IJF special issue: Big Data-Driven Forecasting in the Supply Chain

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IJF special issue: Big Data-Driven Forecasting in the Supply Chain

Forecasts have traditionally served as the basis for planning and executing supply chain activities – sourcing, making, and distributing products and services to customers. Forecasts drive all supply chain decisions and their importance has become critical with increasing customer expectations, shortening lead times, and scarcity of resources. Few areas have been as transformed by big data as forecasting given new and rapid access to data.

Digital technologies (such as digital clickstreams, sensors, tags, beacons, and other smart devices), have enabled firms to collect vast amounts of data in real-time. On the other hand, significant but relevant data is also collected in the public domain – social media traffic on the firm’s products and services, photo and video streams, blog and forum entries, and trend spotting data — that are free and fine-grained. We use the term “big data” for data sets that are large (“volume”); that is collected in near real-time (high “velocity”), and present in myriad forms (“variety”). Big data-driven forecasting holds tremendous promise for supply chain planners. The vast amounts of data collected and analyzed in near real-time can be used to better understand customer behavior, improve forecast accuracy, assess systemic and idiosyncratic risk, and improve supply chain execution. The challenge for both researchers and practitioners of forecasting is to effectively integrate this data into supply chain planning activities; and to develop innovative tools, techniques, and models that can use these big data sets unlock deep insights and value.

This opens up an opportunity to significantly impact the practice of forecasting through fundamental research on: i) how big data can be used to leverage the forecasting process in the supply chain; ii) provide supply chain insights by developing innovative and scalable forecasting techniques to manipulate and analyze large data sets; and iii) finally, explore how such forecasting best practices can be developed, structured, and deployed in organizations and across the supply chain.

Special Issue Goals and Topics

This Special Issue draws upon advances in computing and statistics to improve and deepen our understanding on how big data has impacted the practice of forecasting, especially in supply chain contexts. We welcome a wide variety of topics that address big data-driven forecasting in the supply chain. This could, planning production and distribution, and managing the supply base. Data include predicting customer behavior, forecasting or nowcasting demand for products sets used (real or simulated) should have characteristics of big data (volume, velocity, variety); and the insights that are generated should leverage such newly available data.

We are also interested in papers that describe new forecasting models and techniques to analyze large and unstructured data sets that are collected in a supply chain context. These can include statistical techniques (including modifications of traditional forecasting models), adaptive regression, data mining heuristics, machine learning algorithms, video and text processing, etc. Of particular interest are those methods that scale well with large data sets. New techniques should also be rigorously verified. One example is forecasting performance on out-of-sample data. Empirical evaluations of forecasts should be out-of-sample and include established benchmark comparisons, and be subject to tests of statistical significance. This area is especially important in supply chain research where forecasts are often not evaluated as rigorously as they should be, are not compared against established benchmarks or over a large enough sample to draw general conclusions. Big data offers an opportunity for this.

Finally, we are also interested in papers that rigorously explore how big data-driven forecasting capability can be developed, structured, integrated, and deployed across the supply chain and organization. This includes the process of formulating the big data-driven forecasting strategy, investigating the factors for successful deployment, and the identification of potential pitfalls to implementation.

Sample topics of interest:

Value generation in the supply chain

  • Forecasting/Nowcasting demand for products and services
  • Forecasting consumer behavior (through digital click streams, beacons, sensors, etc.)
  • Trend spotting (using Google Trends for example)
  • Demand and production planning
  • Forecasting and planning for supply chain risk (both systemic and rare events)

Tools and techniques

  • Methods to collect and visualize data that will aid forecasting
  • Methods to codify and incorporate unstructured data into traditional forecasting models
  • Scalable techniques for forecasting in the supply chain context (this could include machine learning algorithms, A/B testing, etc.)
  • Techniques to analyze large and/or unstructured data (geographic, sensor, or near real-time data streams)

Organization & Outlook

  • How best to integrate big-data driven forecasting into the organization?
  • Future outlook for using big data-driven forecasting in the supply chain

Submission Guidelines

To submit a paper for consideration for the Special Issue, please upload your paper online and include a cover letter clearly indicating that the paper is for the special issue on “Big data-driven Forecasting in the supply chain”. The webpage for online submissions is mc.manuscriptcentral.com/ijf. The deadline for receipt of papers is 31 January 2017. All papers will follow IJF’s refereeing process.
Instructions for authors are provided at https://ijf.forecasters.org/authors

For further information about the Special Issue, please contact one of the guest editors.

