breaking down, e.g., by stocking inventory or maintaining a back-up supply base (Tomlin 2006; Wu and Olson 2008; Yang et al. 2009; Tang et al. 2016). Like robustness and differently to stability, resilience is a systemic property, i.e., “a complex, collective, adaptive capability of organizations in the supply network to maintain a dynamic equilibrium, react to and recover from a disruptive event, and to regain performance by absorbing negative impacts, responding to unexpected changes, and capitalizing on the knowledge of success or failure” (Yao and FabbeCostas 2018), in line with the studies by Hosseini et al. (2019b) and Zhao et al. (2019).

6

1

Chapter 1 · Supply Chain Risks, Disruptions, and Ripple Effect

..      Table 1.1  Major analysis concepts for supply chain performance under uncertainty Concept

Definition

Scope

Disturbance in supply chain functions or processes

Disruption in supply chain structures

Supply chain performance

Recovery

Stability

The ability to return to a pre-­ disturbance state within some pre-determined bounds (pendulum-­ like)

Process property

Disturbance of a single function or process, e.g., inventory control policy

Not considered

Not considered

Not considered

Robustness

The ability to withstand a disruption (or a series of disruptions) to maintain the planned performance

Systemic property

Not considered

Disruption in supply chain network, e.g., supplier unavailability

Impact of a disruption on supply chain performance

Not considered

Resilience

The ability to withstand, adapt, and recover from disruptions to meet customer demand and ensure the target performance.

Systemic property

Not considered

Disruption in supply chain network, e.g., supplier unavailability

Impact of a disruption on supply chain performance

Selection of a recovery policy to restore normal operations and planned performance

In summary, stability can be used to analyze supply chain reaction to operational disruptions (e.g., bullwhip effect), while robustness and resilience allow analyzing supply chain reactions to severe disruptions. We can analyze stress-test supply chains regarding their robustness (i.e., the ability to withstand) and recovery (i.e., the ability to restore operations and performance after a disruption) pointing to the central role of resilience in managing supply chains in this volatile world (Peck 2005; Ponomarov and Holcomb 2009; Pettit et al. 2010; Brandon-Jones et al. 2014).

7 1.1 · Uncertainty and Risk

1

►►Example

One example for stability analysis is bullwhip effect. It has been observed that a small deviation in demand leads to even greater deviations in ordering and production quantities upstream the supply chain (Lee et al. 1997). This negatively influences the stability of production-inventory control policies. As for robustness, many firms extended their supply chain by using a multiple-sourcing strategy and by building new facilities on the supply side. Such supply chain segmentation also helps to reduce disruption risk implications (Chopra and Sodhi 2014). ◄

1.1.2

Definition of Risk

The concept of risk is subject to various definitions. Knight (1921) classified under “risk” the “measurable” uncertainty. From the financial perspective of Markowitz (1952), risk is the variance of return. From a project management perspective, risk is a measure of the probability and consequence of not achieving a defined project goal. According to March and Shapira (1987), risk is a product of the probability of occurrence of a negative event and the resulting amount of damage. Generally, in decision theory, risk is a measure of the set of possible (negative) outcomes from a single rational decision and their probabilistic values. In contrast to risk, uncertainty is a more comprehensive term, considering situations that cause both positive (chance) and negative (threats) deviations from an expected outcome. ►►Example

One can compare risks with viruses and resilience with immune systems. Using the immune system analogy, we can understand a difference between risks and resilience. Immune system is an inherent property of any human being to absorb negative events, e.g., a virus. It also helps to recover after an infection. In supply chain terms, risks are negative events (i.e., a kind of a virus that can cause an infection) and resilience is an inherent supply chain property responsible to absorb the negative events and restore the normal operations (i.e., to recover). We have immune systems in order to withstand different viruses and recovery in case of illness. In case of some minor viruses (as an analogy to small operational deviations such as a lead-time fluctuation), only a part or a function of our body is affected (e.g., we get some headaches). However, our body as a system is able to keep operating. We would talk about the stability of supply chains in this case. In case of severe viruses (or a weak immune system), their impact on our abilities and performances can be much higher and disrupt our life on the system level. Here, we encounter the questions of robustness and resilience. A strong immune system helps human beings achieve high performances. Similarly, the supply chain performance depends on the resilience. A weak immune system may result in chronic diseases leading to performance degradation. Low supply chain resilience, if a disruption is experienced, also results in profitability reductions, mismatches of demand and supply, and destabilization of normal operations. ◄

8

1

1.2

Chapter 1 · Supply Chain Risks, Disruptions, and Ripple Effect

Disruption Risks in Supply Chains

1.2.1

Supply Chain Risks

The material flows in supply chains can be vulnerable to disruptions and numerous risks (Tang 2006; Chopra et al. 2007; Craighead et al. 2007; Ivanov et al. 2017b; He et al. 2019; Macdonald et al. 2018; Yoon et al. 2018), which yield different types of supply, production, and logistics disruptions (Sawik 2020). Risk management in supply chains became one of the most important topics in research and practice over the last decade. A number of books (e.g., Waters 2011; Gurnani et al. 2012; Heckmann 2016; Ivanov 2018a) and literature review papers (Ho et al. 2015; Fahimnia et al. 2015; Gupta et al. 2016; Snyder et al. 2016; Ivanov et al. 2017b; Dolgui et al. 2018; Bier et al. 2020; Duong and Chong 2020; Pournader et al. 2020) provide insightful overviews and introductions to different aspects of this exciting field. For example, Chopra and Sodhi (2004) divided potential supply chain risks into nine categories: 55 Disruptions (e.g., natural disasters, terrorism, and war) 55 Delays (e.g., inflexibility of supply source) 55 Systems (e.g., information infrastructure breakdown) 55 Forecast (e.g., inaccurate forecast and bullwhip effect) 55 Intellectual property (e.g. vertical integration), 55 Procurement (e.g., exchange rate risk) 55 Receivables (e.g., number of customers) 55 Inventory (e.g., inventory holding cost, and demand and supply uncertainty) 55 Capacity (e.g., cost of capacity) Quang and Hara (2017) classify the following seven groups of risks: 55 External risks that “deal with threats from an external perspective of supply chain that can be caused by economical, sociopolitical or geographical reasons. Examples are fire accidents, natural catastrophes, economic downturn, external legal issues, corruption, and cultural differentiation.” 55 Time risks referring to delays in supply chain processes. 55 Information risks, e.g., communication breakdown within the project team, information infrastructure complications, distorted information, and information leaks. 55 Financial risks, e.g., inflation, interest rate level, currency fluctuations, and stakeholder requests. 55 Supply risks, i.e., risks related to suppliers, e.g., supplier bankruptcy, price ­fluctuations, unstable quality, and quantity of inputs. 55 Operational risks caused by problems within the organizational boundaries of a firm, e.g., changes in design and technology, accidents, and labor disputes. 55 Demand risks that refers to demand variability, high market competition, customer bankruptcy, and customer fragmentation.

1

9 1.2 · Disruption Risks in Supply Chains

Tang (2006) distinguishes operational risks (e.g., uncertain costs and uncertain demand) and disruption risks (e.g., natural and man-made disasters or general crises). Rao and Goldsby (2009) classify risks into environmental, industry, organizational, problem-specific, and decision-maker related factors. Christopher (2011) divides the supply chain risks into five groups: supply, demand, process, control, and environmental risks. Ho et al. (2015) classify macro, demand, manufacturing, supply, and infrastructural risk factors. Shen and Li (2017) offer a classification of market disruptions in supply chains. Another classification of supply chain risks has been proposed by Ivanov and Dolgui (2019) and is as follows: 55 Network risks: risks imposed by specific types of supply chain networks (e.g., scale-free vs decentralized networks). 55 Process risks: specific parameters of supply chain processes such as inventory and capacity may influence supply chain proneness to disruptions. 55 Supplier risks: risks induced by specific suppliers, e.g., those located in geographical zones with high probability of natural disasters. In . Fig.  1.3, we present our classification of supply chain risks based on the works by Chopra and Sodhi (2004), Tang (2006), Tang and Musa (2011), Ho et al. (2015), and Quang and Hara (2017). Supply chain risks stem from different areas related to material flows such as supply, demand, production, and logistics process. Along with material flow-­ related risks, risks arise in financial and information flows, e.g., due to cyber-attacks or financial crisis (Sawik 2021; Ghadge et  al. 2019; Linkov and Kott 2019). Moreover, supply chains are subject to macro-risks such as climate changes and shortages of critical resources.  

• Production Capacity Breakdowns • Facility Disruptions • Logistics Risks • Strikes • Delivery Delays • Product Quality Risks • Supplier Disruptions

Supply Risks

• Liquidity Risks • Financial Crisis • Credit Risks

• Climate Change and Natural Disasters • Natural Resource Shortages • Epidemics/Pandemics

Financial Risks

Natural Risks

..      Fig. 1.3  Classification of supply chain risks

Process Risks

Supply Chain Risks

Demand Risks

• Price Risks • Demand Fluctuations • Market Disruptions

Information • Information Distortion Risks • Cyber-Attacks

Law and Cultural Risks

• Legal Risks • Cultural Risks • Trust Risks

10

Chapter 1 · Supply Chain Risks, Disruptions, and Ripple Effect

►►Example

1

Practical insights. SC risks are highly intertwined and can induce each other. Consider an example based on Manager Magazine (2021). In January 2021, production stops at automotive assembly plants have been observed due to the supply shortages of semiconductors. The shortages were caused by surges in demand at automotive firms that recovered after the pandemic shock in 2020. However, this fast demand recovery was not well anticipated by semiconductor suppliers, which have reduced or reallocated their capacities by cooperating electronics industry and healthcare SCs in order to substitute the missing demand from automotive industry which suffered from severe shocks in the wake of the COVID-19 pandemic. On the other hand, silicon as a raw material for semiconductor industry is associated with high sourcing risks. Silicon production has been reduced in China in 2020 due to the shortages of electricity generated by hydropower stations, which in turn were caused by long periods of drought. This example illustrates interconnections between different SC risks shown in . Fig. 1.3. Reference: Manager Magazine (2021) ◄  

From the perspective of impact severity, different types of risks in the supply chain can be classified into areas of demand, supply, process, and structure entailing a generalized separation in operational and disruption risks, also referred to as LIHF (low-impact, high frequency) and HILF (high-impact, low-frequency) risks (see . Fig. 1.4). LIHF risks of demand and supply uncertainty are related to random uncertainty and business-as-usual situation. Such risks are also known as recurrent or operational risks (Tang 2006; Chopra et al. 2007). Supply chain managers achieved significant improvements at managing global supply chains and mitigating recurrent supply chain risks through improved planning and execution. However, dis 

..      Fig. 1.4  Operational and disruption supply chain risks

Operational (Recurrent) Risks / Bullwhip Effect Lead time variations

Demand fluctuations

Supply Chain Risks

Structural complexity and connectivity

Lean structures and processes

Disruption (Exceptional) Risks / Ripple Effect

11 1.2 · Disruption Risks in Supply Chains

1

ruption risk management presumes an advanced utilization of supply chain resilience methods. We consider disruption risks in the next section. 1.2.2

Definition and Classification of Disruptions

Definition Disruption is an unexpected event that interrupts the normal flow of goods and materials in a supply chain network and has a severe negative impact on supply chain operations and performance.

Disruption is considered an HILF event (Akkermans and van Wassenhove 2018; Chen et al. 2019; Azadegan et al. 2020; Dolgui and Ivanov 2020; Kinra et al. 2020). Appearance and consequences of disruptions are difficult to anticipate and predict (Ivanov et al. 2017a; Paul et al. 2019; Essuman et al. 2020; Gupta and Ivanov 2020; Ivanov 2020a). There are three different kinds of disruptions. Random disruptions belong to the category of known-known uncertainty, i.e., we know which events can happen, when they can happen, and how likely they are. For example, each summer, countries in Southeast Asia and the associated supplier locations are hit by typhoons. The hazard disruptions are close to known-unknown uncertainty, i.e., we know which events can happen but we do not know when they would happen and what their impact is. An example is the continuously existing danger of earthquakes in Japan, which is however hardly predictable. The deep disruptions are related to the unknown-unknown uncertainty, i.e., we do not know what can happen and when, and what the consequences are. The deep disruptions represent the most complex case for decision-making. One example for deep disruption is the COVID19 pandemic (Golan et al. 2020; Paul and Chowdhury 2021; Queiroz et al. 2020; El Baz and Ruel 2021). Disruption risks are of utmost importance for supply chain managers (Saghafian and Van Oyen 2016; Sahebjamnia et al. 2015; Gianesello et al. 2017; Papadopoulos et al. 2017; Paul et al. 2017). Disruption risks are events caused by natural catastrophes, such as hurricanes, earthquakes, or floods, or by man-made threats, such as terrorist attacks or labor strikes. Disruption risks unpredictably vary in type, scale, and nature; are intermittent and irregular to be identified, estimated, and forecasted well; and may have short- and long-term negative effects (Ho et al. 2015; Torabi et al. 2015; Dolgui et al. 2018; He et al. 2019; Hosseini et al. 2019a; Ivanov and Sokolov 2020). ►►Example

For example, as a result of the tsunami and earthquake that struck Japan in 2011, Toyota’s parts suppliers were unable to deliver parts at the expected volume and time (Marsh et al. 2011). This forced Toyota to halt production for several weeks. Similarly, General Motors was forced to stop production because of a shortage of raw materials from their Japanese suppliers (Huffington Post 2015). Nissan suffered significant set-

12

1

Chapter 1 · Supply Chain Risks, Disruptions, and Ripple Effect

backs: the company had a high level of dependency on raw materials from suppliers who were located in the tsunami and earthquake zone and supplied 12% of Nissan’s engines (BBC News 2011). Nissan had to temporarily shut down production at its Sunderland UK plant. ◄

Disruption risks may adversely affect supply chains because of several reasons. First, globalization and outsourcing make supply chains more complex and less observable and controllable. According to complexity theory, such systems become more sensitive to disruptions. Second, the efficiency paradigms of lean processes (e.g., single sourcing or just-in-time sourcing) can entail an increased proneness to disruptions due to missing structural and process variety. As a consequence, supply chains became more vulnerable. Disruptions in a global supply chain, especially in its supply base, may immediately affect the entire supply chain. Third, with increased specialization and geographical concentration of manufacturing, disruptions in one or several nodes affect almost all the nodes and links in the supply chain. >>Important Observation Disruptions at the tier-1 suppliers have an immediate impact on the OEM (original equipment manufacturer). As such, risky tier-1 suppliers should be identified very carefully. At the same time, a disruption at a tier-N supplier, even at a smaller one, can have devastating impacts comparable with a tier-1 disruption. As such, the search for and identification of such “hidden” suppliers is of an utmost importance (Simchi-Levi et al. 2015; Yan et al. 2015).

Disruption risks are characterized by a very strong and immediate impact on the supply chains since some factories, suppliers, warehouses, and transportation links become temporarily unavailable. Adversely, the resulting material shortages and delivery delays propagate downstream the supply chain, causing the ripple effect and performance degradation in terms of revenue, service level, and productivity decreases (Ivanov et  al. 2014a, 2014b; Garvey et  al. 2015; Sokolov et  al. 2016; Dolgui et al. 2018; Cao et al. 2019; Ivanov et al. 2019b; Pavlov et al. 2019; Dolgui et al. 2020; Goldbeck et al. 2020; Li and Zobel 2020; Gholami-Zanjani et al. 2020). We refer readers to the works by Ho et al. (2015), Fahimnia et al. (2015), ­Heckmann et al. (2016), Ivanov and Dolgui (2019), and Xu et al. (2020) for comprehensive reviews of definitions and core characteristics of supply chain risks. >>Important Observation Risks are the vulnerabilities that should be balanced and recovered by supply chain resilience management. To this end, resilience management is different as risk management. Supply chain risk management is rooted in an event-oriented perspective while supply chain resilience is a system property. One can compare risks with viruses and resilience with immune systems.

In . Table 1.2, some examples of disruptions in supply chains in recent years are presented.  

13 1.2 · Disruption Risks in Supply Chains

..      Table 1.2  Examples of disruptions in supply chains Factor

Example

Impacts

Terrorism Piracy

September 11, 2001 Somali, 2008

Five Ford plants have been closed for a long time Disruptions in many supply chains

Natural disasters

Earthquake in Thailand, 1999 Flood in Saxony, 2002 Earthquake in Japan, 2007

Apple computers’ production in Asia has been paralyzed Significant production decrease at VW, Dresden Production breakdown in Toyota’s supply chains amounted to 55,000 cars

Hurricane Katrina, USA, 2006

This storm halted 10–15% of total US gasoline production, raising both domestic and overseas oil prices

Earthquake and tsunami in Japan, 2011

Massive collapses in global automotive and electronics supply chains; Toyota lost its market leadership position

Floods in Chennai, India, in 2015

Production of academic literature has been stopped at many international publishing houses

Explosion at BASF plant in Ludwigshafen in 2016

15% of raw materials were missing for the entire supply chain Production of some products at BASF has been stopped for many weeks

Transportation disruption in Suez Canal in March 2021

Many ripple effects in global supply chains due to delayed deliveries and destabilization of the global shipment schedules

A fire in the Phillips Semiconductor plant in Albuquerque, New Mexico, in 2000

Phillips’s major customer, Ericsson, lost $400 million in potential revenue

Political crises

“Gas” crisis 2009

Disruptions in gas supply from Russia to Europe, billions of losses to GAZPROM and customers

Financial crises

Autumn 2008

Business bankruptcies; interruptions in supply chains all over the world

Strikes

Strikes at Hyundai plants in 2016

Production of 130,000 cars has been affected

Legal contract disputes

Volkswagen and Prevent Group contract dispute in summer 2016

Six German factories face production halt on parts shortage; 27,700 workers were affected, with some sent home and others moved to short-time working

Epidemics and pandemics

COVID-19 global pandemic in 2020–2021

Worldwide disruptions in supply and demand, devastating effects in many global and local supply chains; ripple effects; supply chain collapses and long-term performance degradation

Man-made disasters

Extended from Ivanov and Sokolov (2010) and Ivanov (2018a))

1

14

1

1.3

Chapter 1 · Supply Chain Risks, Disruptions, and Ripple Effect

Ripple Effect in Supply Chains

In several contexts, disruptions can be localized without an associated cascading effects throughout a network. However, in other situations disruptions in a supplier base propagate to downstream supply chain echelons adversely impacting the performance of individual firms and networks. Research works have analyzed how one or more disruptive events that propagate through the supply chain impact performance, thus placing increased emphasis on the ripple effect in the supply chain (Ivanov et al. 2014a, b; Dolgui et al. 2018; Ivanov and Dolgui 2021). Ripple effect is a specific area of supply chain disruptions and a strong stressor to supply chain resilience. The phenomenon of the ripple effect, immensely existing in practice, is considered in this section. 1.3.1

Definition of the Ripple Effect

The ripple effect occurs when a disruption, rather than remaining localized or being contained to one part of the supply chain, cascades downstream and impacts the performance of the entire supply chain (Dolgui et al. 2018). Ripple effect is followed by supply chain structural dynamics (Ivanov 2018a) and its impact might include lower revenues, delivery delays, loss of market share and reputation, etc., with adverse effects on the profitability of supply chain (Li et  al. 2020, 2021; Llaguno et al. 2021). Definition Ripple effect describes the disruption propagation in the supply chain network entailing unavailability of components at different echelons and an associated performance degradation.

According to Dolgui et al. (2020), the ripple effect “refers to structural dynamics and describes a downstream propagation of the downscaling in demand fulfilment in the supply chain as a result of a severe disruption.” Ivanov et al. (2014b) state that the “ripple effect describes the impact of a disruption on supply chain performance and disruption-based scope of changes in the supply chain structures and parameters.” These definitions imply that the ripple effect refers to multi-stage networks and triggering failures in the network elements as a domino or cascading effect. Between 2010 and 2014, first studies appeared in the area of the ripple effect, along with an increased interest in disruption propagation and correlated disruptions (Liberatore et al. 2012; Chatfield et al. 2013; Ghadge et al. 2013; Ivanov et al. 2014a). The first explicit definition of the ripple effect has been undertaken by Ivanov et al. (2014b) as indicated above. Thus far, much progress has been made in the area deploying different methodologies and obtaining relevant managerial outcomes and recommendations (Fiksel et al. 2015; Van der Vegt et al. 2015; Chaudhuri et  al. 2016; Scheibe and Blackhurst 2018). Dolgui et  al. (2018) and Ivanov and Dolgui (2020, 2021) presented overviews of the ripple effect in supply chains.

15 1.3 · Ripple Effect in Supply Chains

1

Studies on the ripple effects in supply chains have been collated in 2019 in a handbook of ripple effects in supply chains (Ivanov et al. 2019b). The recent works on the ripple effect are multi-faceted and cover the areas of network analysis, design, planning, and control. ►►Example

Consider some examples of the ripple effects. Earthquake and tsunami in Japan in 2011 have disrupted multiple suppliers in the automotive industry and led to production breaks and material shortages worldwide leading to numerous ripple effects in global supply chains (Ivanov 2018a). In May 2017, production at BMW was disrupted as a consequence of a supply shortage of steering gears. BMW’s first tier supplier, Bosch, was unable to deliver the steering gears since an Italian second tier supplier experienced production delays for certain steering parts due to internal machine breakdowns (Moetz et  al. 2019; Dolgui and Ivanov 2021). The COVID-19 pandemic has caused numerous ripple effects. Haren and Simchi-Levi (2020) observed two examples of a ripple effect triggered by COVID-19 immediately after the epidemic outbreak. Fiat Chrysler Automobiles NV halted production at a car factory in Serbia in response to being unable to receive parts from China. As Hyundai stated, it had “decided to suspend its production lines from operating at its plants in Korea … due to disruptions in the supply of parts resulting from the coronavirus outbreak in China.” Further ripple effects have been encountered in the wake of the COVID-19 pandemic, driven by the closures of manufacturing facilities, stores, and logistics activities, and adversely affecting almost all industries and services worldwide (Ivanov and Dolgui 2021). In another example, a transportation disruption in Suez Canal in March 2021 resulted in numerous arrival delays and congestions at international ports leading to longer lead times. As a consequence, suppliers and manufacturers suffered from material shortages which led to an increase in backlogged demands at customers. Moreover, global transportation shipment schedules have been destabilized. ◄

>>Important Observation Detection and mitigation of the ripple effect can be improved by supply chain visibility. It is of crucial importance to observe the supply chain network beyond the tier-1 suppliers.

With the ripple effect, a complex setting should be considered comprising a disruption (or a set of disruptions) and their propagations, analysis of impact of the disruption propagation on operational and strategic economic performance, and deployment of stabilization and recovery policies (. Fig. 1.5). Ripple effects belong to reality of many supply chains. In many practical settings, supply chain disruptions go beyond the disrupted stage; i.e., the original disruption causes disruption propagation in the supply chain, and at times still higher consequences are caused (. Fig. 1.6).  

 

>>Important Observation The ripple effect describes disruption propagation in the supply chain, impact of a disruption on supply chain performance, and disruption-based scope of changes in supply chain structures and parameters.

16

Chapter 1 · Supply Chain Risks, Disruptions, and Ripple Effect

1

..      Fig. 1.5  Ripple effect in the supply chain

Following a disruption, its effect ripples through the supply chain (Basole and Bellamy 2014). The missing capacities or inventory at the disrupted facility may cause missing materials and production decrease at the next stages in the supply chain. Should the supply chain remain in the disruption mode longer than some critical period of time (i.e., time-to-survive (Simchi-Levi et al. 2015)), critical performance indicators such as sales or stock returns may be affected.

17 1.3 · Ripple Effect in Supply Chains

1

..      Fig. 1.6  Disruption propagation in the supply chain (Ivanov et al. 2021)

1.3.2

Reasons and Countermeasures for the Ripple Effect

The reasons for ripple effect are not difficult to find. With increasing complexity and consequent pressure on speed and efficiency, the globalized and lean supply chains are particularly exposed to rippling and its impact on economic performance. The scope and scale of the ripple effect depend both on redundancy (e.g., inventory or capacity buffers) and effectiveness of recovery measures (Gupta et al. 2021; Ivanov 2021; Ivanov et al. 2019a). Therefore, contingency plans (e.g., alternative suppliers or shipping routes) should be prepared and quickly deployed to expedite stabilization and recovery in order to ensure continuity of supply and avoid long-term impacts. In implementing such recovery policies, companies need a tool supported by collaboration and endto-­end supply chain visibility for assessing the disruption impact on the supply chain as well as the recovery effects and costs. . Figure 1.7 and . Table 1.3 summarize the reasons and countermeasures for the ripple effect (Ivanov and Rozhkov 2020; Dolgui et al. 2018; Ivanov 2018a).  

 

18

Chapter 1 · Supply Chain Risks, Disruptions, and Ripple Effect

1

..      Fig. 1.7  Reasons for the ripple effect (Ivanov 2018b)

..      Table 1.3  Reasons and countermeasures for ripple effect (Dolgui et al. 2018) Reasons

Supply chain designs

Ripple effect impact

Countermeasures

Leanness

Single sourcing

In the non-disrupted scenario, it is irrational to avoid lean practices. At the same time, a capacity disruption may result in the ripple effect and performance decreases

Multiple/dual sourcing/ backup suppliers

Without a coordinated contingency policy, disruption recovery and performance impact estimation can be very long lasting and expensive. Coordinated control algorithms are needed to monitor supply chain behavior, identify disruptions, and adjust order allocation rules using a coordinated contingency policy

Geographical sourcing diversification

Low inventory Inflexible capacity Complexity

Globalization Decentralization Multi-stage supply chains

1.3.3

Risk mitigation inventory Postponement

Global supply chain contingency plans Supplier segmentation according to disruption risks

 isruption Tails and Overlays: When Ripple Effect D and Bullwhip Effects Intersect

One interesting observation from intersections of the ripple effect with the bullwhip effect is the so-called “disruption tail.”

19 1.3 · Ripple Effect in Supply Chains

1

Definition A disruption tail is a postponed effect of a residue from a disruption period, such as backlog and delayed orders, which appears in the post-disruption/recovery period and may influence supply chain operations and performance even after the disruption recovery. For example, a highly excessive inventory and destabilization of inventory dynamics can be observed after a capacity disruption recovery as a consequence of backlog orders if an inventory control policy is not adapted accordingly.

Several works (Ivanov 2019; Dolgui et al. 2020; Ivanov and Rozhkov 2020) have observed that non-coordinated ordering and production policies during a disruption period may result in backlog and delayed orders, the accumulation of which causes post-disruption supply chain instability, resulting in further delivery delays and non-recovery of supply chain performance. These residues have been named “disruption tails.” The extant literature suggests that specific “revival” policies must be developed for the transition from the recovery to disruption-free operation mode to avoid these “disruption tails.” On another note, there are interrelations of structural and operational vulnerabilities in the supply chain – the so-called disruption overlays. Definition A disruption overlay is an effect of intersecting operational and disruption risks leading to their mutual impact on each other and amplifying/dampening disruption propagations.

Disruption overlays occur if the negative consequences of changes in a supply chain structure as a result of a disruption are either amplified or mitigated by changes in the operational environment. Ivanov (Ivanov 2020a) proposed that overlays can be both reciprocal (i.e., complementary or mitigating) and aggravate (i.e., concurrent or enhancing). For example, a reciprocal overlay manifests when a single supplier disruption happens at a time of low demand and immediately after an inventory refill. An aggravate overlay can be encountered when simultaneous disruptions occur at the primary supplier (e.g., due to a strike) and a backup supplier (e.g., due to a natural catastrophe) at the times of high seasonal demand and lower inventory levels. If a demand increase is followed by capacity disruption and a demand decrease in the post-disruption period, strong mismatches of demand and supply can be observed both during and after the disruption periods (Ivanov 2021b).

20

1

Chapter 1 · Supply Chain Risks, Disruptions, and Ripple Effect

1.4

 uper Disruptions and Supply Chain Crises: Example S of the COVID-19 Pandemic

Supply chains of many companies showed low resilience and lack of recovery capabilities in the wake of the COVID-19 virus outbreak and the associated global pandemic in 2020–2021. ►►Example

Nearly 94% of the Fortune 1000 companies have been affected by coronavirus-driven supply chain disruptions in February 2020 (Fortune 2020). Supply chains have seen unprecedented vulnerabilities in lead times and order quantities, disruptions in networks structures, and severe demand fluctuations. A survey by ISM (Institute of Supply Management) (data for mid of April 2020) revealed that average lead times in supply chains increased by about 200% as compared to “normal” operations; moreover, Chinese and European manufacturing rates were at about one-half normal capacity, 53% and 50%, respectively. ◄

The COVID-19 pandemic has imposed a new disruption context unlike any seen before. At the pandemic times, for some supply chains, demand has drastically increased and the supply was not able to cope with that situation (e.g., facial masks, hand sanitizer, disinfection spray). As such, the question of market and society survivability was raised. For other supply chains, the demand and supply have drastically dropped resulting in the production stops (e.g., automotive industry), the danger of bankruptcies, and necessities of governmental supports. Here, the questions of supply chain survivability again arose. The pandemic context can be described as a supply chain crisis subject to the following characteristics: 55 Long-lasting crisis with hardly predictable scaling and uncertainty about both short-term and long-term future in the supply chain and its environment, i.e., a deep uncertainty (unknown-unknown) 55 Simultaneous disruptions in supply, demand, and logistics 55 Recovery is performed in the presence of a disruption and its hardly predictable scaling (i.e., coupling of supply chain and disruption dynamics) 55 Simultaneous and/or sequential openings and closures of suppliers, facilities and markets 55 Response and recovery strategies taking potential crisis recurrence and setbacks into consideration 55 Cascading effects of disruptions through the supply chain networks (i.e., the ripple effect). The pandemic represents as specific type of disruption risks, i.e., a super disruption that is characterized by four major aspects that differentiate the pandemic disruption from all other “instantaneous” (i.e., an event of an immediate impact) disruptions as shown in . Table  1.4 (Choi 2020; Ivanov 2020b, c; Ivanov and Dolgui 2020, 2021; Ivanov and Das 2020). With the COVID-19 pandemic, some novel  

21 1.4 · Super Disruptions and Supply Chain Crises…

1

..      Table 1.4  Instantaneous supply chain disruptions and super disruptions (supply chain crises) Instantaneous disruption, e.g., an earthquake or fire

Super disruption (supply chain crises), e.g., a pandemic

Impact

Instant impact

Long-lasting impact with hardly predictable scaling

Scope

Single supply chain echelon (with possible propagations)

Simultaneous disruptions in supply, demand, and logistics

Recovery

Begins when disruption is over

Is performed in the presence of a disruption and its unpredictable scaling

Timing

A single disruptive event

Simultaneous and/or sequential openings and closures of suppliers, facilities, and markets

context has been unveiled which goes beyond an instantaneous event-driven understanding and can be described as a supply chain crisis subject to some specific characteristics. ►►Definition

Supply chain crisis is a long-term disrupted state that is characterized by unstable current situation and uncertainty of future developments in the markets, supply base, and capacities entailing a danger of supply chain collapses and interruption of market provision with goods and services. ◄

First, a pandemic is characterized by a very long existence of disruption and its unpredictable scaling. Unlike other disruptions, the pandemic profile is characterized by gradual degradation and recovery rather than by instant disruptions of high magnitude and immediate reactions, as this is the case, e.g., for natural disasters. Since the pandemic is lasting long and its dynamics can be forecasted, e.g., by SIR models, this may allow for more time to adapt the supply chains. Besides, the gradual and long-lasting pandemic-like disruption profile may allow for avoidance of disruption tails and overlays leading to different insights as compared to instant-­ event disruptions. Second, the recovery begins in the presence of the disruption and its unpredictable scaling. This is different from “instantaneous” disruptions such as an earthquake, which hit the supply chain once, and the recovery begins when the disruption is over. Third, in the pandemic, we have simultaneous disruptions in demand, supply, and logistics infrastructure. This is different from classical disruption risks that usually impose shocks on either supply or demand. Fourth, the pandemic is challenging by the timing of disruption propagation driven by simultaneous disruption and epidemic outbreak propagations with simultaneous and/or sequential openings and closures of suppliers, facilities, and markets. Different supply chain echelons are bit by disruptions (i.e., due to lock-

22

1

Chapter 1 · Supply Chain Risks, Disruptions, and Ripple Effect

downs and quarantines entailing workforce shortages and surges in demand) at different times. This is a novel timing setting with simultaneous and/or sequential openings and closures of suppliers, facilities, and markets (Queiroz et al. 2020).

1.5

Questions and Discussion Points

In this chapter, disruption risks have been introduced as major vulnerability for supply chain networks. Fundamentally, there are four important categories that need to be analyzed in this domain, i.e., uncertainty, risk, disturbance, and disruption. 55 Can you explain how these four elements are mutually connected? 55 Can you summarize major differences and similarities related to stability, robustness, and resilience? 55 Can you summarize uncertainty factors and the corresponding handling ­measures? 55 Can you explain different supply chain risks? We have seen that demand, globalization, lead-time, and lean supply chains can be considered as reasons for risks in supply chains. Explain why we should care of risks in our supply chains? Supply chains are exposed to recurrent operational risks and exceptional disruption risks. Summarize in your own words factors relevant to disruption risks and explain the ripple effect providing some examples. 55 Where does information coordination help, and when would you suggest building up inventory buffers? 55 Is an explosion of a factory an operational or a disruptive risk? 55 What are low-frequency, high-impact risks? Are these operational or disruptive risks? 55 What are high-frequency, low-impact risks? Are these operational or disruptive risks? 55 What are consequences of the ripple effect that can be diagnosed in reduced supply chain performance? 55 What differences can you see between singular disruptions and the ripple effect? 55 Can you explain the notions of “disruption overlays” and “disruption tails”? The COVID-19 pandemic has shed light on a novel context of supply chain disruptions entailing the notion of supply chain crisis. 55 What is the difference between an instantaneous disruption and supply chain crisis? 55 How the firms can prepare for and manage supply chain crises?

23 References

1

References Akkermans, H., & van Wassenhove, L. N. (2018). Supply chain tsunamis: Research on low-­probability, high-impact disruptions. Journal of Supply Chain Management, 54(1), 64–76. Azadegan, A., Mellat Parast, M., Lucianetti, L., & Nishant, R.t; Blackhurst, J. (2020). Supply chain disruptions and business continuity: An empirical assessment. Decision Sciences, 51(1), 38–73. Basole, R. C., & Bellamy, M. A. (2014). Supply network structure, visibility, and risk diffusion: A computational approach. Decision Sciences, 45(4), 1–49. BBC News. (2011). Japan disaster: Supply shortages in three months. http://www.­bbc.­com/news/business-­12782566 Bier, T., Lange, A., & Glock, C. H. (2020). Methods for mitigating disruptions in complex supply chain structures: A systematic literature review. International Journal of Production Research, 58(6), 1835–1856. Bode, C., & Macdonald, J. R. (2017). Stages of supply chain disruption response: Direct, constraining, and mediating factors for impact mitigation. Decision Sciences, 48(5), 836–874. Brandon-Jones, E., Squire, B., Autry, C. W., & Petersen, K. J. (2014). A contingent resource-based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50(3), 55–73. Cao, S., Bryceson, K., & Hine, D. (2019). An Ontology-based Bayesian network modelling for supply chain risk propagation. Industrial Management and Data Systems, 119(8), 1691–1711. Chatfield, D. C., Hayya, J. C., & Cook, D. P. (2013). Stockout propagation and amplification in supply chain inventory systems. International Journal of Production Research, 51(5), 1491–1507. Chaudhuri, A., Srivastava, S. K., Srivastava, R. K., & Parveen, Z. (2016). Risk propagation and its impact on performance in food processing supply chain: A fuzzy interpretive structural modeling based approach. Journal of Modelling in Management, 11(2), 660–693. Chen, H. Y., Das, A., & Ivanov, D. (2019). Building resilience and managing post-disruption supply chain recovery: Lessons from the information and communication technology industry. International Journal of Information Management, 49, 330–342. Choi, T.-M. (2020). Risk analysis in logistics systems: A research agenda during and after the COVID-19 pandemic. Transportation Research Part E: Logistics and Transportation, 140, 101961. Chopra, S., & Sodhi, M.  S. (2004). Managing risk to avoid supply-chain breakdown. MIT Sloan Management Review, 46, 52–61. Chopra, S., & Sodhi, M.  S. (2014). Reducing the risk of supply chain disruptions. MIT Sloan Management Review, 55(3), 73–80. Chopra, S., Reinhardt, G., & Mohan, U. (2007). The importance of decoupling recurrent and disruption risks in a supply chain. Naval Research Logistics, 54(5), 44–555. Christopher, M. (2011). Logistics & supply chain management (4th ed.). Financial Times Prentice Hall. Craighead, C. W., Blackhurst, J., Rungtusanatham, M. J., & Handfield, R. B. (2007). The severity of supply chain disruptions: Design characteristics and mitigation capabilities. Decision Sciences, 38(1), 131–156. Demirel, G., MacCarthy, B.  L., Ritterskamp, D., Champneys, A., & Gross, T. (2019). Identifying dynamical instabilities in supply networks using generalized modeling. Journal of Operations Management, 65(2), 133–159. Disney, S. M., & Towill, D. R. (2002). A discrete transfer function model to determine the dynamic stability of a vendor managed inventory supply chain. International Journal of Production Research, 40, 179–204. Dolgui, A., Ivanov D. (2020). Exploring supply chain structural dynamics: New disruptive technologies and disruption risks. International Journal of Production Economics, 229, 107886. Dolgui, A., & Ivanov, D. (2021). Ripple effect and supply chain disruption management: New trends and research directions. International Journal of Production Research, 59(1), 102–109. Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect in the supply chain: An analysis and recent literature. International Journal of Production Research, 56(1–2), 414–430.

