Proceedings of the Eighth International Forum on Decision Sciences 9811613796, 9789811613791

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Uncertainty and Operations Research

Xiang Li Xiaofeng Xu   Editors

Proceedings of the Eighth International Forum on Decision Sciences

Uncertainty and Operations Research Editor-in-Chief Xiang Li, Beijing University of Chemical Technology, Beijing, China Series Editor Xiaofeng Xu, Economics and Management School, China University of Petroleum, Qingdao, Shandong, China

Decision analysis based on uncertain data is natural in many real-world applications, and sometimes such an analysis is inevitable. In the past years, researchers have proposed many efficient operations research models and methods, which have been widely applied to real-life problems, such as finance, management, manufacturing, supply chain, transportation, among others. This book series aims to provide a global forum for advancing the analysis, understanding, development, and practice of uncertainty theory and operations research for solving economic, engineering, management, and social problems.

More information about this series at http://www.springer.com/series/11709

Xiang Li · Xiaofeng Xu Editors

Proceedings of the Eighth International Forum on Decision Sciences

Editors Xiang Li School of Economics and Management Beijing University of Chemical Technology Beijing, China

Xiaofeng Xu School of Economics and Management China University of Petroleum Qingdao, Shandong, China

ISSN 2195-996X ISSN 2195-9978 (electronic) Uncertainty and Operations Research ISBN 978-981-16-1379-1 ISBN 978-981-16-1380-7 (eBook) https://doi.org/10.1007/978-981-16-1380-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents

Study on the Handling of City Express Packaging . . . . . . . . . . . . . . . . . . . . . Yan-wen Dai, Xu-dong He, and Wen Wang

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Optimization and Analysis of Cold Chain Logistics of Agricultural Products Based on Consumer Satisfaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Jiajia Fu, Xudong He, and Xujie Yang Purchase Channel Decision Based on Prospect Theory in the Context of Showrooming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Xiang Xu, Xiao Zhang, Fangfang Meng, and Yan Zhang A Simulation Analyses for Disruption Risks in the Spare Parts Supply Chain of New Energy Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Chaowen Xia and Guoqing Zhang Does ESBO Omnichannel Strategy Benefit an Online Retailer with Consideration of Return Rate and Experience Service? . . . . . . . . . . . . 41 Jinrong Liu, Qi Xu, and Guoqing Zhang Global Warehouse with Cross-Border Supply Chain . . . . . . . . . . . . . . . . . . . 51 Fawzat Alawneh and Guoqing Zhang The Preference of VMI Contract on Traditional RMI System in an Optimal Healthcare Supply Network: A Comparative Study . . . . . . 59 Mohammed Almanaseer and Guoqing Zhang

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Study on the Handling of City Express Packaging Yan-wen Dai, Xu-dong He, and Wen Wang

Abstract With the rapid development of ecommerce and Internet enterprises, a large number of online shopping by urban residents promotes the explosive growth of urban express business. For a long time, the excessive consumption of resources and the pollution of urban environment caused by the lack of effective treatment of a large number of express packaging wastes are becoming more and more serious. This paper focuses on the optimization of the urban environment and puts forward some Suggestions on the existing problems of the urban express package garbage disposal under the current situation and from the perspective of optimizing its disposal mode. Keywords City express · Green packaging · Green processing mode

1 China’s Urban Express Packaging Garbage and Treatment Status 1.1 Current Situation of Urban Express Packaging Garbage in Our Country China Massive packages have brought huge waste residues, waste of resources and environmental pollution to the city. According to the data released by the environmental protection organization “Greenpeace”, in 2018, the consumption of various types of express packaging materials in China was 9.123 million tons, and paper-based express packaging materials accounted for 90.95% of the packaging materials used. The packaging materials were 851,800 tons, accounting for 9.05% of express packaging materials. According to statistics from the Post Office, China’s express delivery Y. Dai School of Management Engineering, Xuzhou University of Technology, Xuzhou, China X. He (B) Xuzhou University of Technology, Xuzhou, China W. Wang School of Finance, Xuzhou University of Technology, Xuzhou, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 X. Li and X. Xu (eds.), Proceedings of the Eighth International Forum on Decision Sciences, Uncertainty and Operations Research, https://doi.org/10.1007/978-981-16-1380-7_1

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business volume has reached 63 billion in 2019, an increase of 24% year-on-year, of which urban express packaging consumption accounts for a large proportion. The increment of express packaging waste in China’s mega cities (Wuhan, Nanjing, etc.) accounts for 93% of the increase in domestic waste, and in some large cities it is 85–90%. This kind of garbage is mainly paper and plastic, and high-frequency packaging materials such as transparent tape and plastic bags made of non-degradable polyvinyl chloride will seriously pollute the environment if not treated.

1.2 Current Status of Urban Express Packaging Waste Disposal in My Country China According to the survey data, about 60% of urban consumers will discard the packaging after disassembling, 30% of consumers will be sold as waste products, and only less than 10% of consumers will reuse the packaging or submit it to the logistics company. Recycle. At present, there are three main ways to deal with express packaging waste. The first category: the consumer will keep the packaging after disassembling it, accumulate it to a certain amount, and then sell it to the recipient, and then it will be sorted and recycled by the waste recycling station. The second category: After the consumers disassemble the parts, they will discard them in the trash can, and the municipal sanitation department will carry out indiscriminate recycling, and uniformly clear the landfill or incinerate. The third category: to entrust the logistics company’s dispatcher to recycle, and simply process the packaging materials and then flow into the market; if they cannot be used, they will be destroyed or discarded. Judging from the current situation of express packaging waste treatment, at present most cities in China have not yet formed a complete express packaging recycling system, the recycling industry chain is not complete, the recycling is difficult, and the cost of express waste recycling is high. The data shows that in 2017, the actual recovery rate of urban paper and plastic packaging in China was less than 10%, and the overall utilization rate was even lower.

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2 Problems in the Disposal of Urban Express Packaging Waste 2.1 From the Perspective of Government 2.1.1

Lack of Feasible Laws and Policies

China’s logistics enterprises are developing very fast, but due to the late start, the concept of green logistics is not popular, and the relevant legal provisions cannot keep up. Despite the frequent legal and regulatory documents concerning the “express industry” in recent years, there are still shortcomings such as low feasibility, lack of pertinence, insufficient coercive force, and insufficient coverage, and no urban express packaging standards and their discard There are clear regulations on material handling, and most of the clauses cannot attract enough attention from relevant enterprises due to lack of compulsory and enforceability.

2.1.2

City Express Packaging and Recycling System is Not Perfect

At present, only a few large logistics companies are exploring the express packaging and recycling system, and the recycling system in most regions is only in its infancy or even conceived. The existing recycling system cannot balance economic and social benefits. For example, the system composed of waste recycling personnel, recycling stations and sorting centers is concerned with its own economic benefits, while the municipal waste classification and recycling system in charge of government is concerned with social benefits [1]. Part of the recycling system has a problem of lack of professionalism in the process of operation, showing a small, complicated and chaotic phenomenon.

2.2 From the Perspective of Enterprise 2.2.1

The Materials and Packaging Process Are Not Green Enough

Packaging materials with green standards are the key to express packaging. For different types of goods, e-commerce companies often use different packaging materials [2]. The mainstream packaging used in the current market is mainly nondegradable plastic bags, woven bags, etc., and the shockproof materials are mainly film inflatable bags. Most of these packaging materials cannot be reused and are difficult to degrade. The green packaging process is also the focus. In order to reduce the probability of commodities being damaged during transportation, e-commerce companies put too much filler in the packaging and use a large amount of tape to

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wrap it, which not only wastes materials, but also makes it difficult for consumers to unpack the box, which increases packaging and recycling the difficulty of the system.

2.2.2

The Recycling Cost of City Express Packaging is High

The task of courier sites in various areas of the city is to deliver couriers, lacking the necessary space to store packaging and the personnel dedicated to managing old packaging. If the logistics enterprise is equipped with professional staff, planning new storage space, and paying for the various costs of recycling and warehousing, the company’s labor and time costs will rise greatly, which will be a fatal blow to small enterprises. Regardless of the angle from which the evaluation of packaging recycling is not in line with the economic benefits of the enterprise.

2.3 From the Perspective of Consumer 2.3.1

Violent Disassembly Affects Recycling

Due to the excessive packaging of express shipments and the lack of environmental awareness of most consumers, people usually disassemble the express with an eager attitude after receiving the express shipment, unaware that the originally damaged packaging has a secondary use The value is to tear the carton with a more complete structure into a carton that basically loses the value of the second cycle, which affects the recycling of the packaging.

2.3.2

Low Awareness of Packaging and Recycling

It is still not uncommon for urban residents to randomly discard packaging materials after disassembling parts. It can be seen that the significant increase in the amount of urban express packaging waste has not caused people to pay attention to packaging recycling. Most urban residents have ignored the value of packaging recycling, leading to recyclable express packaging such as cartons, cartons and internal fillers being randomly discarded in various corners. Not only did they fail to sort them, but the tape on their packaging. Such packaging also brings certain complexity to the waste sorting and recycling.

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3 The Necessity of Green Management of Urban Express Packaging Garbage in China First, reduce urban environmental pollution. The express delivery industry in many cities has developed in a blowout style, and the surprising increase in express packaging has overwhelmed the urban environment. It is understood that nearly 80% of the packaging materials are mixed into domestic garbage and enter the sanitation system, accounting for more than 50% of the urban solid waste. The packaging that enters the sanitation system has a low degree of harmless treatment, especially plastic waste, which will cause soil compaction and reduced fertility. Plastic particles entering the groundwater circulation will cause water pollution. It poses a great threat to the living environment of urban residents. Second, reduce waste of resources. Behind the increasing volume of urban express delivery business is the astronomical figure of packaging consumption. According to statistics, the demand for corrugated boxes in 2018 was about 13.94 billion pieces. If the express delivery volume is expected to grow at a neutral pessimistic rate of 20%, the resulting demand for corrugated boxes will be approximately 20.07 billion pieces in 2020, an increase of approximately 3 billion pieces per year. Therefore, strengthening the management of urban express packaging waste is of great significance for saving resources.

4 Specific Measures for Green Governance of Urban Express Packaging in China Developed countries with significant express packaging management effects are generally led by the government. For example, Germany, which carries out recycling of packaging waste, has stipulated regulations on materials, packaging, and accountability, and supervised the responsible parties. In light of China’s actual national conditions, the government’s lead is an indispensable link. Here, we use the German packaging governance model to build a green packaging management model in China. As shown in Fig. 1. Forward logistics part: The company conducts business operations in accordance with laws and regulations during production and packaging, including the “4R1D” principle (Reduce, Recovery, Re-use, Recycle, and Degradable). After the consumer obtains the package, the packaging waste is sorted and replayed into a dedicated recycling facility [3]. Reverse logistics part: Third-party enterprises carry out centralized transportation and treatment of waste in the unmanned recycling facilities at the end, process and reproduce the packaging, and then provide usable packaging materials to logistics enterprises for recycling. Information flow part: Government departments pass legislation to enforce constraints, clarify the related responsibilities of logistics enterprises, third-party

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Fig. 1 Schematic diagram of urban packaging governance model

enterprises, and consumers, and supervise the three. As the initiator of forward logistics, logistics enterprises standardize the operation of packaging materials selection and packaging methods in accordance with relevant government regulations; thirdparty enterprises are directly responsible for the recycling of packaging materials and must summarize the data analysis of the recycling process to the government department; consumers can make corresponding feedback according to the satisfaction degree of recycling. Capital Flow: Logistics companies, consumers, and governments provide financial subsidies for the construction of third-party companies and recycling facilities. Advantage analysis: 1.

2.

Leaded by the government, united with third-party enterprises, logistics enterprises, and consumers, with the terminal unmanned recycling facility as the carrier, logistics, information flow, and capital flow as the guarantee, unified recycling of urban express packaging waste. Under the supervision of the government, a reverse logistics network consisting of logistics enterprises, terminal unmanned recycling facilities, regional transfer stations, urban processing centers, and packaging reproduction enterprises will be constructed. The third-party enterprises are professional, through reasonable planning of reverse recycling websites, integrating resources to conduct business, avoiding the repetitive construction caused by major logistics companies independently establishing packaging recycling networks, reducing waste of resources, achieving scale effects and reducing operating costs Improve recycling efficiency.

Based on the collaborative recycling model of government, logistics e-commerce enterprises, and third-party enterprises, the optimization of its operation process is shown in Fig. 2.

