INFORMS gives Wagner Prize to Ford for identifying supply chain risks, reducing disruption
The Institute for Operations Research and the Management Sciences (INFORMS), the leading professional association for analytics professionals, today announced that the winner of the Daniel H. Wagner Prize judged by CPMS, the association's practice section, is Ford Motor for an innovative approach to an important issue facing companies with a global presence: early identification of supply chain risks and a new process to respond when things go wrong.
"Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain" is by David Simchi-Levi, and Peter Y. Zhang, Massachusetts Institute of Technology; William Schmidt, Cornell University; Yehua Wei, Duke University; and Oleg Gusikhin, Don Zhang, Yao Ge, Keith Combs, and Michael Sanders, Ford Motor Company.
Firms are exposed to a variety of low probability/high impact risks that may disrupt their operations and supply chains. For example, following the massive 2011 tsunami, at least one automaker had to halt plant production because of shortages from Japanese suppliers.
These risks are difficult to predict and quantify, and therefore difficult to manage. As a result, managers deploy less-than-perfect countermeasures, leaving their firms exposed to some risks while wasting resources to mitigate others that would not cause significant damage.
In a three-year research engagement with Ford Motor Company, the authors addressed this problem by developing a novel risk exposure model that assesses the impact of a disruption originating anywhere in the firm's supply chain. The approach defers the need to estimate the probability associated with any specific disruption risk until after the company learns how the realization of such a disruption will impair its operations. As a result, the company can make more informed decisions about where to focus its limited risk management resources.
The authors demonstrated to the judges how Ford applied this model to identify previously unrecognized risk exposures, evaluate pre-disruption risk mitigation actions, and develop optimal post-disruption contingency plans, including circumstances in which the duration of the disruption is unknown.
According to the authors, Ford supply risk specialists use the model routinely, for example to prioritize commodities and supplier sites that represent the highest level of exposure during potential disruptions like natural disasters.
The Daniel H. Wagner Prize for Excellence in Operations Research Practice emphasizes the quality and coherence of the analysis used in the practice of operations research and analytics. The late Dr. Wagner strove for strong mathematics applied to practical problems, supported by clear and intelligible writing. The prize recognizes those principles by emphasizing good writing, strong analytical content, and verifiable practice successes.
The other five finalists for the 2014 Wagner Prize were:
- Sabre: Airline Crew Augmentation, Decades of Improvements by Xiaodong Luo, Yogesh Dashora, and Tina Shaw, Sabre Corporation
- P&G: Statistical and Optimization Techniques for Laundry Portfolio Optimization by Ivan Oliveira, Rob Pratt, Ming Zhao, SAS; Kevin Norwood, Nats Esquejo, and Kevin Miller, Procter & Gamble
- Gerrymandering for Justice: Redistricting U.S. Liver Allocation by Sommer Gentry, United States Naval Academy; Eric Chow, Allan Massie, and Dorry Segev, Johns Hopkins University School of Medicine
- CDC: Vaccine Prioritization for Effective Pandemic Response by Eva Lee and Fan Yuan, Georgia Institute of Technology; Ferdinand Pietz and Bernard Benecke, Centers for Disease Control and Prevention
- Intel: Project Portfolio Planning by Siddhartha Sampath, and Karl Kempf, Intel Corporation; Esma Gel and John Fowler, Arizona State University
The INFORMS Daniel H. Wagner Prize was presented in November at the INFORMS annual meeting in San Francisco. Nearly 6,000 academics and professionals attended the conference.
Provided by Institute for Operations Research and the Management Sciences