24

1

Chapter 1 · Supply Chain Risks, Disruptions, and Ripple Effect

Dolgui, A., Ivanov, D., & Rozhkov, M. (2020). Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain. International Journal of Production Research, 58(5), 1285–1301. Duong, L. N. K., & Chong, J. (2020). Supply chain collaboration in the presence of disruptions: A literature review. International Journal of Production Research, 58(11), 3488–3507. El Baz, J., & Ruel, S. (2021). Can supply chain risk management practices mitigate the disruption im-pacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era. International Journal of Production Economics. https://doi.org/10.1016/j. ijpe.2020.107972. Essuman, D., Boso, N., & Annan, J. (2020). Operational resilience, disruption, and efficiency: Conceptual and empirical analyses. International Journal of Production Economics, Available online, https://doi.org/10.1016/j.ijpe.2020.107762. Fahimnia, B., Tang, C. S., Davarzani, H., & Sarkis, J. (2015). Quantitative models for managing supply chain risks: A review. European Journal of Operational Research., 247(1), 1–15. Fiksel, J., Polyviou, M., Croxton, K. L., & Pettit, T. J. (2015). From risk to resilience: Learning to deal with disruption. MIT Sloan Management Review, 56(2), 79–86. Garvey, M. D., Carnovale, S., & Yeniyurt, S. (2015). An analytical framework for supply network risk propagation: A Bayesian network approach. European Journal of Operational Research, 243(2), 618–627. Ghadge, A., Dani, S., Chester, M., & Kalawsky, R. (2013). A systems thinking approach for modelling supply chain risk propagation. Supply Chain Management: An International Journal, 18(5), 523–538. Ghadge, A., Weiß, M., Caldwell, N. D., & Wilding, R. (2019). Managing cyber risk in supply chains: A review and research agenda. Supply Chain Management: An International Journal, 25(2), 223– 240. Gholami-Zanjani, S. M., Klibi, W., Jabalameli, M. S., & Pishvaee, M. S. (2020). A robust location-­ inventory model for food supply chains operating under disruptions with ripple effects. International Journal of Production Research. in press. Gianesello, P., Ivanov, D., & Battini, D. (2017). Closed-loop supply chain simulation with disruption considerations: A case-study on Tesla. International Journal of Inventory Research, 4(4), 257–280. Golan, M. S., Jernegan, L. H., & Linkov, I. (2020). Trends and applications of resilience analytics in supply chain modeling: Systematic literature review in the context of the COVID-19 pandemic. Environment Systems and Decisions. https://doi.org/10.1007/s10669-­020-­09777-­w. Goldbeck, N., Angeloudis, P., & Ochieng, W. (2020). Optimal supply chain resilience with consideration of failure propagation and repair logistics. Transportation Research: Part E, 133, 101830. Gupta, V., & Ivanov, D. (2020). Dual sourcing under supply disruption with risk-averse suppliers in the sharing economy. International Journal of Production Research, 58(1), 291–307. Gupta, S., Starr, M., Zanjirani Farahani, R., & Matinrad, N. (2016). Disaster management from a POM perspective: Mapping a new domain. Production and Operations Management, 25(10), 1611–1637. Gupta, V., Ivanov, D., & Choi, T.-M. (2021). Competitive pricing of substitute products under supply disruption. Omega, 101, 102279. Gurnani, H., Mehrotra, A., & Ray, S. (2012). Supply chain disruptions: Theory and practice of managing risk. London: Springer. Haren, P., D.  Simchi-Levi (2020). How coronavirus could impact the global supply chain by mid-­ march. Harward Business Review 2020., https://hbr.­org/2020/02/how-­coronavirus-­could-­impact-­ the-­global-­supply-­chain-­by-­mid-­march?ab=hero-­subleft-­1. Accessed on 10 Mar 2020. He, J., Alavifard, F., Ivanov, D., & Jahani, H. (2019). A real-option approach to mitigate disruption risk in the supply chain. Omega: The International Journal of Management Science, 88, 133–149. Heckmann, I. (2016). Towards supply chain risk analytics. Wiesbaden: Springer. Ho, W., Zheng, T., Yildiz, H., & Talluri, S. (2015). Supply chain risk management: A literature review. International Journal of Production Research, 53(16), 5031–5069. Hosseini, S., Ivanov, D., & Dolgui, A. (2019a). Ripple effect modeling of supplier disruption: Integrated Markov Chain and Dynamic Bayesian Network Approach. International Journal of Production Research. in press.

25 References

1

Hosseini, S., Ivanov, D., & Dolgui, A. (2019b). Review of quantitative methods for supply chain resilience analysis. Transportation Research: Part E, 125, 285–307. Huffington Post. (2015). Toyota among Japanese automaker extending plant closures. http://www.­ huffingtonpost.­com/2011/03/16/japans-­auto-­plant-­closures_n_836653.­html. Ivanov, D. (2018a). Structural dynamics and resilience in supply chain risk management. New York: Springer. Ivanov, D. (2018b). Revealing interfaces of supply chain resilience and sustainability: A simulation study. International Journal of Production Research, 56(10), 3507–3523. Ivanov, D. (2019). Disruption tails and revival policies: A simulation analysis of supply chain design and production-ordering systems in the recovery and post-disruption periods. Computers and Industrial Engineering, 127, 558–570. Ivanov, D. (2020a). “A blessing in disguise” or “as if it wasn’t hard enough already”: Reciprocal and aggravate vulnerabilities in the supply chain. International Journal of Production Research, 58(11), 3252–3262. Ivanov, D. (2020b). Predicting the impact of epidemic outbreaks on the global supply chains: A simulation-based analysis on the example of coronavirus (COVID-19 / SARS-CoV-2) case. Transportation Research – Part E, 136, 101922. https://doi.org/10.1016/j.tre.2020.101922. Ivanov, D. (2020c). Viable supply chain model: Integrating agility, resilience and sustainability perspectives. Lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research. https://doi.org/10.1007/s10479-­020-­03640-­6. Ivanov, D. (2021). Lean resilience: AURA (active usage of resilience assets) framework for post-­ COVID-­19 supply chain management. International Journal of Logistics Management, forthcoming. Ivanov D. (2021b). Exiting the COVID-19 Pandemic: After-Shock Risks and Avoidance of Disruption Tails in Supply Chains. Annals of Operations Research, https://doi.org/10.1007/s10479-021-04047-7 Ivanov, D., & Das, A. (2020). Coronavirus (COVID-19 / SARS-CoV-2) and supply chain resilience: A research note. International Journal of Integrated Supply Management, 13(1), 90–102. Ivanov, D., & Dolgui, A. (2019). Low-Certainty-Need (LCN) supply chains: A new perspective in managing disruption risks and resilience. International Journal of Production Research, 57(15– 16), 5119–5136. Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915. Ivanov, D., & Dolgui, A. (2021). OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications. International Journal of Production Economics, 232, 107921. Ivanov, D., & Rozhkov, M. (2020). Coordination of production and ordering policies under capacity disruption and product write-off risk: An analytical study with real-data based simulations of a fast moving consumer goods company. Annals of Operations Research, 291(1–2), 387–407. Ivanov, D., & Sokolov, B. (2010). Adaptive supply chain management. London: Springer. Ivanov, D., & Sokolov, B. (2013). Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis, and adaptation of performance under uncertainty. European Journal of Operational Research, 224(2), 313–323. Ivanov, D., & Sokolov, B. (2020). Simultaneous structural-operational control of supply chain dynamics and resilience. Annals of Operations Research, 283(1), 1191–1210. Ivanov, D., Sokolov, B., & Pavlov, A. (2014a). Optimal distribution (re)planning in a centralized multi-stage network under conditions of the ripple effect and structure dynamics. European Journal of Operational Research, 237(2), 758–770. Ivanov, D., Sokolov, B., & Dolgui, A. (2014b). The ripple effect in supply chains: Trade-off ‘efficiency-­ flexibility-­resilience’ in disruption management. International Journal of Production Research, 52(7), 2154–2172. Ivanov, D., Pavlov, A., Pavlov, D., & Sokolov, B. (2017a). Minimization of disruption-related return flows in the supply chain. International Journal of Production Economics, 183, 503–513. Ivanov, D., Dolgui, A., Sokolov, B., & Ivanova, M. (2017b). Literature review on disruption recovery in the supply chain. International Journal of Production Research, 55(20), 6158–6174.

26

1

Chapter 1 · Supply Chain Risks, Disruptions, and Ripple Effect

Ivanov, D., Dolgui, A., & Sokolov, B. (2019a). The impact of digital technology and industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846. Ivanov, D., Dolgui, A., & Sokolov, B. (Eds.). (2019b). Handbook of ripple effects in the supply chain. New York: Springer. Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2021). Global supply chain and operations management: A decision-oriented introduction into the creation of value (3rd ed.). Cham: Springer Nature. Kinra A., Ivanov D., Das A., & Dolgui A. (2020). Ripple effect quantification by supply risk exposure assessment. International Journal of Production Research, 58(19), 5559–5578. Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust value-creating supply chain networks: A critical review. European Journal of Operational Research, 203(2), 283–293. Knight, F. (1921). Risk, uncertainty and profit: Hart, Schaffner and Marx Prize Essays 31. Boston: Houghton Mifflin. Lee, H.  L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43(4), 546–558. Li, Y., & Zobel, C. W. (2020). Exploring supply chain network resilience in the presence of the ripple effect. International Journal of Production Economics, 228, 107693. Li, Y., Zobel, C. W., Seref, O., & Chatfield, D. C. (2020). Network characteristics and supply chain resilience under conditions of risk propagation. International Journal of Production Economics, 223, 107529. Li, Y., Chen, K., Collignon, S., & Ivanov, D. (2021). Ripple effect in the supply chain network: Forward and backward disruption propagation, network health and firm vulnerability. European Journal of Operational Research, 291(3), 1117–1131. Liberatore, F., Scaparra, M.  P., & Daskin, M.  S. (2012). Hedging against disruptions with ripple effects in location analysis. Omega, 40, 21–30. Linkov, I., & Kott, A. (2019). Fundamental concepts of cyber resilience: Introduction and overview. In A.  Kott & I.  Linkov (Eds.), Cyber resilience of systems and networks (pp.  1–25). Cham: Springer. Llaguno, A., Mula, J., & Campuzano-Bolarin, F. (2021). State of the art, conceptual framework and simulation analysis of the ripple efect on supply chains. International Journal of Production Research. https://doi.org/10.1080/00207543.2021.1877842. Macdonald, J. R., Zobel, C. W., Melnyk, S. A., & Griffis, S. E. (2018). Supply chain risk and resilience: Theory building through structured experiments and simulation. International Journal of Production Research, 56(12), 4337–4355. Manager Magazine. (2021). Warum die Chiphersteller die Autoindustrie schmoren lassen. (in German). https://www.­manager-­magazin.­de/unternehmen/autoindustrie/autoindustrie-­warum-­ die-­versorgung-­mit-­mikrochips-­stockt-­und-­die-­produktion-­laehmt-­a-­970562ab-­c6cf-­4b23-­9097-­ f089594eb2d5?utm_source=pocket-­newtab-­global-­de-­DE. Aaccessed on 21 Jan 2021. March, J.  G., & Shapira, Z. (1987). Managerial perspectives on risk and risk taking. Management Science, 33, 1404–1418. Markowitz, H. (1952). Portfolio selection. The Journal of Finance, VII(1), 77–91. Marsh P, Brown K, Anderlini J, Johnston T, Waldmeir P, Jung S, Rickards J, Weitzman H, Ward A (2011) Japan crisis impact on the supply chain: Global industries consider their options. Financial Times Europe: No. 37568 17 March, 16. Moetz, A., Stylos-Duesmann, P., & Otto, B. (2019). Schedule instability in automotive production networks: The development of a network-oriented resequencing method. Proceedings of the 9th IFAC conference on manufacturing modelling, management and control (MIM 2019), edited by Dmitry Ivanov, Alexandre Dolgui, Farouk Yalaoui. IFAC-PapersOnLine, 52(13), 2810–2815. Nair, A., & Vidal, J.  M. (2011). Supply network topology and robustness against disruptions: An investigation using multiagent model. International Journal of Production Research, 49(5), 1391– 1404. Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S. J., & Wamba, S. F. (2017). The role of big data in explaining disaster resilience in supply chains for sustainability. Journal of Cleaner Production, 142(2), 1108–1118.

27 References

1

Paul, S. K., & Chowdhury, P. (2021). A production recovery plan in manufacturing supply chains for a high-demand item during COVID-19. International Journal of Physical Distribution & Logistics Management, 51(2), 104–125. Paul, S. K., Sarker, R., & Essam, D. (2017). A quantitative model for disruption mitigation in a supply chain. European Journal of Operational Research, 257(3), 881–895. Paul, S., Sarker, R., Essam, D., & Lee, P. T.-W. (2019). Managing sudden disturbances in a three-tier manufacturing supply chain: A mathematical modelling approach. Annals of Operations Research, 280, 299–335. Pavlov, A., Ivanov, D., Pavlov, D., & Slinko, A. (2019). Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics. Annals of Operations Research. https://doi.org/10.1007/s10479-­ 019-­03182-­6. Peck, H. (2005). Drivers of supply chain vulnerability: An integrated framework. International Journal of Physical Distribution & Logistics Management, 35(4), 210–232. Pettit, J. T., Fiksel, J., & Croxton, L. K. (2010). Ensuring supply chain resilience: Development of a conceptual framework. Journal of Business Logistics, 31(1), 1–21. Ponomarov, S., & Holcomb, M. (2009). Understanding the concept of supply chain resilience. International Journal of Logistics Management, 20(1), 124–143. Pournader, M., Kach, A., & Talluri, S. (2020). A review of the existing and emerging topics in supply chain risk management literature. Decision Sciences, 51(4), 867–919. Quang, H. T., & Hara, Y. (2017). Risks and performance in supply chain: The push effect. International Journal of Production Research, 56, 1369. Queiroz, M. M., Ivanov, D., Dolgui, A., & Fosso, W. S. (2020). Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research. https://doi.org/10.1007/s10479-­020-­03685-­7. Rao, S., & Goldsby, T. J. (2009). Supply chain risks: A review and typology. The International Journal of Logistics Management, 20(1), 97–123. Rosenhead, J., Elton, M., & Gupta, S. K. (1972). Robustness and optimality as criteria for strategic decisions. Operational Research Quarterly, 23(4), 413–431. Saghafian, S., & Van Oyen, M. P. (2016). Compensating for dynamic supply disruptions: Backup flexibility design. Operations Research, 64(2), 390–405. Sahebjamnia, N., Torabi, S. A., & Mansouri, S. A. (2015). Integrated business continuity and disaster recovery planning: Toward organizational resilience. European Journal of Operational Research, 242(1), 261–273. Sawik, T. (2020). Supply chain disruption management (2nd ed.). New York: Springer. Sawik, T. (2021). A linear model for optimal cybersecurity investment in industry 4.0 supply chains. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1856442. Scheibe, K.  P., & Blackhurst, J. (2018). Supply chain disruption propagation: A systemic risk and normal accident theory perspective. International Journal of Production Research, 56(1–2), 43–59. Shen, B., & Li, Q. (2017). Market disruptions in supply chains: A review of operational models. International Transactions Inoperational Research, 24, 697–711. Simchi-Levi, D., Schmidt, W., Wei, Y., Zhang, P. Y., Combs, K., Ge, Y., Gusikhin, O., Sander, M., & Zhang, D. (2015). Identifying risks and mitigating disruptions in the automotive supply chain. Interfaces, 45(5), 375–390. Simchi-Levi, D., Wang, H., & Wei, Y. (2018). Increasing supply chain robustness through process flexibility and inventory. Production and Operations Management, 27(8), 1491–1496. Snyder, L. V., Atan, Z., Peng, P., Rong, Y., Schmitt, A. J., & Sinsoysal, B. (2016). OR/MS models for supply chain disruptions: A review. IIE Transactions, 48(2), 89–109. Sodhi, M., & Tang, C. S. (2012). Managing supply chain risk. New York: Springer. Sokolov, B., Ivanov, D., Dolgui, A., & Pavlov, A. (2016). Structural quantification of the ripple effect in the supply chain. International Journal of Production Research, 54(1), 152–169. Tan, W. J., Zhang, A. N., & Cai, W. (2019). A graph-based model to measure structural redundancy for supply chain resilience. International Journal of Production Research, 57(20), 6385–6404. Tang, C. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451–488.

28

1

Chapter 1 · Supply Chain Risks, Disruptions, and Ripple Effect

Tang, O., & Musa, S. N. (2011). Identifying risk issues and research advancements in supply chain risk management. International Journal of Production Economics, 133, 25–34. Tang, L., Jing, K., He, J., & Stanley, H. E. (2016). Complex interdependent supply chain networks: Cascading failure and robustness. Physica A, 443, 58–69. Tomlin, B. (2006). On the value of mitigation and contingency strategies for managing supply chain disruption risks. Management Science, 52, 639–657. Torabi, S. A., Baghersad, M., & Mansouri, S. A. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research – Part E, 79, 22–48. Van der Vegt, G., Essens, P., Wahlstrom, M., & George, G. (2015). Managing risk and resilience. Academy of Management Journal, 58(4), 971–980. Waters, D. (2011). Supply chain risk management: Vulnerability and resilience in logistics (2nd ed.). London: Kohan Page. Wu, D., & Olson, D.  L. (2008). Supply chain risk, simulation, and vendor selection. International Journal of Production Economics, 114(2), 646–655. Xu, S., Zhang, X., Feng, L., & Yang, W. (2020). Disruption risks in supply chain management: A literature review based on bibliometric analysis. International Journal of Production Research, 58(11), 3508–3526. Yan, T., Choi, T. Y., Kim, Y., & Yang, Y. (2015). A theory of the nexus supplier: A critical supplier from a network perspective. Journal of Supply Chain Management, 51(1), 52–66. Yang, Z., Aydin, G., Babich, V., & Beil, D. R. (2009). Supply disruptions, asymmetric information, and a backup production option. Management Science, 55(2), 192–120. Yao, Y., & Fabbe-Costas, N. (2018). Can you measure resilience if you are unable to define it? The analysis of Supply Network Resilience (SNRES). Supply Chain Forum: an International Journal, 19(4), 255–265. Yoon, J., Talluri, S., Yildiz, H., & Ho, W. (2018). Models for supplier selection and risk mitigation: A holistic approach. International Journal of Production Research, 56(10), 3636–3661. Zhao, K., Zuo, Z., & Blackhurst, J. V. (2019). Modelling supply chain adaptation for disruptions: An empirically grounded complex adaptive systems approach. Journal of Operations Management, 65(2), 190–212.

29

Managing Supply Chain Resilience Contents 2.1

Historical Development – 31

2.2

 trategic Understanding of S Supply Chain Resilience – 32

2.3

 upply Chain Resilience S Framework – 35

2.4

 esilience Capabilities and R Recovery Strategies – 37

2.5

 ramework of Resilience F Capacity – 42

2.5.1 2.5.2 2.5.3

 bsorptive Capacity – 43 A Adaptive Capacity – 44 Restorative Capacity – 45

2.6

 osts and Value of Supply C Chain Resilience – 45

2.6.1

L CN (Low-Certainty-Need) Supply Chain Framework – 47 Lean Resilience: The AURA (Active Usage of Resilience Assets) Framework – 50

2.6.2

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 D. Ivanov, Introduction to Supply Chain Resilience, Classroom Companion: Business, https://doi.org/10.1007/978-3-030-70490-2_2

2

2.7

 upply Chain Resilience During S a Global Pandemic – 51

2.8

Discussion – 55 References – 55

31 2.1 · Historical Development

2

nnLearning Objectives In this chapter, we introduce the major concepts related to resilience management in supply chains. We explain how to build and use resilience for supply chain recovery. To this end, our learning objectives for this chapter are as follows: 55 Understand supply chain resilience and its strategic importance 55 Explain design and usage of resilience capabilities 55 Analyze resilience capacity concept 55 Decide on the trade-off resilience vs. efficiency 55 Illustrate supply chain resilience issues in the context of a super-disruption on the example of the COVID-19 pandemic

2.1

Historical Development Definition Supply chain resilience is the “ability to maintain, execute and recover (adapt) planned execution along with achievement of the planned (or adapted, but yet still acceptable) performance” (Ivanov 2018a). According to Hosseini et  al. (2019), supply chain resilience is “the firm’s capability to withstand, adapt, and recover from disruptions to meet customer demand, ensure target performance, and maintain operations in vulnerable environments.”

Foundations of supply chain resilience literature were developed in the first decade of 2000s. The works by Rice and Caniato (2003), Christopher and Peck (2004), Blackhurst et  al. (2005), Sheffi (2005), Wagner and Bode (2008), Ponomarov and Holcomb (2009), Pettit et al. (2010), Jüttner and Maklan (2011), Bode et al. (2011), Blackhurst et  al. (2011) provided major definitions that have been rooted in and motivated by a number of crucial supply chain disruptions at the beginning of the twenty-first century. Ivanov (2018a, p. 23) presented a historical overview of severe supply chain disruptions classifying them in natural disasters (e.g., tsunamis), manmade disruptions (e.g., fire or strike), and financial disruptions (e.g., financial crisis or bankruptcy). These events have been considered as severe disruption risks in contrast to more “light” operational risks such as demand fluctuations or delivery delays. The works by Brandon-Jones et  al. (2014), Melnyk et  al. (2014), Scholten and Schilder (2015), Ambulkar et  al. (2015), and Chowdhury and Quaddus (2017) extended supply chain resilience literature in relation to collaboration, agility, and adaptability. Modeling and simulation studies for resilient supplier selection (Silbermayr and Minner 2014; Torabi et al. 2015; Sawik 2011, 2013, 2019; Behzadi et al. 2017, 2018; Hosseini et al. 2019b), resilient supply chain design (Klibi et al. 2010; Losada et al. 2012; Khalili et al. 2016; Yildiz et al. 2016; Azaron et al. 2020), supply chain recovery (Lücker and Seifert 2017; Lücker et al. 2020; Paul and Rahman 2018; Ivanov et al. 2016b), and network theory (Li and Zobel 2020; Chauhan et al. 2021) have extensively dealt with network, process, and supplier resilience analysis.

32

2

Chapter 2 · Managing Supply Chain Resilience

Ivanov et al. (2014a, b) introduced a specific perspective in supply chain resilience that can arise from disruption propagation through the network, i.e., the ripple effect. The ripple effect has been further investigated by Ojha et al. (2018), Li et al. (2021), Hosseini and Ivanov (2019, 2020), and Li and Zobel (2020). To assess the impact of disruptions on supply chain performance, supply chain resilience assessment literature has been developed (Hosseini and Ivanov 2019; Dixit et al. 2020; Behzadi et al. 2020; Fattahi et al. 2020). For analysis of dynamic and feedback aspects of supply chain resilience, control theory (Spiegler et al. 2012; Ivanov et al. 2018; Pavlov et al. 2018) and simulation (Ghadge et al. 2013, Ivanov 2017a, b, Ivanov 2020c, Macdonald et al. 2018) have been used. Interrelations of supply chain resilience and sustainability have been coined by Fahimina and Jabarzadeh (2016), Ivanov (2018b), and Pavlov et al. (2019)). Utilization of supply chain endto-end visibility for supply chain resilience has been studied in the context of big data analytics, digitalization, and Industry 4.0 (Park et al. 2018; Cavalcante et al. 2019; Ivanov et al. 2019; Ivanov and Dolgui 2020c; Ralston and Blackhurst 2020; Dolgui et al. 2020a, b; Dubey et al. 2020) . Supply chains must be designed in a way to withstand disruptions (i.e., supply chain should exhibit low vulnerability) and recover from disruptions quickly and at a minimal cost (i.e., supply chain should offer high recoverability). Indeed, disruption risks such as tsunamis, fires, and strikes may have high impact on supply chain operations and performance. Thus far, a lack of supply chain resilience may result in financial losses, mismatches of demand and supply, and destabilization of normal operational policies in production, distribution, and inventory control in the face of today’s inevitable supply chain disruptions (Ivanov et  al. 2016b, Pavlov et al. 2019; Gupta et al. 2020, Yoon et al. 2020). These disruptions share a common set of attributes, i.e., 55 Discrete-event orientation (i.e., disruptions as singular or combined events) 55 Single feedback control (i.e., normal → disruption → return-to-normal cycle) 55 Finite-dimensional view on economic performance within a fixed time horizon as the major resilience assessment criterion In this chapter, we offer an introduction to major concepts and principles of supply chain resilience. We do not pretend to be encyclopedic and refer the interested reader to the supply chain resilience reviews by Hosseini et al. (2019b), Chowdhury and Quaddus (2017), and Kamalahmadi & Mellat-Parast (2016a, b) for more details. We also provide an extensive bibliography on supply chain resilience which can help to identify relevant studies and methods for resilience analysis.

2.2

Strategic Understanding of Supply Chain Resilience

Ivanov et al. (2021) consider resilience as a major component of supply chain strategies. In particular, they position the notions of design-for-efficiency and design-­ for-­resilience as follows.

33 2.2 · Strategic Understanding of Supply Chain Resilience

2

Definition Design-for-efficiency: efficient and responsive supply chains and operations are coined by lean and agile principles. The key idea of such leagile operations and supply chain designs is to utilize the available resources (i.e., material, time, capital, technology, and workforce) at the highest possible degree of efficiency to avoid waste and maximize p ­ rofitability. Design-for-resilience: resilient supply chain and operations are designed to absorb unexpected, severe disruptions (e.g., natural disasters, fires at facilities, strikes, or pandemic outbreaks) and restore operations thereafter. Resilience helps mitigate the impact of disruptions using some redundancy (e.g., inventory, capacity buffers, or back-up suppliers) and recover to an original or even better performance later.

Understanding of strategic importance of supply chain resilience can be illustrated when considering in detail efficient, responsive, and resilient supply chain strategies (. Table 2.1). The literature on supply chain strategic management usually points to efficiency and responsiveness as two major strategies for supply chains (Fischer 1997). Responsive supply chains focus on demand fulfillment and customer satisfaction while efficient supply chains try to utilize advantages of lean production and economy of scale (Ivanov et al. 2021). Characteristics of responsive supply chains are a fast response to customer demand, low inventory, and flexible suppliers. Also, lead times are reduced to enable a swift reaction to demand fluctuations. Margins are comparatively high to suitably fulfill the company’s financial needs. Responsive supply chains are best fit where market requirements are unpredictable and changeable. When looking at efficient supply chains, almost always the opposite is the case. The market situation is stable in demand and price. The variety of products is also comparatively low. Efficient supply chains concentrate mainly on lowering costs via high capacity utilization, minimizing inventory, and contracting suppliers from low-cost countries. Accordingly, margins are kept at a low level. Efficient supply chain strategy also tries to reduce lead time, but not at the expense of higher costs. In the resilient supply chain, the major focus is directed toward ensuring operations continuity and demanding fulfillment in the presence of disruptions. Such a strategy builds on some redundancies such as risk inventory and capacity buffers as well as on flexibility (e.g., postponement and capacity pooling). Obviously, the companies should strive a balance between efficiency, responsiveness, and resilience in order to design profitable and disruption-resistant value-adding systems.  

►►Example

In practice, firms should target an integrated system that combines elements of efficiency, resilience, and responsiveness. For example, PepsiCo has developed a global supply chain for beverages using coconut water. To utilize the advantages of efficiency, PepsiCo has selected primary suppliers and manufacturers in Southeast Asia. To increase resilience in light of frequent natural disasters in this geographical region, PepsiCo established

34

2

Chapter 2 · Managing Supply Chain Resilience

backup manufacturing capacities in other regions. Moreover, they utilized operations management models to use inventory for protection against disruptions in an efficient way. In terms of responsiveness, PepsiCo has located several packaging plants close to the markets to reduce the lead time. This agility capability can also be considered for resilience since the regional packaging plants can be used in case of a disruption at a central packaging plant. ◄

..      Table 2.1  Efficient, responsive, and resilient supply chain strategies Criterion

Efficient supply chain

Responsive supply chain

Resilient supply chain

Primary goal

Supply demand at the lowest cost

Respond quickly to demand

Ensure demand fulfillment in the presence of disruptions

Network organization

Centralized, global

Decentralized, responsive, local-global balance

Decentralization, structural variety, diversification, localization, segmentation

Product design strategy

Maximize performance at minimum product cost

Create modularity to allow postponement of product differentiation

Re-purposing, postponement to ensure product flexibility, product substitution, capacity pooling

Pricing strategy

Lower margins because price is a prime customer driver

Higher margins because price is not a prime customer driver

Higher prices caused by the costs of resilience

Manufacturing strategy

Lower costs through high utilization

Maintain capacity flexibility to buffer against demand/supply uncertainty

Capacity reservations and flexibility

Inventory strategy

Minimize inventory to lower cost

Maintain buffer inventory to deal with demand/supply uncertainty

Risk mitigation inventory

Lead time strategy

Reduce, but not at the expense of costs

Reduce aggressively, even if the costs are significant

Lead time reservations

Supplier strategy

Select based on cost and quality

Select based on speed, flexibility, reliability, and quality

Supplier risk exposure analysis; backup suppliers and dual sourcing

Based on Fisher (1997) and Ivanov and Dolgui (2019)

2

35 2.3 · Supply Chain Resilience Framework

2.3

Supply Chain Resilience Framework

Recall that supply chain resilience is the “ability to maintain, execute and recover (adapt) planned execution along with achievement of the planned (or adapted, but yet still acceptable) performance is therefore the next objective property of the supply chain” (Ivanov 2018a). Consider . Fig. 2.1 as an illustration. Supply chain resilience looks at maintaining some desired performance despite disruptions. Using some proactive capabilities (i.e., inventory), a supply chain can absorb negative disruption impacts (e.g., supply unavailability) without performance degradation. However, if proactive capabilities do not help, performance (e.g., on-time delivery or revenue) can decline. In this case, reactive capabilities should be employed to restore the performance and operations. This takes time and creates costs. Thus far, building a resilient supply chain is based on mitigating risks, preparedness for disruptions, stabilization, and recovery (see . Fig. 2.2).  

 

Performance

Disruption

Recovered performance time for recovery 0

Time

..      Fig. 2.1  Supply chain resilience concept

Resilience

Resistance

Redundancy

Recovery

Flexibility

Pre-disruption ..      Fig. 2.2  Supply chain resilience

Stabilization

Adaptation

Post-disruption

36

Chapter 2 · Managing Supply Chain Resilience

Case Study: “Supply Chain Resilience Management”

2

A special focus of supply chain management at Toyota is risk and disruption management (Toyota 2021). Many parts of the Toyota’s supply chain are located in areas that are likely to be hit by an earthquake (Marsh et al. 2011). As such, the risk that Toyota’s supply chain might suffer from those disasters is rising, and the damage could severely impact production and other activities. Given this context, one can assume that Toyota would suffer greatly from such a disaster and so should make preparations to affect early recovery. For these reasons, Toyota reassessed its business continuity plan. The foremost premise of Toyota’s business continuity plan is to work on preparedness before and recovery after disaster happens. Learning from previous experiences, Toyota has prepared a nationwide framework that utilizes the warehouses and logistics network throughout Japan for sending relief supplies to disaster-affected areas. In addition to stocking emergency supplies at the distributors nationwide, Toyota has also built a framework for sending relief supplies to the disaster-affected distributors. Taking into consideration possible problems such as motor fuel shortages, this framework is important for delivering quick and reliable support to disaster-affected sites. The risk management committee at Toyota organizes meetings twice a year to identify risks that may affect business activities and to take preventative actions against the negative impacts of those risks. The committee members include the global chief risk officer (CRC), regional CRSs, and all senior managers and chief officers. They work to manage and prevent the major risks in the regions and report on any immediate and serious disruptions. Toyota also has invested in new capabilities to improve supply chain resilience. Working with its partners, the company created a database to visualize supply networks for each component. If disaster strikes, Toyota can immediately identify the network at risk. The database identifies components that are supplied by only one manufacturer and are difficult to replace. The company then decreases dependence on solo providers by reducing the number of unique designs and sharing equipment specifications with parts production facilities and suppliers. Discussion 55 What are the main decisions involved with disruption management? 55 Why is it risky to rely on single sourcing when considering the tier-1 suppliers? 55 How can we prepare a supply chain for maintaining business continuity in a case of disruptions? 55 What digital technologies can help in resilience improvement? Sources: Marsh et al. (2011) Toyota (2021)

2

37 2.4 · Resilience Capabilities and Recovery Strategies

Vulnerabilities (V) A Resilience

C

B Capabilities (C)

Unbalanced High V & Low C

Excessive risk

Balanced Portfolio of C matched to the pattern of V

Improved performance

Unbalanced Low V & High C

Improved performance

..      Fig. 2.3  Balancing vulnerabilities and capabilities (based on Pettit et al. 2010)

Balancing vulnerabilities and capabilities is therefore a major concern in supply chain resilience management (. Fig. 2.3). Recall that one of the main objectives of supply chain management is to increase total supply chain output performance, which is basically referred to as supply chain effectiveness (i.e., sales and service level) and efficiency (supply chain costs). At the same time, achievement of planned performance can be hindered by disruptions in a real-time execution environment. This requires supply chain protection against and efficient reaction to disruptions. Therefore, supply chains need to be planned to be robust and resilient enough to (1) maintain their basic properties and ensure execution and (2) be able to adapt their behavior in the case of disruptions in order to achieve planned performance using recovery actions Ivanov (2021), Ivanov and Das (2020), Ivanov and Dolgui (2021), Panetto et al. (2019), Pavlov et al. (2020), Sawik (2016, 2020), Schmitt et al. (2017), Sokolov et al. (2020). Supply chain resilience management is based on four major areas: Identify, Quantify, Mitigate, and Respond (. Fig. 2.4). The first stage is to identify the risks related to network design structure, integrated supply chain processes, and individual supplier risks. Once the risks are identified, they need to be quantified. Similarly, resilience and the ripple effect are assessed. Next stage is to protect the supply chain by inventory, capacity agility, and backup suppliers to mitigate the negative impact of possible disruptions. If a disruption happens, the objective is to respond by deployment of contingency plans, recovery strategies, and situational adaptations.  

