CSCMP is currently pursuing two complementary research projects to address what big data means for supply chain management. Results from both studies will be presented in 2014 in the form of printed reports, at the CSCMP annual conference, potentially at nearby roundtables, and in other CSCMP publications.
Big Data: What does it mean for Supply Chain Management?
By: Mark Barratt, Marquette University; Anníbal Camara Sodero, University of Arkansas; and Yao Jin, University of Arkansas
Description: this research has three objectives: 1) to develop a taxonomy of data sources containing Big Data that may be considered for decision-making purposes in retail supply chains, 2) to explore the current and potential use of Big Data analytics in and across multiple industries positioned across echelons of retail supply chains, and 3) to identify the general and specific implications of the use of Big Data analytics, and what firms in retail supply chains would need to do differently in order to capture the likely benefits. Literature has overlooked the importance of classifying data sources. In many circumstances, the widespread use of vague terms may make it difficult for scholars and practitioners to communicate among themselves, may hinder the advance of relevant research in the realm of Big Data. The second objective would assist firms to understand where they could begin to incorporate Big Data analytics in order to make inventory planning and replenishment decisions. Traditionally, firms in retail supply chains have used data pertaining to orders placed across echelons to make inventory-related decisions; however, mounting evidence suggests that such an approach is likely to lead to inefficiencies across supply chain echelons, for instance, by giving rise to the bullwhip effect. Finally, the third objective suggests that the use of Big Data analytics will require “a world of radical transparency” and enable “scalable collaboration” based on customers and suppliers allowing access to their own databases. Despite these broad assumptions, there is recognition that functional silos within organizations delay the exploitation of shared data and as such these silos must be broken down.
The What, How and Why of Big Data in Supply Chain Relationships: A Structure, Process, and Performance Study
By: R. Glenn Richey, Jr., The University of Alabama (US) and & Associate Professor, The University of Edinburgh (UK); Chad W. Autry, The University of Tennessee; Frank G. Adams, Mississippi State University; Tyler R. Morgan, The University of Alabama; Kristina Lindsey, The University of Alabama; and Taylor Wade, University of Alabama
Description: a broad based examination of leveraging the big data process for supply chain success. A basic model stems from resource-based analysis and previous research in the Journal of Business Logistics and elsewhere. Initially, industry needs to know where certain types of data come from, be that information strategic or operations oriented. This is largely a function of whom they interface with and what collection mechanism (technology) is used. This part of the Big Data management process Structure is a key feature in the Governance Theory of Supply Chain Relationships (Richey et. al 2010). Following the structuring of the data strategy, one must assess the process of analyzing, sorting, and distributing the data to partners. Three known technological approaches to this process are discussed by Richey et al (2009) and include storage, communication, and (service) customization. Finally, it's important to know what to share, what to protect, and what to reevaluate. This is obviously a question of levels of transparency and safeguarding, both of which are hot and poorly understood topics in SCM. These concepts/stages set the basic frame for a very deep study. As a strategic study, we focus heavily on how data type, technological tools, relationship structure and the process itself all contribute to firm performance.