Abstract
Purpose: This work aims at providing insights to optimise healthcare logistic of the drug management, in order to deal with the healthcare expenditure cut. In this paper the effects of different drug supply chain configurations, on the resulting average stock, service level and Bullwhip effect, of the studied supply chain, is quantitatively assessed. Design/methodology/approach: A case study of an Italian district has been studied, taking into account three echelons: suppliers, central stock, and hospitals. A model of the various supply chain configurations has been created with the use of the simulation. Specifically, 24 supply chain configurations have been examined, stemming from the combination of several supply chain design parameters, namely: transshipment policies (Emergency Lateral Transshipment or Total Inventory Equalization); re-order and inventory management policies (Economic Order Quantity or Economic Order Interval); required service levels (90% or 95%); the number of available vans (one or two). For each configuration, hospital average stock, service level and a “Bullwhip effect” analysis are computed. To know which input variables are statistically significant, a DoE (Design of Experiments) analysis has been executed. Findings: The output of this paper provides useful insights and suggestions to optimize the healthcare logistic and drug supply chain. According to the developed DoE analysis, it can be stated that the introduction of transshipment policies provides important improvement in terms of service and stock levels. To reduce the Bullwhip effect, which results in a service level decreasing, and in a managing stock costs increasing, it is worth to adopt an EOQ re-order policy. Practical implications: This research gives practical recommendations to the studied system, in order to reduce costs and maintain a very satisfactory service level. Originality/value: This paper fulfils an identified need to study which combination of transshipment policies, re-order/inventory management policies and required service levels, can be the best one to reduce costs and maintain a very satisfactory service level, in the specific logistic system.
Highlights
Drug management represents a large portion of the costs in the healthcare system, due to the significant costs of these products and their storage and control requirements
According to the developed Design of Experiment (DoE) analysis, it can be stated that the introduction of transshipment policies provides important improvement in terms of service and stock levels
Despite well-documented evidence of significant competitive advantage and cost reduction resulting from supply chain management (SCM) practices, the healthcare industry has been extremely slow to embrace these practices (Balaji, Lewis & Rai, 2010; Lee, Lee & Schniederjans, 2011); the challenges are many: i. products and medical devices used procedures can be extremely expensive ii. demand in terms of types and amount of product required for procedures can be highly unpredictable due to the diversity in patient characteristics iii. inventory tracking can be difficult due to the urgency of medical procedures iv. product expiration and tracking issues caused by a lack of accountability for products managed under a consignment process (Balaji et al, 2010)
Summary
Drug management represents a large portion of the costs in the healthcare system, due to the significant costs of these products and their storage and control requirements. According to Sinha and Kohnke (2009) there is a gap between the growing demand and available supply of high-quality, cost-effective, and timely health care, in developing and underdeveloped countries and in developed countries. The significance of this problem is heightened when the economy is in recession. Most healthcare organizations focus their attention on reducing the cost of their supply chain management (SCM) by improving the decision making pertaining processes’ efficiencies (AbuKhousa, Al-Jaroodi, Lazarova-Molnar & Mohamed, 2014). Simulation and modeling (SM) has been presented as an alternative approach for supply chain managers in healthcare organizations to test solutions and to support decision making processes associated with various SCM problems
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