Proven Leader: Nada R. Sanders


Nada R. Sanders 
Distinguished Professor, Supply Chain and Information Management
PhD The Ohio State University

Sanders is a renowned expert in business forecasting, data-driven business decision-making, and global supply chain management.

Her research has impact across business industries, and she is frequently called upon as an expert witness in the area of forecasting. She has worked with companies such as NIKE, IDC, Scitex Corporation, and AT&T through her extensive consulting and executive training projects.

Sanders was ranked in the top eight percent of authors in research productivity in U.S. business schools and has authored numerous articles appearing in premier scholarly journals.

She is currently working to identify better ways for organizations to implement big data analytics with a focus on implementing and using available forecast technology and maximizing managerial interaction with such technology.

Q: What are your areas of expertise/research focus?

Business forecasting, forecasting and risk management, supply chain management strategy, the role of information technology in decision-making, and the impact of sustainability on supply chain management.

Q: What industries are or could be impacted by your research?

All industries can benefit. The reason being is that all industries generate forecasts and make decisions that are driven by those forecasts.

Q: What research projects are you currently working on or planning?

I am working on identifying better ways for organizations to implement Big Data analytics; the focus is on implementing and using available forecast technology and analytics, and for managers to interact with this technology.

Q: What are some of your most seminal publications?

Sanders, N.R., “How to Use Big Data to Drive Your Supply Chain,” California Management Review, forthcoming, 2016.

Important as it provides a framework and a ‘how to’ for executives to use Big Data across their supply chains. The press is full of benefits of Big Data but actual implementation is a challenge; this publication is important as it tells executives how to actually implement Big Data.

Sanders, N. R. and Ritzman, L.P. "Judgmental Adjustment of Statistical Forecasts," Principles of Forecasting: A Handbook for Researchers and Practitioners, J. Scott Armstrong (ed), (Kluwer Academic Publishers), 2001, 405-416.

This paper tells executives and managers the best way to conduct judgmental adjustment of statistical forecast. This is a common practice in business, however managers often do it incorrectly. This looks at all relevant research studies and extracts a process that serves as a template for managers.

Sanders, N. R. and G. Graman, “Impact of Bias Magnification on Supply Chain Costs: The Mitigating Role of Forecast Sharing,” Decision Sciences, forthcoming 2015.

This paper is important as it documents the impact of forecast bias on supply chain costs and shows how forecast sharing can mitigate the negative effect. It is especially significant as studies to date only look at the impact of overall forecast errors but do not consider forecast bias, a constant presence in practice. In this study we show its detrimental effects and how they can be mitigated.

Q: What are some awards that you have received?
  • Karl Beidelman Research Award, for highest quality research, Lehigh University, 2012.
  • MBA Professor of the Year, Lehigh University, 2012.
  • Fellow, Decision Sciences Institute, 2008.