SGER: Multiscale Methods for Supply Chain Monitoring

这项探索性研究小额赠款（SGER）为探索多尺度方法提供资金，用于监测供应链中产品和交易的及时和正确移动，即从原始订单到交付给客户。这些方法将使用单个产品的状态信息，这些信息可以通过射频识别（RFID）或其他计算机跟踪技术提供。每个数据元素将被视为产品ID、产品的状态/位置以及状态检查时间的三倍。这些三元组随时间、ID和/或状态的聚合提供了在不同尺度上查看数据的方法，从零件在制造工厂中移动的低级细节，到由一组产品组成的订单从订单到交付给客户的高级状态。聚合和分解的数据将使用专门的多元统计过程控制（SPC）方法进行检查。现有的多变量SPC方法将得到增强，以允许捕获来自供应链网络结构的产品运动中的依赖关系，使用聚类和网络优化算法。这些方法将根据宾夕法尼亚州立大学供应链研究中心的企业成员提供的供应链数据进行测试。该中心的企业成员包括戴尔、潘斯克物流、好时食品和强生公司。这项工作的结果将通过中心会议、会议报告以及供应链和信息系统部的课程来传播。如果成功，这项研究的结果将导致更有效的方法来检测复杂供应链中的不当路线和交付延迟。这些方法的多尺度特征将使它们能够用于在战术和战略层面上管理供应链，并提供供应链问题的快速检测和根本原因诊断。多尺度方法将有助于多元SPC的一般知识，一个广泛使用的六西格玛工具。

This Small Grant for Exploratory Research (SGER) provides funding for the exploration of multiscale methods for monitoring the timely and correct movement of products and transactions through supply chains, that is, from their originating order through their delivery to the customer. The methods will use individual product status information, which can be provided by radio-frequency identification (RFID) or other computerized tracking technology. Each data element will be viewed as a triple of the product ID, the status/location of the product, and the time of the status check. The aggregation of these triples over time, ID, and/or status provide ways to view the data on different scales, from the low level detail of a part moving though a manufacturing plant to the high level status of an order consisting of a group of products as it moves from order to delivery to the customer. The aggregated and disaggregated data will be examined using specialized multivariate statistical process control (SPC) methods. Existing multivariate SPC methods will be enhanced to allow the capture of dependencies in product movement that come from the structure of the supply chain network, using clustering and network optimization algorithms. The methods will be tested against supply chain data provided by corporate members of the Center for Supply Chain Research at Penn State. The center's corporate members include Dell, Penske Logistics, Hershey Foods and Johnson & Johnson. The results of this work will be disseminated through Center meetings, conference presentations, and the curriculum of the Department of Supply Chain and Information Systems.If successful, the results of this research will lead to more effective ways to detect improper routing and delivery delays in complex supply chains. The multiscale character of the methods will allow them to be used to manage the supply chain at both tactical and strategic levels, and to provide quick detection and root-cause diagnosis of supply chain problems. The multiscale methods will contribute to general knowledge about multivariate SPC, a widely used Six Sigma tool.