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Fig. 2 Operation process of urban express packaging green treatment

As an important part of the urban packaging waste recycling system, the recycling mechanism occupies an important position. First of all, from the beginning of consumers unpacking and express delivery, it is necessary to provide consumers with convenient recycling methods. At this time, consumers can choose to directly

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unpack, return the packaging to the station or put it in the unmanned recycling equipment at the end, or choose to delay unpacking, choose to make an appointment for home recycling, place it in the recycling equipment, and reserve it for secondary use. Then, the regional recycling transfer station will carry out unified cleaning, sterilization of packaging waste, and classify them according to the degree of damage to the packaging, and then transport them to a third-party packaging company for reproduction. The processed packaging is processed according to whether it can be recycled. Packaging materials are sold to logistics and e-commerce enterprises at a price lower than the market price for resale and reuse. Non-recyclable packaging materials are handed over to the sanitation department for landfill and incineration. The third-party enterprise conducts the design and development of green packaging while processing and producing the packaging, and directly puts it on the market, enters the packaging circulation system, and gradually replaces the original packaging materials that cannot meet the requirements, thereby achieving a virtuous cycle [4]. This system draws on the “cross-regional management model” of the recycling system of Japanese cities. It establishes transfer stations in various areas of the city, processes the recycled packaging in a unified manner and sends it to the urban processing center. Through intensive management, it can effectively reduce transportation and operating costs. Its advantage lies in the establishment of terminal unmanned express packaging and recycling sites, including residential areas, campuses, shopping malls, and other densely populated areas, to achieve full coverage of the recycling network, and facilitate the delivery of express packaging waste.

5 Suggestions on Green Disposal of Urban Express Packaging Waste 5.1 The Government Introduces Perfect Laws and Regulations as Soon as Possible Governments at all levels urgently need to increase the level of attention to the problem of express packaging waste management and speed up the legislative process. Starting from the concept of sustainable development, we will unify the packaging materials, specifications and other standards in the industry, and establish an evaluation mechanism for green packaging. Strengthen the mandatory and feasibility of relevant laws and regulations, so that each link has norms and standards, and can find the responsible subject.

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5.2 Multi-party Cooperation to Establish a Packaging Waste Recycling System Express packaging waste management is not only a unilateral action of the government, enterprises, and consumers. The lack of multi-party recycling channels is a major drawback that restricts China’s express packaging recycling. The government can act as a sponsor to help logistics enterprises, packaging raw material suppliers and leading enterprises of manufacturers establish cooperative relationships with express packaging recycling systems and continuously optimize the design of the system Program.

5.3 R & D of Green Packaging Materials and Design The use of green packaging materials and optimized packaging design is the starting point of the entire express packaging recycling process, increase investment in related parties, develop new packaging materials, and gradually replace non-degradable packaging materials. Continuously optimize the design of packaging to reduce and simplify packaging, reduce the use of plastic bags, tapes and other non-degradable materials, and comprehensively improve the utilization rate of packaging.

5.4 Raise the National Awareness of Green Packaging and Environmental Protection The country needs to guide enterprises and individuals through various means such as economy, administration, and law, and publicize high-density cities and regions, especially urban residents’ communities. It is possible to enhance the public’s awareness of environmental protection and resource utilization through publicity of online prize-winning knowledge contests, appraisal activities of environmentally friendly communities and individuals. Promote consumers to develop good packaging use and recycling habits, and actively participate in the green link of packaging recycling. Acknowledgements This research was funded by a research granted from Xuzhou University of Technology Students Innovation and Entrepreneurship Training Program (XCX2019162), and Key Projects of Natural Science Research of Jiangsu Higher Education Institutions (18KJA12001), whose support are great appreciated.

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Bibliography 1. Zou X, Li Y (2016) Construction of express packaging recycling system based on circular economy theory. J Packag 8(4):60–66 2. Zhang M (2008) Modern logistics and sustainable development. China Materials Publishing House, Beijing, p 107 3. Chen X, Liang J (2016) Research on express packaging recycling system. Logist Eng Manag 38(7):177–178 + 165 4. Zhu L (2017) Research on the circulation packaging sharing system of the express delivery industry and its recycling model. Logist Technol 9:21–26

Optimization and Analysis of Cold Chain Logistics of Agricultural Products Based on Consumer Satisfaction Jiajia Fu, Xudong He, and Xujie Yang

Abstract With the rapid development of economy, the level of consumption of residents is constantly upgrading, and the upgrading of consumption has also promoted the development process of cold chain logistics from scratch in China. In order to better meet the needs of consumers, this paper takes consumer satisfaction as the starting point to analyze, tap the problems in the cold chain logistics network of agricultural products, and put forward targeted agricultural products cold chain logistics optimization program. Keywords Cold chain logistics · Consumer satisfaction · Optimization

1 Research Background and Significance In recent years, with the continuous improvement of living standards of urban and rural residents. Consumers pay more and more attention to the added value of products, such as the convenience, freshness and nutrition of agricultural products supply, and the impact of consumer satisfaction on cold chain logistics is more and more prominent. But how to ensure the quality and safety of agricultural products for consumers, but many problems in the cold chain have not been well solved, such as the lack of network coordination in the cold chain logistics of agricultural products, local optimization conflicts and the risk of “chain breaking” of the system, etc. This paper will take consumer satisfaction as the core, based on this perspective to improve the quality of cold chain logistics products of agricultural products, while reducing the operation cost of cold chain logistics. I hope this paper can provide ideas for the future research direction of cold chain logistics network optimization of agricultural products.

J. Fu · X. He (B) · X. Yang School of Management, Xuzhou Institute of Engineering, Jiangsu 221008, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 X. Li and X. Xu (eds.), Proceedings of the Eighth International Forum on Decision Sciences, Uncertainty and Operations Research, https://doi.org/10.1007/978-981-16-1380-7_2

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2 Research Status at Home and Abroad The western developed countries are quite mature in the field of cold chain logistics, 3C and other famous theories still play an important role. However, most of the researches in these developed countries focus on the technical links, such as cold chain information technology, agricultural products refrigeration and cold chain logistics industry classification. The field of cold chain logistics in China started late, but in recent years, more and more scholars are committed to this field. For example, Yao and Li (2013) first used the SWTO analysis method of marketing to quantitatively and qualitatively analyze the development of cold chain logistics in China [1]. Zhao (2014) takes the vegetable wholesale market as the center, looking for a mature cold chain logistics model [2]. Wang (2016) took Dalian aquatic products as a model to conduct in-depth research on cold chain logistics of special agricultural products and put forward the development direction and countermeasures [3]. With the efforts of numerous professionals, the level of domestic cold chain logistics is rapidly approaching that of developed countries. However, in the domestic and foreign research, the research on consumer satisfaction is often ignored, and the ability to coordinate consumers with the whole cold chain logistics network is relatively weak. Therefore, from the perspective of consumers, it is necessary to optimize the cold chain logistics network.

3 Problems in Domestic Cold Chain Logistics In recent years, cold chain logistics has developed rapidly, and its market scale is expected to reach 470 billion yuan in 2020. The rapid expansion of market scale, but the supporting facilities of cold chain logistics failed to meet the relevant standards in time, resulting in the service quality of cold chain logistics is always difficult to meet the requirements of customers. See Table 1 for infrastructure comparison between China and the United States. The data shows that the supporting facilities of domestic cold chain logistics are obviously behind the developed countries, but the evaluation of the whole cold chain system cannot take the hardware index as a single index, and the extension of service is also an important part. As consumers, the satisfaction of the whole cold chain logistics system is related to all aspects. Therefore, it is of great significance to improve the service quality of cold chain logistics that how to grasp the key point of consumer perception and then focus on limited resources to optimize cold chain logistics.

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Table 1 Comparison of infrastructure between China and the United States Project

U.S.A

China

Ratio of investment in food industry to total investment in industry

8–10%

3%

Refrigeration capacity

16 million tons

5 million tons (1.4 million tons for refrigeration, 3.6 million tons for refrigeration)

Refrigerated transportation capacity

160,000 refrigerated trains 60,000 heat preservation vehicles

40,000 refrigerated trains 100,000 refrigerated cars Refrigerated ship 100,000 tons 10,000 reefer containers The annual output of insulated and refrigerated cars in China is 1500–2000

Refrigerated transportation rate 80–90%

Railway 25% Highway 15% Waterway 1% Air 0.1%

4 The Construction and Analysis of Consumer Satisfaction Model 4.1 The Concept of Consumer Satisfaction Model Since about 1970, many developed countries have organized research on the field of consumer satisfaction, and formed a more mature scientific theoretical model. Among them, ACSI model is the most authoritative and has become an important reference index in the field of consumer satisfaction in the world.

4.2 Building a Mathematical Model of Cold Chain Logistics Customer Satisfaction In order to be able to quantify consumer satisfaction, a mathematical model is established with reference to the ACSI model. At this time, the mathematical model of customer satisfaction structural equation of cold chain logistics is as follows: η ≡ βη + τ ζ + ζ

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η1 η2



 =

0 0 β21 0



η1 η2



⎛

⎞ γ11   ⎜ γ12 ⎟

 ζ ⎜ ⎟ +⎝ ζ1 ζ2 ζ3 ζ4 + 1 ⎠ γ13 ζ2 γ14

Among them, β represents the relationship between intrinsic latent variables, τ indicates the influence of internal potential variable on external potential variable, ζ represents the error of internal potential variable. β21 is from η1 to η2 diameter coefficient, that is, the degree of influence of customer satisfaction on customer loyalty.

4.3 Design of Questionnaire and Data Collection Xuzhou a company is selected in this survey. It is a commercial logistics enterprise integrating development and operation. The company has established a perfect cold chain logistics system, and the enterprise data is representative. This questionnaire uses Likert five level scale. We divide consumer satisfaction into five levels: very satisfied, satisfied, general, dissatisfied and very dissatisfied. The corresponding scores are 5 points, 4 points, 3 points, 2 points and 1 point respectively. In the process of investigation, a total of 400 questionnaires were distributed and 218 effective questionnaires were collected. See Table 2 for the summary of questionnaire data. Table 2 Customer satisfaction of cold chain logistics Index

Number Very Satisfied Commonly Dissatisfied Very Average satisfied dissatisfied value

Corporate image

218

0.271

0.578

0.092

0.040

0.019

4.042

Enterprise 218 infrastructure

0.033

0.154

0.196

0.420

0.197

2.406

Enterprise product quality

218

0.098

0.198

0.227

0.323

0.154

2.763

Enterprise 218 product price

0.218

0.499

0.210

0.052

0.021

3.841

Enterprise product category

218

0.229

0.369

0.289

0.098

0.015

3.699

Enterprise service attitude

218

0.052

0.182

0.210

0.348

0.218

2.532

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Table 3 Data reliability analysis Latent variable

Corporate image ζ1

Enterprise foundation facilities ζ2

Enterprise products quality η1

Enterprise product price η2

Enterprise product category η3

Enterprise service attitude η4

Reliability value

0.849

0.820

0.712

0.819

0.783

0.841

4.4 Data Reliability Proof of Questionnaire SPSS software was used to analyze the data, and the Cronbach reliability was 0.856, indicating that the data was stable and reliable. Then the latent variables were analyzed, and the results are shown in Table 3, showing that the reliability of the questionnaire meets the calculation requirements.

4.5 Analysis of Service Quality of Cold Chain Logistics The quartile model is often used as an analytical model in favor of qualitative research, also known as an important factor derivation model. As shown in Fig. 1, the arrow direction indicates from low to high, and four areas are divided in the model according to the indicators. They are: A dominant area, B repair area, C opportunity area and D maintenance area. See Fig. 1 for the quarter diagram model.

Fig. 1 Quarter diagram model

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4.6 Calculation Method and Results According to the questionnaire to calculate its logistics service indicators, when calculating the importance of indicators, we take the proportion of the number of indicators mentioned in the survey as the measurement method, and then calculate the satisfaction degree of each indicator, and then we can get the indicator data into the quartile model to analyze it. Measurement method of index importance: Ii =

mi N

Index satisfaction measurement method: Si =

5

j Ri j

j=1

Among them, Ii indicates the importance of the i indicator; N indicates the total number of valid questionnaires; m i indicates the number of mentions of the i indicator; Si indicates the satisfaction degree of i indicators; j indicates the rating level; Ri j indicates the proportion of the number of mentions in the total number of the i indicator with a rating of j. The satisfaction and importance data of each indicator of consumers in Table 4 are respectively substituted into the quartile model, and the model can be obtained as shown in Fig. 2.

4.7 Analysis of Quartogram Results As shown in Fig. 2, the cold chain logistics service quality index quadrant model is constructed. In a advantage area, consumers show a high degree of recognition for the product price ➃ and product type ➄ of the enterprise. It shows that enterprises have a very Table 4 Satisfaction and importance of each index

No.

