US-Austria Doctoral Dissertation Enhancement Project: Improving the Pharmaceutical Supply Chain Through Order-Fulfillment Technology

美国-奥地利博士论文强化项目：通过订单履行技术改善药品供应链

• 批准号: 1037211
• 负责人: Russell Meller
• 金额: $ 0.85万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1037211&HistoricalAwards=false
• 关键词: US; Austria; Doctoral; Dissertation; Enhancement

The pharmaceutical supply chain is critical to the quality of our healthcare system and its performance depends on the correct application of order-fulfillment technology. Industrial Engineering Ph.D. candidate Ms. Jennifer A. Pazour, in conjunction with her advisor Dr. Russell D. Meller, is conducting research that develops analytical models to analyze alternative piece-level order-fulfillment technologies that are prevalent in pharmaceutical distribution. This doctoral dissertation enhancement project is focused on developing improved planning tools for the selection of order-fulfillment technology. The research will be conducted in collaboration with the Technical University of Graz's Institute of Logistics Engineering and SSI Schaefer, a leading order-fulfillment technology provider. Both organizations are located in Graz, Austria. An optimization modeling approach will be developed and incorporated into a decision support tool that integrates Ms. Pazour's current dissertation research with her collaborators' mechanical understanding of the technologies. Not only will the research improve on the selection of current order-fulfillment technology, it also has the potential to identify gaps in the market where new order-fulfillment technology could be developed.

药品供应链对我们医疗保健系统的质量至关重要，其绩效取决于订单履行技术的正确应用。 工业工程博士候选人Jennifer A.帕佐尔和她的顾问罗素·D. Meller正在进行研究，开发分析模型，以分析在药品分销中普遍存在的替代件级订单履行技术。 这个博士论文增强项目的重点是开发改进的计划工具，选择订单履行技术。 该研究将与格拉兹技术大学物流工程学院和领先的订单履行技术提供商SSI Schaefer合作进行。 这两个组织都位于奥地利格拉兹。 将开发一种优化建模方法，并将其纳入决策支持工具，该工具将Pazour女士目前的论文研究与她的合作者对技术的机械理解相结合。 该研究不仅将改进当前订单履行技术的选择，还可能发现市场中可以开发新订单履行技术的差距。