Dr. Sanders is an internationally recognized expert in forecasting, predictive analytics, risk management, and supply chain management. Her research includes identifying best practices in forecasting, developing a corporate technology strategy, and creating a resilient supply chain. Her teaching includes advanced supply chain management problems, supply chain strategy, supply chain analytics, and forecasting. She has taught at a wide range of academic levels, primarily at the MBA and Executive MBA levels, and has designed multiple successful MBA programs
Dr. Sanders has held a range of leadership roles in both academic and professional organizations and has served on numerous Executive Boards. She has provided training and consulting to a range of Fortune 500 companies, including IDG, Nike, AT&T, CIBA Corning, Mattel, MTC Corp., Dell, and many others. She is a frequently called upon keynote speaker and expert witness having worked with firms such as Jones Day; Vorys, Sater, Seymour and Pease; Quinn, Emanuel, Urquhart & Sullivan, LLP and others.
Dr. Sanders is Co-Editor of Production and Operations Management (POM), Special Issue “Big Data Driven Supply Chain Management” (2017); Co-Editor of International Journal of Forecasting (IJF), Special Issue “Big Data Driven Forecasting in Supply Chain Management” (2017) and Co-Editor of Journal of Business Logistics (JBL), Special Issue “Sustainable Supply Chains in a Digital Interconnected World,” (2017). She was Co-Editor of Journal of Business Logistics, Special Issue “Using Interdisciplinary Research to Address Contemporary SCM Problems,” Volume 37 (2), 2016. She also serves as Associate Editor of the Decision Sciences Journal, Journal of Business Logistics, and International Journal of Forecasting. She was co-founder and Associate Editor of Foresight: The International Journal of Applied Forecasting, a journal of the International Institute of Forecasters (www.forecasters.org), 2004 – 2010. She also serves on the Selects Committee for the annual INFORMS Analytics Conference.
Areas of Expertise (4)
Distinguished Professor of Supply Chain Management (professional)
2014 – Present
West Chair and Professor in Supply Chain Management (professional)
M. J. Neeley School of Business, TCU, (2007 – 2009)
Iacocca Chair and Professor of Supply Chain Management (professional)
Lehigh University (2009 – 2014)
MBA Professor of the Year (professional)
Lehigh University (2012)
Beidelman Research Award (professional)
Lehigh University (2012)
Decision Sciences Institute (2008)
The Ohio State University, Fisher College of Business: Ph.D.
The Ohio State University, Fisher College of Business: M.B.A.
Franklin University: B.S., Mechanical Engineering
- Decision Sciences Journal : Associate Editor
- Journal of Business Logistics : Associate Editor
- International Journal of Forecasting : Associate Editor
- Foresight: The International Journal of Applied Forecasting : Associate Editor
- INFORMS Analytics Conference : Selects Committee member
Media Appearances (5)
AI Knows What Customers Want, Transforms Supply Chains
News @ Northeastern online
Say you’re looking for a new jacket that’s affordable and on-trend. What store comes to mind?
For many consumers, the answer is Zara. The Spain-based company is a leader in fast fashion, which refers to retailers that rapidly turn out inexpensive clothing inspired by what’s currently on the runway.
Amazon and Target’s Retail Rumble
Boston Magazine online
It didn’t take more than a few minutes of wandering around the sparkling new Target store in Central Square for it to dawn on me: Somehow, contained in this 21,000-square-foot expanse, is everything that most people, on most days, could ever need. There’s trail mix, school binders, blazers, smartphones, basketballs, Star Wars toys, bedding, toilet paper, Beats by Dre headphones, and at least half a dozen passable button-down shirts. Given the current retail climate, it felt like a store engineered specifically to keep me from buying things on the Internet—an act of war, so to speak, against Target’s archrival, Amazon. Which, it turns out, is exactly the case.
A Step-by-Step Guide to Creating a Culture of Disruptive Innovation at Your Hospital
H&HN Magazine online
Hospitals and health systems are clinging tenaciously to the belief that their economic survival depends on their embracing continuous quality improvement and Lean methods. After all, in a time of ratcheted-down reimbursement, the need for a little belt-tightening and quality improvement seems obvious.
Addressing Supply Chain Risk, Sustainability in Strategic Plans
Environmental Leader online
Although two of the biggest concerns in supply-chain management are risk and sustainability, they are often viewed separately when they should be addressed concurrently in strategic and operational plans, according to a report by The Conference Board.