Index

Importance

Satisfaction

➀

Corporate image

0.166

4.042

➁

Enterprise infrastructure

0.339

2.406

➂

Enterprise product quality

0.710

2.763

➃

Enterprise product price

0.658

3.841

➄

Enterprise product category

0.544

3.699

➅

Enterprise service attitude

0.357

2.532

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Importance

Sasfacon Fig. 2 Quartogram index analysis model

accurate grasp of consumer price sensitivity, and through internal optimization, technology upgrading and other measures to ensure the rich diversity of product categories. In the next development, enterprises should maintain the advantages in this field, upgrade constantly, and improve their competitiveness. B repair area, product quality ➂ has become a key problem to be solved in cold chain logistics. Because in the past, enterprises have been trapped in the misunderstanding of occupying the market with low price. Nowadays, with the upgrading of consumption, consumers have higher requirements for product quality. In opportunity area C, in terms of enterprise infrastructure ➁ and enterprise service attitude ➅, at present, consumers have not paid enough attention to it, but also expressed dissatisfaction. In the aspect of enterprise infrastructure construction, it is not achieved overnight. It is necessary for enterprises to take it as a basic policy for a long time. However, the service attitude of enterprises can be effectively improved in a relatively short time by vigorously promoting the cultivation of professional quality and the drastic reform of personnel system. The improvement of cold chain infrastructure construction and better service will improve consumer satisfaction, which will make the region enter into the advantage area and become the competitiveness of enterprises. D maintenance area, the index of enterprise image ➀ falls to maintenance area, indicating that the input and output of enterprises in this field are not directly proportional at present. Customer's perception of cold chain logistics satisfaction depends more on service quality itself. If we pay too much attention to the construction of its corporate social image, but ignore its own quality level, that is, putting the cart before the horse. Therefore, the region can maintain its advantages without expanding investment.

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5 Optimization of Cold Chain Logistics Based on Customer Satisfaction Based on the current situation of domestic cold chain logistics and the analysis results of consumer satisfaction index, the following optimization and improvement schemes are proposed. (1)

(2)

(3)

(4)

In order to improve the quality of products, it is necessary to continuously invest in improving the infrastructure. Perfect infrastructure is the foundation of improving the quality and efficiency of cold chain logistics, and the most solid guarantee for the healthy development of cold chain logistics. It is necessary to speed up the construction of storage, transportation, processing and other supporting infrastructure in cold chain logistics, in which the construction of refrigeration facilities is the key point. According to the freight transportation pressure of cold chain logistics in different areas, we should plan the construction of cold storage reasonably according to local conditions. In the process of product transportation, the process should be strictly controlled, and the temperature should be strictly monitored to ensure the quality of products. In order to optimize the industrial structure as a whole, it is necessary to establish a complete cold chain logistics system and integrate cold chain logistics resources. Due to the high requirements of cold chain transportation facilities for fresh products, many enterprises prefer the third-party logistics with low cost and high degree of socialization. However, due to the imperfect cold chain system, the degree of adhesion between the third-party logistics and manufacturers, retailers has been low, there are information barriers. In addition, the lagging technology aggravates the “broken chain” phenomenon of cold chain logistics in China. The phenomenon of “broken chain” leads to a high rate of product damage and corruption, which will greatly affect product quality and consumer satisfaction while increasing operating costs. In order to solve the problem of “broken chain” and reduce the operation cost, the third-party logistics enterprises need to integrate the cold chain logistics system, form a strict network system for each link of the logistics industry, regulate the process with the system, run through the whole logistics system, so as to ensure the high quality and competitive price of cold chain products. In order to improve the service quality of cold chain logistics, we should pay attention to the vocational quality education of employees. To some extent, consumer satisfaction depends on the service attitude provided by the staff, so the quality education of the staff is particularly important. Enterprises should vigorously train staff in cold chain logistics related basic knowledge, let them understand the precautions in the process of cold chain logistics, and enhance customer-centered service awareness. And then improve consumer satisfaction for cold chain logistics services. For the sustainable development of industry, advanced technology should be introduced. The sustainable development of cold chain logistics must adapt to the trend, follow the pace of scientific and technological development, rely

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on advanced technology, take the maintenance of product quality and safety as the core, use artificial intelligence, big data, Internet of things and other technologies to fully cover the cold chain logistics supervision without dead ends, and form an intelligent cold chain logistics. Therefore, to improve the innovation ability and technology level of cold chain logistics, to increase the research and development of new cold chain logistics technology, and to promote advanced cold chain logistics facilities and equipment should be the long-term basic policies of enterprises. Acknowledgements This research was funded by a research granted from Xuzhou University of Technology Students Innovation and Entrepreneurship Training Program (xcx2019160), and Key Projects of Natural Science Research of Jiangsu Higher Education Institutions (18KJA12001), whose support are great appreciated.

References 1. Yao B, Li J (2010) SWOT analysis on the development of food cold chain logistics in China. Electron test 14:85–86 2. Zhao J (2014) Research on the development mode of vegetable logistics centered on wholesale market – a case study of Pengzhou, Sichuan Province. Rural Econ 07:116–118 3. Wang J (2016) On cold chain logistics of Dalian water products from the perspective of supply and demand. Labor Secur world 32:62–63

Jiajia Fu (1998–), male, from Xuzhou, Jiangsu Province, School of Management, Xuzhou Institute of Engineering. Xudong He (1970–), male, associate professor, Ph.D., School of Management, Xuzhou Institute of Engineering, Research Direction: Logistics Supply Chain Management. Xujie Yang (1999–), male, born in Taizhou, Jiangsu Province, School of Management, Xuzhou Institute of Engineering.

Purchase Channel Decision Based on Prospect Theory in the Context of Showrooming Xiang Xu, Xiao Zhang, Fangfang Meng, and Yan Zhang

Abstract Consumers are faced with multiple channel choices when purchasing products in the context of multi-channel. “Showrooming” as a market phenomenon in multi-channel retailing has grown in importance over the last few years, and it is also a frequent purchase channel for consumers. Consumers nowadays often evaluate products at brick-and-mortar stores to identify their “best-fit” product but buy it at a competing online retailer (i.e., showrooming). In this paper, the consumers’ psychological behavior is considered in consumer purchase channel decision. Therefore, a consumer utility model is built to study consumer purchase channel based on prospect theory in the context of showrooming. Firstly, consumer aspirations considering product attributes are regarded as the reference points before purchasing. Then the gain and loss of each attribute concerning consumer evaluation are calculated. Furthermore, the prospect value of each attribute is calculated based on prospect theory. Moreover, the consumer utility of each channel is calculated and consumers’ optimal purchase channel is determined according to consumer utility. Finally, a numerical example is used to illustrate the feasibility and validity of the proposed method. Keywords Purchase channel decision · Showrooming · Prospect theory

1 Introduction Nowadays, the behavior of consumer offline-online channel switch is becoming more frequent with the evolution of multichannel retailing. Consumers often evaluate products at brick-and-mortar stores to identify their “best-fit” product but buy it for a lower price at a competing online retailer (Showrooming). In particular, research

X. Xu (B) · X. Zhang · F. Meng · Y. Zhang Department of Management Engineering, School of Economics and Management, Xidian University, Xi’an, Shaanxi, China X. Zhang e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 X. Li and X. Xu (eds.), Proceedings of the Eighth International Forum on Decision Sciences, Uncertainty and Operations Research, https://doi.org/10.1007/978-981-16-1380-7_3

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has shown that 68% of US Internet users have channel-switch behavior when shopping [1], Accenture Seamless [2] finds that 73% of consumers have channel-switch shopping behavior when purchasing products through online and offline in the past six months. Some of the recent media articles document that this trend, referred to as “showrooming,” is on the rise [3]. To our knowledge, theoretic research on consumer “browse-and-switch” behavior termed “Showrooming” was first started by Telser [4], then existing literature has studied “Showrooming” from multiple aspects. Theoretic literature has studied consumer’s purchase decision based on showrooming in recent years [5–8]. Amit et al. [5] build consumer’s utility model based on whether consumers do showrooming and whether they buy in physical stores, then consumer can make optimal decision based on consumer utility model. Liu et al. [6] establish the consumer surplus model via calculating retailer aggregate surplus and marginal profit in a dual-channel system, then consumers decide channel to complete products purchase by comparing the consumer surplus model. Zhang et al. [7] consider the sunk cost effects of consumers, and construct a consumer decision model based on this, therefore consumer can make an optimal decision according to comparing the purchase path utility. Shin [8] considers a consumers’ decision tree by calculating consumer purchase product utility and the optimal option is determined for consumers. In addition, some research has studied the competition between brick-and-mortar stores and online retailers in the context showrooming behavior of consumers [5, 9–11]. Existing literature has studied the consumer purchase channel under showrooming, however, the potential psychological behavior of the consumer has not been considered when consumers purchase products facing multiple channel choices. Numerous studies have shown that the psychological behavior of the consumer plays an important role in purchasing decision making [12–15]. The consumer often purchases the product by setting aspirations on product attributes in practical purchase decisions, and the aspiration-levels can be regarded as reference points of the consumer [13, 15]. Then the consumer will compare the attribute value with the aspiration-level and concern with the deviation of the attribute value from the aspiration-level. If the product meets the aspiration, the consumer will be satisfied and the excess can be regarded as his “gain”; if not, the lack can be regarded as his “loss” [13, 15]. The consumer also has different psychological mirrors for the gain and the loss [14, 16]. Therefore, the potential psychological behavior of the consumer should be considered in purchase channel decision in the context showrooming. In this paper, a purchase channel decision method based on prospect theory in the context of showrooming is proposed. Firstly, the consumer has three product purchase channels under showrooming in the context of multi-channel. In order to making optimal purchase channel for consumer, the consumer’s aspiration of product attribute based on Prospect theory are considered for each product purchase channel. Secondly, the consumer utility model based on Prospect theory in the context of showrooming is determined. Then, the purchase channel is determined by comparing consumer utility of three channels. Thus, according to prospect theory, the consumer’s gains or losses concerning product attributes are transformed into

Purchase Channel Decision Based on Prospect Theory …

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a consumer utility and the highest consumer utility value is the optimal purchase channel for the consumer. The rest of this paper is organized as follows. In Sect. 2, we present our formal model, mainly the description of the problem and research framework. And a numerical example is used to illustrate the feasibility and validity of the proposed method in Sect. 3. Finally, in Sect. 4, we provide concluding remarks and conclude by discussing limitations and potential future research direction.

2 Model Let M = {1, 2 . . . , m}. Let D = {d1 , d2 , . . . di . . . dm } be a finite product attributes set, where di denotes the ith attribute, i ∈ M; W = (W1 , W2 . . . Wi , . . . Wm )T be an attribute weight vector, where Wi denotes the weight or the importance degree of m Wi = 1, and 0 ≤ Wi ≤ 1, i ∈ M. In this paper, the the attribute di , such that i=1 attribute values can be obtained by consumers experiencing directly in the physical store or observing product reviews and ratings online. Let E = (e1 , e2 , . . . ei , . . . em ) be a finite attribute expectation vector, which can be obtained based on information available and expectations for the future, ei denotes the expectation vector of the attribute di , i ∈ M. Let T denotes the visit cost of the consumer in the physical store, let t denotes the wait cost if consumer completes purchase online, besides the Ps and Po represent physical store price and online price respectively, and normalize values to [0, 1]. In this paper, the sequence of the consumer purchase process in the game is as follows. When consumers engage in showrooming, they only have one option: (i) offline-online option (Showrooming), in this case, consumer firstly experiences products at brick-and-mortar stores, then observes online product reviews and buy it at a competing online retailer. The consumer utility is Us−o . However, when consumers do not engage in showrooming, they have two options: (ii) offline-only option, in this case, consumer experiences products at brick-and-mortar stores and buy it directly. The consumer utility is Us ; (iii) online-only option, in this case, consumer observes online product reviews and buy it directly, The consumer utility is Uo . Figure 1 illustrates the stages of a consumer’s purchase channel decision process. To solve the purchase channel decision problem mentioned above, we develop a method based on prospect theory in the context of showrooming. And, the calculation process of gain and loss, prospect value and consumer utility is as follows:

2.1 Case 1: Offline-Only Option Let Q = (q1 , q2 , . . . qi , . . . qm ) be a finite product attribute values vector that consumer experiences and evaluates the product at brick-and-mortar stores, qi and denotes the attribute value of the attribute di . Let aspiration-level ei and attribute

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Fig. 1 Channel decision tree for consumers

valueqi denote interval and crisp numbers, respectively. For the ei , ei = ei , and   ei = eil , eiu , eiu > eil ≥ 0. For the qi , qi = qi , qi ≥ 0, i ∈ M. According to the literature [13], the calculation formula of gain for offline-only is expressed by ⎧   ⎨ qi − eiu , qi > eiu ,  G i = 0, qi < eil , ⎩  l 0, ei ≤ qi ≤ eiu ,

i∈M

(1)