Guest Editors

Nada R. Sanders
Nada Sanders ([email protected])
Distinguished Professor of Supply Chain Management
D’Amore-McKim School of Business, Northeastern University

Tonya Boone
Tonya Boone ([email protected])
Associate Professor of Operations & Information Technology
Raymond A. Mason School of Business, The College of William and Mary

Ram Ganeshan
Ram Ganeshan ([email protected]).
D. Hillsdon Ryan Professor of Business
Raymond A. Mason School of Business, The College of William and Mary


Nada R. Sanders is the Distinguished Professor of Supply Chain Management at the D’Amore-McKim School of Business at Northeastern University in Boston MA. Prior to that, she held the Iacocca Chair at the College of Business and Economics at Lehigh University and as the West Chair at the M.J. Neeley School of Business.

Her research and teaching interests have been in forecasting and the use of data analytics in decision-making within the supply chain context. She has authored over one hundred scholarly works and has served on the editorial boards of prominent journals in her field, including the Journal of Operations Management, Production and Operations Management, Decision Sciences Journal, Journal of Business Logistics, and International Institute of Forecasting. She is a Fellow of the Decision Sciences Institute and was co-founder and Associate Editor of Foresight: The International Journal of Applied Forecasting. She has authored multiple books with the most recent being Big Data Driven Supply Chain Management and has given numerous talks on the subject including a recent HBR webinar. She currently serves on the Board of POMS, having served as both Program and General Chair.

She holds a Ph.D. in Operations Management and Logistics, and an MBA, from the Fisher College of Business at The Ohio State University, as well as a B.S. in Mechanical Engineering.

Tonya Boone is an Associate Professor at the Raymond A. Mason School of Business, The College of William & Mary. Prior to joining the faculty of the College of William and Mary, she was a faculty member at the Fischer College of Business at Ohio State University.

Her research and teaching interests include sustainable operations, knowledge management in professional service organizations, and the management of supply chains in data rich environments. Tonya’s research has been published in the top academic management journals, including Management Science, Journal of Operations Management, POMS Journal, and Decision Sciences. She is also a co-editor of the recent book Sustainable Supply Chain Management: Methods, Models, and Policy Implications.

Tonya is active in her community, serving on the Board of the Williamsburg Economic Development Authority, the Board of the Hampton Roads Incubator, and the Board of the Williamsburg Regional Library. She is a sought-after expert in the local community in the areas of sustainability, public service, and supply chain management.

Tonya Boone has a Ph.D. in Operations and Technology Management from the University of North Carolina at Chapel Hill’s Kenan-Flagler School of Business; a MBA from the College of William and Mary; and a BS in Electrical & Electronics Engineering from the University of Kansas.

Ram Ganeshan is the D. Hillsdon Ryan Professor of Business at the Raymond A. Mason School of Business, The College of William and Mary, Williamsburg, VA.

Ram’s teaching, research and consulting interests are in the areas of supply chain management, data analytics, and logistics strategy, primarily in the chemical, hi-tech, and retail industries.  He is a regular contributor to academic and trade journals and is the co-editor of three books including Quantitative Models for Supply Chain Management which is one of the most highly cited books in supply chain management. In 2001, the Production & Operations Management Society (POMS) awarded him the prestigious Wickham Skinner Award for his research on how supply chains can be efficiently managed. His current research projects extensively rely on big-data techniques to provide insights into making sound decisions for managing both manufacturing and service supply chains. They include: (1) work with online retailers to harness and interpret clickstream data from customer browsing behavior; (2) working with carriers in the transportation sector to reduce cost and carbon footprint; and (3) analyzing large scale project data in professional services to provide strategies for productivity improvement.

He received a Doctorate in Operations and Logistics Management from Penn State; a MSOR degree in Operations Research from the University of North Carolina at Chapel Hill; and an undergraduate degree in Industrial Management from the Birla Institute of Technology & Science in India.

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By |May 19th, 2015|Forecasting News|