 

2.4

Resilience Capabilities and Recovery Strategies

There are three major resilience capabilities (assets): (i) Redundancies (e.g., risk mitigation inventories, subcontracting capacities, backup supply, and transportation infrastructures) (ii) Recovery flexibility and contingency plans (iii) End-to end supply chain visibility

38

Chapter 2 · Managing Supply Chain Resilience

• Identify

2

• Quantify Risk KPIs Network risks Process risks Supplier risks

Contingency plans Recovery strategies • Respond

Adaptability

Resilience KPIs Ripple effect KPIs Inventory Agile capacity Backup facilites • Mitigate

..      Fig. 2.4  Supply chain resilience management

>>Important Observation Supply chain resilience is based on proactive planning of some preparedness measures (e.g., backup suppliers and risk mitigation inventory) and reactive measures (i.e., deployment of contingency plans) to recover in case of disruptions.

In . Table 2.2, we summarize some of the existing views on proactive and reactive resilience capabilities in the supply chain. . Figure 2.5 provides an illustration of major resilience capabilities. Supply chain resilience capabilities can be classified into two groups: redundancy and adaptation. In redundancy, different reserves (material inventory, capacities, and network design redundancy) as well as facility fortification can be named. For example, Lücker and Seifert (2017), Lücker et  al. (2020), and analyzed the issues of risk mitigation inventory and reserve capacity on supply chain resilience. The redundancies are intended to protect the supply chain against disruptions based on certain reserves. This issue is related to the supply chain robustness.  

 

►►Example

Many companies invest in structural redundancy (e.g., Toyota extends its supply chain subject to multiple sourcing and building new facilities on the supply side). ◄

Supply chain adaptation has four major dimensions: scalability (e.g., online retailers used capacity expansions for coping with surges in demand during the COVID19 pandemic), process flexibility (e.g., auto manufacturers re-purposed their

2

39 2.4 · Resilience Capabilities and Recovery Strategies

..      Table 2.2  Resilience capabilities of the supply chain References

Main supply chain drivers

Planning type

Vulnerable part

Spiegler et al. (2012)

Surplus inventory

Proactive

Manufacturing

Carvalho et al. (2012)

Surplus inventory

Proactive

Manufacturing

Sawik (2013)

Surplus inventory

Proactive

Supply

Chowdhury and Quaddus (2015)

Backup supply, alternative transportation routing, quick respond, information integration, cooperation, and collaboration

Proactive and reactive

Supply and manufacturing

Chowdhury and Quaddus (2017)

Quick response, quick recovery, information sharing, backup capacity

Proactive and reactive

Supply

Brusset and Teller (2017)

Integration capabilities, external capabilities, flexibility

Proactive

Supply

Ni et al. (2018)

Backup facility, capacity expansion

Proactive

Facility

Ivanov and Dolgui (2019)

Structural complexity reduction, process and resource utilization flexibility, efficient parametric redundancy

Proactive and reactive

Supply, facility

Hosseini et al. (2019b)

Supplier’s reliability, segregation of suppliers, backup supplier, surplus capacity of supplier, additional restorative capacity of supplier

Proactive and reactive

Supply

Based on Hosseini et al. (2019b)

production from cars to ventilators), structural reconfiguration (e.g., usage of backup suppliers), and intertwining (e.g., collaboration of commercial and healthcare supply chains) (Ivanov 2021b). Besides, product substitution can also be used to adapt supply chains (Gupta et al. 2020). Moreover, organizations can learn from previous disruptions, and so improve future resilience (Chen et al. 2021). Adaptation is connected with flexibility with regard to both operations and resources (Tang and Tomlin 2008). >>Important Observation Backup suppliers belong to standard resilience capabilities. While this approach is relatively easy to implement for the tier-1 suppliers, it might become difficult for firms to find appropriate backups at the tier-2/tier3 levels due to insufficient visibility and expertise. Therefore, in some cases, it can be preferable to have a strategic relationship with a single supplier and invest in recovering from disruption when

40

Chapter 2 · Managing Supply Chain Resilience

Redundancy

2

Adaptation Intertwining

Ecosystem

Network

Network design redundancy

Structural reconfiguration

Flow

Inventory and capacity reservations

Capacity scalability

Node/Arc

Facility fortification

Process flexibility

..      Fig. 2.5  Supply chain resilience capabilities

necessary. Such a cooperative strategy is a very popular approach in industry. For instance, Li & Fung, the global supply chain intermediary, has financed its long-­ term suppliers so that they can initiate production for Li & Fung orders (Tang et al. 2017). In another example, the power generator manufacturer in Sichuan, China, has immediately offered an incentive payment to its disrupted supplier to initiate production recovery after the massive earthquake in May 2008 (Li et al. 2017). In other words, the responsibility for finding backups at tier-2/tier-3 levels in a disruption case can be moved to strategic tier-1 suppliers.

. Figure 2.6 summarizes different elements of redundancy and adaptation in supply chain resilience context based on structural, process, and parametric views The issues of segmentation, diversification, backup suppliers, facility fortification, globalization, and localization are considered important managerial levers to increase supply chain resilience at the proactive and reactive stages (Dolgui et al. (2018), Hosseini et al. (2020a), Ivanov et al. (2016a, 2016b)). Backup and dual sourcing, postponement, product substitution, production capacity flexibility, and coordination are major elements of the contingency processes drivers to be addressed at the process management level. Understanding of resilience capabilities allows design and deploy recovery strategies. In . Table 2.3, elements of supply chain recovery strategies are classified into the areas of robustness and agility.  

 

41 2.4 · Resilience Capabilities and Recovery Strategies

Structural variety

Process flexibility

2

Parametric redundancy

• Decentralisation

• Backup sourcing

• Diversification

• Dual sourcing

• Risk mitigation inventory • Advanced purchasing • Capacity reservation • Lead time reserves

• Localisation • Segmentation

• Postponement and capacity pooling

• Fortification

• Product substitution • Coordination

..      Fig. 2.6  Supply chain resilience capabilities (Ivanov and Dolgui 2019)

..      Table 2.3  Supply chain recovery strategies Robustness

Agility

Source

Backup sourcing Risk inventory

Multiple sourcing Product substitution

Make

Backup production sites Facility fortification Risk inventory

Capacity agility and expansion Human-robot collaboration Additive manufacturing Postponement

Deliver

Backup warehouses Backup routes

Omni-channel Product substitution

►►Example

Businesses have taken steps to strengthen the resilience of their global supply chains to risks posed by natural or industrial HILF events, for example, Hurricane Katrina in 2005, the earthquake and tsunami in Japan in 2011, and an explosion at the BASF plant in 2016. Supply chain resilience has been fortified by investments in risk mitigation inventories; subcontracting capacities; backup supply and transportation infrastructures; and data-driven, real-time monitoring and visibility systems. For example, following Schmidt and Simchi-Levi (2013), Nissan has developed a supply chain resilience program that encompasses supply chain monitoring and visibility, geographic supply diversification, and flexible reallocation of demand and supply in the case of disruptions. Toyota has invested in new capabilities to improve supply chain resilience. Working with its partners, the company created a database to visualize supply networks for each component. If disaster strikes, Toyota can immediately identify the parts at risk.

42

2

Chapter 2 · Managing Supply Chain Resilience

The database identifies components that are supplied by only one manufacturer and are difficult to replace. The company then decreases dependence on solo providers by reducing the number of unique designs and sharing equipment specifications with parts production facilities and suppliers. ◄

Case Study: “Role of Collaboration and Trust in Supply Chain Resilience”

7 http://autonews.­com/article/20180730/OEM10/180739995/&template=print&n ocache=1 Once a supplier makes the cut, BMW is committed to the relationship for the long haul, Murat Aksel said (BMW Americas vice president for purchasing and supplier network). “When they become a supplier, then they are with us on the boat,” even in rough waters, he said. That trust was evident after a fire in May 2018 ripped through a Meridian Magnesium Products of America factory in Eaton Rapids, Michigan. The fire brought auto assembly lines around the country to a halt, affecting Ford Motor Co., General Motors, Fiat Chrysler Automobiles, Mercedes-Benz, and BMW. The Meridian factory produces instrument panel crossbeams  – a structurally important part on which a vehicle’s instrument panel is mounted. “It was not an easy part to source,” Aksel said. “We were threatened with not getting parts because machines were down, tools were broken.” BMW moved swiftly, flying 20 employees from its Landshut, Germany, foundry where the automaker produces castings for its manufacturing plants worldwide. Working with the supplier, the BMW foundry workers located and extracted the affected machining tools and had them transported to a different supplier  – Shiloh Industries in Clarksville, Tennessee. Line workers from BMW’s Spartanburg, SC, plant helped to get the production of the affected part started in the Tennessee plant. The in-house experience and decisive action had bottom-line consequences. BMW did not reveal how many units of vehicle production it lost, but the automaker said the total was “very low.” “We had people who knew exactly how the tool and the equipment worked and how to set it up quickly,” said Aksel. The cooperation BMW received was a result of trust, Aksel said. Meridian helped the automaker extract the tooling equipment from its fire-damaged site, and then Shiloh made its plant available for production.  

2.5

Framework of Resilience Capacity

The notion of resilience capacity has been introduced by Vugrin et al. (2011) and Biringer et al. (2013) considering three categories, each of which represents temporal attributes before, during, and after a disruption: absorptive capacity, adaptive capacity, and restorative capacity.

43 2.5 · Framework of Resilience Capacity

2

Definition Resilience capacity is a concept for analyzing system performance under uncertainty which consists of the resilience enhancement features that could increase the ability of a system to absorb, adapt, and restore itself after disruption.

Resilience capacity principles have been adopted to analyze supply chain resilience by Hosseini et al. (2019b, 2020b). We discuss them now and start our explanation with . Fig. 2.7.  

2.5.1

Absorptive Capacity

Absorptive capacity is related to supply chain robustness. It shows whether a supply chain can absorb disruptions without any changes in the system operations (Vugrin et al. 2011). ►►Example

For example, PepsiCo uses a backup packing plant in the United States and carries a risk mitigation inventory to cope with the disruptions in coconut water supply from South Asia (HBS 2017). ◄

Absorptive capacity refers to the preparedness measured prior to a disruption occurrence. Absorptive capacity can be viewed as the primary or “first line of defense” as it emphasizes on the ability of a system to absorb disruptive shocks.

Disruptive events

Supplier segmentation; multiplesourcing; inventory pre-preositioning

Absorptive capacity (1st line of defence)

Backup supplier; re-routing; capacity expansion Facility restoration; Workforce recovery; Technology restoration

Adaptive capacity (2st line of defence) Restorative capacity (3st line of defence) Resilience capacity

..      Fig. 2.7  Resilience capacity of supply chains with three lines of defense

2

44

Chapter 2 · Managing Supply Chain Resilience

2.5.2

Adaptive Capacity

In some case, absorptive capacity can help to withstand the disruptions. In other cases, the supply chain needs to be adapted to cope with disruptions (Ivanov 2010; Ivanov and Sokolov 2013). Adaptive capacity can be considered the second line of defense against disruptions. It refers to the supply chain capability to adapt during the disruption and recovery periods. ►►Example

For example, in a supply chain, contracts with backup suppliers can enable adaptive production capacity at a manufacturing facility when the original supplier is disrupted and inventory is exhausted. ◄ Case Study of ASOS: Building Resilient Supply Chains by Adaptive Capacity

A good example to display the importance of adaptive capacity in supply chains is the British online fashion retailer ASOS plc. ASOS currently ships to 240 countries and operates one main global distribution center located in Barnsley (UK). Around 70 percent of the stock is hold in Barnsley. The distribution center is crucial to ASOS as every item ASOS sells online is screened and checked there before it is shipped. ASOS also set up satellite warehouses and return centers in Ohio (USA), Sydney, China, and Berlin. On Friday, 20th June 2014, a fire at the central distribution center and warehouse in Barnsley (UK) containing about ten million boxes of packaging forced ASOS to cease trading. This is not the first time ASOS distribution has been affected by a fire incident. Its previous warehouse in Hemel Hempstead was severely damaged after an oil blast at oil depot in December 2005, just before ASOS was targeting the Christmas season. If we compare the two events and the supply chain reactions, we can see that ASOS has introduced an adaptive capacity and has designed its supply chain to deal with disruptions such as fires. While the operations in 2005 were hold off for about a month following the fire in the warehouse, the fire in 2014 forced ASOS to set its website offline and to stop taking orders over the weekend, and they needed only 2 days to resume trading again while about 20% of total stock was destroyed. This is remarkable and proves that a good risk management is in operation at ASOS and that ASOS learned from the fire ­incident in 2005. In comparison to 2005, ASOS’s supply chain design structure included more warehouses. The layout of the main warehouse was redesigned. Finally, contingency plans have been developed. References: Degun (2014) Morrow (2014) Sheree (2014) Wearden (2014)

2

45 2.6 · Costs and Value of Supply Chain Resilience

2.5.3

Restorative Capacity

Restorative capacity, or the third line of defense, refers to the supply chain capability to restore some facilities on the physical level. For example, after sever earthquakes and tsunamis, buildings of factories and suppliers can be physically destroyed, and people can also be affected. The restoration of the supply chain facilities, technological processes, and workers belong to this third line of defense.

Costs and Value of Supply Chain Resilience

2.6

Resilience design and management can be costly but the missing resilience can result in much higher losses if a supply chain is hit by disruptions. This trade-off presents a central issue in resilience management. High inventory, capacity reservations, and lead time reserves may not only help in increasing supply chain resilience, but they can also negatively influence supply chain efficiency (Aldrighetti et al. 2021). >>Important Observation Resilience can be expensive. Missing resilience can be fatal and entail supply chain collapses.

The resilient supply chain management requires a balanced approach to investments in resilience which allows for achieving maximum performance with disruption risk considerations at acceptable redundancy costs. These ideas are exemplified in . Figs. 2.8, 2.9, 2.10 and 2.11. In . Fig. 2.8, a supply chain of a retailer, a warehouse, and a factory is presented. The factory orders 100 units of a product every day from factory that is aligned with the daily production capacity of 100 units. No batching is considered in this simple example. Daily shipments are assumed. The warehouse holds risk mitigation inventory of 700  units as a proactive resilience policy. In . Fig.  2.9, factory capacity is disrupted for 7 days. Considering the risk mitigation inventory of 700 units and daily demand of 100 units, this disruption does not affect supply chain performance in terms of service level, i.e., the ratio of on-time delivered orders to all placed orders. Now let us consider . Fig. 2.10. . Figure 2.10 depicts that in case a factory stops producing for 14 days, service levels will be disrupted since risk mitigation inventory would help for 1 week only. In . Fig. 2.11, it can be observed that usage of backup factory mitigates the ripple effect and performance decrease. However, both risk mitigation inventory and a backup capacity increase supply chain costs It is evident that through the adaptation, supply chain flexibility and robustness are interrelated. From the dynamics point of view, the robustness elements can also be considered as flexibility elements and the flexibility elements can also be consid 

 

 

 

 

 

46

Chapter 2 · Managing Supply Chain Resilience

a Order of 100 units per day

2

Factory

Daily shipments

Capacity 100 units per day

Warehouse

Daily shipments

Retailer

Risk Mitigation Inventory 700 units

Performance Examples: – Annual sales – Service level – On-time delivery

7

14

21

28

35

42 Time (days)

..      Fig. 2.8  Supply chain without disruptions (Ivanov 2018a)

ered as robustness drivers. This is quite natural since both robustness and flexibility serve as “uncertainty cushions” of a supply chain. Balancing the elements of flexibility and robustness at proactive and reactive control loops, different constellations of service level, costs, and resilience can be analyzed (. Fig. 2.12). . Figure 2.12 depicts an example of a typical multi-objective analysis with regard to different proactive and reactive policies. We are interested in investigating the impacts of different combinations of disruptions (i.e., two scenarios in . Fig. 2.12), proactive mitigation strategies, and reactive recovery policies (A, B, C, and D) on the supply chain performance in form of service level and costs. The combinations of proactive mitigation strategies and reactive recovery policies (e.g., A – higher risk mitigation inventory and B – a back-up facility) may differ in costs and service-level impacts for different disruption scenarios. The task of quantitative analysis methods is to decide on what proactive and reactive policies need to be selected. In further chapters of this book, we will consider recent literature advancements and describe our own developments in this research field.  

 

 

2

47 2.6 · Costs and Value of Supply Chain Resilience

b Order of 100 units per day

Factory

Daily shipments

Capacity 100 units per day

Warehouse

Daily shipments

Retailer

Risk Mitigation Inventory 700 units

Factory is disrupted for 7 days Service Level % 100

7

14

21

28

35

42 Time (days)

..      Fig. 2.9  Supply chain with a proactive policy (risk mitigation inventory) (Ivanov 2018a)

2.6.1

LCN (Low-Certainty-Need) Supply Chain Framework

The LCN (low-certainty-need) supply chain framework approaches resilience and efficiency as a complementary setting (Ivanov and Dolgui 2019). Rather than opposing efficiency and resilience, the LCN framework suggests considering their mutual intersections to enhance each other based on synergetic effects in terms of supply chain resileanness. >>Important Observation LCN supply chain framework aims at designing a lean resilience, i.e., a resileanness.

Major costs of resilience are seen in disruption prediction, protective redundancy, and reactive capabilities as a result of a higher need for certainty entailing higher redundancy and recovery efforts. As such, LCN suggests studying these areas from the perspective of efficiency and resilience complementarity. Structural complexity, process inflexibility, and non-flexible usage of resources and insufficient parametric redundancy increase uncertainty and disruption risk propagation in the supply chain. The three key elements of the LCN supply chain framework are as follows:

48

Chapter 2 · Managing Supply Chain Resilience

a Order of 100 units per day

2 Factory

Daily shipments

Capacity 100 units per day

Daily shipments

Warehouse

Retailer

Risk Mitigation Inventory 700 units

Factory is disrupted for 7 days Service Level % 100

7

14

21

28

35

42 Time (days)

..      Fig. 2.10  Disrupted supply chain performance (Ivanov 2018a)

55 Structural simplification and variety 55 Process and resource utilization flexibility 55 Efficient parametric redundancy The ultimate objective of the LCN supply chain design is to develop the ability to operate according to planned performance regardless of environmental changes. As such, the LCN supply chain design possesses two critical capabilities: 55 Low need for uncertainty consideration in planning decisions 55 Low need for recovery coordination efforts >>Important Observation LCN framework constitutes a novel approach to managing supply chain resilience in an efficient manner. The main idea is to actively maintain efficient and agile “ready-­ to-­change” supply chain states in dynamics rather than pre-designing some static and costly “ready-to-absorb,” passive redundancies. Building some actively used redundant functions with a flexible use may help improve resilience better than anticipating disruptions.

2

49 2.6 · Costs and Value of Supply Chain Resilience

b Order of 100 units per day

Retailer

Warehouse

Factory Capacity 100 units per day

Back-up factory Factory is disrupted for 14 days

Service Level % 100

But: both higher safety stock and back-up factory increase costs!

7

14

21

28

35

42 Time (days)

..      Fig. 2.11  Supply chain with a proactive policy (back-up facility) (Ivanov 2018a)

Scenario I

Service level

Scenario II

Service level D

D

B

C

A B

A

C Costs

Costs

..      Fig. 2.12  Efficiency vs effectiveness trade-off in supply chain resilience management (Ivanov 2018a)

50

2

Chapter 2 · Managing Supply Chain Resilience

Structural variety, process flexibility, and parametrical redundancy ensure disruption resistance and recovery resource allocation and allow for supply chain operation in a broad range of environmental states. This means that planning activities in the LCN supply chains do not heavily rely on uncertainty prediction and proactive protection investments. Similarly, recovery coordination efforts are reduced to a minimum. Note that the LCN supply chain design does not necessarily imply higher costs, but rather seeks for an efficient combination of lean and resilient elements. 2.6.2

 ean Resilience: The AURA (Active Usage of Resilience L Assets) Framework

To recap, supply chain resilience is composed of the prediction of disruptive events and their propagating chains (i.e., the ripple effect), building of redundant assets (i.e., inventory) to absorb these anticipated disruptions, and the development of reactive plans for recovery afterward. Notably, redundancies and recovery measures are mostly considered in light of some anticipated events and are treated more or less as passive assets, which are “waiting” for use in case of an emergency. This, however, can be inefficient. Ivanov et al. (2021) conceptualized the design of the AURA (Active Usage of Resilience Assets) framework that takes the lens of lean resilience and is discussed in this section. >>Important Observation The AURA framework is built around two major concepts: (i) shifting the focus from disruption prediction efforts to building adaptable networks and (ii) value creation using resilience capabilities.

The AURA framework contains five areas: plan, source, make, deliver, and return. In all the five areas, the AURA framework provides guidance on how to utilize the resilience capabilities in business-as-usual situations, thereby creating inherent network adaptability that is equally valuable at “normal” and disruption times (Ivanov et  al. 2019; Ivanov and Dolgui 2020c, Hsieh and Chang 2020). Let us summarize the major elements that can be used both for value creation and for resilience. Plan: 55 Multiple structural networks designs 55 Adaptive mechanisms to transition between structural designs (Ivanov 2020b) 55 Supplier monitoring and transportation control using end-to-end visibility and data analytics capabilities to predict and analyze the disruptions Source: 55 Integration of backup suppliers into everyday business processes, e.g., regarding new product development or multiple sourcing for their tighter integration into the supply chain and a faster activation in case of an emergency.

51 2.7 · Supply Chain Resilience During a Global Pandemic

2

55 Product substitution is an important tool in managing resilience during supplier disruptions (Gupta et al. 2020) and can also be used as a sales and marketing instrument. 55 Supplier collaboration portals can be used to enhance end-to-end visibility and support recovery coordination in emergency cases. Make: 55 Capacity agility and flexibility – flexible production lines and the use of postponement principles are important both for market responsiveness and resilience. 55 Digital supply chain twins and Industry 4.0 technologies create end-to-end visibility and so contribute to resilience and value-adding activities (Ivanov et al. 2019; Ivanov and Dolgui 2020c). Deliver: 55 Decentralized logistics network designs help improve efficiency while simultaneously reducing the ripple effect. 55 Omnichannel distribution networks enhance both market responsiveness and resilience. 55 T&T technologies integrate disruption monitoring and real-time transportation control. Return: 55 Recycled materials can be used for resilience and sustainable operations (Hsieh and Chang 2020). 55 Closed-loop supply chain resources can be utilized in disruptions cases and used to enhance firm’s profitability.

2.7

Supply Chain Resilience During a Global Pandemic

The COVID-19 pandemic has been the strongest test to resilience of supply chains. Supply chain resilience capabilities have been established at many firms in response to more and more frequent natural and man-made disasters for the last two decades. Supply chain resilience management is based on the following disruption profile: “normal operations  – disruption  – recovery  – bounce-to-old-normal.” However, the “old” normal cannot always exist anymore in case of ­super-­disruptions and bouncing back can become problematic (Ivanov 2020a). The following resilience management elements have been mostly utilized by firms to cope with the COVID-19 pandemic: 55 Health and safety measures 55 Business continuity plans 55 Global multi-sourcing strategy

52

2

Chapter 2 · Managing Supply Chain Resilience

55 Risk inventory management: Pre-lockdown procurement of essential goods and materials 55 Securing transportation capacities 55 Collaboration with supply chain partners to synchronize decisions on shutting down and ramping up 55 Utilization of digital technologies (additive manufacturing, supply chain visibility, Industry 4.0) Analysis of supply chain operations and performances from January to December 2020 shows that redundant resilience assets (i.e., risk mitigation inventories, subcontracting capacities, backup supply, and transportation infrastructures) have not much helped firms. In automotive industry, most of the processes are organized just in time and inventory was available for a period of about 30 days. Moreover, suppliers and factories are located in different regions subject to different timing of shutdowns and lockdowns (regardless of whether globally or local). As such, even the available inventory or backup capacities were not accessible for longer periods of time. More positive experience has been done with agile capacities and data-driven, real-time monitoring and visibility systems. Agile capacities have enabled firms to repurpose their supply chains. Luxury goods manufacturers have completely transformed their operations to manufacture urgently needed items during the COVID19 virus outbreak and repurposed their perfume and hair gel factories to produce hand sanitizers. Automotive firms shifted their production from automobiles to highly proprietary ventilators and hospital beds by collaborating with local manufacturers. Thus, flexible supply chains played a critical role, including rapid raw material sourcing, product design, development and testing, and distribution. Moreover, data-driven, real-time monitoring and visibility technologies were of great help for companies to map their supply chains and utilize the data when preparing their responses to the COVID-­19 pandemic settings. To summarize, adaptability became a key to survive through the pandemic.  ase Study: Resilient Sourcing Strategy Adoption at  AGCO Corporation: Reacting C to COVID-19 Virus Outbreak

AGCO Corporation is an agricultural equipment manufacturer. The company utilizes multiple sourcing strategy combining local/domestic sources and international suppliers. The volume of local sourcing depends on the costs some other factors that change dynamically. In case of market-specific equipment, local sources are mostly used to increase responsiveness at the times of disruptions. AGGO selects suppliers with consideration of both economic (costs) and risk criteria (Banker 2020). In addition, the company operates a digital sourcing platform to ensure an end-to-end communication with suppliers to identify disruptions in a timely manner. Risk management activities supported by the digital platform include prediction of future scenarios and provision of PPE (personal protection equipment) to suppliers to maintain manufacturing continuity.

53 2.7 · Supply Chain Resilience During a Global Pandemic

Such a preparedness and utilization of resilience practices at the “normal” times have allowed AGGO to survive through the COVID-19 pandemic. In the wake of the pandemic, various elements of resilience strategy to tackle supply chain disruptions by adopting multiple sourcing and supplier diversification strategies have been mobilized. Additional flexibility and adaptability were added by assemble-to-order and postponement production strategies entailing faster and customized deliveries to customers. The COVID-19 action plan adopted by AGCO was as follows. The supply side of AGCO had regular communication with Chinese suppliers which allowed to respond quickly to the upcoming pandemic by searching for and utilizing alternative sourcing channels. A task force was created that contained plant managers, materials and logistics managers, and purchasing, supplier quality, and finance managers. AGCO sourced/produced as many critical parts as possible in anticipation of the shutdowns of Chinese factories, and all the finished goods inventories were moved to European markets which have not been concerned with a pandemic yet. Even unconventional modes of transportation, e.g., railway shipment across Russia rather than using conventional, cheap sea-shipping, have been activated. Even if this emergency ­transportation routes were 85% more expensive as sea freight, this recovery action allowed to avoid material shortages at European factories thereby reducing the ripple effect. In addition, shipment allocations have been centralized in decision-making processes to avoid disputes between different plants in the network regarding supply prioritization. Especially, factories producing equipment with higher gross margins and higher demand were prioritized for emergency supply allocations. AGCO also developed a special lockdown forecasting method and thus predicted the South Korean’s shutdown before it occurred. This helped to accelerate the impacted suppliers’ shipments prior to the plant closures. Learning from this experience, they further improved the lockdown forecasting method and applied it to Europe, especially in Italy, where they predicted the closures 7 days prior to the official measures. AGCO used these 7 days of lead time to scale up deliveries from 240 suppliers prior to the lockdown announcement. Same policies have been adopted later in Brazil and North America. We can see that both preparedness and reactive capabilities helped AGGO to achieve high resilience. They created flexible and adaptable supply chain network management at “normal” times and so were able to adapt efficiently and effectively in the wake of the COVID-19 pandemic that devastated supply chains of many other companies.  iscussion D 55 What are the main resilience capabilities you can identify in this case study? 55 What proactive and reactive measures can you see at AGGO? 55 How can we prepare a supply chain for maintaining business continuity in a case of super disruptions such as a global pandemic? 55 Why is it important for resilience to design and manage supply chains as flexible and adaptable systems at “normal” times?

2

54

2

Chapter 2 · Managing Supply Chain Resilience

To summarize, the analysis of the firms’ operations during the COVID-19 pandemic has shown that measures and actions taken by the companies are in most instances in line with what has been recommended by supply chain resilience management theory. At the same time, the pandemic posed a novel setting, i.e., supply chain crisis, which is characterized by long-term disrupted state in the supply network, unstable current situation and uncertainty of future developments in the markets, supply base, and capacities entailing a danger of supply chain collapses and interruption of providing markets with goods and services. The following general main recommendations can be derived for future pandemic-­like disruptions. Preparedness measures: 1. Do not focus on a single region, neither in supply nor in demand 2. Ramp up inventory and position it strategically correct when a lockdown is anticipated 3. Have a working business continuity plan in place 4. Develop viable supply chain designs and identify potential new (temporary) business opportunities to quickly repurpose the supply chain 5. Manage the supply chain as an adaptive system utilizing new technologies for creating highly flexible and reconfigurable supply networks 1. 2. 3. 4.

Recovery measures: Take care of your employees Collaborate closely with your supply chain partners Repurpose the supply chain Leverage modern digital technologies ►►Example

We illustrate using examples. The Panera Bread chain, having lost about 50% of its largely indoors business to COVID-19, adapted to a new supply chain in order to offer staple groceries along the traditional soups and bread. Burger chain Fuddruckers sold toilet paper, gloves, and bleach at specific locations – products far removed from its regular fast food product line, requiring entirely different supply chain infrastructure (Taylor 2020). Many firms entered non-traditional supply markets for their existing products, in order to meet disruption-induced surges in demand, as well as compensate for sudden deficiencies in their regular supply chains. Examples include intertwining of commercial and healthcare supply chains. Automotive companies intertwined with healthcare supply chains to produce ventilators and face shields. Moreover, intertwinings of competing supply chains have been observed (Wang et al. 2018, Ivanov and Dolgui 2020). Amazon turned to demand decline hit Lyft for warehouse and logistical staffing needs, with the latter directing its employees to Amazon positions (Statt 2020). ALDI found a similar unconventional supply chain partner in McDonalds. When demand surged at ALDI Nord and ALDI Süd due to COVID stocking up customer behavior, ALDI collaborated with demand-constrained McDonald’s Germany to allow the latter’s employees to accept temporary positions at ALDI.  The arrangement was lauded as a “win-win situation” by McDonald’s CEO and elicited a “special times require special solutions’ commendation from ALDI management” (ESM 2020). ◄

55 References

2.8

2

Discussion

In this chapter, supply chain resilience management has been introduced. Fundamentally, there are two major resilience management areas, i.e., proactive and reactive ones. 55 Can you name some examples of redundancy and flexibility measures to increase resilience in supply chains? 55 Analyze resilience capacity concept. We learned the strategic importance of resilience and its capabilities which firms should build to recover after disruptions. 55 Can you name some resilience capabilities? 55 Can you explain how proactive and reactive resilience capabilities are mutually connected? 55 Can you explain their application in different practical settings? 55 What recovery strategies do you know with regard to redundancy, agility, and visibility? Subsequently, we offered insights into practical ways to resolve trade-offs between resilience and efficiency. 55 How would you decide on resilience investments in the presence of the trade-off resilience vs. efficiency? 55 Explain LCN and AURA frameworks. What are their objectives, differences, and commonalities? Finally, we learned that supply chain resilience issues in the context of a super-­ disruption, i.e., the COVID-19 pandemic require a specific treatment 55 What are the specific features of resilience management during a super-­ disruption like a pandemic? 55 Discuss some examples of resilience management in different industry and service sectors which have been observed at the COVID-19 pandemic times

References Ambulkar, S., Blackhurst, J., & Grawe, S. (2015). Firm’s resilience to supply chain disruptions: Scale development and empirical examination. Journal of Operations Management, 33, 111–122. Aldrighetti R., Battini D., Ivanov D., Zennaro I. (2021). Costs of resilience and disruptions in supply chain network design models: a review and future research directions. International Journal of Production Economics, 235, 108103. Azaron, A., Venkatadri, U., & Doost, A. F. (2020). Designing profitable and responsive supply chains under uncertainty. International Journal of Production Research. https://doi.org/10.1080/0020754 3.2020.1785036. Banker, S. (2020). A Very Agile Supply Chain: The Inside Story of AGCO’s Response to COVID-19. https://www.forbes.­com/sites/stevebanker/2020/04/15/a-very-agile-supply-­chain-­the-­inside-­story-­ of-­agcos-­response-­to-­covid-­19/. Accessed on 2 Jan 2021.

56

2

Chapter 2 · Managing Supply Chain Resilience

Behzadi, G., O’Sullivan, M. J., Olsen, T. L., Scrimgeour, F., & Zhang, A. (2017). Robust and resilient strategies for managing supply disruptions in an agribusiness supply chain. International Journal of Production Economics, 191, 207–220. Behzadi, G., O’Sullivan, M.J., Olsen, T.L and Zhang, A. (2018). Allocation flexibility for agribusiness supply chains under market demand disruption. International Journal of Production Research, 56(10), 3524–3546. Behzadi, G., O’Sullivan, M. J., & Olsen, T. L. (2020). On metrics for supply chain resilience. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2020.04.040. Biringer, B.  E., Vugrin, E.  D., & Warren, D.  E. (2013). Critical infrastructure system security and resiliency (1st ed.). CRC Press Taylor & Francis. Blackhurst, J., Craighead, C. W., Elkins, D., & Handfield, R. (2005). An empirically derived agenda of critical research issues for managing supply-chain disruptions. International Journal of Production Research, 43(19), 4067–4081. Blackhurst, J., Dunn, K. S., & Craighead, C. W. (2011). An empirically derived framework of global supply resiliency. Journal of Business Logistics, 32(4), 374–391. Bode, C., Wagner, S. M., Petersen, K. J., & Ellram, L. M. (2011). Understanding responses to supply chain disruptions: Insights from information processing and resource dependence perspectives. Academy of Management Journal, 54(4), 833–856. Brandon-Jones, E., Squire, B., Autry, C. W., & Petersen, K. J. (2014). A contingent resource-based perspective of supply chain resilience and robustness. Journal of Supply Chain Management, 50(3), 55–73. Brusset, X., & Teller, C. (2017). Supply chain capabilities, risks, and resilience. International Journal of Production Economics, 184, 59–68. Carvalho, H., Barroso, A. P., Machado, V. H., Azevedo, A., & Cruz-Mahado, V. (2012). Supply chain redesign for resilience using simulation. Computers & Industrial Engineering, 62, 329–341. Cavalcante, I.  M., Frazzon, E.  M., Forcellinia, F.  A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86–97. Chauhan, V. K., Perera, S., & Brintrup, A. (2021). The relationship between nested patterns and the ripple effect in complex supply networks. International Journal of Production Research, 59(1), 325–341. Chen, K., Li, Y., & Linderman, K. (2021). Supply network resilience learning: An exploratory data analytics study. Decision Sciences, forthcoming. Chowdhury, M.  M. H., & Quaddus, M.  A. (2015). A multiple objective optimization based QFD approach for efficient resilient strategies to mitigate supply chain vulnerabilities: The case of garment industry of Bangladesh. Omega, 57, 5–21. Chowdhury, M. M. H., & Quaddus, M. (2017). Supply chain resilience: Conceptualization and scale development using dynamic capability theory. International Journal of Production Economics, 188, 185–204. Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The International Journal of Logistics Management, 15(2), 1–14. Degun, G. ASOS warehouse fire highlights threat to supply chains; published on 23rd June 2014 on http://www.­supplymanagement.­com/news/2014/asos-­warehouse-­fire-­highlights-­threat-­to-­supply-­ chains.­Last access date 20th Nov 2014. Dixit, V., Verma, P., & Tiwari, M. (2020). Assessment of pre and post-disaster supply chain resilience based on network structural parameters with CVaR as a risk measure. International Journal of Production Economics, 227, 107655. Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect in the supply chain: An analysis and recent literature. International Journal of Production Research, 56(1–2), 414–430. Dolgui, A., Ivanov, D., & Sokolov, B. (2020a). Reconfigurable supply chain: The X-network. International Journal of Production Research, 58(13), 4138–4163. Dolgui, A., Ivanov, D., Potryasaev, S., Sokolov, B., Ivanova, M., & Werner, F. (2020b). Blockchain-­ oriented dynamic modelling of smart contract design and execution control in the supply chain. International Journal of Production Research, 58(7), 2184–2199.