⎧  qi > eiu , ⎨ 0,   , i∈M L i = qi − eil , qi < eil , ⎩  l u 0, ei ≤ qi ≤ ei ,

(2)

and, that of loss for offline-only is expressed by

The prospect value Vsi of the consumer relative to each attribute can be calculated according to Eq. (3). Vsi = (G i )α + [−λ(−L i )β ]

(3)

Finally, the overall prospect value Vs and consumer utility Us are as follow: Vs =

m  i=1

Wi Vsi , i ∈ M

(4)

Purchase Channel Decision Based on Prospect Theory …

Us =

m 

Wi Vsi − T − Ps , i ∈ M

25

(5)

i=1

2.2 Case 2: Online-Only Option Let Y = (y1 , y2 , . . . yi , . . . ym ) be a finite product attribute values vector that consumer evaluates after reading numerous product reviews and ratings online, yi denotes the attribute value of the attribute di . Let aspiration-level ei and attribute value yi denote interval and crisp numbers, respectively. For the ei , ei = ei , and  ei = [eil , eiu ], eiu > eil ≥ 0. For the yi , yi = yi , i ∈ M. The calculation formula of gain for online-only is expressed by ⎧   ⎨ yi − eiu , yi > eiu ,  G i = 0, , i∈M yi < eil , ⎩  l u 0, ei ≤ yi ≤ ei ,

(6)

and, that of loss for online-only is expressed by ⎧  yi > eiu , ⎨ 0,   , i∈M L i = yi − eil , yi < eil , ⎩  0, eil ≤ yi ≤ eiu ,

(7)

The prospect value Voi of the consumer relative to each attribute can be calculated according to Eq. (8). Voi = (G i )α + [−λ(−L i )β ]

(8)

Finally, the total prospect value Vo and consumer utility Uo are as follow: Vo =

m 

Wi Voi , i ∈ M

(9)

Wi Voi − t − Po , i ∈ M

(10)

i=1

Uo =

m  i=1

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2.3 Case 3: Offline-Online Option (Showrooming) Consumers firstly evaluate product at brick-and-mortar stores and then buy it for a lower price at a competing online retailer, so consumers evaluate the product twice: one is experiencing evaluation in the physical store and the other is product evaluation online. Firstly, the loss and gain that consumers experience and evaluate in the physical store are the same as the offline-only. Then the prospect value can be seen as the attribute expectation that consumer reading online. Let X = (x1 , x2 , . . . xi , . . . xm ) denote expectation vector before consumer evaluate product online. Let aspiration  level xi and attribute value yi denote crisp numbers, for x1 , xi = xi and yi , yi = yi , i ∈ M. The calculation formula of gain for offline-online option (Showrooming) is expressed by Gi =

yi − xi , yi ≥ xi , , i∈M 0, yi < xi ,

(11)

and, that of loss for offline-online option (Showrooming) is expressed by Li =

0, yi ≥ xi , , i∈M yi − xi , yi < xi ,

(12)

The prospect value V(s−o)i of the consumer relative to each attribute can be calculated according to Eq. (13).   V(s−o)i = (G i )α + −λ(−L i )β

(13)

Finally, the total prospect value Vs−o and consumer utility Us−o are as follow: Vs−o =

m 

Wi V(s−o)i , i ∈ M

(14)

Wi V(s−o)i − T − t − Po , i ∈ M

(15)

i=1

Us−o =

m  i=1

In summary, the gains and losses and prospect value can be calculated based on prospect theory. Then consumers’ utility of each purchase channel is determined by Eqs. (5), (10) and (15), finally, consumers make optimal purchase channel decision according to the consumer utility model.

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3 Illustrative Example In this section, an example is used to illustrate the use of the proposed method. Consider selling a bunch of new phones in the market, consumer A wants to purchase one, and the phone three attributes that d1 running speed, d2 display effect and d3 battery life are considered, simultaneously. Meanwhile, assume that the attribute weight vector provided by consumer is W = (0.5, 0.3, 0.2)T . And the attribute expectation vector E = ([4, 5], [3, 4], [2, 4]) that the consumer can give based on information available and expectations for the future. And given data, visiting cost T = 0.6 and waiting cost t = 0.4. Physical store price Ps = 0.51 and online price Po = 0.49.

3.1 Case 1: Offline-Only Option Firstly, the attribute expectation vector is E = ([4, 5], [3, 4], [2, 4]) and attributes value of consumer A is Q = (10, 5, 1), then the loss and gain are calculated: L 1 = 0, G 1 = 5, L 2 = 0, G 2 = 1, L 3 = −1, G 3 = 0. Secondly, according to Eq. (3) and we can take α = β = 0.88 and λ = 2.25. Then the prospect values are Vs1 = 4.12, Vs2 = 1, Vs3 = 2.25, and the total prospect value standardized that consumer for all experience attributes is Vs = 1.603. Finally, the utility of consumer A according to Eq. (5) is Us = 0.493.

3.2 Case 2: Online-Only Option Firstly, the attribute expectation vector is E = ([4, 5], [3, 4], [2, 4]), and the attributes value of consumer A is Y = (8, 4.5, 2), then the loss and gain are calculated: L 1 = 0, G 1 = 3, L 2 = 0, G 2 = 0.5, L 3 = G 3 = 0. Secondly, the prospect values are V1 = 2.63, V2 = 0.59, V3 = 0 and the total prospect value standardized is Vo = 1.136. Finally, the utility of consumer A according to Eq. (10) is Uo = 0.246.

3.3 Case 3: Offline-Online Option (Showrooming) Firstly, the loss and gain that consumers experience and evaluate in the physical store are the same as the offline-only option. Secondly the x1 = 4.12, x2 = 1, x3 = 2.25 can be seen as the attribute expectation that consumer reading online. And consumer A’s attributes value after reading reviews online is Y = (10, 6, 1.5), then the loss and gain are calculated: L 1 = 0, G 1 = 5.88, L 2 = 0, G 2 = 5, L 3 = −0.75, G 3 = 0. And the prospect values are V(s−o)1 = 4.75, V(s−o)2 = 4.12, V(s−o)3 = 1.755. And

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the total prospect value standardized is Vs−o = 2.26. Finally, the utility of consumer A according to Eq. (15) is Us−o = 0.77. In summary, the consumer utility of each purchase paths are Us = 0.493, Uo = 0.246, Us−o = 0.77 and Us−o > Us > Uo . So the optimal decision of consumer A is the offline-online option (Showrooming), which is consumer A experiences and evaluates products at the physical stores to collect product information and determine “best-fit” product, but they purchase it online at a lower price.

4 Conclusions This paper has presented a novel method for the purchase channel decision based on Prospect theory in the context of showrooming. In the proposed method, the consumers’ psychological behavior has be considered when consumers purchase products face multiple channel choices. Compared with the existing methods, the proposed method has following distinct characteristics. Firstly, this method is proposed based on showrooming in the context of multichannel, and unlike others, the purpose of method is making optimal channel decision for consumers when purchasing. Secondly, the consumers’ aspirations are considered in the process of purchasing channel decision. The consumer’s gain–loss each attribute is assessed by measuring the perceived difference between the attribute value and the aspiration level. Then, considering the consumer’s different psychological mirrors for gains and losses, the consumer utility of each case is calculated based on prospect theory. Using the proposed method, the result can reflect the consumer’s actual behavior and is more available to practical purchase decision problems. In this paper, the product purchase channel model based on Prospect theory is proposed in the multi-channel, but only three purchase paths are considered. However, in reality, consumers have more than three purchase channels when buying products. Therefore, in future research, scholars can consider the problem in the omni-channel, and which making better purchase channel decision for consumers. Acknowledgements This work was partly supported by the Natural Science Foundation of Shaanxi Province (Program No. 2019JM-110) and the Fundamental Research Funds for the Central Universities (Project Nos. JB190604 and RW180173).

References 1. STATISTA (2016) Share of Internet users in the United States who have utilized showrooming and webrooming as of September 2014. https://www.statista.com/statistics/448677/uswebr ooming-showrooming-penetration. Accessed 12 Feb 2016 2. Prasad S (2016) Showrooming and webrooming: the emerging trends consumer behaviour. Market Express, 23 May 2016. https://www.marketexpress.in/2016/05/showrooming-and-web rooming-the-emerging-trends-consumer-behaviour.html. Accessed 10 July 2016

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3. Bosman J (2011) Book shopping in stores, then buying online. New York Times, Dec 4. https://mediadecoder.blogs.nytimes.com/2011/12/04/book-shopping-in-stores-thenbuying-online/?_r?0 4. Telser LG (1960) Why should manufacturers want fair trade? J Law Econ 3:86–105 5. Amit M, Subodha K, Raju JS (2018) Competitive strategies for brick-and-mortar stores to counter “showrooming.” Manage Sci 64(7):3076–3090 6. Liu ZX, Lu L, Qi XT (2019) The showrooming effect on integrated dual channels. J Oper Res Soc. https://doi.org/10.1080/01605682.2019.1605470 7. Zhang T, Ge L, Gou QL, Chen LW (2018) Consumer showrooming, the sunk cost effect and online-offline competition. J Electron 19(1):55–74 8. Shin J (2007) How does free riding on customer service affect competition? Mark Sci 26(4):488–503 9. Basu P, Basak S, Avittathur B (2017) A game theoretic analysis of multichannel retail in the context of “showrooming.” Decis Support Syst 103:34–45 10. Jing B (2018) Showrooming and webrooming: information externalities between online and offline sellers. Mark Sci 37(3):469–483 11. Jeuland AP, Shugan SM (1983) Managing channel profits. Mark Sci 2(3):239–272 12. Zhou X, Wang L, Liao H (2018) A prospect theory-based group decision approach considering consensus for portfolio selection with hesitant fuzzy information. Knowl-Based Syst 28–38 13. Fan ZP, Zhang X, Chen FD (2013) Multiple attribute decision making considering aspirationlevels: a method based on prospect theory. Comput Ind Eng 65(2):341–350 14. Tversky A, Kahneman D (1992) Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertain 5(4):297–323 15. Fan ZP, Zhang X, Zhao YR, Chen FD (2013) Multiple attribute decision making with multiple formats of attribute aspirations: a method based on prospect theory. Int J Inf Technol Decis Mak 12(4):711–727 16. Ds K, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometric 47(2):263–291

A Simulation Analyses for Disruption Risks in the Spare Parts Supply Chain of New Energy Vehicles Chaowen Xia and Guoqing Zhang

Abstract New energy vehicles (NEVs) will become the main traffic tools, and their service needs to be improved correspondingly. This paper mainly proposes a double circulation loop (DCL) approach to mitigate disruption risks in the supply chain (SC) of NEVs spare parts. The simulation can dynamically illustrate real inventory levels and transportation, which is a new try for spare parts supply chain. The basic idea of the DCL approach is introduced. The experimental results are compared and analyzed between disruptions with and without recovery. Keywords Disruption risk · Double circulation loop · Supply chain simulation · New energy vehicles

1 Introduction It is reported by Lazard that the sale column of the EVs from now up to 2025, compared with traditional automotive enterprises, will range from 8 to 20% in the US, from 20 to 32% in Europe, from 29 to 47% in China. When NEVs reach up to a certain quantity, some SC problems correlated with its spare parts will occur consequently. With a rapid increase in NEVs’ quantity, multiple risks will be exposed. Disruption risk originates from natural disasters and artificial crises, such as earthquakes, tsunami, fires, strikes, wars, accidents, and so on. In 2001, the terrorist attack on the World Trade Center led to significant regulatory changes in the process by which goods could be shipped in and out of the United States. In 2002, the longshoremen’s strike at the LA docks significantly impacted the availability of retail products that were manufactured in the Far East and sold in the United States [1]. How to effectively manage these problems is very crucial to improve the service C. Xia (B) School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, China e-mail: [email protected] G. Zhang Department of Engineering, University of Windsor, Windsor, Canada e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 X. Li and X. Xu (eds.), Proceedings of the Eighth International Forum on Decision Sciences, Uncertainty and Operations Research, https://doi.org/10.1007/978-981-16-1380-7_4

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level and sustainable development of NEVs industry. If we may take multiple technologies and measures in advance so as to avoid unpredictable risks, it will benefit the development of the whole NEVs industry. This paper proposes a new approach of the DCL to counter disruption risks, which will happen in the NEVs spare parts SC. Based on the DCL approach, disruption risks will be monitored in real-time, analyzed and reacted, which can minimize the costs and losses when any disruption occurs. Therefore, the DCL approach may become a useful alternative for enhancing the robustness and resilience of NEVs spare parts SC. The remaining of this paper is organized as follows. Section 2 presents a short literature review. Section 3 introduces the methodology and Sect. 4 presents simulation results and its analysis. The conclusion and future work are proposed in Sect. 5.

