{"title":"Electric vehicle supply chain investment under demand uncertainty: A jointly held real options perspective","authors":"Feng Liu , Carman K.M. Lee , Min Xu","doi":"10.1016/j.cie.2024.110840","DOIUrl":null,"url":null,"abstract":"<div><div>An increasing number of electric vehicle (EV) companies are facing supply chain investment decisions, which are essential for the effective operation and management of their businesses. This study proposes a real options approach to explore the investment timing threshold of the EV supply chain under demand uncertainty. It addresses the limitations of previous studies that primarily focused on the perspective of a single investor under a deterministic demand. To achieve the objective, an analytical real options game model is first presented for investment in the EV supply chain. Then, the investment timing threshold and option value of the EV supply chain are derived under three different scenarios: the integrated case, the revenue-sharing contract case, and the revenue-sharing contract through bargaining. The findings reveal that the investment timing threshold is lower when bargaining occurs between the two parties in the EV supply chain compared to the revenue-sharing contract case. Furthermore, the investment timing threshold exhibits a negative correlation with the drift and learning rates. It also increases with the volatility of the bargaining parameter, risk-free interest rate, and market demand volatility. The option value, on the other hand, shows a positive correlation with the demand-shift and volatility parameters. A bargaining-based revenue-sharing contract is proposed as a means to coordinate the supply chain. These results provide theoretical guidance for investments in the EV supply chain.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"200 ","pages":"Article 110840"},"PeriodicalIF":6.7000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Industrial Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360835224009628","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
An increasing number of electric vehicle (EV) companies are facing supply chain investment decisions, which are essential for the effective operation and management of their businesses. This study proposes a real options approach to explore the investment timing threshold of the EV supply chain under demand uncertainty. It addresses the limitations of previous studies that primarily focused on the perspective of a single investor under a deterministic demand. To achieve the objective, an analytical real options game model is first presented for investment in the EV supply chain. Then, the investment timing threshold and option value of the EV supply chain are derived under three different scenarios: the integrated case, the revenue-sharing contract case, and the revenue-sharing contract through bargaining. The findings reveal that the investment timing threshold is lower when bargaining occurs between the two parties in the EV supply chain compared to the revenue-sharing contract case. Furthermore, the investment timing threshold exhibits a negative correlation with the drift and learning rates. It also increases with the volatility of the bargaining parameter, risk-free interest rate, and market demand volatility. The option value, on the other hand, shows a positive correlation with the demand-shift and volatility parameters. A bargaining-based revenue-sharing contract is proposed as a means to coordinate the supply chain. These results provide theoretical guidance for investments in the EV supply chain.
期刊介绍:
Computers & Industrial Engineering (CAIE) is dedicated to researchers, educators, and practitioners in industrial engineering and related fields. Pioneering the integration of computers in research, education, and practice, industrial engineering has evolved to make computers and electronic communication integral to its domain. CAIE publishes original contributions focusing on the development of novel computerized methodologies to address industrial engineering problems. It also highlights the applications of these methodologies to issues within the broader industrial engineering and associated communities. The journal actively encourages submissions that push the boundaries of fundamental theories and concepts in industrial engineering techniques.