57 References

2

Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., Roubaud, D., & Foropon, C. (2020). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research. https://doi.org/10.1080/0 0207543.2019.1582820. ESM Magazine. (2020). McDonald’s Germany and ALDI Sign Personnel Partnership. https://www.­ esmmagazine.­com/retail/mcdonalds-­germany-­and-­aldi-­sign-­personnel-­partnership-­93198, Mar 24 2020. Accessed 25th Oct 2020. Fahimina, B., & Jabarzadeh, A. (2016). Marrying supply chain sustainability and resilience: A match made in heaven. Transportation Research-Part E, 91, 306–324. Fattahi, M., Govindan, K., & Maihami, R. (2020). Stochastic optimization of disruption-driven supply chain network design with a new resilience metric. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2020.107755. Fisher, M. (1997). What is the right supply chain for your product? Harvard Business Review, 1997, 105–116. Ghadge, A., Dani, S., Chester, M., & Kalawsky, R. (2013). A systems thinking approach for modelling supply chain risk propagation. Supply Chain Management: An International Journal, 18(5), 523–538. Gupta, V., Ivanov, D., & Choi, T.-M. (2020). Competitive pricing of substitute products under supply disruption. Omega. https://doi.org/10.1016/j.omega.2020.102279. HBS. (2017). Will Typhoons in Southeast Asia Stop You from Staying Hydrated This Summer? https:// rctom.­h bs.­o rg/submission/will-­t yphoons-­i n-­s outheast-­a sia-­s top-­you-­f rom-­s taying-­hydrated-­ this-­summer/. Accessed on 5 Jan 2018. Hosseini, S., & Ivanov, D. (2019). Resilience assessment of supply networks with disruption propagation considerations: A Bayesian network approach. Annals of Operations Research. https://doi. org/10.1007/s10479-­019-­03350-­8. Hosseini, S., & Ivanov, D. (2020). Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review. Expert Systems with Applications, 161, 113649. Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research: Part E, 125, 285–307. Hosseini, S., Ivanov, D., & Dolgui, A. (2020a). Ripple effect modeling of supplier disruption: Integrated Markov chain and dynamic Bayesian network approach. International Journal of Production Research, 58(11), 3284–3303. Hosseini, S., Ivanov, D., & Blackhurst, J. (2020b). Conceptualization and measurement of supply chain resilience in an open-system context. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2020.3026465. Hosseini, S., Morshedlou, N., Ivanov D., Sarder, MD., Barker, K., Al Khaled, A. (2019b). Resilient supplier selection and optimal order allocation under disruption risks. International Journal of Production Economics, 213, 124–137. Hsieh, C. C., & Chang, H.-L. (2020). Sourcing with recycled materials: A contingent sourcing model with supply unavailability and setup time uncertainty for ripple effect mitigation. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1822558. Ivanov, D. (2010). A framework for aligning (re)planning decisions on supply chains strategy, design, tactics, and operations. International Journal of Production Research, 48(13), 3999–4017. Ivanov, D. (2017a). Simulation-based the ripple effect modelling in the supply chain. International Journal of Production Research, 55(7), 2083–2101. Ivanov, D. (2017b). Simulation-based single vs dual sourcing analysis in the supply chain with con-­ sideration of capacity disruptions, big data and demand patterns. International Journal of Integrated Supply Management, 11(1), 24–43. Ivanov, D. (2018a). Structural dynamics and resilience in supply chain risk management. New York: Springer. Ivanov D. (2018b). Revealing interfaces of supply chain resilience and sustainability: a simulation study. International Journal of Production Research, 56(10), 3507–3523. Ivanov, D. (2020a). Predicting the impact of epidemic outbreaks on the global supply chains: A simulation-based analysis on the example of coronavirus (COVID-19 / SARS-CoV-2) case. Transportation Research – Part E, 136, 101922. https://doi.org/10.1016/j.tre.2020.101922.

58

2

Chapter 2 · Managing Supply Chain Resilience

Ivanov, D. (2020b). Viable supply chain model: Integrating agility, resilience and sustainability perspectives. Lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research. https://doi.org/10.1007/s10479-­020-­03640-­6. Ivanov, D. (2021a). Lean resilience: AURA (active usage of resilience assets) framework for post-­ COVID-­ 19 supply chain management. International Journal of Logistics Management, ­forthcoming. Ivanov, D. (2021b). Supply chain viability and the COVID-19 pandemic: A conceptual and formal generalisation of four major adaptation strategies. International Journal of Production Research. https://doi.org/10.1080/00207543.2021.1890852. Ivanov, D., & Das, A. (2020). Coronavirus (COVID-19 / SARS-CoV-2) and supply chain resilience: A research note. International Journal of Integrated Supply Management, 13(1), 90–102. Ivanov, D., & Dolgui, A. (2019). Low-Certainty-Need (LCN) supply chains: A new perspective in managing disruption risks and resilience. International Journal of Production Research, 57(15– 16), 5119–5136. Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915. Ivanov, D., & Dolgui, A. (2021). OR-Methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications. International Journal of Production Economics, 232, 107921. Ivanov, D., & Dolgui, A. (2020c). A digital supply chain twin for managing the disruptions risks and resilience in the era of Industry 4.0. Production Planning and Control. https://doi.org/10.1080/09 537287.2020.1768450. Ivanov, D., & Sokolov, B. (2013). Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis, and adaptation of performance under uncertainty. European Journal of Operational Research, 224(2), 313–323. Ivanov, D., Sokolov, B., & Pavlov, A. (2014a). Optimal distribution (re)planning in a centralized multi-stage network under conditions of the ripple effect and structure dynamics, European Journal of Operational Research, 237(2), 758–770. Ivanov, D., Sokolov, B., & Dolgui, A. (2014b). The ripple effect in supply chains: Trade-off ‘efficiency-flexibility-resilience’ in disruption management. International Journal of Production Research, 52(7), 2154–2172. Ivanov, D., Dolgui, A., Sokolov, B., Werner, F., & Ivanova, M. (2016a). A dynamic model and an algorithm for short-term supply chainscheduling in the smart factory industry 4.0. International Journal of Production Research, 54(2), 386–402. Ivanov, D., Sokolov, B., Pavlov, A., Dolgui, A., & Pavlov, D. (2016b). Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies. Transportation Research: Part E, 90, 7–24. Ivanov, D., Dolgui, A., & Sokolov, B. (2018). Scheduling of recovery actions in the supply chain with resilience analysis considerations. International Journal of Production Research, 56(19), 6473–6490. Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846. Ivanov, D., Tang, C.  S., Dolgui, A., Battini, D., & Das, A. (2021a). Researchers’ perspectives on industry 4.0: Multi-disciplinary analysis and opportunities for operations management. International Journal of Production Research, 59(7), 2055–2078. Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2021b). Global supply chain and operations management: A decision-oriented introduction into the creation of value (3rd ed.). Cham: Springer Nature. Jüttner, U., & Maklan, S. (2011). Supply chain resilience in the global financial crisis: An empirical study. Supply Chain Management: An International Journal, 16(4), 246–259. Kamalahmadi, M., & Mellat-Parast, M. (2016a). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. International Journal of Production Economics, 171, 116–133.

59 References

2

Kamalahmadi, M., & Mellat-Parast, M. (2016b). Developing a resilient supply chain through supplier flexibility and reliability assessment. International Journal of Production Research, 54(1), 302–321. Khalili, S. M., Jolai, F., & Torabi, S. A. (2016). Integrated production-disruption planning in two-­ echelon systems: A resilience view. International Journal of Production Research, 55(4), 2017. Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust value-creating supply chain networks: A critical review. European Journal of Operational Research, 203(2), 283–293. Li, Y., & Zobel, C. W. (2020). Exploring supply chain network resilience in the presence of the ripple effect. International Journal of Production Economics, 228, 107693. Li, H., Pedrielli, G., Lee, L. H., & Chew, E. P. (2017). Enhancement of supply chain resilience through inter-echelon information sharing. Flexible Services and Manufacturing, 29(2), 260–285. Li, Y., Chen, K., Collignon, S., Ivanov, D. (2021). Ripple Effect in the Supply Chain Network: Forward and Backward Disruption Propagation, Network Health and Firm Vulnerability. European Journal of Operational Research, 291(3), 1117–1131. Losada, C., Scaparra, M. P., & O’Hanley, J. R. (2012). Optimizing system resilience: A facility protection model with recovery time. European Journal of Operational Research, 217, 519–530. Lücker, F., & Seifert, R. W. (2017). Building up resilience in a pharmaceutical supply chain through inventory, dual sourcing and agility capacity. Omega, 73, 114–124. Lücker F., Chopra, S. and Seifert, R. W. (2020). Mitigating product shortages due to disruptions in multi-stage supply chains. Production and Operations Management. https://doi.org/10.1111/ poms.13286. Macdonald, J. R., Zobel, C. W., Melnyk, S. A., & Griffis, S. E. (2018). Supply chain risk and resilience: theory building through structured experiments and simulation. International Journal of Production Research, 56(12), 4337–4355. Marsh, P., Brown, K., Anderlini, J., Johnston, T., Waldmeir, P., Jung, S., Rickards, J., Weitzman, H., & Ward, A. (2011). Japan crisis impact on the supply chain: Global industries consider their options. Financial Times Europe: No. 37568 17 March, 16. Melnyk, S. A., Closs, D. J., Griffis, S. E., Zobel, C. W., & Macdonald, J. R. (2014). Understanding supply chain resilience. Supply Chain Management Review, 18(1), 34–41. Morrow Robertson, C. ASOS not so secret weapon to success – its supply chain, published on 14th April 2014 on Eye for Transport, http://www.­eft.­com/retailcpgfashion/asos%E2%80%99-­not-­so-­ secret-­weapon-­success-­%E2%80%93-­its-­supply-­chain.­ Last access date 20th Nov 2014. Ni, N., Howell, B. J., & Sharkey, T. C. (2018). Modeling the impact of unmet demand in supply chain resiliency planning. Omega, 86, 1–16. Ojha, R., Ghadge, A., Tiwari, M. K., & Bititci, U. S. (2018). Bayesian network modelling for supply chain risk propagation. International Journal of Production Research, 56(17), 5795–5819. Panetto, H., Iung, B., Ivanov, D., Weichhart, G., & Wang, X. (2019). Challenges for the cyber-­ physical manufacturing enterprises of the future. Annual Reviews in Control, 47, 200–213. Park, H., Bellamy, M. A., & Basole, R. C. (2018). Visual analytics for supply network management: System design and evaluation. Decision Support Systems, 91, 89–102. Paul, S., & Rahman, S. (2018). A quantitative and simulation model for managing sudden supply delay with fuzzy demand and safety stock. International Journal of Production Research, 56(13), 4377–4395. Pavlov, A., Ivanov, D., Dolgui, A., & Sokolov, B. (2018). Hybrid fuzzy-probabilistic approach to supply chain resilience assessment. IEEE Transactions on Engineering Management, 65(2), 303–315. Pavlov, A., Ivanov, D., Pavlov, D., & Slinko, A. (2019). Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics. Annals of Operations Research. https://doi.org/10.1007/s10479-­ 019-­03182-­6. Pavlov, A., Ivanov, D., Werner, F., Dolgui, A., & Sokolov, B. (2020). Integrated detection of disruption scenarios, the ripple effect dispersal and recovery paths in supply chains. Annals of Operations Research. https://doi.org/10.1007/s10479-­019-­03454-­1. Pettit, J. T., Fiksel, J., & Croxton, L. K. (2010). Ensuring supply chain resilience: Development of a conceptual framework. Journal of Business Logistics, 31(1), 1–21.

60

2

Chapter 2 · Managing Supply Chain Resilience

Ponomarov, S., & Holcomb, M. (2009). Understanding the concept of supply chain resilience. International Journal of Logistics Management, 20(1), 124–143. Ralston, P., & Blackhurst, J. (2020). Industry 4.0 and resilience in the supply chain: A driver of capability enhancement or capability loss? International Journal of Production Research, 58(16), 5006–5019. Rice, J., & Caniato, F. (2003). Building a secure and resilient supply network. Supply Chain Management Review, 7(5), 22–30. Sawik, T. (2011). Selection of supply portfolio under disruption risks. Omega, 39(2), 194–208. Sawik, T. (2013). Selection of resilient supply portfolio under disruption risks. Omega, 41(2), 259–269. Sawik, T. (2016). On the risk-averse optimization of service level in a supply chain under disruption risks. International Journal of Production Research, 54(1), 98–113. Sawik, T. (2019). Two-period vs. multi-period model for supply chain disruption management. International Journal of Production Research, 57(14), 4502–4518. Sawik, T. (2020). Supply chain disruption management (2nd ed.). New York: Springer. Schmidt, W., & Simchi-Levi, D. (2013). Nissan Motor Company Ltd: Building operational resiliency (pp. 1–12). Cambridge, MA: MIT Sloan Management. Schmitt, T. G., Kumar, S., Stecke, K. E., Glover, F. W., & Ehlen, M. A. (2017). Mitigating disruptions in a multi-echelon supply chain using adaptive ordering. Omega, 68, 185–198. Scholten, K., & Schilder, S. (2015). The role of collaboration in supply chain resilience. Supply Chain Management: An International Journal, 20(4), 471–484. Sheffi, Y. (2005). The resilient enterprise. Massachusetts: MIT Press. Sheree, H. ASOS warehouse fire reveals the cost of supply chain risk, published on 27th June 2014 on http://www.­s upplychaindigital.­c om/warehousing/3491/ASOS-­Warehouse-­F ire-­Reveals-­t he-­ Costs-­of-­Supply-­Chain-­Risk.­ Last access date 20th Nov 2014. Silbermayr, L., & Minner, S. (2014). A multiple sourcing inventory model under disruption risk. International Journal of Production Economics, 149, 37–46. Sokolov, B., Ivanov, D., & Dolgui, A. (Eds.). (2020). Scheduling in industry 4.0 and cloud manufacturing. New York: Springer, ISBN 978-3-030-43176-1. Spiegler, V.  L. M., Naim, M.  M., & Winker, J. (2012). A control engineering approach to the ­assessment of supply chain resilience. International Journal of Production Research, 50(21), 6162–6187. Statt, N. (2020). Lyft is referring drivers to jobs at Amazon after massive ridership decline. https://www.­ theverge.­c om/2020/3/27/21197699/lyft-­a mazon-­c oronavirus-­r idership-­d ecline-­j ob-­referral-­ warehouse-­grocery-­delivery, March 27 2020. Accessed 25th Oct 2020. Tang, C., & Tomlin, B. (2008). The power of flexibility for mitigating supply chain risks. International Journal of Production Economics, 116, 12–27. Tang, C.  S., Yang, S.  A., & Wu, J. (2017). Sourcing from suppliers with financial constraints and performance risk. Manufacturing & Service Operations Management, 20(1), 70–84. Taylor, K. (2020). Chains like Subway and Panera are selling groceries, including loaves of bread, milk, and even toilet paper, as grocery stores struggle with shortages and long wait times. Business Insider. https://www.­msn.­com/en-­us/foodanddrink/foodnews/chains-­like-­subway-­and-­panera-­ are-­selling-­groceries-­including-­loaves-­of-­bread-­milk-­and-­even-­toilet-­paper-­as-­grocery-­stores-­ struggle-­with-­shortages-­and-­long-­wait-­times/ar-­BB12kjeE. Accessed 25th Oct 2020. Torabi, S. A., Baghersad, M., & Mansouri, S. A. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research – Part E, 79, 22–48. Toyota. (2021). Risk management. https://www.­toyota-­tsusho.­com/english/company/governance/ riskmanagement.­html. Accessed on 13 Feb 2021. Vugrin, E. D., Warren, D. E., & Ehlen, M. A. (2011). A resilience assessment framework for infrastructure and economic systems: Quantitative and qualitative analysis of petrochemical supply chains to hurricane. Process Safety Progress, 30(3), 280–290. Wagner, S.  M., & Bode, C. (2008). An empirical examination of supply chain performance along several dimensions of risk. Journal of Business Logistics, 29(1), 307–325.

61 References

2

Wang J., Dou R., Dou, Muddada, R.R., & Zhang, W. (2018). Management of a holistic supply chain network for proactive resilience: Theory and case study. Computers and Industrial Engineering, 125, 668–677. Wearden, G. ASOS warehouse fire in South Yorkshire leads company to halt sales, published on 22nd June 2014 on www.­theguardian.­com/business/2014/jun/22/asos-­warehouse-­fire-­south-­yorkshire/.­ Last access date 20th Nov 2014. Yildiz, H., Yoon, J., Talluri, S., & Ho, W. (2016). Reliable supply chain network design. Decision Sciences, 47(4), 661–698. Yoon, J., Talluri, S., & Rosales, C. (2020). Procurement decisions and information sharing under multi-tier disruption risk in a supply chain. International Journal of Production Research, 58(5), 1362–1383.

63

Modeling Supply Chain Resilience Contents 3.1

Modeling Methods – 64

3.2

End-to-End Visibility, Digital Technology, and Resilience – 67

3.3

Optimization: Recovery Model of a Multi-stage Supply Chain – 71

3.3.1 3.3.2

 roblem Context – 71 P Mathematical Model – 72

3.4

 imulation: Ripple Effect S Prediction During the COVID-19 Pandemic – 79

3.4.1 3.4.2 3.4.3

 roblem Context – 79 P Simulation Model – 80 Managerial Insights – 87

References – 89

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 D. Ivanov, Introduction to Supply Chain Resilience, Classroom Companion: Business, https://doi.org/10.1007/978-3-030-70490-2_3

3

64

Chapter 3 · Modeling Supply Chain Resilience

nnLearning Objectives Supply chains are complex networks that operate subject to uncertainties. Modeling resilience in such complex systems can be a challenging task. We explain in this chapter the existing approaches to supply chain resilience modeling such as optimization, simulation, and network analysis. To this end, our learning objectives for this chapter are as follows: 55 Understand the management insights that can be obtained by optimization, simulation, and network analysis methods of resilience modeling in supply chains 55 Utilize mathematical optimization for supply chain recovery 55 Use digital supply chain twins as a combination of optimization, simulation, and data analytics 55 Explain the usage of simulation techniques to identify the impact of disruptions on supply chain performance

3

3.1

Modeling Methods

Modeling methods for supply chain resilience analysis can be classified according to network, process, and control levels (Ivanov and Dolgui 2021) as shown in . Fig. 3.1. The methods at the network level primarily look at unlocking associations between network structures and risk propagations (Li et al. 2020, 2021; Basole and Bellamy 2014). Network modeling also allows to detect disruption scenarios and identify critical nodes (or combinations of nodes), the failure of which would lead to supply chain discontinuities and operational collapse (Kim et al. 2015). At the network level, supply chain robustness and resilience under disruption propagation and structural dynamics can be analyzed and measured. Along with the stress-­ testing of existing supply chain designs, the network-level analyses can offer guid 

Network and complexity theories

Mathematical optimization

Simulation

Bayesian networks Complexity theory Reliability theory Petri nets Markov chains

Mixed-integer linear programming

Agent-based simulation

Robust optimization Stochastic optimization

Discrete-event simulation Systems dynamics

Network-wise analysis

Planning decisions

Process control

..      Fig. 3.1  Modeling methods for supply chain resilience

65 3.1 · Modeling Methods

3

ance to enhance resilience, e.g., through supplier diversification (Hosseini et  al. 2019). With the use of Bayesian networks, it becomes possible to model dependencies and inter-dependencies in supply networks with consideration of both vulnerabilities and recovery (Garvey et al. 2015; Ojha et al. 2018; Qazi et al. 2018; Liu et al. 2020). An integration of Markov chains and Bayesian networks allows to model the node’s behaviors along with the overall network dynamics (Hosseini et al. 2019). >>Important Observation The network- and graph-theoretical studies allow us to understand potential weaknesses in supply chain designs, taking into account the structure, connectivity, and dependence within the supply chain.

Compared to the network level, the methods at the process level take a more specific perspective (Dolgui et al. 2020b; Ivanov and Dolgui 2020; Ivanov and Sokolov 2010, 2012, 2020; Ivanov et al. 2021a; Paul & Chowdhury 2021). For example, Garvey and Carnovale (2020) argue that “managers should focus more of their attention on control or mitigation of exogenous events […] and spend less of an effort and resources on mitigating the propagation of exogenous risk...” The mathematical optimization studies consider supply chain networks, which may vary structurally and parametrically over time, and optimize flow reconfigurations under disruption propagation (Sawik 2011; Ivanov et al. 2013, 2017). Optimization models presume some parametrized structures to balance demands, processing capacities, and supply (Ivanov et al. 2015; Paul et al. 2017; Sawik 2019; Azaron et al. 2020; Sawik 2013; Ivanov et al. 2016; Khalili et al. 2016; Gupta et al. 2020). The analysis at the process level is usually supported by mathematical optimization (Paul et al. 2019), and system dynamics (e.g., Ghadge et al. (2013), along with the overall impact of the ripple effect on performance (Giannoccaro et al. 2018; Dolgui et al. 2018; Hosseini et al. 2020). As the most desirable outcome, process-level analysis seeks to identify and test resilient supply chain designs to sustain disruptions, which range from optimistic and pessimistic scenarios (Ivanov et  al. 2014) to probability-based disruptions (Pariazar et  al. 2017) to worst-case scenarios in robust optimization (Zhao and Freeman 2019; Özçelik et  al. 2020). In some settings, the authors solve inverse problems and search for the elements in supply chain structures that should be strengthened to withstand disruption propagation (Liberatore et al. 2012; Ivanov et al. 2013). Some models include recovery costs (Ivanov et al. 2016) and sustainability issues (Pavlov et al. 2019). >>Important Observation The process-level methods help to analyze measures for disruption propagation mitigation. As the most desirable outcome, process-level analysis seeks to identify and test resilient supply chain designs to withstand disruptions.

The control-level studies are distinctively characterized by the inclusion of inventory control and production-ordering dynamics in the analysis (Ivanov 2018, Ivanov and Rozhkov 2020). At this level, simulation methods are the most dominant

66

Chapter 3 · Modeling Supply Chain Resilience

(Ivanov 2019; Dolgui et  al. 2020a). Dynamic supply chain behaviors and time dependencies in disruption propagation and responses (Ivanov 2017a, b, 2020a, b) can be modeled conveniently using simulation techniques. In . Table 3.1, we summarize major methods, their outcomes, and managerial applications.  

3

..      Table 3.1  Methods, outcomes, and managerial insights Level of analysis

Methods

Outcomes

Managerial insights

Network level

Graph theory

Associations between network structures and resilience Analysis of critical network elements leading to supply chain discontinuities Modeling of interdependencies in supply chains State dynamics within supply chain nodes Assessment of supply chain robustness and resilience to disruptions with considerations of ripple effect

Identification of disruption scenarios of different severity Stress-testing of supply chain designs Proneness of specific supply chain designs to disruption risk propagation Identification of critical suppliers and facilities for maintaining supply chain operations Selection and proactive enhancements of supply chain designs to sustain certain levels of disruptions Adaptation of supply chain designs according to environmental changes

Optimal reconfigurations of material flows according to disruption scenarios Impacts of ripple effect and structural dynamics on service level and costs Optimal re-allocation of supply and demand under disruptions

Stress-testing of supply chain production-distribution plans within differently disrupted network designs Analysis of contingency-­ preparedness plans Recovery plan selection

Impacts of disruptions on service level, inventory levels, and costs Time-dependent effect of disruptions on supply chain behaviors and performance in dynamics Individual behavior of firms in supply chains

Building resilient supply chains for new, post-pandemic business models Analysis of disruption propagation in dynamics with consideration of production and inventory control policies Simulation of operation policies during disruption, in transition to recovery, and in post-­recovery periods

Complexity theory Entropy Petri nets Bayesian networks Markov chains Reliability theory/ statistical analysis

Process level

Stochastic optimization Robust optimization Linear/mixedinteger programming

Control level

Optimal control Systems dynamics Agent-based simulation Discrete-event simulation

Based on Ivanov and Dolgui (2021))

67 3.2 · End-to-End Visibility, Digital Technology, and Resilience

3

>>Important Observation The control-level methods help to analyze the dynamics of disruptions and recovery.

3.2

End-to-End Visibility, Digital Technology, and Resilience

Supply chains are evolving toward technology-driven networks and digital ecosystems (Queiroz et  al. 2019; Roeck et  al. 2020; Wamba and Queiroz 2020). Data analytics, additive manufacturing, and Industry 4.0 allow creating end-to-end visibility for supply chains based on dynamically reconfigurable material flows and digital information flows. For example, digital supply chain financing with the use of blockchain (e.g., deep tier financing) creates an end-to-end supply chain visibility that is of vital importance for supply chain viability and resilience. Based on the literature, digital technology for supply chain resilience management can be classified into the following: 55 Visualization 55 Early warning systems 55 Blockchain and supply chain financing 55 Real-time disruption-detection systems (Sheffi 2015; Dolgui et al.) Data-driven disruption modeling provides a basis for proactive, resilient supply chain design in anticipation of disruptions and structural-parametrical adaptation in the event of disruptions. The modeling combines simulation, optimization, and data analytics to create a digital supply chain twin and thereby manages disruption risks. Definition Digital supply chain twin is a computerized model that at each point of time represents the physical supply chain with the actual transportation, inventory, demand, and capacity data and can be used for planning and real-time control decisions.

In the digital supply chain twin, model-based decision-making support enables the simulation of the supply chain’s dynamic behavior in the event of disruption (Ivanov and Dolgui 2020c). In addition, before a disruption occurs, potential impacts on supply chain performance can be evaluated, and then recovery policies can be optimized. Data analytics is used at the proactive stage for building realistic disruption scenarios based on risk data about historical disruptions and other data (e.g., supplier reliability data from ERP systems) during the supply chain design phase. At the reactive stage, data analytics is used for disruption identification in real time using process feedback data, e.g., from sensors, T&T, and RFID (Papadopoulos et al. 2017; Panetto et al. 2019; Fragapane et al. 2020; Ivanov et al. 2019, 2021b, c).

68

Chapter 3 · Modeling Supply Chain Resilience

According to the principles described above, the following modeling framework for resilient supply chain design and recovery planning, which contains the conceptually integrated data, can be proposed (. Fig. 3.2). . Figure 3.2 depicts data sources, information systems, and the parameters of models for resilient supply chain design and recovery planning where the respective data is used. We illustrate the digital supply chain twin principles and data-driven disruption modeling framework from . Fig. 3.2 on example of the digital supply chain twin anyLogistix (. Fig. 3.3). The combination of anyLogistix and data analytics is based on a mapping of the risk data with geographical locations in the supply chain structure as previously discussed in Ivanov et al. (2019). This mapping is considered at the resilient supply chain design and resilience recovery stages. The DSS (decision-support system) uses disruption risk data to assess supplier and transportation disruption risks, predicting possible supply chain interruptions. This data is used for computing alternative supply network topologies and backup routes with assessment of estimated times of arrival in anyLogistix. In the dynamic mode, simulation is applied using real-time data to analyze the disruption impact on supply chain performance and alternative supply chain designs that contain non-disrupted network nodes and arcs depending on real-time inventory, demand, and capacity data. Furthermore, the interaction of data analytics and simulation-optimization tools is not limited to updating model data. Considering the output of simulation modeling, simulation results can be transferred to an ERP system or a business intelligence (BI) tool in order to analyze the performance impact of disruptions. Additionally, the simulation models can activate some BI algorithms. For example, if service level decreases to a certain level in the supply chain’s simulation model, the digital twin might activate a BI algorithm to search for the cause of that problem and the necessary data updated to resolve the problem. . Figure 3.4 demonstrates three major areas of supply chain disruption risk management covered in the digital twin proposed, i.e., disruption identification, disruption modeling, and disruption impact assessment. . Figure 3.4 shows the mapping of risk data identification, the locational supply chain model, and performance impact analysis. At the analysis stage, historical risk data from external databases (e.g., natural disaster events in the past and geographical regional risk assessments) and internal sources (e.g., ERP data about supplier reliability performance) are collected. This data can be used to build disruption scenarios for supply chain resilience analysis which then can be examined and tested in simulation-optimization models. In the real-time mode, the resilience analytics system is used to search for relevant disruption data that might affect supply chain resilience. The simulation can be subsequently run to observe the impact of such a disruption on supply chain performance. Moreover, some recovery policies, such as activation of alternative supply chain designs during the disruption, are simulated. Data collected from different sources in real time are used to update model parameters, such as capacity, inventory, and lead times in terms of production and shipment capacities and inventory availability in the supply chain. Finally,  

 

3

 

 

 

 

Planned lead-time

Safety stock

Trace and tracking

Disruptions

Early warning systems

Risk data

Sensors

Actual demand Big data analytics

Demand forecast

Demand

Costs

Processing capacity in production

Actual capacity

Throughput (for FMCG-fast moving Consumer goods), e.g. cross-docking

Resilient supply chain design and Recovery planning

Actual inventory

Storage capacity (for SMCG-slow moving consumer goods)

Actual lead-time

Lead-time

Actual transportation capacity

Planned transportation capacity

Suppliers and sourcing policy

ERP

ERP WMS

Business intelligence

Sensors

69

..      Fig. 3.2  Data structure in supply chain disruption risk modeling framework (Ivanov and Dolgui 2020c)

ERP APS WMS

Trace and Tracking RFID

Supplier collaboration plattform

RFID

3.2 · End-to-End Visibility, Digital Technology, and Resilience

3

70

Chapter 3 · Modeling Supply Chain Resilience

Flood

Fire

Real-time (monitoring)

Statistics (analysis)

Disruption data

3

Storm

Finance disaster

Machine learning

Constraint System

Events

Reactive recovery planning

Proactive resilient supply chain design

ERP

FILTER Learning feedback

Big data analytics

SIMULATION MODEL

Learning feedback

OPTIMIZATION MODEL

Power outage

Prediction and analysis

Strike

Real-time control

BI

Performance analysis ..      Fig. 3.3  Digital supply chain twin for managing disruption risks

..      Fig. 3.4  Interrelations between risk data, modeling, and performance analysis. (Based on Ivanov and Dolgui 2020c)

3

71 3.3 · Optimization: Recovery Model of a Multi-stage Supply Chain

data analytics can be used as data-driven learning system to use past experiences to manage future disruptions and so utilizing cyber-physical, artificial intelligence, and machine learning principles and technologies (Cavalcante et al. 2019; Panetto et al. 2019).

3.3

 ptimization: Recovery Model of a Multi-stage O Supply Chain

3.3.1

Problem Context

We consider a supply chain network that is composed of two factories (nodes 1 and 6), a central distribution hub (node 4), two regional warehouses (nodes 2 and 3), a rented regional warehouse (node 7), and a customer (node 5) in line with Ivanov et al. (2013). The process dynamics in each of the nodes and transportation arcs are limited by maximal production and warehouse capacity, processing intensity, and transportation intensity (see . Fig. 3.5). In . Fig. 3.5, triangles refer to the warehouse capacity, and numbers on the arcs refer to maximal transportation intensity. The suppliers first deliver materials to the factories 1 and 6. Then, the goods are processed at the central distribution hub 4. The goods from hub 1 are additionally processed at warehouses 2 and 3. From hub 4, the goods are moved to the customer at node 5, which has a certain demand in each of the periods (i.e., 100 units). In order to take into account possible disruptions in the channel 4-5, a rented warehouse is used as a backup facility (i.e., a proactive resilience capacity) for deliveries to customer 5. Besides, it is possible to move small quantities (maximal 30 units) directly from node 2 to node 5.  

 

..      Fig. 3.5 Production-distribution network structure (Ivanov et al. 2013)

20

20 50

1

2 30

50

30 20

20

3

4 50

10

50

100

5 100 40

40

20 6

50

7

72

Chapter 3 · Modeling Supply Chain Resilience

The transportation volumes are constrained by maximal transportation intensity (as noted on the arcs in . Fig. 3.5). The stocking volume is constrained by maximal warehouse capacities as shown by the triangles in . Fig. 3.5. The in- and outbound processing is constrained subject to maximal in- and outbound processing intensities. Suppliers deliver certain order quantities to nodes 1 and 6 at the beginning of each period, and many periods are involved in the planning horizon. The primary problem context is to find optimal plan for supply chain recovery through a reconfiguration of the aggregate product flows to be moved from factories through the intermediate stages to the customer subject to maximizing the service level at node 5 and minimizing the total cost under (i) constrained capacities and processing rates and (ii) disruptions for a multi-period case. The secondary problem consists of inverse analysis to determine the optimal supply chain network design and its operational parameters for some required resilience level.  

 

3

3.3.2

Mathematical Model

In this section, we introduce the main and dual models for the problem context identified above based on Ivanov et  al. (Ivanov et  al. 2013, 2014a, Ivanov et  al. 2016). Let us define the following notations. zz Indexes:

55 k  =  {1, 2, …, Lχ} is the number of a time interval (i.e., interval of structural constancy) in the planning horizon T = (t0; tf]. 55 χ is the number of a disruption scenario. 55 i = {1, 2, …, nχ} is the number of the delivering node in the supply chain, i Î N c- .

55 j = {1, 2, …, nχ} is the number of the receiving node in the supply chain, j Î N c+ . 55 ρ ∈ P = {1, 2, …, p} is the number of a commodity in the supply chain. zz Sets:

55 Xχ(t) = {Axi(t), i ∈ Nχ} is the set of nodes on the supply chain. 55 Eχ(t) = {exij(t),  i, j ∈ Nχ} is the set of arcs in the supply chain. 55 Wχ(t) = {wχij(t),  i, j ∈ Nχ} is the set of operations characteristics for the transportation (if i ≠ j) or processing at warehouse (if i = j). 55 N c+ik is the set of node numbers for the nodes transmitting products to Aχi at time interval k. 55 N c-ik is the set of node numbers for the nodes receiving products from Aχi at time interval k. 55 Δχβ = {δχ} is the set of possible supply chain recovery plans.

3

73 3.3 · Optimization: Recovery Model of a Multi-stage Supply Chain

zz Variables:

55 γρ is the variable that denotes the importance of the product ρ. 55 λρ is the variable that denotes the urgency of the product ρ. 55 eχ(t) is the time-spatial matrix function to define the links between Axi and Axj, and exij(t) is equal to 1, if a transportation from Axi to Axj is possible, 0 – otherwise. 55 Iχk is the total ordered quantity from all suppliers in scenario χ within the interval k. 55 Vχi(t) is the maximal warehouse capacity of the node Aχl. 55 ωχijρ(t) is the maximal transportation channel intensity for the product ρ between Aχi and Aχj. 55 ψχiρ(t) is the maximal inbound processing intensity for the product ρ in Aχl. 55 ϕχiρ(t) is the maximal outbound processing intensity for the product ρ from Aχi. It is assumed that each network element within the subintervals (structure constancy intervals) is indicated by these characteristics that do not change within this interval. The following costs are included in the analysis: 55 cχijρ(t) is the transportation cost for the product ρ from Aχi to Aχj. 55 hχiρ(t) is the inventory cost for the product ρ at the node Aχi. 55 πχiρ(t) is the processing cost for the product ρ at the node Aχi. 55 rχiρ(t) is the return (utilization) cost for product ρ at the node Aχi. 55 fi, fij are the fixed costs of the node Aχi and channel from Aχi to Aχj, ­respectively. zz Decision variables:

55 xχijρk is the amount of product ρ process in factory Aχi, transmitted from Aχi to Aχj and received at Aχj at time interval number k. 55 yχjρk is the product ρ amount relating to the node Aχj and to be stored at warehouse at time interval number k. 55 gχjρk is the product ρ amount relating to the node Aχj and to be delivered at time interval k. 55 zχjρk is the product ρ amount relating to the node Aχj and to be returned (as caused by the missing capacity of supply chain nodes and channels) at time interval number k. The main model can be written as balance Eqs. (3.1)–(3.2) and capacity constraints (3.3): I c i r k + yc i r ( k -1) + +

å wc jir k × uc jir k = fc ir k ×Jc ir k

jÎN c-ik

å wc ij r k × uc ij r k + yc ir k + zc ir k ,

jÎN c+ik

æ ç å xc ij r k ç jÎN + è cik

(3.1)

ö

å xc jir k ÷÷ + ( yc ir k - yc ir ( k -1) ) + g c ir k + zc ir k = I c ir k .

jÎN c-ik

ø

(3.2)

74

Chapter 3 · Modeling Supply Chain Resilience

p

0 £ xc ij r k £ wc ij r k × ( tk - tk -1 ) ; 0 £ å yc i r k £ Vc i ; 0 £ g c i r k £ fc i r k × ( tk - tk -1 ) ; r =1

z c i r k ³ 0,

3

(3.3)

and objectives (3.4)–(3.7) as follows: p

nc Lc

r =1

i =1 k =1

p

nc Lc

r =1

i =1 k =1

p

nc Lc

r =1

i =1 k =1

J c 1 = åg r ååz c i r k ;

J c 2 = ålr ååg c i r k ; J c 3 = åg p åå yc i r k ; p nc nc

(3.4)



(3.5)



(3.6)



Lc

p nc

Lc

p nc

Lc

r =1 i =1

k =1

J c 4 = åååcij r åxc ij r k + ååhi r å yc i r k + ååp i r åg c i r k r =1 i =1 j =1 p nc

Lc

nc

r =1 i =1

k =1

i =1

k =1

k =1 r =1 i =1 nc nc

+ ååri r åz c i r k + å fi + åå fij i =1 j =1



(3.7)

Optimal supply chain recovery plan δχ under disruption scenarios can be denoted as follows:

d c = xc ,g c ,yc ,z c = xc ij r k ,g c i r k ,yc i r k ,z c i r k . Then, Eqs. (3.1)–(3.3) define a set of feasible supply chain recovery plans Δχβ = {δχ} in the disruption scenario Scχ. The search for optimal supply chain recovery plan D*cb Í D cb is performed under preference relations (e.g., weights) based on the following criteria: (Jχ1(δχ) → min, Jχ2(δχ) → max, Jχ3(δχ) → min, Jχ4(δχ) → min). For the procedure of the multi-objective resolution, we refer to Ivanov et al. (2013). Now we turn to dual problem formulation. Dual problem formulation is a practical need in at least two cases. First, in the case of unsatisfied demand (i.e., no feasible solution), the bottlenecks can be identified and strengthened by investment in new facilities or capacity expansion. Second, such models can be applied by analyzing future investments in new facilities/capacities. The basis of this analysis is balancing demand and capacities at aggregate level. In practice, the following parameters may influence the network planning results: 55 Vχi(t) is the maximal warehouse capacity of the node Aχl. 55 ωχijρ(t) is the maximal transportation channel intensity for the product ρ between Aχi and Aχj. 55 ψχiρ(t) is the maximal inbound processing intensity for the product ρ in Aχl.