2 Literature Review Many authors have focused on many types of disruptions. Reference [2] analyzed the international technological trend of NEVs, compared with the industrialization progresses between top global counties. With the development of global multilateral trade, researches on SC disruption risks have been popular since 2007. We can find 152 articles using the keyword of engineering from 2005 to 2018, as shown in Fig. 1. Some have studied many strategies to counter disruption risks utilizing modeling, simulation, and case study. Reference [3] presented a resilient method to manage disruption risks in his book and presented the definition and characteristics of disruption risks. References [4, 5] proposed a quantitative metrics or model to analyze the SC resilience and market response during post-disruption. Due to the increasing diversity and growing size of modern industrial SCs, problems of identification, assessment, and mitigation of disruption risks become challenging goals, as in Ref. [6]. As many cases indicated, disruptions could be catastrophic to the global economy. Therefore, it is to monitor and mitigate these risks with contingency plans, as in Ref. [7]. Reference [8] provided a systematic conceptual framework that reflected the joint activities of risk assessment, economic loss estimations and risk mitigation strategies. Reference [9] developed an optimization model to provide effective remedies as well as quantify and assess sustainability performance to increase SC resilience. Researches on NEVs spare parts will be a benefit to the robust development of NEVs industry. Fig. 1 Articles or reviews about SC disruption risks

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Fig. 2 The DCL design

3 Methodology 3.1 The Basic Idea About the DCL Approach The double circulation loop consists of two circles: the outside one indicates the stage of pre-disruption, including some tasks: risk identification, record, quantification, assessment and monitoring; the inner circle indicates the stage of post-disruption, including other tasks: risk detection, classification, reaction, recovery and analysis, which is shown in Fig. 2. The outside circle is responsible for monitoring. When a disruption occurs, a signal will be sent to the inner circle, which is responsible for taking the appropriate recovery method to mitigate the risk. After being analyzed, the result will be sent to the outside circle. In the procedure of simulation, the system will continuously monitor and analyze the running process of SC. Some performances may be dynamically illustrated and modified, which will provide managers with an intuitive and acceptable method to counter any disruption risks.

3.2 Case Selection To analyze and compare the differences in inventory cost of each DC, we design a three-echelon supply chain to dynamically illustrate, compare and analyze the difference of inventory cost under the conditions with and without the recovery policy.

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Fig. 3 The three-echelon system structure

3.2.1

System Structure Assumption

Here, we design a three-echelon system structure, which is shown in Fig. 3. For the sake of simplicity, we consider two factories, one first-tier distribution center, and six second-tier distribution centers in during the experiment. When risks occur, three overseas emergency suppliers will be activated to replenish these distribution centers.

3.2.2

Configuration of Factories and Distribution Centers

Firstly, How to select reasonable factory locations is a difficult task, and we need to take two factors into account: quantitative factor and qualitative factor. In this paper, we are supposed that two factories are located in any appropriate places based on the conception of the non-central downtown area. And two kinds of parts are only produced respectively: one is the core parts, and the other is the general parts. At the same time, we assume that the replenishment interval is distributed normally. Distribution centers are assumptively configured at the south of Ontario province in Canada, and the local traditional car service centers can be considered as NEVs service centers in the future. Here, the first-tier distribution center (FDC) is located near Toronto, and the second-tier distribution centers (SDCs) are located in Windsor, London, Gravenhurst, Kingston, Eganville, and Ottawa respectively. The overseas emergency suppliers are considered to locate in Detroit, Buffalo and Watertown.

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Table 1 The basic parameters to run simulation procedure Parameters

Part 1

Part 2

Initial inventory level, in unit

294

2556

Mean demand daily, in unit

10

87

Standard deviation of demand daily, in unit

3

26

Ordering quantity, in unit

217

1887

Reorder point of two types parts, in unit

217

1887

Mean lead time, in day

14

14

Standard deviation of lead time, in day

4

4

Unit cost of two types of parts

1577

100

Holding cost of two types of parts, per unit

0.87

0.05

Fixed cost of two types of parts, per unit

50

1

Shortage cost of two types of parts, per unit

0.013

0.013

Safety stock of two types of parts, in unit

77

669

3.3 Simulation Process 3.3.1

Simulation Setting

Simulation Software Here, we utilize the software of simulation with Arena to simulate the counter strategy. Arena is a discrete event simulation software, which allows us to quickly analyze a process or system’s behavior overtime. In this experiment, we can dynamically demonstrate, analyze, and modify some performances.

Parameter Setting To realize the process of simulation, we need to set some parameters, which are shown in Table 1. Here, for the sake of simplicity, we only take uncertain demand and continuous review replenishment policy into consideration, which are all normally distributed. And we select an SDC to analyze its inventory level, lead time, cycle service level and total cost.

Simulation Assumption First, customers’ demand for each SDC daily is distributed normally and almost generated simultaneously. Second, SDC has a certain stock respectively, Backlog order is allowed when inventory level is not sufficient. Third, the transportation interval from the first-tier distributor to second-tier distributors is distributed normally

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with a mean of 14 days and a standard deviation of 4 days to reduce the transportation cost, each truckload is limited, and no further restrictions are considered. Forth, factories are supposed that production capacity is limited, factory location is at an uncertain place which might be civil or oversea, and the time interval is considered as the same normal distribution.

3.3.2

Simulation Process of the Pre-disruption and Post-disruption Stages

Figures 4 and 5 shows the simulation process and flow figure of the DCL approach. In every step of the run process, utilizing drop-list dialog to illustrate every day’s Fig. 4 Simulation process of DCL

Fig. 5 Flow figure of DCL

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status in the simulation process, to judge when disruption risks will occur. Then corresponding approaches of recovery will be taken. And based on recovery policies, what performance will be induced, which can be shown by utilizing figures or tables.

4 Simulation Analysis During the simulation process, we can dynamically illustrate the variation of inventory level and transportation, and this process is shown in Fig. 6. Of course, we can illustrate other information using the Plot module too.

4.1 Simulation Analysis Without Recovery Policy When the event of disruption occurs without recovery policy, we observed from Fig. 6 that inventory level would become negative, the shortage costs will be a constant value during the period of disruption. Meanwhile, the lead time will become longer and longer until the disruption stops.

Fig. 6 Dynamically illustrated logistics and indicators

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Fig. 7 Performance without a recovery policy

4.2 Simulation Analysis with Recovery Policy The recovery policy has many methods. Here, we take into account one kind of oversea suppliers, which (for which) we select three oversea locations, showed in Fig. 6. When the event of disruption occurs with recovery policy, we can observe from Fig. 7 that inventory level will be normal because of replenishment utilizing oversea emergency suppliers. The cycle service level will always be 100%, and the lead time will be steady too.

4.3 Comparison of SC Performances Under Two Policies Observing Figs. 7 and 8, we find that under two kinds of recovery policies, SC performances, such as inventory level, lead time, cycle service level and costs are different. The difference in some performance indicators may be concluded in Table 2.

5 Conclusion Many scholars have done much related theory researches on the supply chain of auto parts. This paper proposed a new approach of DCL to study strategies to counter disruption risks utilizing the software of simulation with Arena. This is a new try to dynamically illustrate and analyze the simulative process of NEVs spare parts SC with disruption risks.

A Simulation Analyses for Disruption Risks in the Spare Parts …

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Fig. 8 Performance with a recovery policy

Table 2 Difference of some performances under two different recovery policy Indicators

Performance without recovery policy

Performance with recovery policy

Inventory—backlog

37,847

44,306

Cycle service level, %

90

100

Profits, CAD $

18,356,285.08

16,822,860.78

In this experiment, we only considered two types of parts. Meanwhile, the stockout, the starting time and period of disruption are set in advance. When disruption risks occur, measures of reaction and recovery are specified in advance. These gaps should be resolved in future work. Acknowledgements This research is partially supported by the Shanghai University of Engineering Sciences and the Natural Sciences and Engineering Research Council of Canada (RGPIN-201403594).

References 1. Vakharia AJ, Yenipazarli A (2008) Managing supply chain disruptions. In: Foundations and trends® in technology, information and operations management, vol 2, no 4, pp 243–325 2. Du J, Ouyang D (2017) Progress of Chinese electric vehicle industrialization in 2015: a review. Appl Energy 188:529–546 3. Ganguly A, Chatterjee D, Rao H (2018) The role of resiliency in managing supply chains disruptions. In: Supply chain risk management: advanced tools, models, and developments, pp 237–251

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4. Zavala A, Nowicki D, Ramirez-Marquez JE (2018) Quantitative metrics to analyze supply chain resilience and associated costs. Proc IMechE Part O J Risk Reliab 1–14 5. Vinayak A, Mackenzie CA (2018) A quantitative model for analyzing market response during supply chain disruptions. In: Supply chain risk management: advanced tools, models, and developments, pp 139–153 6. Levner E, Ptuskin A (2018) Entropy-based model for the ripple effect: managing environmental risks in supply chains. Int J Prod Res 56(7):2539–2551 7. David B, Robert V, Marija B (2013) The adaptation of extended net present value theory and solvency II in risk management. In: Proceedings of the 12th international symposium on operational research in Slovenia, SOR 2013, pp 287–292 8. Kleindorfer PR, Saad GH (2005) Managing disruption risks in supply chains. Prod Oper Manag 14(1):53–68 9. Jabbarzadeh A, Fahimnia B, Sabouhi F (2018) Resilient and sustainable supply chain design: sustainability analysis under disruption risks. Int J Prod Res 56(17):5945–5968

Does ESBO Omnichannel Strategy Benefit an Online Retailer with Consideration of Return Rate and Experience Service? Jinrong Liu, Qi Xu, and Guoqing Zhang

Abstract Today, omnichannel and customer experience are the norm. Does an online retailer benefit from opening an offline showroom with consideration of return rate and experience service? In this study, we establish four profit models before and after providing omnichannel ESBO (Experience in Showroom and Buy Online) and considering return rate and experience service, analyze the effect of the new channel ESBO on demand and profit. The results show that the online retailer can benefit from offering omnichannel ESBO when the return rate and experience service are not considered. Otherwise, the retailer can benefit from providing omnichannel ESBO only if the online return rate and experience service effort are high enough. Furthermore, if the cost coefficient of experience service is less than a certain threshold, the increase in total profit is greater when the return rate and experience service are considered. Keywords ESBO · Omnichannel · Experience service · Return rate

1 Introduction Nowadays, more and more online-first brand retailers are opening stores and showrooms to enrich customer value proposition and improve operational efficiency [1]. If an online-first retailer opens an offline showroom, then he can provide the omnichannel mode ESBO (Experience in Showroom and Buy Online) for customers. Many online retailers have implemented ESBO, such as Bonobos, Paul Evans and J. Liu School of Economics and Trade, Shanghai Urban Construction Vocational College, Shanghai, China J. Liu · Q. Xu Glorious Sun School of Business and Management, Donghua University, Shanghai, China e-mail: [email protected] J. Liu · G. Zhang (B) Department of Mechanical, Automotive and Materials Engineering, Supply Chain and Logistics Optimization Research Centre, University of Windsor, Windsor, Canada e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 X. Li and X. Xu (eds.), Proceedings of the Eighth International Forum on Decision Sciences, Uncertainty and Operations Research, https://doi.org/10.1007/978-981-16-1380-7_5

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Warby Parker, etc. The showroom not only delivers sales the probability of individuallevel conversion of try-ons to sales improves by 1%. And returns in the trading area of a showroom decrease by 1% [2]. Offline channel can increase overall demand, and customers who migrate to offline are the most cost customers in other channels, and locations contained within the trading area of a showroom can increase in overall demand [3]. Physical showrooms may prompt retailers to reduce store inventory, which increases availability risk and discourages store patronage [4]. The service experience has a positive impact on behavioral intentions [5], but online channel often has higher return rates due to lack of product experience services [6]. But no author has integrated shopping cost and experience service perceived value to build consumer utility function and profit model. Moreover, opening an offline showroom and providing ESBO omnichannel service will bring certain service efforts cost. Therefore, in this paper we focus on the following research question: Does an online retailer benefit from opening an offline showroom when the return rate and experience service considered or not?

2 Model Description An online retailer sells products to customers through the Online (or simply “O”) channel; if he opens an offline showroom in a region, he can sell goods to customers at the same price through O and ESBO two channels as follows: (1) under O channel, customers can enjoy a little virtual experience service and directly buy online, then the retailer delivers the product to a customer-specified address; and (2) under ESBO channel, customers can enjoy the ultimate experience service and order online in an offline showroom, then the retailer delivers the product to a customer-specified address. We definite the notations used in the modeling as follows: p is price of the product, c is cost of the product, o is shopping costs of O channel, θ is online return rate (0 ≤ θ < 1), h is shopping inconvenient costs of ESBO channel, s is experience service effort of ESBO channel, r is the ratio of customer perceived experience service effort of O channel relative to ESBO channel, k1 and k2 are the cost coefficient of experience service effort under O and ESBO channel (0 ≤ k1 < k2 ≤ 1), v is product value for a purchase, Ui j is consumer utility under channel i in case j (i = O, E S B O; j = 1, 2), Q i j is demand of channel i in case j (i = O, E S B O; j = 1, 2), Q i and i are total demand and total profit in case j ( j = 1, 2). The online retailer only has O channel to sell products to customers before providing ESBO channel, and he has O and ESBO two channels to sell products to customers after providing ESBO channel. Since the return rate of online shopping is much higher than the return rate of store channel due to the absence of experience [7], we assume that the return rate of ESBO channel are negligible. For ease of analysis, we assume the salvage value of return products of O channel is normalized to zero [8].

