The adoption of new technologies for sustainable risk management in logistics planning: A sequential dynamic approach. (November 2022)

• Record Type: Journal Article
• Authors: Yousefi, Samuel; Tosarkani, Babak Mohamadpour
• Is Part Of: Computers & industrial engineering. Volume 173(2022)
• Publication Date: 2022-11
• Subjects: Risk assessment -- Sustainable risk mitigation -- Logistics planning -- Bayesian belief network -- Dempster-Shafer evidence theory -- Z-number theory; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108627
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Investigating the impact of the adoption of new technologies in risk mitigation. Defining new technology-based mitigation measures to improve logistics planning. Developing a sequential dynamic model to assess the criticality of risks. Modeling causal relationships between logistics risks using Bayesian belief network. Calculating basic probability assignments using Z-Dempster-Shafer evidence theory. Abstract: Nowadays, organizations seek to effectively manage their logistics processes to increase customer satisfaction and achieve a competitive advantage. To improve the performance and sustainability of logistics processes, managers should analyze the risks in such processes and implement risk mitigation measures to reduce their negative impact on system integrity. The adoption of new technologies in risk mitigation planning can help managers improve the performance of logistics processes. This study proposes a sequential dynamic approach for analyzing the risks and assessing the effectiveness of risk mitigation measures in an uncertain environment. The causal relationships between risks of logistics processes are extracted after the risk identification based on the process-oriented perspective. Afterward, a multi-expertise team employs the Z-number theory-based linguistic variables to determine the value of triple risk parameters for each risk (i.e., probabilities of destructiveness, occurrence, and undetectability). In this phase, the multi-expertise team's opinions are aggregated using an extended version of Dempster-Shafer's evidence theory in the Z-number environment to consider uncertainty and reliability simultaneously. Then, the sequential dynamic model of the identified risks is developed using the Bayesian belief network. This model enables decision-makers to identify the critical risks in each logistics sub-process and assess risk mitigation measures, defined by focusing on new technologies. The proposed model is used to estimate the effectiveness of risk mitigation measures after determining the impressionable risks in their implementation. The results of this study imply that new technology-based solutions can improve critical sub-processes (i.e., resource planning, procurement, and production planning) significantly.