3

75 3.3 · Optimization: Recovery Model of a Multi-stage Supply Chain

55 ϕχiρ(t) is the maximal outbound processing intensity for the product ρ fromAχi. 55 cχijρ(t) is the transportation cost intensity for the product ρ from Aχi to Aχj. 55 hχiρ(t) is the inventory cost for the product ρ at the node Aχi. 55 πχiρ(t) is the processing cost intensity for the product ρ at the node Aχi. 55 rχiρ(t) is the return (utilization) cost for the product ρ at the node Aχi. 55 fi, fij are the fixed costs of the node Aχi and channel from Aχi to Aχj, respectively. Subject to pessimistic and optimistic scenarios, the lower bound ζ of performance indicator is to minimize as shown in Eqs. (3.8) and (3.9): p

n+ L+

p

n+ L+

r =1

i =1 k =1

r =1

i =1 k =1

p

n- L-

p

n- L-

r =1

i =1 k =1

r =1

i =1 k =1

a 3a 2 ålr ååg + i r k - a 3a1 åg r ååz+ i r k - a 4u+ ³ z , a 3a 2 ålr ååg -i r k - a 3a1 åg r ååz-i r k - a 4u- ³ z .

(3.8)



(3.9)



In addition, flow-related variables are to meet the constraints (3.10) and (3.11):

d+ =

d- =

x+ ij r1 x+ ij r 2 ¼ x+ ij r L+ g + i r1 g + i r 2 ¼ g + i r L+ y+ i r1 y+ i r 2 ¼ y+ i r L+

T

z+ i r1 z+ i r 2 ¼ z+ i r L+ u+ n + i h+ i1h+ i 2 ¼h+ iL+



x-ij r1 x-ij r 2 ¼ x-ij r L- g -i r1 g -i r 2 ¼ g -i r L- y-i r1 y-i r 2 ¼ y-i r Lz-i r1 z-i r 2 ¼ z-i r L- u- n -i h-i1h-i 2 ¼h-iL-

(3.10)

T



(3.11)

We assume that nodes in different execution scenarios have the same pre-­functioning history, i.e., R+0i = R−0i = R0i ∀ i = 1, …, n, n = n+ = n−; and capacities and processing/transportation intensities are equal for both pessimistic and optimistic scenarios, i.e., V+i = V−i = Υi ∀ i, ω+ijρk = ω−ijρk = ϖijρ, ϕ+iρk = ϕ−iρk = φiρ ∀i, j, ρ, k. With regard to the above-mentioned aspects, the dual problem for optimal supply chain redesign with structure dynamics considerations can be formulated as shown in Eqs. (3.12)–(3.25): p n p n n æ n ö a 5z - a 6 ç åL i U i + ååPi r ji r ( t f - t0 ) + åååRij rv ij r ( t f - t0 ) ÷ ® max (3.12) ç i =1 ÷  r =1 i =1 r =1 i =1 j =1 è ø p

n+ L+

p

n+ L+

r =1

i =1 k =1

r =1

i =1 k =1

p

n- L-

p

n- L-

r =1

i =1 k =1

r =1

i =1 k =1

a 3a 2 ålr ååg + i r k - a 3a1 åg r ååz+ i r k - a 4u+ ³ z , a 3a 2 ålr ååg -i r k - a 3a1 åg r ååz-i r k - a 4u- ³ z ,





(3.13)

(3.14)

76

Chapter 3 · Modeling Supply Chain Resilience

æ ö ç å x+ ij r k - å x+ ji r k ÷ + y+ i r k - y+ i r ( k -1) + g + i r k + z+ i r k ç jÎN + ÷ jÎN +-i è +i ø = I + i r k , i Î N , r Î P, k = 1, ¼, L+ , 

(

3

)

æ ö ç å x-ij r k - å x- ji r k ÷ + y-i r k - y-i r ( k -1) + g -i r k + z-i r k ç jÎN + ÷ jÎN --i è -i ø = I -i r k , i Î N , r Î P, k = 1, ¼, L- , 

(

p

n

)

L+

p

L+

k =1

r =1

k =1

L+

p

L+

k =1

r =1

k =1

u+ - åårij r åx+ ij r k - åp i r åg + i r k - v+ i = R0i , i Î N , r =1 j =1 p

n

u- - åårij r åx-ij r k - åp i r åg -i r k - v-i = R0i , i Î N , r =1 j =1





p

å y+ir k + h+ik = U i , i Î N , k = 1,¼, L+ 

(3.15)

(3.16)

(3.17)

(3.18)

(3.19)

r =1 p

å y-ir k + h-ik = U i , i Î N , k = 1,¼, L- 

(3.20)

r =1

(

)

0 £ x+ ij r k £ v ij r × tk+ - tk+-1 , 0 £ g + i r k £ ji r k ×

(

tk+

- tk+-1

) , i, j Î N , r Î P, k = 1,¼, L , +

(



(3.21)



(3.22)

)

0 £ x-ij r k £ v ij r × tk- - tk--1 , 0 £ g -i r k £ ji r k ×

(

tk-

- tk--1

) , i, j Î N , r Î P, k = 1,¼, L , -

y+ i r k ³ 0, z+ i r k ³ 0, v+ i ³ 0,h+ ik ³ 0,u+ ³ 0, i Î N , r Î P, k = 1, ¼, L+ , 

(3.23)

y-i r k ³ 0, z-i r k ³ 0, v-i ³ 0,h-ik ³ 0,u- ³ 0, i Î N , r Î P, k = 1, ¼, L- , 

(3.24)

çàä 0 £ U i £ U içàä , 0 £ v ij r £ v ijçàä r , 0 £ ji r £ ji r , i, j Î N , r Î P, (3.25)

where, Λ is the fixed cost, Π is the processing cost, and R is the transportation cost in the dual model. Priority coefficients α5, α6 ≥ 0  (α5 + α6 = 1) in the goal function (3.12) characterize supply chain efficiency subject to variable and fixed costs. The analysis of problem (3.12)–(3.25) shows that this is a linear programming model with two-side constraints.

3

77 3.3 · Optimization: Recovery Model of a Multi-stage Supply Chain

Experimental Results

3.3.2.1

We now illustrate the optimization model application to resilience analysis. Let us consider two disruption scenarios of different severity defined in . Figs. 3.6 and 3.7 in line with Pavlov et al. (2020). . Figures 3.8 and 3.9 show the optimal recovery plans obtained for the two disruption scenarios. The optimal solution for the less severe scenario allows a delivery of 300 units (which is the total demand over three periods with a period demand of 100 units) which is equal to a fulfillment rate of 100%. For the more severe disruption scenario, the optimal solution allows a delivery of 250 units which is equal to a fulfillment rate of 83.3%. As such the decision-makers can prepare recovery plans for the disruption scenarios of different severities and efficiently deploy them by relating a real disruption to one of the previously detected disruption scenarios. However, in some cases, the supply chain managers can be interested in achieving some desired level of resilience and thus redesigning their supply chains. In particular, the following questions can be considered: 55 What elements are critical for the supply chain design and what elements are redundant and can be removed without decreasing the service level? 55 Do additional costs in supply chain design elements pay off by the performance increase?  

 

The analysis shown below has been performed based on the dual problem formulation for the developed main model. To answer the first question, in . Fig. 3.10, a redesigned supply chain structure is presented. The analysis of the dual variables in the models for optimistic and pessimistic scenarios and the solving of the inverse problem allowed synthesizing the “ideal” supply chain to meet the demand of 300 units in both scenarios and thus ensure resilience (see . Fig. 3.10). In . Figs. 3.11 and 3.12, the optimized optimal supply chain recovery plans for optimistic and pessimistic disruption scenarios are presented.  

 

 

5

4

[100]

50

20

50 40

40

[100]

4

3

7

Disrupted nodes Customer demand

20

6 20

10

50

2

0

5

10

20

1

30

50 20

50

30 50

1

4

[100]

40

Maximum shipment capacities

20

Maximum warehouse capacities

3

20

50

7

20

6

2

10

20

Incoming flows

..      Fig. 3.6  Low disruption severity scenario (Pavlov et al. 2020)

50

30 50

0

10

5

[100]

20

20

4

3

20

10

50

7

50

0

10

50

50 20

50

30

30

60

2

20

50

20

50

100

1

50

6 20

78

Chapter 3 · Modeling Supply Chain Resilience

50

4

40

40

7

[100]

4

10

6 20

2

5

Customer demand

20

3 50

4

30 50

50 20

50

40

40

40

Maximum shipment capacities

20

Maximum warehouse capacities

20

6

2

50

10

50

20

50

4

30

20

7

[100]

Disrupted nodes

[100]

20

20

1

30

5

40

50

7

40

[100]

20

3

50

0

10

5

20

50

20

1

60

3

50 20

50

30 50

30

50

3

50

2

50

100

20

20

1

6

10

20

Incoming flows

50

..      Fig. 3.7  High disruption severity scenario (Pavlov et al. 2020)

100 90

50

100

4 [100]

50

3

50

4

40 40

5

[100]

20

10

7

6

10

Customer demand

2 30

50

20

Disrupted nodes

Incoming flows

20

40

20

50

20

30 20

50 20 20 100

50

20

10

3

50

4

100

7

20

1

20

90

5

[100]

50

1

20

6

10

2 30

20

20

50 100

50

20

20

50

10

3

50

4

60

5

[100]

100

10

7

Maximum shipment capacities

20

Maximum warehouse capacities

actually used shipment capacities

10

actually used warehouse capacities

50

20

20

60

50

20

50

30

50

2 30

20

1

50

20

50

100

50

6

10

20

actually delivered quantities

..      Fig. 3.8  Recovery plan for a less severe disruption scenario (Pavlov et al. 2020)

With the redesigned supply chain, the profit is achieved in both the optimistic and the pessimistic scenarios due to 100% service level (300 units of the delivered goods), decreased fixed costs (63$ instead of 90$), and inventory costs (8.4$ instead of 12.6$). In the optimistic scenario, the transportation cost has been decreased from 28.6$ to 27.8$. In the pessimistic scenario, transportation cost has increased from 27.0$ to 33.6$ mainly due to the increase in the volume of the delivered goods. It can be concluded that through the capacity alignment and fixed cost reduction, both high resilience and by tendency lower inventory and transportation cost can be achieved.

3

79 3.4 · Simulation: Ripple Effect Prediction During the COVID-19…

90

40 40

5

[100]

100

4 [100]

50

3

50

4

100

20

7

6

10

20

Disrupted nodes Customer demand Incoming flows

20

2

5

[100]

50

20

50

30 20

50 50 20

20

30

50

20

1

20 10

3

10

50

4

6

40 40

90

20 10 50

20 50

20

20

30

20

7

2

50

20

5

[100]

10

50

4

20

3

50

50

10

1

60

20

20

50

50

20

50

30

50

2 30

50

100

20

20

1

50

6

40 40

60

7

Maximum shipment capacities

20

Maximum warehouse capacities

actually used shipment capacities

10

actually used warehouse capacities

10

20

actually delivered quantities

..      Fig. 3.9  Recovery plan for a more severe disruption scenario (Pavlov et al. 2020) ..      Fig. 3.10  “Ideal” supply chain network design (Ivanov et al. 2013)

10

10 1

60

2

10 3

20

4 60

6

3.4

35

15

30

10

20

70

5 100

70

70

7

 imulation: Ripple Effect Prediction During the COVID-19 S Pandemic

3.4.1

Problem Context

The COVID-19 pandemic generated supply chain disruptions on a scale unlike any seen before. Numerous ripple effects have been seen at the pandemic times (Ivanov 2020b; Ivanov and Das 2020). The pandemic has quite unique implications for supply chains. In contrast to geographically centered, singular occurrence natural/ industrial disasters, a pandemic is not limited to a particular region or confined to a particular time period. Different components of a supply chain are affected sequentially or concurrently  – manufacturing, DCs, logistics, and markets can

80

Chapter 3 · Modeling Supply Chain Resilience

3

..      Fig. 3.11  Optimal supply chain recovery plans for the redesigned supply chain in an optimistic scenario (Ivanov et al. 2013)

..      Fig. 3.12  Optimal supply chain recovery plans for the redesigned supply chain in a pessimistic scenario (Ivanov et al. 2013)

become paralyzed within overlapping time windows. This section illustrates the ripple effect and its consequences for supply chain resilience and performance in the COVID-19 pandemic setting. 3.4.2

Simulation Model

In this section, we analyze the results of a simulation that examines the impact of a pandemic on a global supply chain that has upstream facilities located in China (Ivanov 2020b; Ivanov and Das 2020). We model a global supply chain of a company selling some equipment, in total five different products (. Fig. 3.13). This is a multi-stage supply chain with suppliers, factory, distribution centers (DC), and customers located in different geographic zones (. Fig. 3.14).  

 

3

81 3.4 · Simulation: Ripple Effect Prediction During the COVID-19…

..      Fig. 3.13  Supply chain design for simulations

Suppliers in China

Distribution centers

USA

USA

Germany

Europe

Brazil

Suppliers Customers

DC

Customers

Factories

..      Fig. 3.14  Supply chain design (screenshot from anyLogistix™)

South America

82

3

Chapter 3 · Modeling Supply Chain Resilience

Our supply chain network design comprises upstream manufacturers in China, using multi-modal transportation to ship products to DCs in Brazil, Germany, and USA. The average lead transportation time from China to a DC is 30 days with some variations. The lead times from DCs to customers are about 3–7 days. DCs truck the goods to their customers. We modeled three scenarios and are as follows 55 Scenario I: Epidemic originates in and is limited to China producer regions. 55 Scenario II: Epidemic propagates to DCs all over the world. 55 Scenario III: Epidemic propagates to customers – demand disrupts by 50%. Scenarios could coexist or exist independently at different points in time. However, the market disruptions happen in the same time frame as the downstream DC disruptions caused by quarantine measures. For analysis, we use the timeline of coronavirus dispersal which was found in different Internet sources starting from middle of January 2020 until March 12, 2020: 55 January 25 Production stop at suppliers in China 55 February 3 Assembly stops in China 55 February 11 Port operations stop in China 55 February 25 Shortage in DCs worldwide 55 March 11 Production restarts in China 55 March 13 Extended quarantine measures in Europe and the USA We are interested in examining the epidemic outbreak impact on the supply chain subject to some scenarios which were likely to happen after March 13, assuming mitigation of epidemic outbreak in China. The ripple effect was considered in three aspects: the speed of epidemic propagation, the resultant duration of the disruption at DC node, and the duration of the reduced (50% drop) demand period. We examined different disruption ­durations and scales of epidemic propagation. In total, we investigated 39 simulation setups and are as follows (. Fig. 3.15). Scenario I considers three different epidemic durations and the resulting production stops at the producers in China of 45 days, 60 days or 90 days. These numbers are based on the actual or forecasted quarantine times in China from January to March 2020. Scenario II extends scenario I by adding epidemic propagation to USA, Germany, and Brazil, which results in 12 different simulation setups. First, we looked at two different setups with fast and slow epidemic propagations of 30 and 60 days, respectively. These times are based on the actual numbers on the start of the quarantine measures outside China. For example, if an epidemic outbreak begins in China on January 15, we set up epidemic outbreak downstream the supply chain with a delay of either 30 days (i.e., begin on February 15) or 60 days (i.e., begin on March 15). Second, we set up two different lengths of disruption periods in the USA, Germany, and Brazil of 45 or 90 days. These numbers are based on the actual or forecasted durations of the quarantine measures (e.g., in Germany the quarantine measures were introduced on March 16 until April 20, with a declared possibility of prolongation for another 45 days if the epidemic outbreak will not be dampened by April 20). An example of such a combined setup is a disruption in  

3

83 3.4 · Simulation: Ripple Effect Prediction During the COVID-19…

epidemic duration in China

45 days Factory I: disruptions only in China

DC

Market

60 days 90 days

45 days II: disruptions in China, USA, South America and Europe

60 days

Time between epidemic outbreaks in China and US/Europe/South America :30 days

Epidemic duration in US/Europe/South America: 45 days

Time between epidemic outbreaks in China and US/Europe/South America: 60 days

Epidemic duration in US/Europe/South America: 90 days

90 days

III: disruptions in China, USA, South America, Europe, and in the markets

Factory

45 days

DC

Market

Time between epidemic outbreaks in China and US/Europe/South America: 30 days

Epidemic duration in US/Europe/South America: 45 days

Market disruption: 45 days

Time between epidemic outbreaks in China and US/Europe/South America: 60 days

Epidemic duration in US/Europe/South America: 90 days

Market disruption: 90 days

60 days 90 days

Factory

DC

Market

..      Fig. 3.15  Case study scenarios for simulation (Ivanov 2020b)

China for 45 days (say from January 15 to February 29), beginning of an epidemic outbreak in Germany in 60  days after the epidemic outbreak in China (i.e., on March 15), and the resulting disruption duration at the DC in Germany of 90 days (i.e., from March 15 to June 15). In total, we have 12 different setups considering combinations of the upstream disruption duration, propagation speed of the epidemics downstream, and the downstream disruption duration. Finally, in scenario III, we extended the 12 setups from the scenario II by adding demand disruption of 50% in the markets in the USA, Germany, and Brazil of a short or long duration of 45 or 90 days, respectively. We assume that the market disruptions occur in the same time frame as the downstream DC disruptions. The rationales behind these setups are the observed trends for demand and capacity decreases during the quarantine times. This extension resulted in 24 new simulation setups. . Table 3.2 presents selected, specimen simulation runs. A fuller simulation is available in Ivanov (Ivanov 2020b). Expectedly, scenario I, where the epidemic’s impact is still confined to China, sees performance declines, stockouts, and price variability. In such conditions, the duration of the (yet limited) disruption affects supply chain performance (cf. line 1, . Table 3.2). Interesting insights emerge into scenario II. Now supply chain performance is seen to be a function of pandemic propagation velocity and the duration of pandemic-induced downstream disruption. Consider the following illustration for lines 2 and 3  in . Table  3.2 (see . Figs. 3.16 and 3.17). . Figures 3.16 and . Fig. 3.17 respectively contrast the difference in supply chain performance under the 60- and 90-day downstream disruption duration  

 

 

 

 

 

45

60

45

60

90

7

8

9

10

11

45

45

III

6

5

60

45

II

2

4

45

I

1

45

0

No disruption

3

Upstream disruption duration

Scenario

60

60

60

30

60

60

30

60

60

60

0

0

Delay in epidemic outbreak downstream the supply chain

90

90

90

45

90

45

45

45

90

45

0

0

Downstream disruption duration

90

90

90

45

45

45

45

0

0

0

0

0

Duration of market disruption (demand drops by 50%)

69

69

75

82

70

82

82

71

64

75

84

89

On –time delivery, %

77,490

77,490

87,484

97,026

90,947

98,031

97,026

92,259

88,072

102,130

108,028

108,100

Revenue, $

3

No.

..      Table 3.2  Results of specimen simulation runs

−734

−268

2133

12,106

3789

9448

12,431

7241

−215

11,969

19,005

28,568

Profit, $

84 Chapter 3 · Modeling Supply Chain Resilience

3

85 3.4 · Simulation: Ripple Effect Prediction During the COVID-19…

a

b

c

d

..      Fig. 3.16  Supply chain performance in scenario II with 45 days of upstream disruption, 60 days of delay in epidemic outbreak downstream, and 45 days of downstream disruption (line 2 in . Table 3.2) (screenshot from anyLogistix) (Ivanov and Das (2020)). (a) Production-inventory dynamics. (b) Customer (ELT service level) performance. (c) Financial performance. (d) Lead-time performance  

cases (other conditions identical) in scenario II. Performance improves in terms of profit, service level, and lead times, when the timing of facility recovery at different echelons in the supply chain is synchronized. For example, in the case with 45 days disruption in China, 60 days delay in epidemic propagation, and 45 days of disruption downstream, we may have a situation when China production stops on January 25, the DCs downstream close on March 25, China production resumes on March 10, and DC operations resume on May 10. We can observe in . Fig.  3.16 that inventory dynamics minimize backlogs to a single backlog event, allowing a quick recovery. The production quantities for five different products are depicted in the bar chart of production inventory dynamics with different colors. Inventory dynamics stabilize quickly, while on-time delivery (i.e., the ELT service level)  

86

Chapter 3 · Modeling Supply Chain Resilience

a

3 b

c

d

..      Fig. 3.17  Supply chain performance in scenario II with 45 days of upstream disruption, 60 days of delay in epidemic outbreak downstream, and 90  days of downstream disruption (line 3  in . Table  3.2) (screenshot from anyLogistix) (Ivanov and Das (2020)). (a) Production-inventory dynamics. (b) Customer (ELT service level) performance. (c) Financial performance. (d) Lead-time performance  

improves. Performance thus appears to be a function of the timing of closure and opening of upstream production and downstream DC facilities. A complete shutdown of the supply chain is avoided, since there are material flows in the supply chain at every point in time.

87 3.4 · Simulation: Ripple Effect Prediction During the COVID-19…

3

On the contrary, a longer downstream disruption (90 days instead of 45 days), keeping upstream disruption duration and pandemic propagation speed unchanged, results in performance deterioration. Profits dip into negative territory while inventory dynamics remain unstable for several months. Lead times increase and destabilize accompanied by a decline and non-recovery of ELT service level (cf. . Fig. 3.17). Our simulation thus suggests that as epidemics propagate, supply chain performance depends on the timing (e.g., 30 or 60 days between the upstream and downstream epidemic outbreaks) and scale of disruption propagation (i.e., the ripple effect), as well as the sequence of facility closing and opening at different supply chain echelons. The disruption duration of upstream facilities does not impact as strongly on performance. Scenario III introduces the added uncertainty of market disruption of varying durations in an attenuated demand situation (demand drops by 50%) against the general backdrop of variable upstream and downstream disruption times and variable pandemic velocity. Backorder and lost sales costs are not included in this preliminary examination. Interestingly, the combinatorial effects of negative events, happening concurrently, may actually improve supply chain performance. The best case in terms of supply chain performance is seen when facility recovery at different echelons in the supply chain is synchronized across time (see lines 5 and 8 in . Table 3.2). The worst performance is seen in cases with extended DC facility and demand disruption durations, irrespective of the disruption period in the upstream China-based production (see lines 10 and 11 in . Table 3.2).  

 

 

3.4.3

Managerial Insights

. Table 3.3 provides a summary of the managerial insights obtained through this study which can be used by supply chain managers to predict the impact of super-­ disruptions (i.e., a global pandemic) on their supply chains.  

88

Chapter 3 · Modeling Supply Chain Resilience

..      Table 3.3  Managerial insights (Ivanov 2020b) Scenario I: epidemic outbreak only in China

Scenario II: epidemic outbreak in China, the USA, and Europe (stops at all facilities simultaneously)

Scenario 3: epidemic outbreak in China, the USA, and Europe: simultaneous epidemic crises (stops at all facilities and demand disruption in markets)

Performance impact

Performance decrease is proportional to the duration of the upstream disruption

Longer delays in epidemic propagation and shorter disruption durations downstream the supply chain result in the lowest performance degradation

The lowest decrease in the supply chain performance can be observed in cases when the facility recovery at different echelons in the supply chain is synchronized in time. The most negative impact on the supply chain performance is observed in the cases with very long facility and demand disruption durations downstream the supply chain regardless of the disruption period in the upstream part

Duration of supply chain disruption time

The total supply chain disruption time is about 30% longer as an upstream disruption duration; the supply chain disruption time is proportional to the length of an upstream disruption

The longer delays in the epidemic outbreaks increase the total supply chain disruption time; faster disruption propagation and shorter disruption durations downstream the supply chain reduce the total supply chain disruption time

Simultaneous disruptions in downstream demand and supply may have positive effect on the total supply chain disruption time due to backlog reductions. Longer delays in disruption propagation and long-lasting disruptions downstream the supply chain are more dangerous as the disruption duration upstream the supply chain

Role of the scope and timing of disruption propagation

In this case, there is no disruption propagation

The performance reaction depends on the timing and scale of disruption propagation (i.e., the ripple effect) as well as the sequence of facility closing and opening at different supply chain echelons rather than on the disruption duration upstream the supply chain

Simultaneous disruptions in demand and supply may have positive, synergetic effects on supply chain performance as a reaction to an epidemic outbreak, especially for short-term disruptions and a synchronized recovery timing

3

89 References

3

References Azaron, A., Venkatadri, U., & Doost, A. F. (2020). Designing profitable and responsive supply chains under uncertainty. International Journal of Production Research. https://doi.org/10.1080/0020754 3.2020.1785036. Basole, R. C., & Bellamy, M. A. (2014). Supply network structure, visibility, and risk diffusion: A computational approach. Decision Sciences, 45(4), 1–49. Cavalcante, I.  M., Frazzon, E.  M., Forcellinia, F.  A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86–97. Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect in the supply chain: An analysis and recent literature. International Journal of Production Research, 56(1–2), 414–430. Dolgui, A., Ivanov, D., & Rozhkov, M. (2020a). Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain. International Journal of Production Research, 58(5), 1285–1301. Dolgui, A., Ivanov, D., & Sokolov, B. (2020b). Reconfigurable supply chain: The X-network. International Journal of Production Research, 58(13), 4138–4163. Fragapane, G., Ivanov, D., Peron, M., Sgarbossa, F., & Strandhagen, J. O. (2020). Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics. Annals of Operations Research. https://doi.org/10.1007/s10479-­020-­03526-­7. Garvey, M.  D., & Carnovale, S. (2020). The rippled newsvendor: A new inventory framework for modelling supply chain risk severity in the presence of risk propagation. International Journal of Production Economics, forthcoming. Garvey, M. D., Carnovale, S., & Yeniyurt, S. (2015). An analytical framework for supply network risk propagation: A Bayesian network approach. European Journal of Operational Research, 243(2), 618–627. Ghadge, A., Dani, S., Chester, M., & Kalawsky, R. (2013). A systems thinking approach for modelling supply chain risk propagation. Supply Chain Management: An International Journal, 18(5), 523–538. Giannoccaro, I., Nair, A., & Choi, T. (2018). The impact of control and complexity on supply network performance: An empirically informed investigation using NK simulation analysis. Decision Science, 49(4), 625–659. Gupta, V., Ivanov, D., & Choi, T.-M. (2020). Competitive pricing of substitute products under supply disruption. Omega. https://doi.org/10.1016/j.omega.2020.102279. Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research: Part E, 125, 285–307. Hosseini, S., Ivanov, D., & Dolgui, A. (2020). Ripple effect modeling of supplier disruption: Integrated Markov Chain and Dynamic Bayesian Network Approach. International Journal of Production Research, 58(11), 3284–3303. Ivanov, D. (2017a). Simulation-based the ripple effect modelling in the supply chain. International Journal of Production Research, 55(7), 2083–2101. Ivanov, D. (2017b). Simulation-based single vs dual sourcing analysis in the supply chain with con-­ sideration of capacity disruptions, big data and demand patterns. International Journal of ­Integrated Supply Management, 11(1), 24–43. Ivanov, D. (2018). Structural dynamics and resilience in supply chain risk management. Springer, New York. Ivanov, D. (2019). Disruption tails and revival policies: A simulation analysis of supply chain design and production-ordering systems in the recovery and post-disruption periods. Computers and Industrial Engineering, 127, 558–570. Ivanov, D. (2020a). “A blessing in disguise” or “as if it wasn’t hard enough already”: Reciprocal and aggravate vulnerabilities in the supply chain. International Journal of Production Research, 58(11), 3252–3262.

90

3

Chapter 3 · Modeling Supply Chain Resilience

Ivanov, D. (2020b). Predicting the impact of epidemic outbreaks on the global supply chains: A simulation-based analysis on the example of coronavirus (COVID-19 / SARS-CoV-2) case. Transportation Research – Part E, 136, 101922. https://doi.org/10.1016/j.tre.2020.101922. Ivanov, D. (2020c). Viable supply chain model: Integrating agility, resilience and sustainability perspectives. Lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research. https://doi.org/10.1007/s10479-­020-­03640-­6. Ivanov, D., & Das, A. (2020). Coronavirus (COVID-19 / SARS-CoV-2) and supply chain resilience: A research note. International Journal of Integrated Supply Management, 13(1), 90–102. Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915. Ivanov, D., & Dolgui, A. (2020c). A digital supply chain twin for managing the disruptions risks and resilience in the era of Industry 4.0. Production Planning and Control. https://doi.org/10.1080/09 537287.2020.1768450. Ivanov, D., & Dolgui, A. (2021). OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications. International Journal of Production Economics, 232, 107921. Ivanov, D., & Rozhkov, M. (2020). Coordination of production and ordering policies under capacity disruption and product write-off risk: An analytical study with real-data based simulations of a fast moving consumer goods company. Annals of Operations Research, 291(1–2), 387–407. Ivanov, D., & Sokolov, B. (2010). Adaptive supply chain management. London: Springer. Ivanov, D., & Sokolov, B. (2012). Structure dynamics control approach to supply chain planning and adaptation. International Journal of Production Research, 50(21), 6133–6149. Ivanov, D., & Sokolov, B. (2020). Simultaneous structural-operational control of supply chain dynamics and resilience. Annals of Operations Research, 283(1), 1191–1210. Ivanov, D., Sokolov, B., & Pavlov, A. (2013). Dual problem formulation and its application to optimal re-design of an integrated production-distribution network with structure dynamics and ripple effect considerations. International Journal of Production Research, 51(18), 5386–5403. Ivanov, D., Sokolov, B., & Dolgui, A. (2014). The ripple effect in supply chains: Trade-off ‘efficiencyflexibility-resilience’ in disruption management. International Journal of Production Research, 52(7), 2154–2172. Ivanov, D., Sokolov, B., & Pavlov, A. (2014a). Optimal distribution (re)planning in a centralized multi-stage network under conditions of the ripple effect and structure dynamics. European Journal of Operational Research, 237(2), 758–770. Ivanov, D., Sokolov, B., Hartl, R., Dolgui, A., Pavlov, A., & Solovyeva, I. (2015). Integration of aggregate distribution and dynamic transportation planning in a supply chain with capacity disruptions and ripple effect considerations. International Journal of Production Research, 53(23), 6963–6979. Ivanov, D., Sokolov, B., Pavlov, A., Dolgui, A., & Pavlov, D. (2016). Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies. Transportation Research: Part E, 90, 7–24. Ivanov, D., Pavlov, A., Pavlov, D., & Sokolov, B. (2017). Minimization of disruption-related return flows in the supply chain. International Journal of Production Economics, 183, 503–513. Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846. Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2021a). Global supply chain and operations management: A decision-oriented introduction into the creation of value (3rd ed.). Cham: Springer Nature. Ivanov, D., Sokolov, B., Chen, W., Dolgui, A., Werner, F., & Potryasaev, S. (2021b). A control approach to scheduling flexibly configurable jobs with dynamic structural-logical constraints. IISE Transactions, 53(1), 21–38. Ivanov, D., Tang, C.  S., Dolgui, A., Battini, D., & Das, A. (2021c). Researchers’ perspectives on Industry 4.0: Multi-disciplinary analysis and opportunities for operations management. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1798035.

91 References

3

Kim Y., Chen Y. S., Linderman K. (2015). Supply network disruption and resilience: a network structural perspective. Journal of Operational Management, 33–34:43–59. Khalili, S. M., Jolai, F., & Torabi, S. A. (2016). Integrated production-disruption planning in two-­ echelon systems: A resilience view. International Journal of Production Research, 55(4), 2017. Li, Y., Zobel, C. W., Seref, O., & Chatfield, D. C. (2020). Network characteristics and supply chain resilience under conditions of risk propagation. International Journal of Production Economics, 223, 107529. Li, Y., Chen, K., Collignon, S., & Ivanov, D. (2021). Ripple effect in the supply chain network: Forward and backward disruption propagation, network health and firm vulnerability. European Journal of Operational Research, 291(3), 1117–1131. Liberatore, F., Scaparra, M.  P., & Daskin, M.  S. (2012). Hedging against disruptions with ripple effects in location analysis. Omega, 40, 21–30. Liu, M., Liu, Z., Chu, F., Zheng, F., & Chu, C. (2020). A new robust dynamic Bayesian network approach for disruption risk assessment under the supply chain ripple effect. International Journal of Production Research, forthcoming. Ojha, R., Ghadge, A., Tiwari, M. K., & Bititci, U. S. (2018). Bayesian network modelling for supply chain risk propagation. International Journal of Production Research, 56(17), 5795–5819. Özçelik, G., Yılmaz, Ö. F., & Yeni, F. B. (2020). Robust optimisation for ripple effect on reverse supply chain: An industrial case study. International Journal of Production Research. https://doi.org/ 10.1080/00207543.2020.1740348. Panetto, H., Iung, B., Ivanov, D., Weichhart, G., & Wang, X. (2019). Challenges for the cyber-­ physical manufacturing enterprises of the future. Annual Reviews in Control, 47, 200–213. Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S. J., & Wamba, S. F. (2017). The role of big data in explaining disaster resilience in supply chains for sustainability. Journal of Cleaner Production, 142(2), 1108–1118. Pariazar, M., Root, S., & Sir, M. Y. (2017). Supply chain design considering correlated failures and inspection in pharmaceutical and food supply chains. Computers and Industrial Engineering, 111, 123–138. Paul, S. K., & Chowdhury, P. (2021). A production recovery plan in manufacturing supply chains for a high-demand item during COVID-19. International Journal of Physical Distribution & Logistics Management, 51(2), 104–125. Paul, S. K., Sarker, R., & Essam, D. (2017). A quantitative model for disruption mitigation in a supply chain. European Journal of Operational Research, 257(3), 881–895. Paul, S., Sarker, R., Essam, D., & Lee, P. T.-W. (2019). Managing sudden disturbances in a three-tier manufacturing supply chain: A mathematical modelling approach. Annals of Operations Research, 280, 299–335. Pavlov, A., Ivanov, D., Pavlov, D., & Slinko, A. (2019). Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics. Annals of Operations Research. https://doi.org/10.1007/s10479-­ 019-­03182-­6. Pavlov, A., Ivanov, D., Werner, F., Dolgui, A., & Sokolov, B. (2020). Integrated detection of disruption scenarios, the ripple effect dispersal and recovery paths in supply chains. Annals of Operations Research. https://doi.org/10.1007/s10479-­019-­03454-­1. Qazi, A., Dickson, A., & Gaudenzi, B. (2018). Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks. International Journal of Production Economics, 196, 24–42. Queiroz, M. M., Telles, R., & Bonilla, S. H. (2019). Blockchain and supply chain management inte-­ gration: A systematic review of the literature. Supply Chain Management, 25(2), 241–254. Roeck, D., Sternberg, H., & Hofmann, E. (2020). Distributed ledger technology in supply chains: A transaction cost perspective. International Journal of Production Research, 58(7), 2124–2141. Sawik, T. (2011). Selection of supply portfolio under disruption risks. Omega, 39(2), 194–208. Sawik, T. (2013). Selection of resilient supply portfolio under disruption risks. Omega, 41(2), 259–269. Sawik, T. (2019). Two-period vs. multi-period model for supply chain disruption management. ­International Journal of Production Research, 57(14), 4502–4518.

92

Chapter 3 · Modeling Supply Chain Resilience

Sheffi, Y. (2015). Preparing for disruptions through early detection. MIT Sloan Management Review, 57, 31. Wamba, S. F., & Queiroz, M. M. (2020). Industry 4.0 and the supply chain digitalisation: A blockchain diffusion perspective. Production Planning & Control, 1–18. Zhao, M., & Freeman, N. K. (2019). Robust sourcing from suppliers under ambiguously correlated major disruption risks. Production and Operations Management, 28(2), 441–456.

3

93

Measuring Supply Chain Resilience Contents 4.1

 easures of Supply Chain M Resilience – 95

4.2

Complexity Theory: EntropyBased Assessment of Supply Chain Adaptability – 101

4.2.1

 efinition of Supply Chain D Adaptability – 101 Quantitative Estimation of Supply Chain Adaptability: Basic Computation – 102 Quantitative Assessment of Supply Chain Adaptability: An Extension – 105

4.2.2

4.2.3

4.3

 easuring Supply Chain M Resilience Using Bayesian Networks – 107

4.3.1 4.3.2

 roblem Context – 107 P Methodology of Bayesian Networks – 107 Resilience Metric – 109

4.3.3

Supplementary Information The online version of this chapter (https://doi.org/10.1007/978-­3-­030-­70490-­2_4) contains supplementary material, which is available to authorized users. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 D. Ivanov, Introduction to Supply Chain Resilience, Classroom Companion: Business, https://doi.org/10.1007/978-3-030-70490-2_4

4

4.4

 ipple Effect Exposure R Quantification – 118

4.5

 etwork Design Characteristics N and Their Relations to Supply Chain Resilience – 121

4.6

Discussion – 124 References – 124

4

95 4.1 · Measures of Supply Chain Resilience

nnLearning Objectives Measuring supply chain resilience is important in different settings. First, if we have quantitative measures to assess supply chain resilience, we can compare resilience of different supply chains or different alternative supply chain designs. Second, resilience measures can be used for comparing resilience of our supply chains with the competitor’s resilience. Third, we can use resilience measures to assess our efforts in improving resilience over time. In this chapter, we explain the existing approaches to supply chain resilience and ripple effect measurement. To this end, our learning objectives for this chapter are as follows: 55 Understand probabilistic and non-probabilistic approaches to supply chain resilience measurement 55 Be able to quantify supply chain resilience using Bayesian networks 55 Apply entropy analysis to measure supply chain adaptability as a key resilience capability 55 Quantify the ripple effect in supply chains using supplier risk exposure index

4.1

Measures of Supply Chain Resilience

In this section, we review the measures that have been used to quantify resilience. A comprehensive review of definitions and measures of system resilience has been compiled by Hosseini et al. (2016). Resilience in the context of supply chain management is usually quantified using some metrics bounded between [0,1]. Many of the existing methods to measure supply chain resilience are based on the work of Bruneau et al. (2003) who measured the loss of community resilience following an earthquake (Eq. 4.1): t1

RL = ò (100 - Q ( t ) ) dt ,

(4.1)

t0

 where Q(t) is the expected quality of community infrastructure at time t, t0 is the time at which the earthquake occurs, and t1 is the time at which community infrastructure returns to its stable condition. Some research addresses the consideration of gradual performance degradation and recovery as an inherent supply chain resilience property (Ivanov et al. 2018; Pavlov et al. 2018). >>Important Observation Supply chain resilience assessment should include consideration of recovery strategies. Otherwise, we talk about robustness analysis if a supply chain reaction to disruption is analyzed with consideration of some redundancies only and without recovery actions.

How to use the resilience estimates? First, the resilience metrics can be used to compare resilience of different alternative supply chain designs or the effectiveness of different recovery strategies. Second, the metrics are of value when comparing resilience performance over several years. Third, we can use resilience indexes to compare resilience of different companies.

96

Chapter 4 · Measuring Supply Chain Resilience

T

X

4

0

Performance loss

1.00

tinitial

t0

t0 +T

t0 +T*

..      Fig. 4.1  Predicted resilience concept. (Based on Zobel et al. 2021)

Consider some metrics of supply chain resilience. Zobel et al. (2021) extended the Bruneau’s concept by defining a predicted disaster resilience measure to be the “relative amount of functionality retained by the system over time” (Eq. 4.2 and . Fig. 4.1).  

R ( X ,T ) =

XT 2 = 1 - XT * 2T * T

T* -

X Î [ 0,1] , T Î éë0,T * ùû

(4.2)  where X is the initial loss in system performance as a fraction and T is the time until recovery. Note that in Eq. (4.2), instantaneous loss and a constant rate of recovery are assumed. Based on a modified predicted resilience concept, Torabi et al. (2015) developed a metric that allows to quantify supply chain resilience with regard to supplier disruptions. Let’s assume that the amount of lost capacity is denoted by A, B, and C, respectively, and that TA, TB, and TC denote the time of receiving items associated with the A, B, and C recovery strategies. The loss of resilience RE′ can be shown graphically as the shaded area in . Fig. 4.2 and can be mathematically calculated by Eq. (4.3):  

RE¢ = A ´ TA + B ´ TB + C ´ TC 

(4.3)

It is clear that a lower value of RE′ results in higher supply resilience. The authors then calculated the resilience of the supply base by Eq. (4.4): RE = 1 -

RE¢ Q ´T

(4.4) 

4

97 4.1 · Measures of Supply Chain Resilience

Q

Amount of shortage

C

B

A

TA TB TC Time ..      Fig. 4.2  Measuring resilience by losses and recovery. (Based on Torabi et al. 2015)

where Q is the total amount of items the manufacturer needs from the supplier (i.e., the material shortage due to the disruption) and T is the length of the recovery process (. Fig. 4.2). Consider a numerical example. A supply chain experiences a disruption on June 23, 2020, caused by a supplier disruption due to the COVID-19 pandemic which is the day #175 in the year 2020. Due to the disruption, a shortage of materials of 140  units is observed. The disruption is expected to be recovered in 130  days. During the disruption period, the material shortage should be compensated by means of redirecting the material flows to a backup supplier. The firm considers two alternative backup suppliers, A and B.  Supplier A is capable of delivering 20  units within next 25  days after the disruption, 30  units within next 15  days, 40 units within next 30 days, and 50 within next 40 days. Supplier B can deliver 70 units within next 10 days after the disruption and 70 units within next 20 days. 55 Compute resilience for both supplier A and supplier B cases. 55 Which supply chain recovery strategy would entail a high resilience: supplier A or supplier B?  

(a) Compute resilience if recovery by supplier A is selected. RE¢ = 20 ´ ( 200 - 175 ) + 30 ´ ( 215 - 175 ) + 40 ´ ( 245 - 175 ) +50 ´ ( 285 - 175 ) = 100000

98

Chapter 4 · Measuring Supply Chain Resilience

RE ( supplier A ) = 1 -

RE ¢ 10000 = 1= 1 - 0.55 = 0.45 Q ´T 140 ´ 130

(b) Compute resilience if recovery by supplier B is selected. RE¢ = 70 ´ (185 - 175 ) + 70 ´ ( 205 - 175 ) = 2800

4

RE ( supplier B ) = 1 -

RE ¢ 2800 = 1= 1 - 0.15 = 0.85 Q ´T 140 ´ 130

We can observe that the recovery strategy with redirecting material flow to supplier B implies a higher supply chain resilience. Ojha et  al. (2018) developed a metric to quantify resilience as a measure of service level loss after a disruption. Suppose there are n nodes (suppliers) in the supply network, and the resilience index of node n denoted by RIk is measured by Eq. (4.5): æ

wn

SL

ö

å w= w ç1 - SLkw ÷ k0 ø è w w ( n 0) 0

RI k = 1 -

(4.5)

 where w0 and wn are the weeks when disruption occurs at the supply chain node and the time when disruption ends plus the time to recover from the disruption. SLk0 and SLkw are the service levels of node k prior to and after the disruption. The similarity that can be observed across these two measures, proposed by Torabi et al. (2015) and Ojha et al. (2018), is that resilience is calculated by 1 minus fraction of loss, so both metrics are bounded between 0 and 1. Torabi et  al. (2015) measured the loss of supplier capacity, while Ojha et al. (2018) considered the loss of service level. Ivanov et al. (2018) developed a bi-criteria method using attainable sets from control theory to measure supply chain resilience as a reaction to variations in both supply chain design and recovery control policies. Their resilience index is calculated as the area of intersection of two rectangles, i.e., an approximated attainable set and the extremal limits of two performance indicators, e.g., service level and profit (see . Fig. 4.3). The first (gray) rectangle is constructed on the basis of the extremal (e.g., minimum or maximum) values of the performance indicators, e.g., the minimum service level for a supply chain. After running the control algorithm with different recovery strategies for different execution scenarios, different attainable sets (i.e., the sets that include all possible performance outcomes for different scenarios – the blank rectangles in . Fig. 4.3). The greater the intersection region of the two rectangles, the less resilient is the supply chain. Ideally, the square of intersections of the two rectangles should be zero, meaning that a supply chain is capable of withstanding to all disruption scenarios considered. The larger the distance between the two rectangles, the more unnecessary (i.e., excessive) redundancy the supply chain contains.  

 

4

99 4.1 · Measures of Supply Chain Resilience

a

b J2

J2

Approximated attainable set

Area of resilience loss

Jb1

Net profit

Net profit

Approximated attainable set Jb1

Area PJ of low bounds for planned performance

Area PJ of low bounds for planned performance

Ja1 J1 Service level Attainable set analysis for supply chain structure #1

Ja1 J1 Service level Attainable set analysis for supply chain structure #2

..      Fig. 4.3  Different resiliencies of the SC structures: (a) a non-resilient case and (b) a resilient case (Ivanov et al. 2018)

scenario

Network design I

Network design II

Sales

Profit

RIX

Sales Profit

Scenario 1

42

11

0

37

12

0

Scenario 2

40

10

0

35

9

0

Scenario 3

38

7

0

29

7

1

Scenario 4

27

5

9

18

5

36

Example for network design I and scenario 4: Intersection square = RIX

RIX

8 6 Profit

Uncertainty

4 2 0 0

5

10

15 Sales

20

25

30

..      Fig. 4.4  RIX index computation

Consider an example. Target performance of a supply chain is sales of 30,000 units and profit of 8 million euro. Two supply chain network designs with associated backup recovery strategies are evaluated for resilience. Simulations of four different disruption scenarios for each of the two network designs resulted in the following performance outcomes: zz Network design I:

Scenario 1: profit = 11,000; sales = 42,000/Scenario 2: profit = 10,000; sales = 40,000/ Scenario 3: profit = 7000; sales = 38,000/Scenario 4: profit = 5000; sales = 27,000 zz Network design II:

Scenario 1: profit = 12,000; sales = 37,000/Scenario 2: profit = 9000; sales = 35,000/ Scenario 3: profit = 7000; sales = 29,000/Scenario 4: profit = 5000; sales = 18,000 The resilience indexes RIX are then computed as shown in . Fig. 4.4. The RIX index equals to the intersection square of two rectangles, i.e., the performance targets and performance outcomes under disruptions. For example, in case of network design I and scenario 4, RIX = (30–27) × (8–5) = 9. Obviously, the  

100

4

Chapter 4 · Measuring Supply Chain Resilience

higher the RIX value, the lower the resilience. We note that sales and profit numbers of the computations shown above display the worst-case outcomes for each uncertainty scenario. In reality, there are multiple outcomes of possible attainable performance in each scenario subject to variations of uncertainty factors and recovery policies. These sets of the outcomes form attainable sets that are then approximated (e.g., in form of rectangles). The selection of a particular point on the approximated attainable set (i.e., best case, worst case, average) depends on the risk aversion of a decision-maker. >>Important Observation How to use the resilience estimates? First, the resilience metrics can be used to compare resilience of different supply chain designs or the effectiveness of some recovery strategies. Second, the metrics are of value when comparing the firm’s resilience over several years. Third, based on the resilience metrics, one can judge whether a resilience of a particular supply chain is acceptable/non-acceptable. In order to identify the “acceptable” values or ranges of resilience (say 0.95 or between 0.93 and 0.98), the latter analysis is usually supported by an associated performance analysis, e.g., demand fulfillment or service level. If a supply chain is capable of achieving some desired service level under some disruption scenarios and recovery actions, the resilience values can be considered satisfactory.

Other approaches to resilience measurement in supply chains can be found in the works of Chen and Miller-Hooks (2012), Fattahi et al. (2020), Pavlov et al. (2018), Hosseini et al. (2020), Behzadi et al. (2020). One difficulty in assessing and managing supply chain resilience is the relative rarity of the risk event. Existing risk prediction and assessment methodologies attempt to estimate the probability of a disruption occurring together with its potential effects. However, the HILF events are too varied in type and nature and too intermittent and irregular to identify, estimate, or forecast well. These challenges are especially profound because of the nature of HILF events, where historical data are either unavailable or too inadequate to allow inference of statistical probabilities. While contributions to the domain of supply chain disruption research have emerged from probabilistic theory and stochastic programming (Klibi et al. 2010; Sawik 2020; He et al. 2019), the uncertainty of event occurrence combined with the wide variety of risk events renders estimation of causes and underlying probabilities a difficult task (Hosseini et al. 2019a, 2019b; Ivanov and Dolgui 2019; Ivanov et al. 2017; Ivanov 2020a). To overcome these limitations, researchers have begun to suggest shifting the focus of supplier risk estimation from risk event antecedents to risk event exposure (Simchi-Levi et al. 2014). Simchi-Levi et al. (2015) introduced a new model that measures supply chain resilience using supplier risk exposure while avoiding a major inconvenience, namely the need to estimate the likelihood of a disruption. Their model uses two major notions: time-to-recover (TTR) and time-to-survive (TTS). TTS is considered as the maximum time that the supply chain could operate and match supply with demand after a disruption. In contrast, TTR is the time it would take for a particular node to be restored to full functionality after a disrup-

4

101 4.2 · Complexity Theory: Entropy-Based Assessment...

tion (Simchi-Levi et al. 2015). TTR and TTS are used to “identify bottleneck suppliers for which it’s critical to obtain accurate TTR information and distinguish them from other suppliers where even plus or minus 30% error in TTR information will have very little impact on the supply chain” (Simchi-Levi et al. 2015). >>Important Observation If TTS > TTR, then a supply chain can be considered resilient. In other words, the existing resilience capabilities such as risk mitigation inventory, capacity agility, and backup sourcing allow maintaining supply chain operations and surviving during the recovery time.

Kinra et  al. (2020) extended the TTR/TTS approach toward ripple effect assessment. Their model is based on possible maximum loss rather than on the probability of a disruption occurrence. We will consider the Kinra’s et al. model in 7 Sect. 4.4.  

4.2

 omplexity Theory: Entropy-Based Assessment of Supply C Chain Adaptability

4.2.1

Definition of Supply Chain Adaptability

The concept of supply chain adaptability can be formulated in line with Arkhipov and Ivanov (2011) and Ivanov (2018)) as follows. Definition Adaptability is the structural property that characterizes the decoupling or branching degree of supply chain processes and the possibilities of process adaptation to a real execution environment.

Supply chain adaptability concept is close to the supply chain structural controllability approach modeled as spanned cactus (Liu et al. 2011). Supply chain adaptability is a kind of general (abstract) characteristic, like the concept of uncertainty itself. Therefore, its quantitative estimation should be perceived as a relative measure that has a local value in the context of the considered supply chain design problem. Due to this consideration, various approaches to the specified measure construction exist based on subjective considerations. We use supply chain adaptability concept for estimating the supply chain’s ability to compensate for the uncertainty level of its functioning conditions (predicted for a corresponding time interval) by using a multi-variant approach while selecting the trajectories of process realization development. If uncertainty conditions are formed under the influence of uncontrollable environmental factors, the adaptability is a kind of process and structure variety that is introduced into the supply chain to maintain the possibility of choosing alternative trajectories, thus ensuring flexibility to cope with disruptions (Ivanov et al. 2019a, b, 2021; Ivanov 2020b; Ivanov and Dolgui 2020).

102

Chapter 4 · Measuring Supply Chain Resilience

Taking into account the semantic affinity of uncertainty and adaptability concepts, we explain in this section how to use the entropy measure applied in statistical physics and the theory of information. Definition Entropy is a measure of a complex system state’s relative variety in the quantitative estimation of adaptation potential (Shannon and Weaver 1963; Lim 2007).

4 Allesina et  al. (2010), Isik (2010), Levner and Ptuskin (2018), and Hosseini and Ivanov (2019) applied the entropy concept to analyze supply chain complexity and structural dynamics. 4.2.2

 uantitative Estimation of Supply Chain Adaptability: Q Basic Computation

Let us assume a network structure for a period divided into Т intervals. There are nt supply chain elements within the t-interval of the planning period which can be used for network design. Assume that a preference system of the supply chain structuring is unknown; therefore, the probability p of choosing any path in a ­supply chain is equal. A set of objects and links between them in a supply chain represent a set of possible trajectories of supply chain process realization, e.g., alternative transportation routes. Trajectories are designed according to the set system of logical links between adjacent time interval elements in the planning period. Network structural complexity as a measure of system process variety, equivalent to a variety of choices of alternative sets, can be estimated with the help of an indicator known as entropy Н of a supply chain (Eq. 4.6): N

H = -å pi ln pi

(4.6)  where pi is the probability of i-state of system or, in our case, selection probability of i-trajectory of process realization in a supply chain, i = 1,2,…,N. In order to compute the entropy index H, first, the probabilities of each trajectory realization in the supply chain should be determined and, second, the logarithm of this probability should be found. This will be explained further in a later section using a numerical example (see . Fig. 4.5). As the entropy assessment of supply chain adaptability is performed as a basis for the further adaptation potential estimation, any of the log bases can be used. We use the natural (normal) ln because it is the most convenient way for computing experiments in entropy as shown in Shannon and Weaver (1963)). It is not difficult to calculate the index of network entropy when the network is designed, and the i =1

 

103 4.2 · Complexity Theory: Entropy-Based Assessment...

a

b

N=6

c

4

N=10

d N=10

..      Fig. 4.5  Examples of supply chain structures

hypothesis about the choice of equally probable operations at each planning stage is accepted. As a result, an estimation can be received that indirectly characterizes the network adaptation potential. Let us consider a simple example as shown in . Fig. 4.5a, b.  

104

Chapter 4 · Measuring Supply Chain Resilience

Let a supply chain have the structure presented in . Fig. 4.5a. For this network, the entropy index is Н = 1.74 according to Eq. (4.6). There are two options to transit from the node of the initial stage to the 1st stage. Therefore, the probability is 0.5. In considering the upper node of the 1st stage, there are four alternative links to reach the 2nd stage. Therefore, the probability will be 0.25. Hence, the selection probability of each of the four ways through the upper node of the 1st stage is p = 0.5 × 0.25 = 0.125. Now, the logarithm from this probability can be taken as follows: ln0.125 = 2.08. We get 0.125 × 2.08 = 0.26. Since four alternative links exist, 0.26 × 4 = 1.04. Analogously, in considering the bottom node of the 1st stage, there are two alternative links to reach the 2nd stage. Therefore, the probability will be 0. 5. Hence, the selection probability of each of the two ways through the upper node of the 1st stage is p = 0.5 × 0.5 = 0.25. Now, the logarithm from this probability can be taken: ln0.25 = 1.39. We get 0.25 × 1.39 = 0.35 since two alternative links exist, 0.35 × 2 = 0.7. The sum of the two nodes in the 1st stage is, therefore, 1.04 + 0.7 = 1.74. The maximum value for a network with a set number of supply chain elements at each stage will be achieved when all the variants of transitions from i-operation elements to (i  +  1)-operation elements are admissible and equally probable (see . Fig. 4.5b). For this case, we will have Hmax = 2.30. For a network that has a more complex structure (see . Fig. 4.5c), the entropy index is Н = 4.52. In analyzing a real supply chain, it is useful to have relative (normal) estimations Н(о) as well as absolute estimations of a variety level (entropy) Н, previously having constructed a supply chain with the same quantity of elements of planned period stages (network knots), but with all the possible links between them and equal probabilities of all the trajectories: Н(о) = Н/Нmax. Absolute estimations allow us to compare supply chain structures that differ in the quantity of supply chain stages (the number of stages of suppliers, production, distribution, wholesalers, and retailers), the quantity of elements at each supply chain stage, and a variety of links in between elements. The relative estimations characterize, in essence, the degree of affinity between the varieties of links in a concrete supply chain to the maximum value. However, the index (4.6) does not reflect one important supply chain structural characteristic – the maintenance of a high service level, which is important for the adaptation possibilities for the execution of customer orders. Processes in the supply chain are always carried out under increased uncertainty. Therefore, supply chain adaptation possibilities need to increase as supply chain process execution nears the customer. The entropy index (4.6) does not reflect this idea. Let us introduce another supply chain structure, but with the same number of elements and links (see . Fig. 4.5c). Indeed, the estimations calculated for networks, presented in . Figs. 4.5b, c, are identical and equal Н = 2.30. At the same time, these two structures essentially differ because the choice possibilities change in different ways while going downstream the networks. It can be supposed that the heuristic estimation of the choice variety value for the supply chain presented in . Fig. 4.5b should be higher, as it has more choice possibilities at the downstream stages than the supply chain shown in . Fig. 4.5c. As such, we consider this issue and extend the present formulation in the next section.  

4

 

 

 

 

 

 

4

105 4.2 · Complexity Theory: Entropy-Based Assessment...

4.2.3

 uantitative Assessment of Supply Chain Adaptability: Q An Extension

An extended estimation can be received after some transformations of Eq. (4.6), considering each trajectory as a number of consecutive operational blocks (supply chain units) and, accordingly, presenting the i-trajectory selection probability as the multiplication of its units’ probabilities pi = pi1 x pi2 x …..x piТ. Eq. (4.6) will be transformed as follows in Eq. (4.7): N

H = -å pi1 xpi 2 x ¼ xpiT ln ( pi1 xpi 2 ¼ xpiT ) i =1

N

= å pi1 xpi 2 x ¼ xpiT ln ( pi1 xpi 2 ¼ xpiT ) i =1

ö æ ÷ ç N ç T -1 T -1 T -1 ÷ = å ç Õ pik xpil ln pil + Õ pik xpi 2 ln pi 2 +¼+ Õ pik xpik ln pik ÷ ÷ k =0 k =0 i =1 ç k = 0 ÷ ç k ¹t k ¹t k ¹t ÷ çN T -1 T -1 ø è æ ö = åå ç Õ pik ÷ x ( pit ln pit ) i =1 t = 0 è k = 0 ø

(4.7)



In Eq. (4.7), symbol ∏ means the multiplication of i-trajectory units’ probabilities except for one probability “connected” to the algorithm. As it can be observed in Eq. (4.7), supply chain variety is expressed through the probabilities of a trajectory unit’s selection; each item, corresponding to a separate unit (or stage, which is the same in this case), is calculated like the entropy estimation. This allows us to introduce weights, which reflect the subjective considerations about the choice variety value at each planning period stage. We will designate these weights as wt, and we will consider them normal as 0 ≤ wt ≤ 1, ∑ wt = 1. Let us call the obtained estimation the supply chain weighted variety (the weighted entropy) and designate it in Eq. (4.8) as Нw. æ ö ç T -1 ÷ H w = -ååwt ç Õ pik ÷ x ( pit ln pit ) ç k =0 ÷ i =1 t = 0 ç k ¹t ÷ è ø  N T -1

(4.8)

The index of weighted variety, calculated using Eq. (4.8), can also be named the supply chain absolute adaptation potential; we designate it as А and А = Нw. For a supply chain with the maximum value of weighted entropy (this supply chain has

106

Chapter 4 · Measuring Supply Chain Resilience

..      Table 4.1  Examples of supply chain variety and adaptation potential estimations for different network structures (weights wt considered in direct proportion to the t interval number)

4

Structure variant (Fig. no.)

Variety estimations, Н, (Нmax)

Weighted variety estimation (absolute adaptation potential), А = Нw

Maximum estimation of weighted variety, Нwmax

Relative adaptation potential estimation, А(о)

. Figure 4.5a

1.74; (2.30)

0.94

1.31

0.72

. Figure 4.5b

2.30; (2.30)

1.31

1.31

1.00

. Figure 4.5c

2.30; (2.30)

0.99

0.99

1.00

. Figure 4.5d

1.98; (3.69)

0.68

1.39

0.49

 

 

 

 

the same quantity of actions as the initial network and the maximum number of equally probable trajectories), this formula becomes simpler as shown in Eq. (4.9). T -1

H wmax = - åwt ln pit

(4.9)  Let us consider the following estimation as the indicator of the supply chain’s relative adaptation potential (Eq. 4.10): t =0

Hw 0 A( ) = max Hw

(4.10)

 In order to illustrate the proposed technique, we introduce several numerical examples. Supply chain structure variants are presented in . Fig.  4.5; the necessary estimations of network variety and adaptation potential are shown in . Table 4.1. As it can be observed, the adaptation potential index is sensitive to a change in the supply chain knots (elements), the allocation of these knots within planning period intervals, and the variety of variants in choice possibilities. Changes in supply chain potential estimations correspond to intuitive considerations about this indicator’s dependence on the specified parameters. Let us pay attention to absolute and relative potential estimation characteristics (see previous remarks on these indicators). In particular, we would like to note that the relative estimations with the maximum variety of links are identical and equal to 1, no matter how many stages and actions it has. This fact limits their application possibilities to a certain extent, but nevertheless they still keep their analytical role, supplementing absolute adaptation potential estimations. The proposed supply chain adaptation potential indicators can be used as criteria for selecting supply chain structures at the resilient network design stage.  

 

107 4.3 · Measuring Supply Chain Resilience...

4.3

4

 easuring Supply Chain Resilience Using Bayesian M Networks

4.3.1

Problem Context

While development of resilient supply chain designs is desirable and indeed critical to withstand the disruptions, exploiting the resilience capabilities to achieve the target performance outcomes through effective recovery is becoming increasingly important. A more detailed vulnerability analysis can be achieved when the impacts of disruption caused by the suppliers’ suppliers (tier 2 and tier 3 suppliers) are quantified, i.e., considering the disruption propagation or ripple effect in the s­ upply chain. We explain in this section a measure of supply chain resilience with a multistage assessment of suppliers’ proneness to disruptions and test the developed notion of supply chain resilience as a function of supplier vulnerability and recoverability using a Bayesian network (BN) and considering the ripple effect (Hosseini and Ivanov 2020). 4.3.2

Methodology of Bayesian Networks

BNs are structured based on Bayes’ theorem and conditional probability theory. Bayes’ theorem enables us to reason in a logical, rational, and consistent way by computing the posterior probability of input data, given new data input in a specific state. Bayes’ theorem can be represented as shown in Eq. (4.11): P (q |D ) =

P ( D|q ) P (q ) P (D)

(4.11) 

For data  and variable θ, P (q |D ) is the posterior probability of θ in light of the observed data  , P ( D|q ) is the likelihood function of the probability of new data  given θ,  (q ) is prior (unconditional) probability distribution of parameter θ, and P ( D ) is marginal likelihood (evidence). With the use of Bayes’ rule and in light of the data, we are able to update our beliefs about the variable θ to a posterior belief. BNs can be used for representing the impact of evidence on existing data through probabilistic expressions describing the causal relationship among variables (Pearl 1998, 2000; Fenton and Martin Neil 2012). To mathematically represent the structure of BN, consider a DAG (directed acyclic graph) represented by G, where G = (V, E) and V = {X1, X2, …, Xn} represents a set of random variables (nodes) and E is a set of arcs (Hosseini et al. 2020). An outgoing arc from Xi to Xj indicates the dependency or causal relationship between these two variables, such that Xi is the parent of Xj and Xj is the child of Xi. Generally speaking, there are three classes of nodes in BNs: (i) nodes without any child that are called leaf nodes, (ii) nodes without any parent nodes that are

108

Chapter 4 · Measuring Supply Chain Resilience

..      Fig. 4.6  An illustrative example of BN with four variables (Hosseini and Ivanov 2020)

X3

4

X2

X1

X4

called root nodes, and finally (iii) nodes with parent and child nodes that are called intermediate nodes. For the example given in . Fig.  4.6, X2 and X3 are the root nodes, X4 is the leaf node, and X1 is the intermediate node. The dependency between a child node and its parent nodes can be quantified by a conditional probability table (CPT). For nodes without any parents, unconditional probabilities or prior probabilities are specified. The dependencies among variables of a BN can be quantified by conditional probability distributions. Consider a BN with n variables X1, X2, …, Xn. The general expression for joint probability distribution can be represented as shown in Eq. (4.12):  

P ( X 1 ,X 2 , ¼ ,X n ) = P ( X 1 | X 2 , X 3 , ¼ , X n ) P ( X 2 | X 3 , ¼ , X n ) ¼ P ( X n -1|X n ) P ( X n )

(4.12)



Equation (4.12) can be rewritten as Eq. (4.13): n

P ( X 1 ,X 2 , ¼,X n ) = ÕP ( X i |,X i +1|, ¼|,X n )

(4.13)  The joint probability distributions of BN represented in Eq. (4.13) can be further simplified based on the knowledge of which parent nodes belong to which child node. For example, if node X1 has exactly two parents, X2 and X3, then P(X1| X2, …, Xn) can naturally be substituted with P(X1| X2, X3). As such, the joint probability distribution can be simplified to Eq. (14). i =1

n

P ( X 1 ,X 2 , ¼,X n ) = ÕP(X i |parents ( X i ))

(4.14)

i =1

 The full joint probability distribution, for example, illustrated in . Fig. 4.6 can be written as shown in Eq. (4.15):  

P ( X 1 , X 2 , X 3 ,X 4 ) = P ( X 2 ) P ( X 3 ) P ( X 1 | X 2 , X 3 ) P ( X 4 | X 1 )

(4.15)  In this case, we need the conditional probability (from a CPT) for P(X1| X2, X3) and P(X4| X1) and unconditional probability (or prior probability) for P(X2) and P(X3). The marginal distribution of each variable (node) can be computed by the margin-

4

109 4.3 · Measuring Supply Chain Resilience...

alization of the joint probability distribution. For example, the formula for marginalization of variable X2 is given in Eq. (16): P( X2 ) =

å

X1 , X 3 , X 4

P ( X 2 ) P ( X 3 ) P ( X 1 |X 2 , X 3 ) P ( X 4 |X 1 )

(4.16)

 Note that marginalization is a distribution operation over combinations. This implies that global joint probability can be performed by marginalizing the local node probability. For the example given in . Fig. 4.6, P(X2) can be calculated as (4.17):  

æ æ æ ööö P ( X 2 ) = ç åP ( X 3 ) ç åP ( X 1|,X 2 |,X 3 ) P ( X 3 ) ç åP ( X 4 |X 1 ) P ( X 1 ) ÷ ÷ ÷ ÷÷÷ çX çX ç X3 è 4 øøø  è 1 è 4.3.3

(4.17)

Resilience Metric

The relationship q → r represents the material flow supplied from supplier q to supplier r. This also means that a disruption at supplier q can cause a disruption at supplier r, as materials flow from supplier q to supplier r. While an upstream supplier can disrupt a downstream supplier (e.g., q disrupting r), it is assumed that a disrupted supplier downstream will not disrupt an upstream supplier. Represent supplier node i with Xi in a supply network with n suppliers, i = 1, …, n and OEM is denoted by O (this is also the target node of the supply network). Each supplier Xi can be either operational or disrupted. Generally, node Xi, whose parents G are in state g, is in state x with the probability P(x| g) and

åP ( x|g ) = 1 x

for every realization of the states of parent nodes. The conditional probabilities P(x| g) are called risk parameters. Assume that each node has two binary states (True or False). Therefore, there are 2n risk parameters at a node with n parents. It should be noted that True represents disrupted state while False represents operational states. Consider a simple BN model consisting of OEM (node O) and two supplier nodes (X1, X2) as represented in . Fig.  4.7. Node O is conditioned on supplier nodes X1 and X2, which means that disruption of either supplier can cause the disruption of the OEM. The prior probability of each supplier is assumed to be 3%, suggesting each supplier has a 3% likelihood of failing to supply the OEM.  Disruption of a supplier induces disruption at the OEM with a specific probability. . Table  4.2 lists the conditional probabilities of disruption at the OEM due to the i- supplier disruption. According to . Table 4.2, the probability that the OEM is disrupted if supplier 1 is disrupted and supplier 2 is operational is 0.12. This probability changes to 0.21 when both suppliers are disrupted. The prior and joint distribution probabilities at supplier and the OEM are represented in . Fig. 4.8. The marginal probability of an OEM disruption is 1.54%, calculated from the conditional probabilities illustrated in . Table 4.2 and based on Eq. (4.17).  

 

 

 

 

110

Chapter 4 · Measuring Supply Chain Resilience

..      Fig. 4.7  A simple BN with two suppliers and one OEM (Hosseini and Ivanov 2020)

X1 (supplier 1)

O (OEM)

4 X2 (supplier 2)

..      Table 4.2  CPT of OEM disruption Supplier 1 (X1)

Operational

Supplier 2 (X2)

Operational

Disrupted

Operational

Disrupted

OEM disrupted

0.01

0.08

0.12

0.21

OEM operational

0.99

0.92

0.88

0.79

..      Fig. 4.8  Prior and joint disruption probabilities of suppliers and the OEM calculated using CPT (Hosseini and Ivanov 2020)

Disrupted

X1 (supplier 1) 3.00%

True False

97.00%

X2 (supplier 2) 3.00%

True False

97.00%

O (OEM) 1.54% 98.46%

True False

4

111 4.3 · Measuring Supply Chain Resilience...

