A stochastic programming model with endogenous uncertainty for selecting supplier development programs to proactively mitigate supplier risk

Rui, Zhou; Bhuiyan, Tanveer Hossain; Medal, Hugh R.; Sherwin, Michael D.; Yang, Dong

doi:10.1016/j.omega.2021.102542

Cited by 18 publications

(6 citation statements)

References 48 publications

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“…This linearized reformulation allows the problem to be recast into a mixed-integer program that can be solved exactly. Zhou et al [25] utilized the same approach for a supplier development program selection problem where supplier performance depends on the decision maker's level of development investment in the supplier. The same technique is used in [2] to solve a fuel incentivization reduction program to reduce wildfires where a landowner's acceptance probability depends on the offer amount decided in the first stage.…”

Section: Related Workmentioning

confidence: 99%

A Successive Refinement for Solving Stochastic Programs with Decision-Dependent Random Capacities

Medal,

Affar

2024

Preprint

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We study a class of two-stage stochastic programs in which the second stage includes a set of components with uncertain capacity, and the expression for the distribution function of the uncertain capacity includes first-stage variables. Thus, this class of problems has the characteristics of a stochastic program with decision-dependent uncertainty. A natural way to formulate this class of problems is to enumerate the scenarios and express the probability of each scenario as a product of the first-stage decision variables; unfortunately, this formulation results in an intractable model with a large number of variable products with high-degree. After identifying structural results related to upper and lower bounds and how to improve these bounds, we present a successive refinement algorithm that successively and dynamically tightens these bounds. Implementing the algorithm within a branch-and-cut method, we report the results of computational experiments that indicate that the successive refinement algorithm significantly outperforms a benchmark approach. Specifically, results show that the algorithm finds an optimal solution before the refined state space become too large. Mathematics Subject Classification (2020) 90-08 · 90-10

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Section: Related Workmentioning

confidence: 99%

A Successive Refinement for Solving Stochastic Programs with Decision-Dependent Random Capacities

Medal,

Affar

2024

Preprint

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“…These programs usually include training, consulting, and joint project teams (Saghiri & Wilding, 2021). These trainings aim to define customer expectations in terms of determining the possibilities of reducing risk and improving the efficiency and effectiveness of processes (Jafarian, Lotfi, & Pishvaee, 2021;Zhou, Bhuiyan, Medal, Sherwin, & Yang, 2022).…”

Section: Expectations Of Manufacturing Companiesmentioning

confidence: 99%

Expectations of manufacturing companies towards suppliers in terms of implementing improvement activities

Urbaniak,

Zimon,

Madzik

2024

CEMJ

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PurposeThis article aims to map the expectations of manufacturing companies towards suppliers in terms of implementing improvement activities. The article poses two research questions: RQ1: What kind of improvement of activities do the surveyed producers expect from their suppliers? RQ2: Do factors such as size, capital or implemented systems influence different assessments of the analyzed requirements toward suppliers?Design/methodology/approachThe Computer Assisted Telephone Interview (CATI) technique was used to collect data. The sample consists of 150 producers (employing over 50 people) who were suppliers for enterprises from the automotive, electromechanical and chemical sectors operating in the Polish business-to-business (B2B) market. We analyzed 11 improvement activities, while their correlation structure was examined by exploratory factor analysis.FindingsWe have identified three latent factors – risk reduction, product innovation and increasing efficiency – which summarize the main expectations of manufacturing companies towards suppliers. Expectations for these factors are independent of the implemented management system, although the analysis showed higher expectations for product innovation in organizations with the implementation of Kaizen.Originality/valueThe article fills the research gap in the literature. The research results presented in the literature so far have focused on the expectations of enterprises towards suppliers in terms of meeting the criteria for their initial and periodic assessment. The research gap in the article is the result of empirical research presenting the expectations of manufacturers towards suppliers in terms of improving their processes. Based on the findings of the presented study, development trends and implications for managers responsible for purchasing processes and relationships with suppliers can be determined.

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“…Ahmad et al [28] presented a mixed-integer nonlinear model for the supplier selection problem. Zhou et al [29] introduced a stochastic programming model to develop suppliers' performance and implemented the model on a small manufacturing company. They concluded that the evaluation as well as the correct selection and management of suppliers could greatly reduce the impact and probability of risks related to suppliers.…”

Section: Literature Reviewmentioning

confidence: 99%

Supplier Selection in Downstream Oil and Gas and Petrochemicals with the Fuzzy BWM and Gray COCOSO Methods Considering Sustainability Criteria and Uncertainty Conditions

Parsa Rad,

Khalilzadeh,

Banihashemi

et al. 2024

Sustainability

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A project always needs the participation of suppliers to provide the goods and services needed by the project. As the size of the project increases, the need to use suppliers also increases. Choosing the appropriate suppliers for the project is always a difficult task, and considering different criteria along with the existence of multiple alternatives always increases the difficulty and complexity of the supplier selection problem. This study deals with the supplier selection problem, which is one of the important issues in projects and organizations in the downstream field of oil, gas, and petrochemicals. The purpose of this paper was to investigate and improve the supplier selection procedure in this field by considering real-world uncertain conditions. To this end, the fuzzy set theory and gray numbers were taken into account. In the present study, the criteria were first determined by expert judgment; then, the fuzzy best–worst method (FBWM) was exploited to rank the criteria; finally, the suppliers were prioritized and ranked by using the gray COCOSO method The results showed that the highest and the lowest weights were obtained for the “price” and “attention to environmental issues” criteria, respectively. Also, the selected supplier was a supplier with a high score in all criteria. One of the practical benefits of this research is to provide a solution to speed up, facilitate, and improve the process of selecting suppliers for companies in the downstream field of oil, gas, and petrochemicals.

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A stochastic programming model with endogenous uncertainty for selecting supplier development programs to proactively mitigate supplier risk

Cited by 18 publications

References 48 publications

A Successive Refinement for Solving Stochastic Programs with Decision-Dependent Random Capacities

A Successive Refinement for Solving Stochastic Programs with Decision-Dependent Random Capacities

Expectations of manufacturing companies towards suppliers in terms of implementing improvement activities

Supplier Selection in Downstream Oil and Gas and Petrochemicals with the Fuzzy BWM and Gray COCOSO Methods Considering Sustainability Criteria and Uncertainty Conditions

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