Sensing Abnormal Resource Flow Using Adaptive Limit Process Charts in a Complex Supply Network*. Issue 5 (9th September 2015)

• Record Type: Journal Article
• Authors: Sawaya, William J.; Pathak, Surya; Day, Jamison M.; Kristal, Mehmet Murat
• Is Part Of: Decision sciences. Volume 46:Issue 5(2015)
• Page Start: 961
• Page End: 979
• Publication Date: 2015-09-09
• Subjects: Decision making -- Periodicals; Policy sciences -- Periodicals; 658.40305
• DOI: 10.1111/deci.12162
• Languages: English
• ISSNs: 0011-7315

Abstract

<abstract abstract-type="main"> <title>ABSTRACT</title> <p>Supply networks are becoming increasingly complex with multiple overlapping relationships between firms that may span across industries. Consequently, inventory management is becoming more difficult as managers have to cope with variability in the supply flows that originate from different parts of the network. Managers that quickly sense abnormal flows may intervene and adapt their inventory policies in response to system changes. In this article, we present a framework for sensing abnormal flows originating within the upstream supply network of a focal organization. Our framework combines time series modeling with process charts to identify abnormal flow patterns in the incoming supply streams. It is a flexible framework that uses off‐the‐shelf technology to provide managers with a process that can be employed for monitoring multiple individual or aggregated data streams originating within any complex system such as complex adaptive supply networks. We illustrate our framework on four years of longitudinal supply data from the second largest food bank in the United States. We identify multiple instances of abnormal supply flows and validate our results through rigorous inventory analysis as well as field‐based expert interviews. We discuss the implications of our findings for inventory management in complex supply networks, both from academic and practitioner points of view.</p> </abstract>