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3 Model Building 3.1 Return Rate and Experience Service Are Not Considered In this case, the online retailer does not consider the return rate and experience service. When a customer buys the product directly through O channel, his payoff is v − p − o; when a customer buys the product through ESBO channel, his payoff is v − p − h. According to the research in [8, 9], we can get consumer utility functions as the following under O and ESBO channels: U O1 = v − p − o.

(1)

U E S B O1 = v − p − h.

(2)

Without loss of generality, we normalize the total market size to 1. For analytical tractability, we assume v and h follow uniform distribution on [0, 1]. The purchasing preference for the customers can be analyzed using the indifference curve a between No-buy and purchase from O channel before providing ESBO channel, as shown in Fig. 1i; and the purchasing preference for the customers can be analyzed using the three indifference curves a, b and c after providing ESBO channel, as shown in Fig. 1ii. According to Fig. 1i and the assumption of v and h follow uniform distribution, we can obtain the demand function and profit function as following: Q 1A = Q O1A = 1 − p − o.

(i): Under single online channel

(ii): Under O and ESBO channels

Fig. 1 The return rate and experience service are not considered

(3)

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1A = ( p − c)Q 1A = ( p − c)(1 − p − o).

(4)

Similarly, according to Fig. 1ii, we can obtain the demand and profit functions as following: Q O1B = (1 − p − o)(1 − o).

(5)

Q E S B O1 = (1 − p − o)o + o2 /2.

(6)

Q 1B = Q O1B + Q E S B O1 = 1 − p − o + o2 /2.

(7)

1B = ( p − c)Q 1B = ( p − c)(1 − p − o + o2 /2).

(8)

3.2 Return Rate and Experience Service Are Considered In this case, the online retailer considers the return rate and experience service. When a customer buys the product directly through O channel and does not return the product, his payoff is v − p − o + r s; otherwise, he will return the product, his payoff is −2o. When a customer buys the product through ESBO channel and does not return the product, his payoff is v − p − h + s; otherwise, he will return the product, his payoff is −2h. Therefore, the consumer benefits under O and ESBO channels as shown in Fig. 2. According to Fig. 2, we can get consumer utility functions as the following under O and ESBO channels: U O2 = (1 − θ )(v − p + r s) − o(1 + θ ).

Fig. 2 Consumer benefits under O and ESBO channel

(9)

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U E S B O2 = v − p − h + s.

(10)

The purchasing preference for the customers can be analyzed using the indifference curve a between No-buy and purchase from O channel before providing ESBO channel, as shown in Fig. 3i; and the purchasing preference for the customers can be analyzed using the three indifference curves a, b and c after providing ESBO channel, as shown in Fig. 3ii. According to Fig. 3i, we can obtain the demand function and profit function as following: Q 2 A = Q O2 A = 1 − p + r s −

1+θ o. 1−θ

2 A = (1 − θ )Q 2 A ( p − c) − k1 s 2 /2   1+θ o − k1 s 2 /2. = (1 − θ )( p − c) 1 − p + r s − 1−θ

(11)

(12)

Similarly, according to Fig. 3ii, we can obtain the demand and profit functions as following:    1+θ 1+θ o 1 − s + rs − o . Q O2B = 1 − p + r s − 1−θ 1−θ    1+θ 1+θ Q E S B O2 = 1 − p + r s − o s − rs + o 1−θ 1−θ   1+θ 2 o /2. + s − rs + 1−θ

(i): Under single online channel

(ii): Under O and ESBO channels

Fig. 3 The return rate and experience service are considered

(13)

(14)

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Q 2B = Q O2B + Q E S B O2 = 1 − p + r s −

  1+θ 2 1+θ o + s − rs + o /2. 1−θ 1−θ (15)

2B = (1 − θ )( p − c)Q O2B + ( p − c)Q E S B O2 − (k1 + k2 )s 2 /2    1+θ 1+θ = (1 − θ )( p − c) 1 − p + r s − o 1 − s + rs − o 1−θ 1−θ   1+θ o − (k1 + k2 )s 2 /2 + ( p − c) 1 − p + r s − 1−θ      1+θ 2 1+θ o + s − rs + o /2 . s − rs + (16) 1−θ 1−θ

4 Comparison and Analysis 4.1 Effects of ESBO Channel When the Return Rate and Experience Service Are Not Considered When an online retailer opens a new channel ESBO and does not consider the return rate and experience service, as shown in Fig. 1, we can get the following proposition 1 by simply comparing Eqs. (3)–(4) and (5)–(8). Proposition 1 After providing the ESBO channel, the demand and profit of the retailer have such changes: (i) (ii)

The online demand is decreased by o(1 − p − o), the total demand is increased by o2 /2; the total profit is increased by ( p − c)o2 /2. The change of total demand and profit are positively correlated with the shopping costs of the O channel o.

Proof (i)

(ii)

Due to Q O1B − Q O1A = −o(1 − p − o) < 0, Q 1B − Q 1A = o2 /2 > 0, and 1B − 1A = ( p − c)o2 /2 > 0. Therefore, when offering ESBO channel, the online demand of the online retailer will decrease, the total demand and the total profit will increase. Due to ∂(Q 1B∂o−Q 1A ) = o > 0, ∂(1B∂o−1A ) = ( p − c)o > 0, therefore, the change of total demand and total profit are positively correlated with o.

Proposition 1 shows that when the retailer does not consider the return rate and experience service, the new ESBO channel attracts customers by lower shopping costs, which causes some customers switch over from O channel to ESBO channel.

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Moreover, this new channel can generate new demand which was not there before. As a result, the retailer’s total demand and total profit will increase due to the absence of return loss and experience service costs. Furthermore, when providing ESBO channel, the higher the shopping costs of O channel, the more the total demand and total profit will increase.

4.2 Effects of ESBO Channel When the Return Rate and Experience Service Are Considered When an online retailer opens a new channel ESBO and considers the return rate and experience service, as shown in Fig. 3, we can get the following proposition 2 by simply comparing Eqs. (11)–(12) and (13)–(16). Proposition 2 After providing ESBO omnichannel, the demand and profit of the retailer have such changes: (i) (ii)

2 The online demand is decreased by AB, the total demand is increased  by A /2; k2 s 2 −( p−c)A2 p−c A. the total profit increases if and only if θ > 2( p−c)AB and s > k2 The change of total demand and total profit is positively correlated with θ and o.

where A = s − r s + (1 + θ )o/(1 − θ ), B = 1 − p + r s − (1 + θ )o/(1 − θ ). Proof (i)

Let A = s −r s + (1 + θ )o/(1 − θ ), B = 1 − p +r s − (1 + θ )o/(1 − θ ), we can get Q O2B − Q O2 A = −AB < 0, Q 2B − Q 2 A = A2 /2 > 0, and 2B −2 A =

s −( p−c)A [( p − c)(A + 2θ B)A − k2 s 2 ]/2, that is, if θ > k2 2( and s > p−c A, p−c)AB k2 then 2 − 1 > 0. Thus, when offering ESBO channel, the online demand of the online retailer will decrease and total demand will increase, the total profit will increase under a certain condition. Let C = (1 − θ )(1 − r )s + (1 + θ )o, D = (1 − p + r s)(1 − r )s, E = (1 + θ )(1 − 4θ + θ 2 )o2 , F = 1 − p + s, G = F(1 + 2θ − θ 2 ) + 2r sθ (2 − θ ), H = (1 − r )(s − r s + θ − pθ + 2r sθ ), I = o(1 + θ )(1 − r − θ + 2r θ ), J = o(1 + θ )(1 − 2θ ), K = (1 − p)θ + s(1 −r − θ + 2r θ ), we can get ∂(Q 2B∂θ−Q 2 A ) = )C 2oC > 0, ∂(Q 2B∂o−Q 2 A ) = (1+θ)C > 0, ∂(Q 2B∂s−Q 2 A ) = (1−r > 0, ∂(2B∂θ−2 A ) = (1−θ)3  (1−θ) 1−θ 2 +(1−θ)K } E Go > 0, ∂(2B∂o−2 A ) = ( p−c)(1+θ){J > 0, ( p − c) D + (1−θ) 3 + (1−θ)2 (1−θ)2

( p−c)[(1−θ)H +I ] ∂(2B −2 A ) I and if s < , then = ( p − c) H + 1−θ − k2 s > 0, k2 (1−θ) ∂s therefore, the change of total demand is positively correlated with θ , o, and s; the change of total profit is positively correlated with θ and o, and if s is not too high, the change of total profit is positively correlated with s. 2

(ii)

2

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Proposition 2 shows that the ESBO channel attracts customers by offering ultimate experience service, which causes some customers switch over from O channel to ESBO channel to get high experience service and reduce the return loss. Moreover, this new channel generates new demand which was not there before. As a result, the retailer’s total demand increases. Furthermore, when the online return rate and experience service effort reach a certain threshold, the retailer will benefit from providing ESBO channel. In addition, when providing ESBO channel, the higher the online return rate and the shopping costs of O channel, the more the total profit will increase.

5 Conclusions Through analysis in Sect. 3, we find that when the return rate and experience service are not considered, the online retailer can benefit from opening an offline showroom and providing ESBO channel, and the higher the shopping cost of online channel, the experience service and online return rate, the greater the benefit that the online retailer get from opening a showroom. However, when the return rate and experience service are considered, if and only if the online return rate and the experience service effort are high, the online retailer can benefit from opening an offline showroom and providing ESBO channel. In the second case, the increase in total demand of retailers is greater, and the increase in total profit is greater when the cost coefficient of experience service is less than a certain threshold. Since the omnichannel and consumer experience is the norm, and the promotion cost and return cost of online channel are relatively high, brand online retailers can obtain greater profits by opening the low-rent and high-service showroom and providing ESBO omnichannel. From the perspective of the network layout, considering where and how many showrooms to open and analyzing their impacts on online shop is a practical and interesting direction for future research.

References 1. Bell DR, Gallino S, Moreno A (2014) Showrooms and information provision in omni-channel retail. Prod Oper Manag 24(3):359–368 2. Bell DR, Gallino S, Moreno A (2018) Offline showrooms in omnichannel retail: demand and operational benefits. Manage Sci 64(4):1629–1651 3. Bell DR, Gallino S, Moreno A (2014) How to win in an omnichannel world. MIT Sloan Manag Rev 56(1):45–53 4. Gao F, Su X (2017) Online and offline information for omnichannel retailing. Manuf Serv Oper Manag 19(1):84–98 5. Vanasanan V, Huang CH (2018) Effects of service experience on behavioral intentions: serial multiple mediation model. J Hosp Mark Manag 27(8):997–1016 6. Li Y, Xu L, Li D (2013) Examining relationships between the return policy, product quality, and pricing strategy in online direct selling. Int J Prod Econ 144(2):451–460

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7. Guide VDR, Souza GC, Van Wassenhove LN, Blackburn JD (2006) Time value of commercial product returns. Manage Sci 52(8):1200–1214 8. Gao F, Su X (2017) Omnichannel retail operations with buy-online-and-pickup-in-store. Manage Sci 63(8):2478–2492 9. Chiang WYK, Chhajed D, Hess JD (2003) Direct marketing, indirect profits: a strategic analysis of dual-channel supply-chain design. Manage Sci 49(1):1–20

Global Warehouse with Cross-Border Supply Chain Fawzat Alawneh and Guoqing Zhang

Abstract The impact of global suppliers and the effect of the cross-border time on the warehouse are studied. A cross-border dual-channel warehouse model in a dual-channel supply chain context is proposed. In addition to demand and lead time uncertainty, the cross-border time is included as a stochastic parameter. Numerical results and managerial insights are also presented for this problem. Keywords Multichannel warehousing · Cross-border · Supply chain

