Supplier selection under disaster uncertainty with joint procurement - Brown, Camille D. (Kansas State University, 2020)
Health care organizations must have enough supplies and equipment on hand to adequately respond to events such as terrorist attacks, infectious disease outbreaks, and natural disasters. This is achieved through a robust supply chain system. Nationwide, states are assessing their current supply chains to identify gaps that may present issues during disaster preparedness and response. During an assessment of the Kansas health care supply chain, a number of vulnerabilities were identified, one of which being supplier consolidation. Through mergers and acquisitions, the number of suppliers within the health care field has been decreasing over the years. This can pose problems during disaster response when there is a surge in demand and multiple organizations are relying on the same suppliers to provide equipment and supplies. This thesis explores the potential for joint procurement agreements to encourage supplier diversity by splitting purchasing among multiple suppliers. In joint procurement, two or more customers combine their purchases into one large order so that they can receive quantity discounts from a supplier. This research makes three important contributions to supplier selection under disaster uncertainty. The first of these is the development of a scenario-based supplier selection model under uncertainty with joint procurement. This optimization model can be used to observe customer purchasing decisions in various scenarios while considering the probability of disaster occurrence. Second, the model is applied to a set of experiments to analyze the results when supplier diversity is increased and when joint procurement is introduced. This leads to the third and final contribution: a set of recommendations for health care organization decision makers regarding ways to increase supplier diversity and decrease the risk of disruption associated with disaster occurrence.
Keywords: Health care preparedness and response; Descriptive analytics; Supplier selection