P ( OEM disrupted ) =

å P (Supplier disrupted|,X1|,X 2 ) ´ P ( X1 ) ´ P ( X 2 )

X1 , X 2

= P ( OEM disrupted|, X 1 = disrupted|, X 2 = disrupted ) ´P ( X 1 = disrrupted ) ´ P ( X 2 = disrupted ) + P ( OEM disrupted|, X 1 = disrupted|,X 2 = operational ) ´P ( X 1 = disrupted ) ´ P ( X 2 = operational ) + P ( OEM disrupted|, X 1 = operational|,X 2 = disrupted ) ´P ( X 1 = operational ) ´ P ( X 2 = disrupted ) + P ( OEM disrupted|, X 1 = operational|,X 2 = operational ) ´P ( X 1 = operational ) ´ P ( X 2 = operational ) = ( 0.21´ 0.03 ´ 0.03) + ( 0.12 ´ 0.03 ´ 0.97 ) + ( 0.08 ´ 0.97 ´ 0.03) + ( 0.01´ 0.97 ´ 0.97 ) = 1.54%

(4.18)



Note that the CPT of an OEM disruption as represented in . Table 4.2 requires 23 = 8 risk parameters, as there are n = 3 nodes, where each node has two binary states (True or Disrupted) vs. (False or Operational). In practice, constructing a CPT from an OEM disruption can be challenging because the OEM may receive materials from many suppliers, meaning that the disruption of an OEM is conditioned on the disruption of many suppliers. To deal with this issue, we utilize the noisy-OR model (Pearl 1998) to build the causal relationship between disruption at parent and child nodes in large supply networks. The main advantages of utilizing noisy-OR model include the following: (i) it significantly reduces the computational efforts in large supply networks, particularly when OEM is conditional based on dozens of suppliers, and (ii) the number of required elicitation probabilities is much lower relative to a BN built using a CPT. Suppose that there are n suppliers, X1, X2, …, Xn, that affect the status of O (OEM). Assume that there is a probability associated with O being disrupted when one and only one Xi (supplier i) is disrupted and all suppliers other than Xi are operational. The noisy-OR model for the O node can be expressed as shown in Eq. (4.19):  

(

NoisyOR X 1 ,vO| X1 ,X 2 ,vO| X 2 , ¼,X i ,vO| X i , ¼,X n ,vO| X n ,qO

)

(4.19)

where each i, vO| X i = P (O = disrupted|X i = disrupted, X j = operational for each j ¹ i ) is the conditional probability of the OEM being disrupted if, and only if, the supplier is alone disrupted and other suppliers are operational. There is a leak variable θO that represents the probability that the OEM is disrupted when all suppliers are operational. The leak variable is taken into account because the disruption of the OEM does depend not only on supplier disruption but also on several other disruptions that may occur at manufacturing sites (e.g., machine failures, labor strikes, economic collapse of manufacturer, and natural disaster). The leak variable is defined as given in Eq. (20):

112

Chapter 4 · Measuring Supply Chain Resilience

qO = P(O = disrupted|X 1 = operational, X 2 = operational, ¼, X n = operational)

4

(4.20)  By applying the noisy-OR model, we assume that each supplier operates independently of others in terms of their effects. To understand how the noisy-OR model is utilized, consider an example of an OEM with two suppliers 1 and 2 that feed materials to the OEM. The conditional probability of a disruption at the OEM due to disruption at supplier i, ∀i = 1, 2, is represented by vO| X i . The leak probability of OEM (node O) is θO. The prior probability of the disruption at supplier i is denoted by ηi, and the marginal distribution probability of the O node is represented by FO. Assume that vO| X1 = 40%,, vO| X 2 = 55%, qO = 5% , η1 = 3%, and η2 = 4%. The marginal probability of an OEM disruption is calculated in . Table 4.3. In . Table 4.3, there are four states, g1, …, g4. In state g1, both suppliers are operational, (X1, X2). In the second state, the first supplier is fully operational (X1 is 100% in the False state), but the second is fully disrupted ( X 2 is 100% in the True state). In the third state, supplier 1 is fully disrupted ( X 1 ) and supplier 2 is operational (X2). Finally, in the fourth state, both suppliers are disrupted, ( X 1 ,X 2 ) . The  

 

probabilities of two suppliers and an OEM modeled using noisy-OR model are illustrated in . Fig. 4.9. As shown in . Fig. 4.9 and . Table 4.3, the probability FO of OEM disruption is 8.2%. The resilience of OEM in this paper is measured as a function of its vulnerability and recoverability when its supplier fails to supply because of a disruption. Let denote the resilience of the OEM corresponding with supplier Xi, and let VO| X i and O| X i represent the vulnerability and recoverability indices, respectively, of the OEM given that supplier Xi is disrupted. is then expressed as a ­function of VO| X i and O| X i , .  

 

 

..      Table 4.3  Calculation of the marginal distribution probability of OEM, FO using noisy-OR technique States g

P(O| g)

P(g)

g1 = {X1, X2}

θO = 0.05

(1 − η1)(1 − η2) = 0.93

g 2 = { X 1 ,X 2 }

1 - (1 - qO ) 1 - vO| X 2 = 0.573

g 3 = { X 1 ,X 2 }

1 - (1 - qO ) 1 - vO| X1 = 0.43

g 4 = { X 1 ,X 2 }

1 - (1 - qO ) 1 - vO| X1 1 - vO| X 2 = 0.74

(

)

(1 − η1)η2 = 0.039

(

)

η1(1 − η2) = 0.029

(

)(

)

FO = åP ( O|g ) ´ P ( g ) = 0.082, FO = 8.20% g

η1η2 = 0.001

4

113 4.3 · Measuring Supply Chain Resilience...

..      Fig. 4.9  Prior probabilities of two suppliers and marginal distribution probabilities of OEM calculated using noisy-OR model (Hosseini and Ivanov 2020)

X1 (supplier 1) 3.00%

True False

97.00%

X2 (supplier 2) 4.00%

True False

96.00%

O (OEM) 8.20% 91.80%

True False

The vulnerability index, VO| X i , quantifies the disruption risk increase at the OEM (marginal disruption probability) when supplier i ( X i ) is disrupted. To calculate FO ( X i ) , we enter evidence describing supplier i and set its state to be True. This means that we make an observation about supplier i when it is disrupted and update the marginal probability of OEM through propagation (21).

(

VO| X i = FO ( X i ) - FO

)

(4.21)

The recoverability index O| X i measures the decrease in disruption risk (marginal disruption probability) when supplier i is fully operational. To calculate O| X i , the state of supplier i is changed to 100% False, and the impact is propagated BN to determine the disruption risk of the OEM.  In essence, the recoverability index quantifies the effect on the disruption risk at the OEM when supplier i is fully operational. The recoverability index is calculated as shown in Eq. (4.22): O|Xi = ( FO - FO ( X i ) )

(4.22)  The resilience value of the OEM corresponding with supplier i is calculated as the ratio of the recoverability and vulnerability indices (4.23):

114

Chapter 4 · Measuring Supply Chain Resilience

(4.23)  To understand how the resilience index is calculated, consider an OEM that is conditioned on four suppliers (X1, X2, X3, X4). The prior disruption probabilities of the four suppliers are η1 = 3 % ,  η2 = 4 % ,  η3 = 5 % , and η4 = 6%, respectively. The disruption probabilities of the OEM, given the disrupted suppliers, are

4

VO| X1 = 31.04%, VO| X 2 = 35.3%, VO| X 3 = 39.56%, and VO| X 4 = 43.83% , respectively. The probability of the leak variable associated with OEM is θO = 2%. The disruption risk or marginal disruption probability of the OEM is then FO = 9.53%, as illustrated in the baseline BN model in . Fig. 4.10. To calculate how much the disruption risk probability of the OEM increases if supplier i is disrupted, we set the value of each supplier to True (True state = 100%) and propagate the impact of this observation throughout the BN to measure the impact of this observation on the OEM’s risk of disruption. For example, Table 16 shows that the disruption risk of the OEM is FO ( X 1 ) = 40.57% when we have evidence that supplier 1 is fully disrupted. VO| X1 is calculated as the difference  

between FO and FO ( X 1 ) as calculated in Table 16. The vulnerability index for sup-

plier 1 is 31.04%, which means that the disruption risk of the OEM increases by 31.04% when supplier 1 is disrupted or that the vulnerability of the OEM with respect to supplier 1 is 31.04%. For suppliers 2, 3, and 4, the vulnerability indices are 35.3%, 39.56%, and 43.83%, respectively. A simple comparison between these four suppliers indicates that VO| X 4 > VO| X 3 > VO| X 2 > VO| X1 , suggesting that a disruption at supplier 4 increases OEM’s risk of disruption. As such, supplier 4 plays a key role in determining the OEM’s level of disruption risk. The vulnerability index value can be obtained by

Supplier 1 (X1) Supplier 1 (X1)

3.00% 97.00%

Supplier 4 (X4) True False

6.00% 94.00%

True False

9.53% 94.47%

True False

Supplier 2 (X2) Supplier 2 (X2) Supplier 3(X3)

OEM(O)

4.00% 96.00%

OEM(O) True False

Supplier 3(X3) Supplier 4 (X4)

5.00% 95.00%

True False

..      Fig. 4.10  BN model, prior probabilities of four suppliers, and marginal disruption probability of OEM (Hosseini and Ivanov 2020)

115 4.3 · Measuring Supply Chain Resilience...

4

performing inference from cause (supplier i) to effect (OEM) by setting evidence that supplier i is 100% disrupted (True state) and measuring the resulting impact of this observation on the posterior distribution probability of the OEM. Considering the illustrative example of the BN model in . Fig.  4.10, the probability of the OEM being disrupted under normal conditions is FO = 9.53%. A vulnerability index comparison across the four suppliers highlights the importance of reducing the probability of disruption at supplier 4 by analyzing the threats that can lead to its disruption and developing a pre-disaster strategy (e.g., extra inventory prepositioning, fortifying the physical location of supplier) and post-disaster resilience strategies (e.g., contracting with backup suppliers). The recoverability of the OEM with respect to each supplier is calculated using Eq. (4.22). To calculate FO(Xi), we set the state of each supplier i to their False states by assuming that supplier i is 100% operational and propagate this impact to the risk of disruption at the OEM.  The recoverability index of the OEM with respect to each supplier i is calculated in . Table  4.4. Finally, the resilience of the OEM with respect to each supplier i is calculated using Eq. (4.23). Finally, we illustrate the resilience metric using a complex, real-life example. Auto supply networks can be very large since OEMs can have somewhere between 50 and 500 suppliers: analyzing multi-tier supply networks with a large number of suppliers and a causal relationship between suppliers can be a difficult task. We utilize the noisy-OR formulation to lessen the computational burden of analyzing a BN developed for the 29 suppliers of this OEM supply network, as illustrated in . Fig. 4.11. Using our method, we could both identify resilience of the OEM and cluster the suppliers as shown in . Fig. 4.12. The multi-quadrant chart offers us a convenient way to identify which suppliers are important, but less resilient. This plot is divided into four quadrants. The most critical suppliers are the ones located in the upper left quadrant (suppliers 2, 9, 19, 21, 22, 26), since they are highly important with regard to the OEM, but not sufficiently resilient. As such, the OEM can request them to improve their predisaster and post-disaster resilience strategies to reduce the risk of disruption propagation throughout the supply network. Suppliers located in the upper right quadrant are also considered important but probably do not need major revisions in their resilience strategies. Finally, suppliers located in the lower left quadrant lack resilience, but are not as important as other less resilient suppliers. Such an analysis allows for identifying critical suppliers and developing respective resilience strategies.  

 

 

 

Supplier 4

Supplier 3

Supplier 2

FO ( X 1 ) - FO =

Supplier 1

53.36 %  − 9.53 %  = 43.83%

FO ( X 4 ) - FO =

49.09 %  − 9.53 %  = 39.56%

FO ( X 3 ) - FO =

44.83 %  − 9.53 %  = 35.3%

FO ( X 2 ) - FO =

40.57 %  − 9.53 %  = 31.04%

Vulnerability index ( VO| X i )

FO − FO(X4)= 9.53 %  − 6.73 %  = 2.8%

FO − FO(X3)= 9.53 %  − 7.44 %  = 2.09%

FO − FO(X2)= 9.53 %  − 8.05 %  = 1.48%

FO − FO(X1)= 9.53 %  − 8.57 %  = 0.96%

Recoverability index ( O| X i ) Resilience index

4

Supplier i

 

..      Table 4.4  The calculations of vulnerability, recovery, and resilience index of BN model given in . Fig. 4.10

116 Chapter 4 · Measuring Supply Chain Resilience

4

117 4.3 · Measuring Supply Chain Resilience...

X27

X25

X9

X22

X6

X14

X21 X5 X20

X8

X12 X4

X7

X2

X1

OEM (0)

X24

X29

X19

X11

X3

X17

X26

X18

X23

X15

X13

X10

X28

..      Fig. 4.11  The BN model of center console supply network (Hosseini and Ivanov 2020)

100%

Supplier 14

Supplier 7 Supplier 27

90%

Supplier 5 Supplier 2

80%

Supplier 22

70% Percentile of Importance

Supplier 13

Supplier 26

Supplier 25 Supplier 11 Supplier 21

60%

Supplier 18 Supplier 9

50%

50.00%

Supplier 19

Supplier 12

Supplier 24 Supplier 16

40%

Supplier 8 Supplier 10

Supplier 23

30%

Supplier 15

20%

Supplier 6

Supplier 1 Supplier 20

10%

Supplier 4 Supplier 29

Supplier 3

Supplier17

Supplier 28

50.00%

0% 0%

10%

20%

30% 40% 50% 60% Percentile of Resilience

70%

80%

90%

100%

..      Fig. 4.12  Quadrant plot analysis of percentile resilience versus percentile of importance (Hosseini and Ivanov 2020)

118

4.4

Chapter 4 · Measuring Supply Chain Resilience

Ripple Effect Exposure Quantification

While the resilience assessment shown in 7 Sect 4.2 is based on probabilistic theory assuming that we can fairly estimate likelihood of disruptions at different suppliers, in this section, we explain a non-probabilistic approach based on supplier risk exposure assessment. Kinra et al. (2020) proposed a new model to measure the ripple effect of a supplier disruption based on possible maximum loss. The constructed ripple effect exposure (REE) model quantifies the ripple effect, comprehensively combining features such as impacts of financial, customer, and operational performance, consideration of multi-echelon inventory, disruption duration, and supplier importance. . Figure 4.13 shows the computational logic of the REE model. We introduce the following notations in line with Kinra et al. (2020): 55 REE is the ripple effect exposure of the supply chain 55 𝑆𝐸𝑖 is the supplier exposure 55 𝑖 is the supplier index, 𝑖∈[1,…,𝑛]. 55 𝑗 is the product index, 𝑗∈[1,…,]. 55 𝑙𝑗 is the part needed for product 𝑗, 𝑙∈[1,…,𝑗]. 55 𝑞𝑙i, is the number of units of part 𝑙 sourced from supplier 𝑖 at stage k or the number of missing parts resulting from supplier disruption when considering stage k  +  1 according to downstream disruption propagation in the supply chain. 55 Q𝑙i, is the total number of units of part 𝑙. 55 m is the total number of units of part 𝑙𝑗 sourced across all suppliers. 55 𝑉𝑗 is the demand for 𝑗.  

4

 

Daily profit ($ per day)

Business impact value ($ per day) Supplier (#) Exposure ($) Ripple effect exposure ($)

Supplier importance ratio (%)

Average units sold per day (# units per day) Profit per unit ($ per day)

Unit profit margin (%)

Number of units procured from supplier(#)

Total number of units for part across all supplier(#) Possible maximum loss - product A ($)

Supplier (#) Exposure ($) Possible maximum loss - product B ($)

Residual business interruption time (# day)

Inventory on hand (# days of inventory) Business continuation time Downstream inventory (# days of inventory) (# days of inventory) Upstream inventory (# days of inventory) Business interruption time (# days)

..      Fig. 4.13  REE model for ripple effect measurement (Kinra et al. 2020)

Price per unit ($ per unit)

4

119 4.4 · Ripple Effect Exposure Quantification

55 𝜋𝑗 is the profit margin for product 𝑗 in % of revenue. 55 𝑝𝑗 is the sales price of product 𝑗. 55 𝑐l is the business interruption time. 55 𝐼𝑘 is the inventory (measured, e.g., as weeks of supply) held at process step 𝑘. 55 𝑘 is the supply chain echelon, 𝑘∈[1,…,𝑗]. REE is considered a function of SE to risk. Assessment of SE is based on the analysis of “possible maximum loss” (PML), resulting from upstream disruptions in the supply chain (Eq. 4.24): PMLlj = BIVlj × RBITlj

(4.24)  PML is a compounding function of two variables: residual business interruption time (RBIT) and “business impact value” (BIV), a financial impact variable. The BIV computation is shown in Eq. (4.25). BIVlj = The term

qli ×V j × p j × p j Ql

(

)

(4.25) 

qli in BIV shows the supplier importance ratio (SIR). The higher the Ql

supplier spending, the higher the SIR and the higher the BIV. The second part of the BIV computation is related to the lost sales due to the supplier disruptions. The second factor in determining the PML in the model is RBIT. RBIT consists of two separate parts, i.e., the BIT (business interruption time) and BCT (business continuation time). Their meaning is close to TTR/TTS time introduced by Simchi-­ Levi et al. (2015). BIT is the amount of time for which the supplier is expected to be unavailable due to a disruption and during which no supply substitute can be established. BCT is the amount of time the focal company can continue to meet demand in spite of a disruption in supply, e.g., using some inventory on-hand. Thus, RBIT considers the maximum time the system is affected, given some recovery strategies. The model sets negative RBIT values to 0  in order to ensure that instances with BCT > BIT, i.e., making the RBIT negative, are not included as negative PML values, i.e., profit gains. The computation of the RBIT is outlined in Eq. (4.26).

((

) )

RBITlj = max BITlj - BCTlj ,0

(4.26)

Now we compound the Eqs. (4.24)–(4.27) to the SE and REE indexes (Eq. 4.27). s

sj ææ q æ æ ööö ö ç ç li × V j × p j × p j ÷ × max ç 0; ç c - ålk ÷ ÷ ÷ , ÷÷ ç ç l Î 1,¼,rj ) ç Ql ø j =1 j ( k =1 ø ø ÷ è è èè ø m

REE = åSE k ; SEi = å k =1

(4.27) 

where, SEs are computed at each supplier. REE extends the analysis toward a multi-echelon setting. REE is considered as a compounding function for the dis-

120

Chapter 4 · Measuring Supply Chain Resilience

ruption propagation (i.e., the ripple effect) downstream in the supply chain, taking into account individual SE assessments. Depending on the decision-maker’s risk-­ aversion, REE can reflect either the total impact, as a worst-case scenario where all suppliers would experience a disruption, or the average impact among all possibly disrupted suppliers. zz Practical Example

4

A three-stage retail supply chain with five suppliers, one distribution center (DC), and two customers with equal demand is considered. Each supplier delivers exactly one product (i.e., water, drinks, juice, yogurt, and milk) to the DC, and the DC delivers these products to customers. Analyze the REE and PML with consideration of total impact and the importance of each homogeneous node in the supply network. Data for the analysis and computations of REE/SE are shown in . Fig. 4.14. . Figure 4.14 displays five supplier-product combinations with different average disruption durations (BITs), supplier importance ratios, average inventory in terms of days of supply, and profits based on analysis of historical company data over 3 years. The average daily demand of the DC is 1310 boxes for water, 1900 boxes for drinks, 980 boxes for juices, 1200 boxes for yogurt, and 850 boxes for ESL (extended shelf life) milk. Average inventory in supplier data refers to the inventory of the supplier’s products at the DC.  Analogously, average inventory in the DC data refers to the inventory of the DC’s products at the customers. We first compute SEs on the supplier level. PML equals 0 for the two cases (water and yogurt) because average inventory enables compensation after the dis 

 

..      Fig. 4.14  Input data and REE/SE computation example (Kinra et al. 2020)

121 4.5 · Network Design Characteristics and Their Relations...

4

ruption during the BIT (i.e., average inventory in days > BIT and RBIT = 0). For drinks, juices, and ESL-milk, PML is positive and its total is $769,200. As such, the total SE (i.e., the case if all suppliers would be disrupted simultaneously) is $769,200. Considering the ripple effect of the supplier disruptions, we now move to the DC level. The total daily demand at the customers (i.e., the supermarket stores) is 2620, 3800, 1960, 2400, and 1700 boxes for water, drinks, juices, yogurt, and ESL-­ milk, respectively. Since, in reality, two DCs deliver to the stores, the average daily demand at the DC considered is lower (1310, 1910, 980, 1200, and 850 boxes, respectively). The disruption-driven shortage is identical to those in the upper part of the computational sheet. The REE of the supply chain is $1,027,650, which is the maximum possible financial loss that our supply chain can suffer from supplier disruptions. In other words, REE quantifies the costs of disruptions for a given level of resilience (Aldrighetti et al. 2021, Ivanov 2021a, b). A number of management recommendations can be derived using the REE and SE indicators. In terms of sourcing strategy, identifying critical suppliers and/or replacing/re-planning inventory in the supply chain can be recommended in cases with high REE and SE. At a strategic level, the REE exposure analysis can be used to determine an improved supplier strategy (e.g., dual/backup supply) by identifying which suppliers will have the greatest impact on performance, when disrupted. The model can also be applied at the operative level in two ways. First, it can provide critical information for adjusting inventory placements and levels in the supply chain, subject to RBIT.  Second, using the REE index can enable quick estimations of possible impacts of disruptions on supply chain performance. Such estimation can also be useful when deciding on the timing and scale of deployment of a contingency policy, e.g., the timing of installation of a backup source and its scope.

4.5

 etwork Design Characteristics and Their Relations N to Supply Chain Resilience

In several practical decision-making settings, resilience of supply chains can be analyzed indirectly by quantification of some network design characteristics without considering any operational processes or recovery strategies (Sokolov et  al. 2006; Ivanov et al. 2013). In this section, we introduce two coefficients known from the graph theory, i.e., connectivity and reachability that allow to judge about supply chain resilience to some extent (Eqs. 4.28 and 4.29). We introduce the following notations in line with Sokolov et al. (2016): 55 A = ‖aij‖ is the adjacency matrix. 55 A* = aij* = A( 0 ) Å A Å A( 2 ) żŠA( n ) is the reachability matrix. 55 n is the number of elements in the graph

122

Chapter 4 · Measuring Supply Chain Resilience

Connectivity coefficient (J1): J1 =

1 ååaij 2 ( n - 1) i j

(4.28)

 Reachability coefficient (J2):

4

ååaij* J2 =

i

j 2

(4.29)  Connectivity coefficient (J1) characterizes the relation of total arc number to the minimal arc number when a connected graph with the required number of nodes can be maintained. The higher the connectivity coefficient, the higher the network ability to maintain flow continuity in case of disruptions. The coefficient (4.28) is relevant for the resilience of the supply chain network design since it characterizes the ability to maintain operations continuity despite the disruptions. Reachability coefficient (J2) characterizes the ability to achieve all the nodes in the graph (Eq.  4.29). This coefficient can be interpreted as flexibility of the supply chain design. We illustrate using an example. Consider four supply chain design structures as shown in . Fig. 4.15. The structures #1–4 are composed of a central distribution center (CDC), regional distribution centers (RDC), local distribution centers (LDC), and customers (C). The resulted coefficients J1 and J2 are presented in . Table 4.5. The structure #1 is a fully connected graph leading to the highest values of connectivity, reachability, and centralization. However, such a network design can also entail high costs and with high coordination complexity. Analysis of other structures and their performance indicators can support managers in finding a balance between resilience and structural investments. The structure #2 allows for a relatively high flexibility (J2) despite the absence of both LDCs in the network design. This can be an indication for management to reconsider the necessity of having the LDC in the supply chain network. We can also observe that the absence of the CDC (structure #3) significantly reduces connectivity (J1) and flexibility (J2). A reduction in transportation links between the RDCs and customers (structure #4) also negatively influences the connectivity and flexibility of the supply chain network making these network designs potentially exposed to disruptions. n

 

 

4

123 4.5 · Network Design Characteristics and Their Relations...

Structure #1 X1

X3

C1

X2

X5

C2

X4

LDC

X7

C3

X8

RDC

X6

X9

C4

X11

C5

X10

RDC

C6

LDC

CDC

Structure #2 X1

C1

X2

X4

C2

X3

C3

X7

X7

RDC

X5

X8

C4

X9

C5

C6

RDC

CDC

Structure #3 X1

X2

C1

X4

C2

X3

X5

C3

X6

RDC

X7

C4

C5

X8

C6

RDC

Structure #4 X1

C1

X2

X4

LDC

C2

X5

C3

X6

X7

C4

X8

RDC

..      Fig. 4.15  Supply chain network design structures (Sokolov et al. 2016)

C5

X9

X10

RDC

C6

124

Chapter 4 · Measuring Supply Chain Resilience

..      Table 4.5  Connectivity and reachability coefficients

4

Structures

J1

J2

1

2.2000

1.0000

2

1.2500

1.0000

3

0.8571

0.5313

4

0.7778

0.3400

4.6

Discussion

This chapter was devoted to supply chain resilience quantification and measurement. We learned how to assess and quantify supply chain resilience using different techniques. Measuring supply chain resilience is important in different settings. First, if we have quantitative measures to assess supply chain resilience, we can compare resilience of different supply chains or different alternative supply chain designs. Second, resilience measures can be used for comparing resilience of our supply chains with the competitor’s resilience. Third, we can use resilience measures to assess our efforts in improving resilience over time. Finally, we explained in this chapter the existing approaches to supply chain resilience and the ripple effect measurement. 55 Why is it important to quantify supply chain resilience? 55 Explain probabilistic and non-probabilistic approaches to supply chain resilience measurement 55 How can we assess supply chain resilience using Bayesian networks? 55 What is entropy and how it can be used to quantify? 55 What is the difference between probabilistic and non-probabilistic approaches to supply chain resilience assessment? 55 Explain the principles and techniques to quantify the ripple effect in supply chains using supplier risk exposure index

References Aldrighetti R., Battini D., Ivanov D., Zennaro I. (2021). Costs of resilience and disruptions in supply chain network design models: a review and future research directions. International Journal of Production Economics, 235, 108103. Allesina, S., Azzi, A., Battini, D., & Regattieri, A. (2010). Performance measurement in supply chains: New network analysis and entropic indexes. International Journal of Production Research, 48(8), 2297–2321. Arkhipov, A., & Ivanov, D. (2011). An entropy-based approach to simultaneous analysis of supply chain structural complexity and adaptation potential. International Journal of Shipping and Transport Logistics, 3(2), 180–197. Behzadi, G., O’Sullivan, M. J., & Olsen, T. L. (2020). On metrics for supply chain resilience. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2020.04.040.

125 References

4

Bruneau, M., Chang, S. E., Eguchi, R. T., Lee, G. C., O’Rourke, T. D., Reinhorn, A. M., Shinozuka, M., Tierney, K., Wallace, W. A., & von Winterfeldt, D. (2003). A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake Spectra, 19(4), 733–752. Chen L., Miller-Hooks E. (2012). Resilience: An Indicator of Recovery Capability in Intermodal Freight Transport. Transportation Science 46(1):109–123. Fattahi, M., Govindan, K., & Maihami, R. (2020). Stochastic optimization of disruption-driven supply chain network design with a new resilience metric. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2020.107755. Fenton, N., & Martin Neil, M. (2012). Risk assessment and decision analysis with Bayesian networks. CRC Press. He, J., Alavifard, F., Ivanov, D., & Jahani, H. (2019). A real-option approach to mitigate disruption risk in the supply chain. Omega: The International Journal of Management Science, 88, 133–149. Hosseini, S., & Ivanov, D. (2019). Resilience assessment of supply networks with disruption propagation considerations: A Bayesian network approach. Annals of Operations Research. https://doi. org/10.1007/s10479-­019-­03350-­8. Hosseini, S., & Ivanov, D. (2020). Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review. Expert Systems with Applications, 161, 113649. Hosseini, S., Barker, K., & Ramirez-Marquez, J. E. (2016). A review of definitions and measure of system resilience. Reliability Engineering and System Safety, 145, 47–61. Hosseini, S., Morshedlou, N., Ivanov, D., Sarder, M.  D., Barker, K., & Al Khaled, A. (2019a). Resilient supplier selection and optimal order allocation under disruption risks. International Journal of Production Economics, 213, 124–137. Hosseini, S., Ivanov, D., & Dolgui, A. (2019b). Review of quantitative methods for supply chain resilience analysis. Transportation Research: Part E, 125, 285–307. Hosseini, S., Ivanov, D., & Dolgui, A. (2020). Ripple effect modeling of supplier disruption: Integrated Markov chain and dynamic Bayesian network approach. International Journal of Production Research, 58(11), 3284–3303. Isik, F. (2010). An entropy-based approach for measuring complexity in supply chains. International Journal of Production Research, 48(12), 3681–3696. Ivanov, D. (2018). Structural dynamics and resilience in supply chain risk management. New  York: Springer. Ivanov, D. (2020a). Predicting the impact of epidemic outbreaks on the global supply chains: A simulation-based analysis on the example of coronavirus (COVID-19/SARS-CoV-2) case. Transportation Research – Part E, 136, 101922. https://doi.org/10.1016/j.tre.2020.101922. Ivanov, D. (2020b). Viable supply chain model: Integrating agility, resilience and sustainability perspectives. Lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research. https://doi.org/10.1007/s10479-­020-­03640-­6. Ivanov D. (2021a). Supply Chain Viability and the COVID-19 Pandemic: A Conceptual and Formal Generalisation of Four Major Adaptation Strategies. International Journal of Production Research. https://doi.org/10.1080/00207543.2021.1890852. Ivanov D. (2021b). Lean Resilience: AURA (Active Usage of Resilience Assets) Framework for PostCOVID-19 Supply Chain Management. International Journal of Logistics Management. https:// doi.org/10.1108/IJLM-11-2020-0448. Ivanov, D., & Dolgui, A. (2019). Low-Certainty-Need (LCN) supply chains: A new perspective in managing disruption risks and resilience. International Journal of Production Research, 57(15– 16), 5119–5136. Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915. Ivanov, D., Sokolov, B., Solovyeva, I. (2013). Application of control theoretic tools to supply chain disruption management. Proceedings of the IFAC Conference on Manufacturing Modelling, Management and Control (MIM’13), St. Petersburg, Russia, June 19–21, 2013, A.  Dolgui, N. Bakhtadze (Eds.), Elsevier Science, pp. 1926–31. Ivanov, D., Dolgui, A., Sokolov, B., & Ivanova, M. (2017). Literature review on disruption recovery in the supply chain. International Journal of Production Research, 55(20), 6158–6174.

126

4

Chapter 4 · Measuring Supply Chain Resilience

Ivanov, D., Dolgui, A., & Sokolov, B. (2018). Scheduling of recovery actions in the supply chain with resilience analysis considerations. International Journal of Production Research, 56(19), 6473–6490. Ivanov, D., Dolgui, A., & Sokolov, B. (Eds.). (2019a). Handbook of Ripple Effects in the Supply Chain. New York: Springer. Ivanov, D., Dolgui, A., & Sokolov, B. (2019b). The impact of digital technology and industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846. Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2021). Global supply chain and operations management: A decision-oriented introduction into the creation of value (3rd ed.). Cham: Springer Nature. Kinra, A., Ivanov, D., Das, A., & Dolgui, A. (2020). Ripple effect quantification by supply risk exposure assessment. International Journal of Production Research, 58(19), 5559–5578. Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust value-creating supply chain networks: A critical review. European Journal of Operational Research, 203(2), 283–293. Levner, E., & Ptuskin, A. (2018). Entropy-based model for the ripple effect: Managing environmental risks in supply chains. International Journal of Production Research, 56(7), 2539–2551. Lim, A. (2007). Relative entropy, exponential utility, and robust dynamic pricing. Operations Research, 55(2), 198–214. Liu, Y. Y., Slotine, J. J., & Barabasi, A. L. (2011). Controllability of complex networks. Nature, 473, 167–173. Ojha, R., Ghadge, A., Tiwari, M. K., & Bititci, U. S. (2018). Bayesian network modelling for supply chain risk propagation. International Journal of Production Research, 56(17), 5795–5819. Pavlov, A., Ivanov, D., Dolgui, A., & Sokolov, B. (2018). Hybrid fuzzy-probabilistic approach to supply chain resilience assessment. IEEE Transactions on Engineering Management, 65(2), 303–315. Pearl, J. (1998). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo: Morgan Kaufman Publisher. Pearl, J. (2000). Causality: Models, reasoning and inference. Cambridge, UK: Cambridge University Press. Sawik, T. (2020). Supply chain disruption management (2nd ed.). New York: Springer. Shannon, C.  E., & Weaver, W. (1963). The mathematical theory of communication. Urbana: The University of Illinois Press. Simchi-Levi, D., Schmidt, W., & Wei, Y. (2014). From superstorms to factory fires: Managing unpredictable supply chain disruptions. Harvard Business Review, 92(1–2), 96. Simchi-Levi, D., Schmidt, W., Wei, Y., Zhang, P. Y., Combs, K., Ge, Y., Gusikhin, O., Sander, M., & Zhang, D. (2015). Identifying risks and mitigating disruptions in the automotive supply chain. Interfaces, 45(5), 375–390. Sokolov B. V., Ivanov D.A., Zaychik E.M. (2006) The formalization and investigation of processes for structure-dynamics control models adaptation of complex business systems, in: 20th European Conference on Modeling and Simulation ESMS 2006, May 28-31, 2006, Bonn, Sankt Augustin, Germany, Proceedings. pp. 292–5. Sokolov, B., Ivanov, D., Dolgui, A., & Pavlov, A. (2016). Structural quantification of the ripple effect in the supply chain. International Journal of Production Research, 54(1), 152–169. Torabi, S. A., Baghersad, M., & Mansouri, S. A. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research – Part E, 79, 22–48. Zobel, C. W., MacKenzie, C. A., Baghersad, M., & Li, Y. (2021). Establishing a frame of reference for measuring disaster resilience. Decision Support Systems, 140, 113406.

127

Supply Chain Viability Contents 5.1

System-Theoretic Foundations of Supply Chain Resilience and Viability: Multi-Structural Dynamics – 128

5.2

Viable Supply Chain – 130

5.2.1 5.2.2

S upply Chain Viability – 130 Viable Supply Chain Model – 136

5.3

I ntertwined Supply Networks and Their Viability – 138

5.4

 iability and Adaptation of V Supply Chains: The Climate Change Challenge – 141

5.5

Discussion – 143 References – 143

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 D. Ivanov, Introduction to Supply Chain Resilience, Classroom Companion: Business, https://doi.org/10.1007/978-3-030-70490-2_5

5

128

Chapter 5 · Supply Chain Viability

nnLearning Objectives

5

In some settings, disruptions in supply chain offer decision-making situations which go beyond the traditional understanding of resilience. Besides, many supply chains have a shared supplier base and intersect with each other forming intertwined structures. In this chapter, we aim at learning the following topics: 55 Understand system-theoretic basics of supply chains as multi-structural dynamic networks 55 Define the concept of supply chain viability 55 Explain the differences and commonalities between viability and resilience 55 Design a viable supply chain model 55 Understand the intertwining of supply chains and its influence on the viability

5.1  System-Theoretic Foundations of Supply Chain Resilience

and Viability: Multi-Structural Dynamics

In real life, the corporations have several supply chains such as a product supply chain, a project supply chain, a service supply chain, and a digital supply chain. The system-theoretic view on the supply chain networks offers a useful lens in their presentation and analysis using a multi-structural dynamics notion (Ivanov et al. 2010; Ivanov 2018) (. Fig. 5.1). Supply chains represent complex coordinated networks that operate in uncertain environments and are therefore exposed to different risks and disruptions. Notable, supply chain networks are composed of different structures that are interconnected. It can be observed from . Fig. 5.1 how the multi-dimensional dynamic space along with coordinated and distributed decision-making guides us to the understanding of supply chain resilience in terms of multi-structural dynamic systems (Ivanov et al. 2010; Dolgui and Ivanov 2020). The main supply chain structures are as follows: 55 Product structure (bill-of-materials) 55 Process structure (structure of management functions and business-processes) 55 Organizational structure (structure of facilities, enterprises, managers and workers) 55 Technological structure (structure of technological operations for manufacturing, structure of machines, devises etc.) 55 Logistics structure (roads and multi-mode transportation ways) 55 Informational structure (information flows according to a coordination strategy in the supply chain) 55 Financial structure (structure of costs and profit centres)  

 

Additional structures, e.g., energy structure, can be added within specific contents. The above-mentioned description of supply chains as dynamic systems with structural changes can be used as a framework for analysis of supply chain viability and resilience.