1 Introduction Efficient and flexible supply chains are a vital survival factor for business success nowadays. The logistics industry must keep up with the efficiency level, visibility, and control over the uncertainty sources in the supply chain, such as demand forecasting or delivery times. Supply chain optimization is a key objective in the new digital era. We live in a very competitive world; manufacturers need to optimize their operations to remain competitive. One key aspect is to have mitigation strategies for many sources of uncertainty in the dynamic world where we are living. Cross-border delay is a key source of uncertainty especially now that more firms extend globally. Uncertainty of the border crossing time impacts the viability of supply chains. Hence, it is of extreme importance to have the correct response to the uncertainty of lead times in global supply chain networks. The cost of uncertainty in border crossings is a major problem for global supply networks. The delay cost might include penalties imposed by buyers, the cost of inventory holding and warehousing, and the cost of buffer times—early arrival at the border crossing in making deliveries which leads to higher fuel consumption, F. Alawneh (B) · G. Zhang Department of Mechanical, Automotive and Materials Engineering, University of Windsor, Windsor, Canada e-mail: [email protected] G. Zhang e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 X. Li and X. Xu (eds.), Proceedings of the Eighth International Forum on Decision Sciences, Uncertainty and Operations Research, https://doi.org/10.1007/978-981-16-1380-7_6

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and more environmental impact as the emissions increase. Buffer time strategy is the most used strategy to overcome the border crossing uncertainty [1, 2]. Some research was conducted to identify the causes behind the border crossing time uncertainty, its impact, and the measurements that should be implemented to minimize its impact [1, 3, 4, 2, 5, 6, 7]. However, none has considered the effect of the cross-border time on a global warehouse in terms of storage capacity and inventory levels. Firms are in urgent need to quantify the cost of the cross-border process. Additionally, there is a need for analytical tools to help optimize the cross-border supply chain considering border crossing time variability and its associated delays. Some researchers have investigated the application of new technologies such as the use of Radio Frequency Identification (RFID) in the cross-border supply chain [8, 9, 10, 11, 12, 13, 14]. However, there is still a gap in literature to address the dual channel warehouse with cross border supply chain. In the global supply chain optimization, it is not only preferred to minimize the total operational cost (including operation, transportation, and holding costs) but also it is necessary to optimize other factors such as border crossing costs, environmental considerations, CO2 emissions, truck idle time, and pollution. Many firms are looking to adopt the concept of green supply chain. Therefore, multi-criteria models should be developed, and appropriate solution approaches should be utilized including the environmental cost. This research makes contributions in the following three aspects. First, we analyzed the structure of the cross-border dual-channel global supply distribution centers, taking into account the border crossing lead times and the development of an inventory policy for the distribution center. Second, we developed a mathematical model that jointly determines multi-item products order quantities of the cross-border distribution center thereby minimizing the total expected cost taking into account the border crossing uncertainty, stochastic lead times and the uncertain demands. Third, we provided a closed-form solution for the normal distribution demand. Our proposed model is also an effective performance evaluation tool for any cross-border warehouse system. Finally, this model evaluates the impact of cross-border delays, and assists in the decision-making process as it is a very effective tool that converts the delays’ impacts into cost impacts as necessary. The paper is structured as follows: In Sect. 2, the mathematical model is defined. Numerical examples and results are provided in Sect. 3. Finally, conclusions are presented in Sect. 4.

2 Mathematical Model The problem can be described as follows: To design a cross-border global dual channel warehouse including the cross-border transportation system and lead time uncertainty as shown in Fig. 1. In addition to the notations and assumptions presented in [15], we have the new notations related to the cross border crossing time. The cross-border cost is made up of the following components [1]:

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Fig. 1 The cross-border supply chain

ci : Compliance cost which is the cost of membership in a trusted shipper program defined on a per shipment basis for item i. αi : Mean delay cost which is the average cost of a truck driver’s time, wasted fuel and idled time at the border crossing for item i. In determining the trusted shipper program cost, we did include the environmental impact as well when crossing the border. Logically if the shipper is a member of trusted shipper program, the idle time and therefore CO2 emissions will be decreased. The total cost is: C (Qi , Ri ) be the total expected cost per year, then the total expected cost is formulated as follows in terms of the decision variables Qi , Ri . C(Qi2 , Ri2 , Qi1 , Ri1 ) =

 [Ai2 + ci + αi ]Di2 Qi2 i

  Ai1 Di1   Qi2    + Ri2 − μxi2 + + hi2 Qi1 2 i i    Qi1    + Ri1 − μxi1 hi1 + 2 i ⎡∞ ⎤  bi2 Di2  ⎣ (xi2 − Ri2 ) f (xi2 )dxi2 ⎦ + Qi2 i Ri2 ⎡∞ ⎤  bi1 Di1  ⎣ (xi1 − Ri1 ) f (xi1 )dxi1 ⎦ + (1) Qi1 i Ri1

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Subject to P

 





γi2 (Q i2 + Ri2 − xi2 ) + γi1 (Q i1 + Ri1 − xi1 ) ≤ S ≥ β

(2)

i

The first term of the objective function (1) refers to the annual ordering cost, which is basically the order cost multiplied by the number of cycles, the ordering cost includes the membership cost in the trusted shipper program defined on per shipment basis, and the mean delay cost which is composed of the truck driver’s time, wasted fuel and idled capital in queues at the border crossing. The second term refers to the annual holding cost, which is equal to the holding cost multiplied by the average cycle inventory plus the safety inventory. The integration limits of the safety inventory to infinity represent a good approximation of the safety inventory as they will end up equivalent to the reorder point R minus the mean of the demand during the lead time. The third term represents approximated annual backorder costs, which are equal to the backorder costs multiplied by the expected number of shortages per cycle. Additionally, we considered the warehouse capacity constraint. Due to uncertain demand, we set the probability that the total simultaneous items inventory within the warehouse space will not be smaller than β.

3 Numerical Examples and Results In this section, we will present a numerical example to demonstrate the effectiveness of the proposed model and some parameters discussion. The solution methodology used in this paper was presented in [15]. Consider as an example a single item in the cross-border warehouse inventory system where the demand is normally distributed. The goal is to find the reorder points for cross-border warehouse taking into account the cross-border crossing time. We analyzed the case of six different scenarios for Fast and Secure Trade (FAST) and NON-FAST suppliers within North America. The input data used in this article is available upon request.

3.1 Insights About Safety Stock for All Scenarios Figure 2 shows the safety stock level for each scenario. Note that the safety stock level dropped dramatically from 1618 units to just 38 units. This huge variability in the safety stock level is mainly due to extreme variability in the border crossing processes times.

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Fig. 2 Safety stock level per scenario

3.2 Insights About FAST Program Cost Figure 3 shows the total cost of the cross-border dual channel warehouse systems for a FAST company with different membership and delay costs. As we can see, we solved the proposed model for incremental membership and delay cost, we changed the membership cost from $3.8 to $85 and the delay cost from $1.2 to $35 and the system is still cost effective with a total cost of $16,508 compared to NON FAST case (scenario 3) which has a total cost of $17,236.

Fig. 3 Total cost versus compliance and delay costs

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4 Conclusion In this paper, a cross-border inventory control model is proposed to determine the ordering quantity and the safety stock minimizing the ordering costs, holding costs, backorder costs, and cross-border costs. In our proposed model, the uncertainty in demand and the replenishment lead-time are considered using normal probability distribution. Moreover, a closed-form solution has been developed to solve the model. Numerical results have shown the effectiveness of the proposed model in determining the order quantity for the cross-border warehouse system. Numerical examples are used to demonstrate the method of using the proposed model to evaluate the performance of the cross-border warehouse systems. Analysis is also conducted to highlight the impact of uncertainty of demand and lead-time where the cost of the system increased significantly. We compare the performance of the cross-border warehouse system in six different scenarios and whether or not the company is a FAST or NON-FAST participant. Adopting the proposed inventory policy in the cross-border warehouse systems, we demonstrate that participation in the FAST program will add supply chain flexibility and can lead to overall reduction in the ordering costs, inventory holding costs, backorder costs, and cross-border costs. Acknowledgements This research is supported by Natural Sciences and Engineering Research Council of Canada discovery grant (Grant No. RGPIN-2014-03594).

References 1. Anderson B, Coates A (2010) Delays and uncertainty in freight movement at US–Canada border crossings. In: 45th annual conference of the Canadian transportation research forum, Toronto 2. Goodchild A, Globerman S, Albrecht S (2007) Service time variability at the Blaine, Washington. In: International border crossing and the impact on regional supply chains. Border Policy Research Institute, Western Washington University, pp 1–50 3. Cedillo-Campos MG, Sánchez-Ramírez C, Vadali S, Villa JC, Menezes MBC (2014) Supply chain dynamics and the cross-border effect: the U.S.–Mexican border’s case. Comput Ind Eng 72:261–273 4. Chung W, Talluri S, Kovács G (2018) Investigating the effects of lead-time uncertainties and safety stocks on logistical performance in a border-crossing JIT supply chain. Comput Ind Eng 118(118):440–450 5. Hedaoo SM (2015) Mathematical modelling and a meta-heuristic for cross border supply chain network of re-configurable facilities. UWindsor electronic theses and dissertations. Paper 5640 6. Lee C, Lim A (2014) The solution and impact of RFID enabled fast border crossing procedure. Working report. Department of Industrial Engineering and Logistics Management, Hong Kong University of Science and Technology 7. Sardar S, Lee Y (2015) Modeling the impact of border crossing bottlenecks on supply chain disruption risk. Int J Eng Technol 7(2):692–707 8. Chen JC, Cheng CH, Huang PB (2013) Supply chain management with lean production and RFID application: a case study. Expert Syst Appl 40(9):3389–3397

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9. Daduna JR (2012) Applying radio frequency identification technology in retail trade from a logistics point of view—an overview over opportunities and limitations. In: International conference on computational logistics, vol 7555, Shanghai, pp 104–119 10. Hardgrave BC, Aloysius JA, Goyal S (2013) RFID-enabled visibility and retail inventory record inaccuracy: experiments in the field. Prod Oper Manag 22(4):843–856 11. Laosirihongthong T, Punnakitikashem P, Adebanjo D (2013) Improving supply chain operations by adopting RFID technology: evaluation and comparison of enabling factors. Prod Plan Control 24(1):90–109 12. Peru C (2008) Tracking cross-border shipments. Working report 2008/SOM3/SCCP/005. AsiaPacific Economic Cooperation 13. Sarac A, Absi N, Dauzere-Peres S (2010) A literature review on the impact of RFID technologies on supply chain management. Int J Prod Econ 128(1):77–95 14. Zhu X, Mukhopadhyay SK, Kurata H (2012) A review of RFID technology and its managerial applications in different industries. J Eng Technol Manag 29(1):152–167 15. Alawneh F, Zhang G (2018) Dual-channel warehouse and inventory management with stochastic demand. Transp Res Part E 112:84–106

The Preference of VMI Contract on Traditional RMI System in an Optimal Healthcare Supply Network: A Comparative Study Mohammed Almanaseer and Guoqing Zhang

Abstract This research studies the performance and outcomes of the Vendor Managed Inventory (VMI) system with comparison to the traditional Retailer Managed Inventory (RMI) system. In the proposed model of the VMI system, we integrate the role of the VMI system in the location-inventory assignment problem with the replenishment policy assignment for a chain of hospitals. The healthcare supply chain network under a study composed of a single vendor provides multiple commodities of medical implants to multiple hospitals with a VMI system under a deterministic demand environment. The vendor needs to determine the location of VMI, the number of products, and the number of orders in a way to have a minimal total cost of transportation, inventory, and other associated operating costs. In this study, we compare the total cost of the VMI system with traditional RMI systems for a chain of hospitals. We consider the total cost for each system as a tool of performance measure between VMI and RMI systems. In each case, we developed a mathematical model as a mixed integer non-linear programming for the VMI and RMI with continuous review policies. The two models were solved using GAMS, and the computational results and sensitivity analysis are provided. The results for this case study show that the application of the VMI system is more justified and works better in terms of the cost, same being lower than the cost of traditional RMI systems. Keywords VMI · Healthcare system · SC network · Location-inventory assignment problem · RMI

1 Introduction Total global spending on healthcare has increased by an average of four percent per year from 2000 to 2009; out of that increase, hospitals account for 29% [11]. Inventory logistics costs are the second-highest cost segment in hospitals [8]. Researchers have M. Almanaseer · G. Zhang (B) Department of Mechanical, Automotive and Materials Engineering, Supply Chain and Logistics Optimization Research Centre, University of Windsor, Windsor, ON, Canada e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 X. Li and X. Xu (eds.), Proceedings of the Eighth International Forum on Decision Sciences, Uncertainty and Operations Research, https://doi.org/10.1007/978-981-16-1380-7_7

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recognized that location-inventory assignment problems in supply chain networks have been recognized in the last twenty years as an essential decision tool to minimize health care expenditures [2, 10]. Vendor Managed Inventory (VMI) system is an emerging replenishment solution through which the vendor monitors and decides the time and the quantity of the inventory replenishment of its customers subject to their demand information exchange. The vendor and hospitals are motivated to adopt VMI contracts; the vendor can manage its long-term inventory and production plan by having full access to the hospitals’ demand information, and the hospitals can eliminate the stress of managing the inventory to fulfill all of their demand regardless of the size of the demand and the location of the hospital. Practically, many retailers such as Walmart, Kmart, and Proctor & Gamble have adopted VMI contracts in their inventory operations [4, 12]. In this paper, we study a real-world problem arising from the world’s leading medical implants supply company, applied to a chain of hospitals in the province of Ontario. This healthcare network is composed of a single vendor and multiple hospitals (the chain of hospitals under study consists of 147 hospitals located in Ontario, Canada). We present in this research a comparative study to compare a VMI contract in a location-inventory assignment problem and integrate it with the replenishment policy assignment in which we assign the VMI policy to the hospitals with assigned storage facilities and assign the direct delivery policy to the hospitals with no assigned storage facilities. Our analytical and numerical results in this case study provide insight into the choice of supply chain arrangements to improve the vendor supply chain performance. More specifically, we find that using our suggested VMI model based on the integration of the location-inventory assignment problem can motivate both the vendor and hospitals to adopt it; in this way, the vendor will gain a significant cost saving, and the hospital will benefit by eliminating the inventory operation stress and having better pricing for the medical implants thanks to the cost savings of the vendor. This paper is organized as follows: a review of the relevant literature is presented in the next section, followed by a presentation of the proposed models for both the traditional inventory system represented by RMI and the VMI system in Sect. 3. Computational results have been carried out to analyze and illustrate the findings, benefits, and sensitivity analysis in Sect. 4. The last section contains concluding remarks and some avenues for future research.