JUST AROUND THE BEND: Expansion of Panama Canal to magnify rail and truck traffic in eastern Pa., but can our infrastructure and warehouses handle it?
Lehigh Valley Business online
With the completion of the $5.3 billion Panama Canal expansion planned for next year, much more rail freight and truck traffic could be headed throughout eastern Pennsylvania as cargo increases at New York City and Philadelphia ports.
Boone, T., Ganeshan, R., Hicks, R. & Sanders, N.R.
In this issue, Cui et al. (2017) show how the quantity and quality of user‐generated Facebook data can be used to enhance product forecasts. The intent of this note is to show how another type of user‐generated content—customer search data, specifically one obtained from Google Trends—can be used to reduce out‐of‐sample forecast errors. Based on our work with an online retailer, we bolster Cui et al. (2017) result by showing that adding customer search data to time series models improves out‐of‐sample forecast errors.
Gu, T., Sanders, N.R and A. Venkateswaran
This study explores how suppliers adjust their relation‐specific investments (RSI) in response to the different risk‐taking incentives provided by the customer firm to its CEO, during normal and transition periods. We investigate this relation using 17,553 customer–supplier transactions over the 1993–2013 period. We find strong evidence consistent with the risk‐taking argument. Specifically, we find that an increase in the risk‐taking incentives of customer CEOs leads to a decline in suppliers’ RSI in normal periods, but an increase in RSI during transition periods. We employ the FAS‐123R mandate to show that an exogenous reduction in customer CEO's incentive pay increases suppliers’ RSI. We reaffirm the effect with the passage of the Sarbanes–Oxley Act as a secondary quasi‐natural experiment. Finally, we examine several scenarios that either amplify or attenuate the observed relation, based on factors such as financial constraints, distress, growth opportunities, industry competition, and other firm characteristics. Our study contributes to the literature that examines the interplay between corporate policy and product market relationships.
Nada R. Sanders
Big data analytics has become an imperative for business leaders across every industry sector. Analytics applications that can deliver a competitive advantage appear all along the supply chain decision spectrum—from targeted location-based marketing to optimizing supply chain inventories to enabling supplier risk assessment. While many companies have used it to extract new insights and create new forms of value, other companies have yet to leverage big data to transform their supply chain operations. This article examines how leading companies use big data analytics to drive their supply chains and offers a framework for implementation based on lessons learned.
Sanders, N. R. and G. Graman
The impact of forecast error magnification on supply chain cost has been well documented. Unlike past studies that measure forecast error in terms of forecast standard deviation, our study extends research to consider the impact of forecast bias, and the complex interaction between these variables. Simulating a two‐stage supply chain using realistic cost data we test the impact of bias magnification comparing two scenarios: one with forecast sharing between retailer and supplier, and one without. We then corroborate findings via survey data. Results show magnification of forecast bias to have a considerably greater impact on supply chain cost than magnification of forecast standard deviation. Particularly damaging is high bias in the presence of high forecast standard deviation. Forecast sharing is found to mitigate the impact of forecast error, however, primarily at higher levels of forecast standard deviation. At low levels of forecast standard deviation the benefits are not significant suggesting that engaging in such mitigation strategies may be less effective when there is little opportunity for improvement in accuracy. Furthermore, forecast sharing is found to be much less effective against high levels of bias. This is an important finding as managers often deliberately bias their forecasts and underscores the importance of exercising caution even with forecast sharing, particularly for forecasts that have inherently large errors. The findings provide a deeper understanding of the impact of forecast errors, suggest limitations of forecast sharing, and offer implications for research and practice alike.
Sanders, N.R., Zach, Z.G., and B. Fugate
To accelerate research discoveries—those required to address paramount challenges facing business today—researchers from diverse disciplines must work together. Interdisciplinary research (IDR) is a research that involves bringing together perspectives from two or more disciplines in an integrative manner to address complex and multifaceted supply chain management (SCM) problems. IDR is needed to address contemporary business challenges. We look at SCM research through the lens of the Behavioral Theory of the Firm (BTF) drawing parallels in research evolution and noting similar antecedents in theoretical development. We point to the advances BTF has offered to organizational theory built on IDR and consider the possibilities for SCM. We make a case for methodological diversity in supporting this research, further paralleling lessons from BTF. Last, we describe the state of IDR in SCM today, discuss the objective of this special issue, and showcase the five contributing papers.