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|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|1405||2011||8 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 38, Issue 5, May 2011, Pages 4790–4797
This paper establishes a simulation model for the supply chain of the hospital logistic system (SCHLS) based on the dynamic Taguchi method. The model derives optimal factor level combinations in the SCHLS setting when establishing adjustable contracting capacity between SCHLS and the pharmaceutical company. To attempt the goal, this study adopts the SCHLS case in the North Alliance of the Department of Health of Taiwan. The data collection uses a simulation based on Taguchi’s L18 orthogonal table for SCHLS. This work also proposes an optimal approach, including the neural network (NN) and genetic algorithm (GA), to obtain an optimal robust design in achieving optimal SCHLS multi-performance. The research results show that the predicted response of multi-performance in the optimal approach is better than that in Taguchi.
Over the past two decades, various industries have explored the supply chain (SC) to save on total system cost. Mabert and Venkataramanan (1998) defined SC as the “relationship” in buyer–seller activities, including all “upstream” suppliers, every “downstream” customer, and a “value chain” approach. In other words, its process includes all control activities spanning raw materials procurement and production/manufacturing to distribution of end-products to customers. Thus, to decrease SC’s total system cost, the eliminating inaccurate information in the inventory record is the most crucial job. Several bodies of research (Chen et al., 2000, Joshi, 2001 and Simchi-Levi et al., 2000) have shown that the information shared between suppliers’ provision supply and end-customers’ demand may significantly reduce total system cost of the bullwhip effect. Ganeshan, Boone, and Stenger (2001) and Brown, Inman, and Calloway (2001) also pointed out that an inaccurate inventory record significantly impacts SC performance. To understand SC’s dynamic behavior, a widely used approach to study its dynamics has been adopted based on the dynamic systematic simulation methodology. Wang, Pokharel, and Wang (2004) built a mathematical model to simulate manufacturers for the purpose of improving SC’s positioning strategies. Meanwhile, Reddy and Rajendran (2005) considered a simulation study of the dynamic order-up-to policies in an SC through non-stationary customer demand and information sharing. Chan and Chan (2005) defined five common SC models, tested with simulation for comparative evaluation of SC management strategies. Zhang and Zhang (2007) developed a simulation approach that quantifies firms’ business operations and performances in a multi-tier SC. In general, the results show that with high demand variance, low demand correlation, and/or high demand covariance, the supply chain without intermediate tiers performs better than the one with an intermediary. However, most SC’s simulation behavior belongs to the industrial field, while only a few studies are in the hospital research field. They believe that hospitals’ cost reduction and quality control measurement is a competitive strategic issue that influences all facets of the healthcare medical market. Nylander, Suominen, Nenonen, Rintanen, and Pelanteri (2002) analyzed the hospital’s total system cost and showed that improving the hospital SC system’s affecting factors increases overall efficiency and effectiveness. Gilbert (2001) emphasized that establishing an e-health system helps hospitals to decrease procurement cost. However, buyers and suppliers must work together toward standardization, and agree on a universal product numbering system. Essentially, e-health is an information communication tool to assist in decision-making and contains existing register data about suppliers, hospitals, and customers that can be used as a reference (More & McGrath, 2002). Based on the above literature study, this paper sets up a SC simulation model of the hospital logistics system (SCHLS) to obtain optimal factor level combinations in a SCHLS setting when establishing an adjustable contracting capacity between the SCHLS and the pharmaceutical company. The SCHLS’ research case is based on the SCHLS of the North Alliance of the Department of Health of Taiwan. Owing to the SCHLS’ dissimilar situation with the manufacturing industry, most medicines have patent right limitations and market monopolies. Hence, the SCHLS must pay attention to medical capacity when asking pharmaceutical companies to contract a supply quantity to the SCHLS. In turn, the pharmaceutical company must also reserve medical capacity for other hospitals since it is quite impossible to reserve all of it for a certain SCHLS. Otherwise, a medicine shortage may occur and eventually cause a medicine scarcity with other hospital demands, resulting in a very serious problem and threat to patient safety. This paper considers medical capacity as adjustable from the SCHLS contracting with the pharmaceutical company. Moreover, the current study uses the dynamic Taguchi research method to seek optimal factor level combinations in the SCHLS setting for treating adjustable contracting capacity. The next section discusses SCHLS and the relationship of experimental factors within the simulation. Section 3 describes the research methodology. Section 4 analyzes the development of optimal approach and collected data. Lastly, Section 5 presents the summary of findings, followed by a presentation of research limitations and contributions.
نتیجه گیری انگلیسی
This paper uses simulation to construct a dynamic SCHLS model. Simulation models are often used when uncertain supply chain characteristics cannot be figured out easily with analytical models or when stochastic variables have to be incorporated (Riddalls, Bennett, & Tipi, 2000). After collecting data from the simulation, the dynamic Taguchi method obtained the optimal combination setting of the control factor levels based on the optimal approach, NN, and GA, of this paper. Overall, the most significant contribution of this paper is that SCHLS characteristics, the medicine patent, and contracting capacity, are different from the SC of other manufacturers. Hence, the adjustable contracting capacity between the hospital and pharmaceutical company becomes agile when medicine demand fluctuates, or when medical product capacity is limited. Moreover, this “just in time” feature establishes a cooperative relationship for the contracting supplier. On the other hand, the dynamic Taguchi method is applied in this SC issue. However, most researchers only use this method in solving a quality-engineering problem. Meanwhile, the optimal approach, which combines GA and NN, is used to solve the multi-performance problem. This study has its limitations; it was not able to consider the problem of ordering a variety of medicines. Furthermore, this paper only uses estimated data for simulation because most hospital costs are difficult to obtain. In spite of these limitations, this paper was able to present an innovation method to solve the SCHLS problem. Therefore, future studies can extend the simulation situation and pay more attention to several different situations between the SCHLS and other SC manufacturers. Lastly, this paper introduces a different research perspective that future research can use in the hospital SC field.