2 Literature Review Mathematical modelling is used to identify and compare the benefits of VMI and a traditional inventory system. Hong et al. [4] studied the two-echelon distribution work composed of multiple vendors and retailers in traditional and VMI systems, and the results illustrate that VMI total system cost is lower than a traditional RMI system. Yu et al. [14] have presented an Economic Order Quantity (EOQ) based model to

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analyze how much VMI is better than RMI in a global environment. They concluded that VMI does not perform better than RMI all the time when VMI cannot reduce the ordering, delivery, and holding cost. In our research, we implemented our VMI integrated model based on the location-inventory assignment problem and concluded that the numerical results in our case study show that the new VMI integrated model performs better than the traditional RMI system. It is worth applying the VMI strategy thanks to the high cost savings obtained, in which such savings will reflect directly on the pricing of the medical implants that the vendor will supply to the hospitals. Such findings are limited to our case study in the healthcare sector, and further research is required to validate such findings in other industries. Liao et al. [6] studied an integrated location-inventory distribution network problem by integrating the impact of the facility location, distribution, and inventory issues under a VMI contract on the inventory decisions; they present a multiobjective location-inventory problem model. They tested the multi-objective evolutionary algorithm to solve the model, using a multi-objective approach to present the location assignment as a strategic decision and inventory management assignment as operational decisions with VMI policy as a single replenishment policy. The VMI system has been studied by scholars in deterministic demand environments. Kannan et al. [5] used a real-world case study from the pharmaceutical industry to analyze the outcomes of using VMI; two cases were under study: one using the traditional RMI system and the other one using VMI policy. Razmi et al. [7] developed a model for a VMI system to analyze the performance of VMI when customer demand is normally distributed. Yao et al. [13] developed a mathematical model to investigate the importance of the VMI system on supply chain parameters related to cost savings in a deterministic environment. Darwish and Odah [1] analyzed a VMI case of a single vendor and multiple retailers and formulated a mathematical model to optimize the total cost to be minimal under a deterministic demand environment. Sadeghi et al. [9] extended the problem conducted by [1] to include multiple vendors and multiple retailers with a single and limited-space storage facility; they optimized the number of orders between the vendors and retailers using genetic algorithms and particle swarm optimization algorithms. Gümü¸s et al. [3] developed a mathematical model with deterministic demand to study the benefits of joining VMI and consignment inventory (CI) systems. Based on the findings reached in the literature, the major aims of this paper are to present a comparative study and quantitative analysis, enabling us to quantify the impact of the VMI contract on the performance of the location-inventory assignment problem.

3 Mathematical Model The objective of this paper is to project the possible total cost differences between traditional RMI and VMI systems numerically by developing mathematical models for the systems under study.

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In this research, we assumed that all demands of the hospitals are deterministic, and the demand should be fulfilled regardless of its size or location; in addition, VMI with the continuous policy will be assigned at the hospitals with assigned storage facilities, and the direct delivery policy will be assigned at the hospitals with no storage facilities.

3.1 Mathematical Modelling Indices i j

An index for hospital; i = 1, 2 …, r An index for product; j = 1, 2 …, n.

Notations hi j Ri Fi FCi C A PT r uck Di j fi M Ki gi j Ai ci j 3.1.1

Holding cost for hospital i for product j Rental space cost per f 3 per time unit The total space size of the assigned warehouse at the hospital i per time unit ( f 3 ) Fixed setup cost for having a warehouse at the hospital i The capacity of truck used for delivery to hospital i Demand rate of hospital i of product j per time unit The volume of one item of the product j ( f 3 ) The capability of m i having a maximum integer number Unit truck shipment and ordering cost for hospital i Minimal Safety Stock level for product j at the hospital i with assigned warehouse Ordering costs for hospital i Transportation cost per item to hospital i for product j.

Traditional RMI System with a Continuous Review Policy

Decision variables Qi j mi

Order size quantity of product j delivered to hospital i with the assigned warehouse The number of shipments to hospital i with the assigned warehouse.

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Objective function Min T C V M I =

r 

Ki mi +

i=1

r  n 

 hi j

i=1 j=1

Qi j + SSi j 2

 +

r 

(FCi + Fi Ri )

i=1

s.t.

(1) n 

Q i j ≤ Captr uck

f or ∀ i = 1, . . . , r

(2)

j=1 n  (Q i j + SSi j ) f j ≤ Fi

f or ∀ i = 1, . . . , r

(3)

j=1

SSi j = gi j Q i j m i ≥ Di j Qi j , mi ≥ 0

f or ∀ i = 1, . . . , r and j = 1, . . . , n

(4)

f or ∀ i = 1, . . . , r and ∀ j = 1, . . . , n

(5)

f or ∀ i = 1, . . . , r and j = 1, . . . , n

(6)

The objective function (1) minimizes the total cost by minimizing the following terms: the cost of the combined ordering and trucking transportation cost for all orders delivered to all hospitals with assigned warehouses, and the holding cost per item for the summation of average order quantity and the safety stock level for all products at the hospitals with assigned warehouses and the total costs of the fixed cost of setting up the warehouse at hospitals and their rental rate costs. Constraints (2) represent truck space constraint, constraints (3) represent the warehouse space constraint, constraints (4) compute the lower bound safety stock level constraint, constraints (5) represent the demand satisfaction constraint, and constraints (6) represent the non-negativity of the decision variables.

3.1.2

VMI System with a Continuous Review Policy

Decision variables Ywi Qi j mi SSi j

1 if hospital i has a storage facility with VMI policy, 0 otherwise (the hospital i has no storage facility and with direct delivery policy) Order size quantity of product j delivered to hospital i with the assigned warehouse The number of shipments to hospital i with the assigned warehouse Safety Stock level at the hospital i with assigned warehouse for product j.

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Objective function Min T C V M I =

r  r n   (Ai + ci j )Di j (1 − Ywi ) + Ki mi i=1 j=1

+

r  n 



hi j

i=1 j=1

Qi j + SSi j 2

i=1



r 

+

(FCi + Fi Ri )Ywi

i=1

s.t.

(7) n 

Q i j ≤ Captr uck

f or ∀ i = 1, . . . , r

(8)

j=1 n  (Q i j + SSi j ) f j ≤ Ywi Fi

f or ∀ i = 1, . . . , r

(9)

j=1

SSi j = Ywi gi j

f or ∀ i = 1, . . . , r and j = 1, . . . , n

m i ≤ Ywi M Q i j m i ≥ Di j Ywi Q i j , m i , SSi j ≥ 0

f or ∀ i = 1, . . . , r

(10) (11)

f or ∀ i = 1, . . . , r and ∀ j = 1, . . . , n

(12)

f or ∀ i = 1, . . . , r and j = 1, . . . , n

(13)

Ywi ∈ [0, 1]

(14)

The object function (7) is to minimize the total cost. The first term represents the sum of ordering and courier delivery costs per item for all products’ demands and all hospitals with no assigned warehouses. The second term represents the cost of the combined ordering and trucking transportation cost for all orders delivered to all hospitals with assigned warehouses. The third term represents the holding cost per item of the summation of average order quantity and the safety stock level for all products at the hospitals with assigned warehouses. The fourth term represents the sum of the totals costs of the fixed cost of setting up the warehouse at hospitals and their rental rate costs. Constraints (8) represent truck space constraint, constraints (9) represent the warehouse space constraint, constraints (10) compute the lower bound safety stock level constraint, constraints (11) represent the upper bound of the number of orders constraint, constraints (12) represent demand satisfaction constraint, constraints (13) represent the non-negativity of the decision variables, and constraint (14) represents the binary decision variable.

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4 Analysis and Discussion In this computational study, we use two mathematical models with deterministic demand and with continuous review policy. First, we formulate the location-inventory assignment problem with the VMI system as a Mixed Integer Non-Linear Programming (MINLP); we integrate the assignment of the storage facility at the hospital with the replenishment policy assignment in which the VMI policy is assigned for the hospitals with assigned warehouses and direct delivery policy for the hospitals with no assigned warehouses. In the second model, we formulate the traditional RMI system as MINLP, in which all hospitals have storage facilities. Both problems are solved using GAMS. Our numerical results for this real-world problem show that the total cost for the VMI system is lower than the traditional RMI system for continuous review policy. The total cost of the traditional RMI is always higher than the integrated VMI system in which the vendor can benefit by integrating to optimize the order quantity and number of orders so as to reduce the ordering cost and by utilizing the space of the storage facilities at the hospitals. Hence, the vendor would prefer the VMI system to the traditional RMI system. Table 1 summarizes the values of the total costs for VMI and traditional RMI systems and the percentage of cost increase for traditional RMI systems compared to the VMI system. Also, we studied the sensitivity of some parameters including the holding, ordering, trucking transportation and set up costs and their impact on the total costs of the VMI and traditional RMI systems. We fix i = 1, 2 so as to have a small size sample of a network of two hospitals to test if the VMI system performs better than the traditional RMI system and to test the impact of the parameters’ cost variations on the total cost of VMI and traditional RMI systems. Figures 1, 2, 3 and 4 monitor the influence of parameters’ cost variations in the total cost in VMI and traditional RMI systems and show that regardless of parameter cost variations at hospitals, the proposed VMI system in this real-world problem performs better than the traditional RMI system. Table 1 Computational results of the total costs for VMI and traditional RMI systems

Activity

VMI system

RMI system

Total cost ($)

1054.884

1,243,543

Cost increase (%)

0

15.10

# of hospitals with VMI

77

147

# of hospitals with DDP

70

0

Absolute gap

0

1.00E−09

Relative gap

0.1

0.1

GAMS solver

COUENNE

BARON

CPU time (s)

9513.094

6833.35

66 Fig. 1 The impact of holding cost variation on TC

Fig. 2 The impact of ordering cost variation on TC

Fig. 3 The impact of transportation cost variation on TC

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Fig. 4 The impact of setup cost variation on TC

5 Conclusions In this paper, we investigate two different types of inventory systems in a chain of hospitals: the VMI system based on integrating the location assignment of the storage facility with the allocation assignment of inventory replenishment policy, and the traditional RMI system with continuous review policy and storage facilities at all hospitals. We also implemented a numerical study based on a real-world problem. In addition, we examined several cost variations on the total costs of the systems related to holding, ordering, trucking transportation, and setup costs. In our research, we used a mixed integer non-linear mathematical programming (MINLP) to present the VMI system and traditional RMI system with continuous review policy. The aim of modelling those mathematical models in VMI and traditional RMI systems is to present the performance and outcomes of these systems by comparing total costs of the systems and analyzing the impact of the cost variations of the parameters on the total cost. The numerical results in our case study show that the vendor’s optimal total cost is obtained in the case of implementing the VMI system, and the traditional RMI system has a higher total cost than the integrated VMI system. Hence, we could expect that the vendor would prefer the VMI system to the traditional RMI systems given the high level of cost saving that will reflect on the pricing of the medical implants offered to the hospitals. More work is needed to analytically compare our proposed VMI model with other inventory systems and for other industries. Acknowledgements This research is supported by Natural Sciences and Engineering Research Council of Canada discovery grant (Grant No. RGPIN-2014-03594, RGPIN-2019-07115).

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