Big Data in Supply Chain Management

Guest Editors:
Nada R. Sanders, D'Amore-McKim School of Business, Northeastern University
Ram Ganeshan, Raymond A. Mason School of Business, The College of William and Mary
Deadline: December 15, 2015

Motivation
We see three trends that have fueled the Big Data revolution in the supply chain:

• There has been an explosion of data available within the company and outside the company in the public Internet. In addition to the data generated by traditional transaction-based enterprise systems (POS, RFID, GPS), supply chain planners now have access to vast amounts of data generated from unstructured data sources such as digital clickstreams, camera and surveillance footage, imagery, social media postings, blog/wiki entries, and forum discussions. This data is growing exponentially, doubling every two years (Moore's Law).
• Supply chains today are heavily instrumented — sensors, tags, trackers, and other smart devices are collecting data in real time on a wide variety of business processes. Gartner estimates that by 2020, there will be around 26 Billion such devices in the supply chain monitoring and connecting supply chain operations — from supplier operations, manufacturing, to distribution and point of sale.
• Advances in computing architecture (such as cluster computing and cloud computing) have enabled the storage, retrieval, analysis, sharing, and distribution of data and insights easier and cheaper.

This data and scalable techniques to analyze them — Big Data Analytics — holds significant promise for improving supply chain management. Analyzing vast amounts of varied data in real time helps firms understand their customer better, reduces cost to serve, helps better manage risk, and can lead to revenue generating sources not imagined before. While most firms are keenly aware of the potential, only a small fraction of firms have successfully integrated big data analytics into their operations and across their supply chains.

 

This provides a significant opportunity to the POMS community to impact practice through fundamental research on how big data can be leveraged and deployed to provide supply chain insights.

Scope and Topics
The purpose of this special issue is to publish rigorous and relevant research that draws on advances in data collection and analytics to deepen our understanding on how supply chains can be managed in data-rich environments. We welcome papers that adequately demonstrate how new insights on managing the supply chain can be generated from big data analytics. Of particular interest are those insights that would otherwise not be possible prior to the big-data era.

Any method of inquiry is accepted, including analytical modeling, case method, fieldwork, econometric analysis, experimental, or behavioral techniques. For papers that use datasets, we ask that the data have some characteristic of "big data" (volume, variety, velocity). While we prefer actual data sets (publicly available, purchased, or proprietary), we also welcome papers that use simulated data — but they need to demonstrate that any methodology is sufficiently scalable and can be rigorously verified.

We welcome a wide variety of topics in the supply chain area. A non-exhaustive list appears below. Example Topics include:

• Product forecasting (use of Google Trends, using unstructured data, etc.)
• Planning product assortments and recommendation engines (might include clickstream analysis, machine learning algorithms, dynamic optimization, etc.)
• Dynamic resource allocation
• Using browsing and/or sensor data to manage inventory and replenishment
• Strategies to improve supply chain operations
• Vehicle loading, routing, and monitoring safety (optimization that incorporates real-time data)
• Managing and mitigating supply chain risk (managing disruptions from sensor data, for example)
• Supplier risk assessment, evaluation, and supplier portfolio development
• Managing after-sales service

Important Dates
Submission Deadline: December 15, 2015. Authors will be strongly encouraged to revise and resubmit their papers within three months. Acceptance will be made within two review cycles.

 

Submission procedure
Please prepare the article following POM's submission guidelines (http://www.poms.org/journal/author_instructions/) and submit the file online at the POM Manuscript Central site (http://mc.manuscriptcentral.com/poms). Please ensure that you select Nada Sanders as the Department Editor and specify in your cover letter that the manuscript is for the special issue. Questions on this special issue can be sent to any of the guest editors: Nada R. Sanders (n.sanders@neu.edu) or Ram Ganeshan (ram.ganeshan@mason.wm.edu).