Optimizing Multi-Item Inventory Management Decisions in Healthcare FacilitiesEsmaili, Nazanin (2017) Optimizing Multi-Item Inventory Management Decisions in Healthcare Facilities. Doctoral Dissertation, University of Pittsburgh. (Unpublished) This is the latest version of this item.
AbstractHealthcare costs in the United States continue to grow at a significant rate. In many healthcare settings material supply and inventory management represent significant areas of opportunity for managing healthcare costs more effectively. In this dissertation, we explore three topics related to these areas. In the first chapter, we propose methodologies to help clinicians store medications and medical supplies optimally in space-constrained, decentralized Automated Dispensing Cabinets (ADCs) located on hospital patient floors. This is significant for many reasons: first, locating and storing medical supplies and pharmaceutical products within automated dispensing devices on patient floors is often not done efficiently and these devices are not utilized optimally. The primary purpose of an ADC is to ensure ready access of pharmaceuticals and medical supplies at floor locations within a hospital. However, the allocation of the limited space within an ADC to these items is typically not planned systematically and this often results in wasted staff effort as clinical personnel must expend effort in locating and retrieving them from a hospital's central pharmacy/storage location. A second major issue in using these devices is human error associated with the selection of pharmaceuticals from floor storage. In the next chapter, we investigate a class of inventory control systems which are used in inventory management systems at points of use (POUs) in hospitals. This class of inventory control systems is characterized by stochastic demand, periodic reviews with fractional (or very small) lead time, expedited delivery when stockouts occur, limited storage capacity, and service level requirements. We develop discrete time Markov chain models of different inventory control systems that deal with all of these characteristics while minimizing the total expected replenishment effort at POUs. We have derived closed form solutions and propose an exact algorithm to calculate the limiting probability distribution by locally decomposing the state space. We investigate the structural results and based on our approach we propose an algorithm that is much easier to use in practical applications compared to solving the steady state equations in Markov models, and the computational effort required for finding the replenishment policy parameters is reduced. In the final chapter, we address the management of inventory for multiple non-perishable medical supplies in floor storage by selecting the optimal inventory policy for each item along with its corresponding operating parameters. In practice, hospitals tend to assign the same overall inventory control policy to all or the majority of the items. This simplistic approach often leads to wasted staff effort and ineffective policies. The objective of our research is to minimize the average labor effort required to count and replenish all of the items, while providing an acceptably high level of service (avoiding stock outs) and taking into account constraints on available space. We consider four policies: PAR, $(R,s,S)$, $(R,s,Q)$, and a two-bin Kanban system. We illustrate the model with actual data from a healthcare setting and propose some practical insights and guidelines on how to choose a hybrid inventory system based on demand and system characteristics. Share
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