{"title":"Optimal portfolio choices to split orders during supply disruptions: An application of sport's principle for routine sourcing","authors":"Sidhartha S. Padhi, Soumyatanu Mukherjee","doi":"10.1111/deci.12511","DOIUrl":null,"url":null,"abstract":"<p>Sourcing in the face of supply chain disruptions has been one of the most challenging tasks in supply chain management, particularly when such disruptions occur due to natural calamities, such as flood, fire, and earthquake, affecting both the primary and the backup suppliers. Invariably, such disruptions lead to reduced supply from the primary supplier, encouraging the supplier to place fresh orders with the backup suppliers. In order to mitigate the adverse effect of supply disruption, in this article we use the concepts underlying the well-known Duckworth–Lewis–Stern method, used in cricket, to revise the supply target of the primary supplier and to decide a target for the backup supplier. We simulated the supply disruption scenarios in an experimental setting by conducting a two-round questionnaire survey among 300 purchase managers. The means and variances of the participants’ estimates of probabilities of meeting the revised targets within the scheduled time for various model-generated supply scenarios were used to find the participants’ risk preferences. In the second-round survey, the participants, clustered in groups of 10, ranked their own risk preferences. These ranks were used to find the optimal portfolio choices. Finally, we validated the theoretical predictions for the risk options using two approaches—one, at the group level by estimating the within- and the between-group risk preferences of buyers, and, two, at the aggregate level, by considering all the participants, fitting quantile regression model to the experimental results, and estimating the risk preference structures for different quantiles of the relative risk–return trade-off distributions.</p>","PeriodicalId":48256,"journal":{"name":"DECISION SCIENCES","volume":"53 6","pages":"1068-1087"},"PeriodicalIF":2.8000,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/deci.12511","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"DECISION SCIENCES","FirstCategoryId":"91","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/deci.12511","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 7
Abstract
Sourcing in the face of supply chain disruptions has been one of the most challenging tasks in supply chain management, particularly when such disruptions occur due to natural calamities, such as flood, fire, and earthquake, affecting both the primary and the backup suppliers. Invariably, such disruptions lead to reduced supply from the primary supplier, encouraging the supplier to place fresh orders with the backup suppliers. In order to mitigate the adverse effect of supply disruption, in this article we use the concepts underlying the well-known Duckworth–Lewis–Stern method, used in cricket, to revise the supply target of the primary supplier and to decide a target for the backup supplier. We simulated the supply disruption scenarios in an experimental setting by conducting a two-round questionnaire survey among 300 purchase managers. The means and variances of the participants’ estimates of probabilities of meeting the revised targets within the scheduled time for various model-generated supply scenarios were used to find the participants’ risk preferences. In the second-round survey, the participants, clustered in groups of 10, ranked their own risk preferences. These ranks were used to find the optimal portfolio choices. Finally, we validated the theoretical predictions for the risk options using two approaches—one, at the group level by estimating the within- and the between-group risk preferences of buyers, and, two, at the aggregate level, by considering all the participants, fitting quantile regression model to the experimental results, and estimating the risk preference structures for different quantiles of the relative risk–return trade-off distributions.
期刊介绍:
Decision Sciences, a premier journal of the Decision Sciences Institute, publishes scholarly research about decision making within the boundaries of an organization, as well as decisions involving inter-firm coordination. The journal promotes research advancing decision making at the interfaces of business functions and organizational boundaries. The journal also seeks articles extending established lines of work assuming the results of the research have the potential to substantially impact either decision making theory or industry practice. Ground-breaking research articles that enhance managerial understanding of decision making processes and stimulate further research in multi-disciplinary domains are particularly encouraged.