129 5.1 · System-Theoretic Foundations of Supply Chain Resilience…

5

Product structure

Process structure

Organizational structure

Technological structure

Logistic structure

Financial structure

Informational structure

..      Fig. 5.1  Multi-structural supply chain dynamics (Based on Ivanov et al. 2010)

►►Example

During the COVID-19 pandemic, supply chain networks have experienced tremendous structural dynamics. Despite high inventory and backup infrastructures, many SCs have frequently demonstrated severe shortages, chaotic behaviors, and a high exposure to the ripple effect during shock events, especially long-lasting events with drastically increased demands. The COVID-19 pandemic has been a stress test for SCs and revealed their weak responsiveness to severe demand shocks (Craighead et al. 2020). Attempts to substitute supply resulted in designing ad-hoc SCs using resources and capacities of intertwined and even competing networks. Time delays, high coordination efforts, and long shortage periods have been seen during these adaptive transformations. Since many suppliers became unavailable due to lockdown measures or bankruptcies, firms had to adjust the organizational structures of supply networks along with the product structures to react to demand disruptions. Technological and process structures should

130

Chapter 5 · Supply Chain Viability

..      Table 5.1  Supply chain resilience as a system property

5

System principles

Cybernetic principles

Supply chain resilience principles

Goals-­directedness

The ability to retain control is determined by “available” requisite variety; viable system model (VSM) presumes adequacy of system functioning to manage variety and the relationship between recursion and requisite variety

The ability to absorb disruptions by structural variety and parametric redundancy

Performance control

Monitoring of system states according to the goals in interaction with a perturbed environment

The ability to maintain planned performance once disrupted

Self-­adaptation & homeostasis

Feedback-driven control

Recovery control and viability assurance

Based on Hosseini et al. (2020)

also be changed, e.g., when the firms re-purposed their traditional operations to manufacturing of new products (e.g., ventilators instead of cars like Ford or hand sanitizers instead of perfumes like Gucci). Closure of major global harbors leads to redesigning of the logistics structures. Missing liquidity in supply chains entailed financial structure dynamics, e.g., by changing the contract schemes. Moreover, frequent cyber-attacks on the information systems in the supply chain influence the information structures and brought the issues of cyber-security into the forefront of discussion. ◄

Theoretical underpinning of supply chain resilience can be seen through the lens of the systems theory as shown in . Tables 5.1 and 5.2. Analysis of major systems and cybernetics principles such as “requisite variety” (Ashby 1956), the viable system model by Beer (1981), and complexity in large-­ scale systems (Casti 1979) allows for the understanding of supply chain resilience from systemic risk positions. These principles are used to develop the notion of the viable supply chain that will be discussed further in this chapter.  

5.2  Viable Supply Chain 5.2.1

Supply Chain Viability

To recapitulate, supply chain resilience is considered the supply chain’s ability to bounce back from a severe disruption and recover to an “old normal” (Fiksel 2006; Hosseini et al. 2019). Notably, resilience theory has been developed for managing disruptions, which are usually defined as events. As a new super-disruption, the COVID-19 pandemic raised novel questions and decision-making contexts, which

131 5.2 · Viable Supply Chain

5

..      Table 5.2  Supply chain resilience in the systemic context Cybernetics principles

Cybernetic treatment of resilience

Notion of supply chain resilience in the open system context

Requisite variety

The ability to retain control is determined by “available” requisite variety

Resilient supply chain design using structural and process variety to ensure controllability in different environments

Viable system model

VSM is based upon continuous system performance monitoring (failures and successes) to control operations executions

Performance and state monitoring; measuring supply chain resilience in the viability setting as a balance of resilience design and control

Second-­order cybernetics

Proactive planning and control; simultaneous modeling of the environment and the control object in this environment; feedbackdriven resilience control

Proactive control and self-adaptation of the supply chain; integration of uncertainty at different supply chain layers; information feedback-based resilience control

Based on Hosseini et al. (2020)

frequently exceeded the resilience scope. Ivanov (2020a) identified four major characteristics of a pandemic that distinguish it from other supply chain disruptions, i.e., long duration, unpredictable scaling, and uncertain demand and supply. Moreover, recovery needs to be organized in the presence of disruption and according to a significantly changed market and supply environment (i.e., a so called “new normal”). An analysis of the extant literature shows that the COVID-19 pandemic has laid out a set of novel decision-making situations that have not been previously considered in resilience theory and principally go beyond its scope. In situations where SCs were literally crumbling, the question no longer concerned bouncing back and recovering to some “normal” state, but rather how to adapt and survive in radically changed internal and external conditions. To answer this and many other related questions, a novel theoretical underpinning is required, which builds on and extends the supply chain resilience. With regard to the global pandemic context, we posit that adaptation plays the central role in supply chain operations during a pandemic, and certain aspects of this pandemic-related context can be approached using the notion of supply chain viability. Adaptation in supply chains is as vital for firms as adaptation mechanisms are for living organisms. Adaptation mechanisms continuously monitor, anticipate, and adjust to dynamic environments. Similarly, organizations are also exposed to and affected by changes in environmental and operational factors. In addition, such systems evolve through adaptation and reconfiguration of their structures, i.e., through structural dynamics (Ivanov 2018). Adaptation helps to survive and be viable at a longer timescale (Ivanov and Dolgui 2020b). Recent literature has offered the concept of supply chain viability, which we consider in this chapter.

132

Chapter 5 · Supply Chain Viability

Viability can be considered a central lens to design and manage supply chains in the post-pandemic world (. Fig. 5.2). Initially developed in terms of leanness and agility (and their combination as leagility), supply chain research has been extended by the perspectives of resilience and sustainability, followed by the advanced utilization of digital technologies and Industry 4.0. The current state-of-the-art supply chain management results from a number of remarkable transformations. In . Fig. 5.3, these transformations are framed in a historical perspective. Being lean, responsive, and globalized in structural designs, supply chains have also learned a great deal about how to act in line with nature and societal interests (i.e., becoming sustainable), how to strengthen their resilience during disruptions triggered by severe natural or man-made disasters, how to recover and manage the ripple effects, and how to utilize the advan 

 

5

Triggers; Lean; responsiveness

Natural and manmade disasters

Climate changes; society and economics

Industry 4.0; data analytics

Global pandemic

Viability Digitalization Sustainability Resilience Leagility 1990

2005

2010

2015

2020

..      Fig. 5.2  Transformation of supply chain research over time (Ivanov 2020b)

Super-disruption and structural changes in the ,,old normal’’

Impact assessment

Disruption

Search for and adaptation to a ,,new normal’’

Back tonormal

Stabilization

Stabilization in the ,,new normal’’ and performance recovery

Recovery Supply Chain Resilience: Closed-System View bouncing back to an ,,old normal’’ after a disruption or the ripple effect to recover a planned performance

Supply Chain Viability: Open System View adaptation to a ,,new normal’’ to survive in radically changed internal and external conditions

..      Fig. 5.3  Viability and resilience concepts (Ruel et al. 2021)

133 5.2 · Viable Supply Chain

5

tages of digital technologies in supply chain management. However, in 2020, the leagility, resilience, and sustainability of supply chains have been put to the test. Supply chains worldwide have experienced an unprecedented series of shocks caused by the COVID-19 virus outbreak and global pandemic, which have been a new instigator of supply chain disruptions, unlike any seen in recent times. The COVID-19 outbreak and global pandemic have immensely affected all areas of the economy and society, raising a series of completely novel decision-making settings for supply chain researchers and practitioners (Ivanov and Das 2020; Ivanov and Dolgui 2019, 2021; Ivanov and Sokolov 2013). Moreover, as we defined in 7 Chaps. 1 and 2, the COVID-19 pandemic context goes beyond the event-driven understanding of disruptions and posed the notion of supply chain crisis which is distinctively different from instantaneous disruptions by long-term existence and high uncertainty of current and future developments in the markets, capacities, and supplier base. The notion of supply chain crisis is very closely associated with the supply chain viability. The concept of viability in supply chain management is relatively new and was coined by Ivanov (2020b) and Ivanov and Dolgui (2020b).  

Definition Viability is the ability of a supply chain to maintain itself and survive in a changing environment through a redesign of structures and replanning of performance with long-term impacts (Ivanov and Dolgui 2020b; Ivanov 2020b).

Viability can be considered an overarching resilience perspective, which extends the supply chain resilience notion from a closed system, “bounce-back” view to an open system, “bounce-forward-and-adapt” notion (. Fig. 5.3). Despite several common features such as disruption-orientation, recovery, and system-view, viability and resilience have some key differences as shown in . Table 5.3. Supply chain resilience is usually considered the ability to bounce back once disrupted and recover to a known normal state. In contrast, viability looks at the ability to bounce forward and adapt once disrupted when a normal state cannot be reached at a reasonable time scale, and a search for an unknown “new normal” is required. While resilience is triggered by a disruption, the viability is linked to a supply chain crisis. To this end, resilience is considered a disruption-driven supply chain property related to single, discrete, exceptional disruptive events within a closed system setting. These analyses are performance-oriented for some fixed time-windows and mostly linear, single-flow directed supply chain systems. Viability is a behavior-driven property (continuous system change) of a system with structural dynamics. It considers system evolution (i.e., open system context) through disruption-reaction balancing in the open system context. Speaking in terms of chaos theory, with the supply chain viability, we have a case of searching for new attractors to stabilize the system which was thrown into a crisis. Next, resilience is related to the level of individual supply chains or supply networks, whereas viability is concerned with intertwined supply networks “that encapsulate entireties of  

 

134

Chapter 5 · Supply Chain Viability

..      Table 5.3  Differences between supply chain resilience and supply chain viability

5

Resilience

Viability

Trigger

Supply chain disruption

Supply chain crisis

Subject of analysis

Discrete, instantaneous disruption-reaction analysis within a closed system setting

Continuous evolution through disruptionreaction balancing in the open system context

Target of analysis

Performance-oriented

Survival-oriented

Time horizon of disruptions

Short-term (single or correlated disruption events)

Long-term (radically changed external and internal environments)

Notion

Ability to bounce back once disrupted and recover to a known normal state

Ability to bounce forward and adapt once disrupted when a normal state cannot be reached in a reasonable time scale, and a search for an unknown “new normal” is required. In terms of chaos theory, we have a case of searching for new attractors to stabilize the system.

Profile

Closed system view: Disruption → performance degradation → restoration actions → performance recovery

Open system view: Disruption → performance and structure degradation → adaptation-to-survive → search for “new normal” → stabilization in the “new normal” → restoration actions → performance recovery

Assessment criterion

Degree of performance degradation and recovery

Survivability of a supply chain

Management objective

Maintaining performance of a supply chain or a firm

Maintaining provisions for a society with critical services such as mobility (survival of car manufacturing companies), communication (survival of electronic companies), and food (survival of food supply chains)

Preparedness

One can prepare a contingency and recovery plan to overcome the disruption period and survive in the disrupted mode

One cannot plan for super-disruptions; adaptation is the only way to survive

Object

Individual supply chains

Individual supply chains and intertwined supply networks (ISN)

135 5.2 · Viable Supply Chain

5

interconnected supply chains, which, in their integrity, secure the provision of society and markets with goods and services.” Further, we retain the focus of supply chain resilience toward performance in a fixed time window and in a closed system setting, while supply chain viability considers the structural dynamics of continuous system change in an open system context wherein the time window is open to reach survivability. In supply chain resilience, one can prepare a contingency and recovery plan to overcome the disruption period and survive in the disrupted mode. In supply chain viability, one cannot plan for super disruptions, and adaptation is the only way to survive. Since survivability is the central dominant in the supply chain viability, we shall define the survivability in supply chains as follows. Definition Survivability of the supply chain is its ability to continue to exist and secure the provision of society with products and services of vital needs by adapting network designs and functions to radically changed environments.

>>Important Observation Viability can be considered in a narrow and broad perspective. In the narrow perspective, viability is an extension of supply chain resilience toward survivability under super disruptions. In the broad perspective, the viable supply chain encompasses resilience, sustainability, and leagility angles guaranteeing viability of the value-adding networks over their whole life cycle and securing providing society with goods and services.

To illustrate, consider an example of an automotive supply chain. From the positions of resilience, a car manufacturer can establish a supply chain with some backup facilities, inventory buffers, flexible capacities, and a visibility control ­system to enable the robustness and recovery against, e.g., severe natural disasters which may temporarily, adversely affect in- and outbound material flows. The resilience would be assessed by a performance of the car manufacturer, e.g., annual revenues or service level. From the positions of viability, the supply chain of a car manufacturer should ensure leagility and profitability, be resilient, and deliver the mobility service to society at a long-term perspective. ►►Example

Commonalities and differences between resilience and viability can be illustrated in a simple format using the following example. In an operational view, we are driving cars to get faster at some destinations at a short lead-time. Strategically, we are using cars to stay mobile. If a car breaks down, then it should be repaired. For the time of service, one can receive another car as a substitute. Then one receives the car back in an old, normal state. This is a classical resilience profile “disruption – backup – recovery – old normal.” However, if the car cannot be repaired any more, we should adapt structurally. One can start using public transportation to stay mobile. Or one can purchase a new car. In both

136

Chapter 5 · Supply Chain Viability

cases, the decisions to adapt are driven by the strategic objective to stay mobile rather than by the operational objective to get faster at some destination. And this is the viability profile: “disruption – performance and structure degradation – adaptation-to-survive (i.e., to ensure/secure some strategically important service; the mobility)  – search for “new normal” – stabilization in the “new normal” – restoration actions – performance recovery.” ◄

5.2.2

5

Viable Supply Chain Model

The viable supply chain (VSC) model was proposed to address the issues of dynamically adaptable and structurally changeable value-adding networks. The VSC model comprises three major perspectives: a viable SC ecosystem, a multilevel supply chain network design, and a set of viable supply chain capabilities (. Fig. 5.4). Across these there perspectives, the VSC model is based on three cycles – the leagility cycle, resilience cycle, and survivability cycle – and the transition/adaptation mechanisms between them. We posit that survival and adaptation in confronting such super-disruptive changes require a special supply chain property  – the capability to survive, to remain viable. Mechanisms to adapt seem to be most critical for healthcare SCs in a pandemic setting; however, little is known about these adaptation mechanisms. The VSC model is based on adaptable structural supply chain designs for supply-­demand allocations and, most importantly, the establishment and control of adaptive mechanisms for transitions between the structural designs. Ivanov (2020b) defined VSC as follows: “Viable Supply Chain (VSC) is a dynamically adaptable and structurally changeable value-adding network able to (i) react agilely to positive changes, (ii) be resilient to absorb negative events and recover after the disruptions, and (iii) survive at the times of long-term, global disruptions by adjusting capacities, utilizations, and their allocations to demands in response to internal and external changes in line with the sustainable developments to secure the provision of society and markets with good and services in a long-term perspective.” The VSC framework thus takes the supply chain ecosystem perspective. The interactions in these supply chain ecosystems are very complex and are triggered by mutual interrelations and feedback between supply chains, nature, society, and the economy. The VSC model is based on the following three cycles (cf. . Fig. 5.4): 55 Leagility-oriented cycle 55 Resilience-oriented cycle 55 Survival-oriented cycle  

 

►►Example

Ford utilizes three levels of the viable supply chain model. Its major supply chain design is based on cost-efficiency and profitability by utilizing the advantages of lean production such as just-in-time, agility (i.e., locating factories close to the markets), and global sourcing. To be prepared for disruptions and to transit to a resilient supply chain design,

5

137 5.2 · Viable Supply Chain

Workforce (+)

Strikes (-)

output

Leagility (+) Resilience (-)(+)

Digital supply chain

Society needs

Recession (-)

Volacity (+)(-)

Economic growth (+)

Twin

Digital

Data (+)

Economy & governance

Capital (+)

Financial shocks (-)

visiblity and mapping

Multi-level supply chain network design

Leagile supply chain design lean agility responsiveness product variery

Resilient supply chain design risk inventory capacity buffers backup suppliers

Adaptation

Survivable supply chain design production re-purposing localisation re-design of supplier base and logistics

Recovery

Viable supply chain model

Secure society needs (-)

Intertwined supply network

Taxation / Regulation (+)(-)

Make profits (+) Fulfill demand (-)(+)

Survivability (-)

Cyber threats (-)

Supply Chain Ecosystem

Investments

Emissions (-)

Products and services

Supply chain input

Epidemics (-)

Society Employment

Nature Natural disasters (-)

Natural resources (+)

Technology / Innovation (+)

Adaptation

Viability capabilities

Financial capability - Liquidity reserves - Business-government collaboration - Revenue management

Organizational capability - Backup suppliers - Backup subcontractors - Facility fortification Workforce resilience

Informational capability - Digital twins - Data analytics - Visibility - Supplier portals - Blockchain

Technological capability - Additive manufacturing - Robotics - Smart manufacturing and warehousing Industry 4.0

Process-funtional capability - Inventory and capability pre-positioning - Flexible capacities and sourcing - Omni-channel - Product diversification and substitution - Production re-purposing

..      Fig. 5.4  Viable supply chain model. (Based on Ivanov 2020b)

Ford evaluates the supplier risks, identifies critical suppliers, analyzes the supply chain resilience in terms of time-to-survive and time-to-recover, and utilizes visibility technology to identify disruptions. In case of severe crisis such as COVID-19 pandemic, Ford has launched the survivable supply chain design while the production systems and supplier base have been re-purposed to produce face shields and ventilators instead of cars using the available equipment and technology. ◄

138

5

Chapter 5 · Supply Chain Viability

Although the lean (i.e., cost-efficient) and resilient SC network designs, as well as the transitions from lean to resilient through deployment of some recovery capabilities (e.g., backup suppliers or risk mitigation inventories), have progressed significantly over the last two decades, little is known about the transition from lean to survivable or from resilient to survivable since this novel context has been just framed through the pandemic times. We note that it might be a very challenging task to operate and control three supply chain designs simultaneously, both in terms of efficiency and complexity. In addition, it is nearly impossible to predict all possible future scenarios and respective supply chain designs for matching supply and demand in these scenarios. As such, the main role in the VSC belongs to adaptation and recovery mechanisms, their design, establishment, training, and implementation. It might be instructive for firms to “virtually” design and simulate the supply chain structures for resilience and survivability and focus on the adaptation trainings to practice the supply chain changeability. The principal ideas of the VSC model are adaptable structural supply chain designs for situational supply-demand allocations and, most importantly, establishment and control of adaptive mechanisms for transitions between the structural designs (Ivanov 2021). The VSC model can help firms in guiding their decisions on recovery and rebuilding of their supply chains after global, long-term crises such as the COVID-19 pandemic. ►►Example

Luxury goods manufacturers have completely transformed their operations to manufacture urgently needed items during the COVID-19 virus outbreak in 2020. LVMH, L’Oreal, and Coty repurposed their perfume and hair gel factories to produce hand sanitizers. Giorgio Armani, Burberry, Gucci, and Prada altered their designer clothing factories in Italy to produce masks, gloves, and nonsurgical gowns. Similarly, many automotive giants like Ford and Tesla shifted their production from automobiles to highly proprietary ventilators and hospital beds by collaborating with local manufacturers. Thus, flexible supply chains played a critical role, including rapid raw material sourcing, product design, development and testing, and distribution. In addition, some companies resolved shortages of parts for life-saving ventilators and masks by using 3D printers. This triggers the role of new production technology of additive manufacturing in spare parts supply chains (Schatteman et al. 2020). ◄

5.3  Intertwined Supply Networks and Their Viability

A supply chain intertwining can be observed when firms are involved with different supply chains at the same time (Nair et al. 2009). Such an intertwining can also be encountered in industrial symbiosis as well as in circular and sharing economies. For example, a symbiosis of commercial and humanitarian logistics can exist when several business and humanitarian supply chains share warehouse facilities. As seen during the COVID-19 pandemic, suppliers in the automotive sector can

139 5.3 · Intertwined Supply Networks and Their Viability

5

simultaneously become producers of valves for respirators as well (Ivanov and Dolgui 2020b). Despite the increasing practical utilization of the above concepts, the supply chain management literature has not yet framed these new kinds of network integrities in a specific concept. To address this, Ivanov and Dolgui (2020b) introduced the new term, “intertwined supply network.” Definition An intertwined supply network (ISN) is an entirety of interconnected supply chains, which, in their integrity, secure the provision of society and markets with goods and services (Ivanov and Dolgui 2020b).

Indeed, value-adding systems rarely represent linear, isolated chains or networks (Wang et  al. 2018). Rather, they are open systems characterized by structural dynamics. In contrast to linearly directed supply chains or supply networks (cf. . Fig. 5.5) with static structures, the firms in the ISNs may exhibit multiple behaviors in buyer-supplier relations (i.e., behavioral dynamics) by acting as buyers and suppliers in interconnected or even competing supply chains simultaneously. From the position of viability, the ISNs as a whole provide services to society (e.g., food service, mobility service, or communication service), which are required to ensure long-term survival. The analysis of survivability at the level of the ISN requires consideration at the same large scale as the resilience of individual supply chains. The example of the COVID-19 outbreak clearly shows the necessity of this new perspective.  

>>Important Observation Consideration of supply chain viability and intertwined supply networks means that we are looking beyond the traditional resilience (bounce back to old normal) of a supply chain of a single firm toward viability (adapt and bounce forward to new normal) in the context of more complex networks that contain intersecting supply

Linear supply chain

Suppliers

Supply chain network

Manufacturers

Interwined supply network

Customers

..      Fig. 5.5  Different types of supply networks. (Based on Ivanov and Dolgui 2020b)

140

Chapter 5 · Supply Chain Viability

chains. These intertwined supply chains as a whole can ensure provision of society with critical services (food service, mobility service, communication service). To this end, the analysis focus shifts from how a particular firm can return its SC performance to an old normal to how the supply chain ecosystems can adapt to secure the society with critical services. ►►Example

5

Repurposing is a concept of utilizing the existing manufacturing and logistics capacities for production of a new, untypical product in a supply chain. The concept of re-­ purposing has been used extensively during the COVID-19 pandemic in 2020. Healthcare supply chains for PPE (personal protection equipment) items have not been able to cope with an increased demand due to the COVID-19 epidemic outbreak. To address the PPE shortages that have been hampering the healthcare sector worldwide, governments have appealed to commercial companies which resulted in the creation of ad-hoc supply chains to combat these shortages. A number of cases to repurpose production systems and supply chains during the COVID-19 have been observed to meet the demand of critical items. ◄ Case Study Ford: Supply Chain Repurposing to Save Lives During the COVID-19 Pandemic

In the wake of the COVID-19 pandemic in the USA, Ford repurposed their production line to help healthcare sector to cope with a shortage of PPE (personal protection equipment) within 2 weeks. Starting on March 20, designers and prototypers at Ford began to review and refine face shield concepts (Ford 2020). They identified suppliers and engaged engineering and program managers. In collaboration with the hospital managers, on March 21 face shields designs have been identified that could be built by a repurposed Ford’s supply chain. On March 22, material planning and logistics started planning inbound and outbound deliveries for the newly established product. Suppliers started delivering materials to production sites. On the same day, the first prototype was completed, and an initial prototype production run was established. On March 23, new suppliers have been identified to resolve capacity problems with supply for some materials needed for the face shield production. On March 24, the first 5000 face shields arrived at the hospitals. Some problems needed to be operatively resolved. The supply of elastic bands was identified as not sufficient for full speed production. Alternative product designs have been validated, and new suppliers have been identified and contacted. On March 25, production continued reaching 32,000 units per day on March 27. On March 28, a secondary design was approved, which removed the need for staples. Thus, in 1 week, Ford was able to go from idea to mass production. Production rate reached 225,000 shields per day, and the overall delivery of over 1 million units by April 5 has been realized. Reference: Ford (2020)

141 5.4 · Viability and Adaptation of Supply Chains: The Climate…

5

Discussion 1. What are the major decisions involved with supply chain when launching a new product? Which decisions have been done at Ford? 2. What are the differences between developing a new product in a supply chain and repurposing the existing one? 3. What is the role of time-criticality in the case of Ford? 4. Which major bottle-necks can you identify which hindered to ramp-­up the new supply chain for face shields? How they have been resolved at Ford? 5. Discuss on the importance of collaboration between commercial supply chains, healthcare supply chains, and governments. 6. What steps could be taken in future to quickly repurpose the supply chain? Do you think that some preparedness measures (in terms of production planning, supplier management, visibility, and digital technology) could help to react more effective and efficient?

>>Important Observation Viability-based supply chain designs have the potential to rapidly serve new markets and/or pivot to new supply chains for new products for business survival.

5.4  Viability and Adaptation of Supply Chains: The Climate

Change Challenge

Climate change represents one of the major challenges for the humanity in the twenty-first century, and so the supply chain viability is tightly related to the climatic changes on the earth. Numerous studies forecast radical changes in climate by 2050 and 2100 which will directly and explicitly influence the supply chains (DHL 2012; Ghadge et al. 2020; McKinsey 2020). Because of the climate change, many supply chain activities in manufacturing and logistics could be restricted or even impossible for long periods of time at many locations worldwide due to increase in severity and duration of extremal weather conditions. In these settings, global supply chain management would not be focused on efficient production only, but rather it would need to ensure the existence of production and logistics as such providing society with goods and services through dynamic reconfigurable supply chain networks (Dolgui et al. 2020). Besides, climate change is distinctively different from other types of supply chain risks since it is probably the most long-­ term disruption the supply chains will confront with (. Fig. 5.6).  

142

5

Chapter 5 · Supply Chain Viability

Examples of disruptions

Time horizon

Climate change

Long-term

Pandemic

Mediumterm

Fire

Short-term

Perspectives of supply chain resilience and viability Macro perspective Meso perspective

Micro perspective

Coping strategies Adaptation and multistructural network transformation

Adaptation and SC survivability

Recovery and firm’s resilience

..      Fig. 5.6  Micro-, meso-, and macro-perspectives of supply chain viability

Supply chain disruptions and crises can be divided into micro-, meso-, and macro-perspectives. For example, a fire at an assembly plant represents an instantaneous disruption (i.e., the micro-perspective). The usual coping strategies for this kind of disruptions are recovery and firm’s resilience. A pandemic is an example for a long-lasting, super disruption or supply chain crisis (i.e., the meso-perspective). The meso-disruptions are coped by adaptation and supply chain survivability. Climate change takes the macro-perspective of supply chain viability that is coped by adaptation and multi-structural network transformations, e.g., using reconfigurable supplier, manufacturing, and logistics bases and so forming a structurally reconfigurable supply chain. For example, if some regions are affected by severe weather disruption, it is likely that all suppliers in this region will be disrupted. As such, to maintain supply chain continuity, a backup supplier base (i.e., a set of suppliers) somewhere else in the world will be needed in order to situationally reconfigure the supply chain flows to this backup supplier base. Smooth and efficient adaptations will be possible using both preparedness plans and digital technology. Finally, supply chains can mediate the severe impacts of the climate change by developing sustainable practices and so contributing to reducing emissions and waste of natural resources. Energy-efficient manufacturing, sustainable logistics, and flexible, dynamically reconfigurable supply chain designs with situational networking of distributed supplier, manufacturing, and logistics bases can be seen as directions to match resilience, viability, and sustainability across micro-, meso-, and macro-perspectives.

143 References

5

5.5  Discussion

In this chapter, we presented supply chain viability as an overarched resilience modeling. First, we explained system-theoretic view of supply chain resilience which is important for further understanding of supply chain viability in the open system context. 55 Explain the multi-structural view of supply chain networks In some settings, disruptions in supply chain offer decision-making situations which go beyond the traditional understanding of resilience. To this end, we then presented the supply chain viability framework and explained the notions of intertwined supply networks and the viable supply chain model. An extended resilience perspective is viability which is based on adaptation of supply chains to super-­ disruptions. Viability is an overarched resilience perspective in supply chains. Viability is the ability of a supply chain (supply chain) to maintain itself and survive in a changing environment through a redesign of structures and replanning of performance with long-term impacts. 55 How is viability different from resilience? 55 Explain the viable supply chain model. 55 How do you understand the notion of survivability in the supply chain resilience and viability context? Besides, many supply chains have a shared supplier base and intersect with each other forming intertwined structures. Thus far, our attention was directed to understand the intertwining of supply chains and its influence on the viability. 55 What is an intertwined supply network? 55 Elaborate on the chances and challenges of resilience management when considering intertwined supply networks rather than linear supply chain systems. Finally, we learned three viability perspectives, i.e., micro, meso, and macro. 55 Explain three viability perspectives. 55 How can supply chains cope with climate change?

References Ashby, W. R. (1956). An introduction to cybernetics. London: Chapman & Hall. Beer, S. (1981). Brain of the firm. Chichester: Wiley. Casti, J. L. (1979). Connectivity, complexity and catastrophe in large-scale systems. New York/London: Wiley-Interscience. Craighead, C. W., Ketchen, D. J., Jr., & Darby, J. L. (2020). Pandemics and supply chain management research: Toward a theoretical toolbox. Decision Sciences, 51(4), 838–866.

144

5

Chapter 5 · Supply Chain Viability

DHL. (2012). The world in 2050 – A scenario study. https://www.­dhl.­com/content/dam/dhl/global/ core/documents/pdf/study_logistics_2050.­pdf. Access on 1 Jan 2021. Dolgui, A., & Ivanov, D. (2020). Exploring supply chain structural dynamics: New disruptive technologies and disruption risks. International Journal of Production Economics, 229, 107886. Dolgui, A., Ivanov, D., & Sokolov, B. (2020). Reconfigurable supply chain: The X-Network. International Journal of Production Research, 58(13), 4138–4163. Fiksel, J. (2006). Sustainability and resilience: Toward a systems approach. Sustainability: Science, Practice, & Policy, 2(2), 1–8. Ford. (2020). https://www.­at.­ford.­com/content/dam/atford/fna/images/articles/2020/04/face_shields/ Face_Shield_Timeline.­pdf Ghadge, A., Wurtmann, H., & Seuring, S. (2020). Managing climate change risks in global supply chains: A review and research agenda. International Journal of Production Research, 58(1), 44–64. Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research: Part E, 125, 285–307. Hosseini, S., Ivanov, D., & Blackhurst, J. (2020). Conceptualization and measurement of supply chain resilience in an open-system context. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2020.3026465. Ivanov, D. (2018). Structural dynamics and resilience in supply chain risk management. New  York: Springer. Ivanov, D. (2020a). Predicting the impact of epidemic outbreaks on the global supply chains: A simulation-based analysis on the example of coronavirus (COVID-19 / SARS-CoV-2) case. Transportation Research – Part E, 136, 101922. https://doi.org/10.1016/j.tre.2020.101922. Ivanov, D. (2020b). Viable supply chain model: Integrating agility, resilience and sustainability perspectives. In Lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations: Research. https://doi.org/10.1007/s10479-­020-­03640-­6. Ivanov, D. (2021). Supply chain viability and the COVID-19 pandemic: A conceptual and formal generalisation of four major adaptation strategies. International Journal of Production Research. https://doi.org/10.1080/00207543.2021.1890852. Ivanov, D., & Das, A. (2020). Coronavirus (COVID-19 / SARS-CoV-2) and supply chain resilience: A research note. International Journal of Integrated Supply Management, 13(1), 90–102. Ivanov, D., & Dolgui, A. (2019). Low-Certainty-Need (LCN) supply chains: A new perspective in managing disruption risks and resilience. International Journal of Production Research, 57(15– 16), 5119–5136. Ivanov, D., & Dolgui, A. (2020b). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915. Ivanov, D., & Dolgui, A. (2021). OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications. International Journal of Production Economics, 232, 107921. Ivanov, D., & Sokolov, B. (2013). Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis, and adaptation of performance under uncertainty. European Journal of Operational Research, 224(2), 313–323. Ivanov, D., Sokolov, B., & Kaeschel, J. (2010). A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations. European Journal of Operational Research, 200(2), 409–420. McKinsey. (2020). Could climate become the weak link in your supply chain? https://www.­mckinsey.­ com/business-­f unctions/sustainability/our-­i nsights/could-­c limate-­b ecome-­t he-­weak-­l ink-­i n-­ your-­supply-­chain. Access on 1 Jan 2021. Nair, A., Narasimhan, R., & Choi, T. (2009). Supply networks as a complex adaptive system: Toward simulation-based theory building on evolutionary decision making. Decision Sciences, 40(4), 783–815.

145 References

5

Ruel, S., El Baz, J., Ivanov, D., & Das, A. (2021). Supply chain viability: Conceptualization, measurement, and nomological validation. Annals of Operations Research, forthcoming. Schatteman, O., D. Woodhouse, & J. Terino. Amid crisis and disruption, supply networks designed for low-cost and minimal inventory pose a major risk. 27 April 2020. [Online]. Available: https:// www.­bain.­com/insights/supply-­chain-­lessons-­from-­covid-­19/. Accessed 18 June 2020. Wang, J., Dou, R., Muddada, R. R., & Zhang, W. (2018). Management of a holistic supply chain network for proactive resilience: Theory and case study. Computers & Industrial Engineering, 125, 668–677.

147

Supplementary Information Index – 149

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 D. Ivanov, Introduction to Supply Chain Resilience, Classroom Companion: Business, https://doi.org/10.1007/978-3-030-70490-2

149

A–M

Index A Absorptive capacity  43 Active Usage of Resilience Assets (AURA)  50 Adaptability  52, 101 Adaptable structural supply chain design  138 Adaptation  39, 66, 131 Adaptive capacity  44 Additive manufacturing  41 Agent-based simulation  66 Agility 40

B Backup sourcing  41 Bayesian network  65, 107–115 Bayes’ theorem  107 Blockchain 67 Business intelligence (BI)  68

C Capacity agility  41 Capacity flexibility  40 Conditional probability  111 Connectivity 121 Contingency plan  17 Coordination 40 Coronavirus (COVID-19) pandemic  20, 79, 82 Critical network elements  66 Critical nodes  64 Critical supplier  66

D Data analytics  67 Deep tier financing  67 Deep uncertainty  4 Demand risk  8 Design-for-efficiency 33 Design-for-resilience 33 Deviation 5 Digital supply chain twin  67 Discrete-event simulation  66 Disruption 11 –– data 68 –– identification 68 –– impact assessment  68 –– modeling 68 –– overlay 19

–– propagation 17 –– risks 11 –– scenarios  66, 68 –– tail 19 Disturbance 5 Dual sourcing  40 Dynamics of disruptions  67

E Early warning system  67 Efficient supply chain  34 End-to-end supply chain visibility  67 Entropy 102 Epidemic 82 External risk  8

F Facility fortification  40 Financial risk  8

H Hazard uncertainty  4

I Industry 4.0  132 Information risk  8 Intertwined supply network  133, 139 Inventory 38 Inventory control  65

K Known-known uncertainty  11 Known-unknown uncertainty  11

L Lean resilience  50 Localization 40 Low-certainty-need (LCN) supply chain  47

M Markov chain  65 Mathematical optimization  65

150

Index

Measuring supply chain resilience  107–115 Modeling 64 Multi-strucrual supply chain dynamics  129

N Network risk  9 Network structures  64

O Open system  133 Operational risk  8, 10

P Parametrical redundancy  50 Possible maximum loss  101, 119 Postponement 40 Proactive planning  38 Process flexibility  38, 50 Process risk  9 Product substitution  40

R Random uncertainty  4 Reachability 121 Reactive measures  38 Real-time disruption-detection  67 Real-time inventory  68 Recoverability 112 Recovery  68, 138 –– costs 65 –– plan 66 –– strategies 40 Recurrent risks  10 Redundancy 38 Repurposing 140 Requisite variety  131 Resilience  3, 5 –– capabilities 37 –– capacity 42 –– supply chain  34 Responsive supply chain  34 Ripple effect  12, 14, 82 –– assessment 101 –– exposure 118 –– prediction 79–89 Risk  4, 7 –– data 68

–– management 8 –– mitigation inventory  38 Risk-aversion 100 Robustness 5

S Scalability 38 Segmentation 40 Service level  45 Simulation 68 Stability 5 Stress-testing 66 Structural complexity  102 Structural reconfiguration  39 Structural redundancy  38 Structural variety  50 Super disruption  20, 135 Supplier risk  9 Supply chain –– adaptability 101 –– crisis  21, 133 –– discontinuities 64 –– ecosystem 136 –– recovery plan  72, 74 –– resilience  31, 37 –– risk 8 –– viability  131, 139 Supply risk  8 Survivability  135, 139 System dynamics  65

T Time risk  8 Time-to-recovery 100 Time-to-survive 100

U Uncertainty  3, 4 Uncertainty factors  4 Unknown-unknown 11

V Viability  132, 133 Viable supply chain  135–137 Viable system model  131 Visualization 67 Vulnerability  37, 112