Improved Acquisition for System Sustainment: Resilient Supplier Evaluation and Selection with Bayesian Network

Abstract

Several criteria, including quality, delivery, performance history, and price, have been used for decades to evaluate and select suppliers of component parts and services in supply chains. Despite the increasing vulnerability of modern supply chains to disruptions, no particular accounting for resilience is offered in the current supplier selection criteria literature. Resilience is often thought of as the ability of a process or system to withstand against and recovery timely from a disruption, and measures of resilience are increasingly seen in the literature across many fields. An ability to withstand and recover from a disruption is important for effective risk management of disruptive events and should be an intrinsic part of supplier selection. The objective of this research is to (i) explore Bayesian networks as a means to quantify resilience from the perspectives of absorptive, adaptive, and restorative capacities, (ii) extend this measure to a means to evaluate supplier resilience, in addition to other important supplier selection characteristics, and (iii) deploy sensitivity analysis to identify supplier characteristics to improve, and by how much, to enhance resilience. This research is important to the study of multi-sourced supplier selection from the perspective of resilience, including choosing resilient suppliers and understanding what supplier characteristics make them more resilient. We account for multiple dimensions of traditional supplier selection, and supplement these traditional measures to include resilience. And the proposed modeling approach, Bayesian networks, offers (i) a new perspective not previously used in supplier selection studies, which (ii) can model the relationships among uncertain variables (e.g., supplier characteristics). The framework conceptualized here could address supplier selection problems in industry as well as government supply chains

Document Details

Document Type
DoD Grant Award
Publication Date
Dec 27, 2016
Source ID
N002441710005

Entities

People

  • Kash Barker

Organizations

  • United States Navy
  • University of Oklahoma

Tags

Readers

  • Industrial Economics
  • Psychological Intervention/Treatment for Stress, Anxiety, PTSD, and Related Emotional and Cognitive Health Symptoms.
  • Psychometric Testing or Psychological Assessment.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy