Impact on Practice: Examples

Sales Forecast Calibration amid the COVID-19 Pandemic for AB Inbev: In 2020, Simchi-Levi and reserchers in the MIT Data Science Lab collaborated with AB Inbev to develop a new online non-parametric regression method to calibrate sales forecast during the COVID-19 pandemic period. The new method brings together two different approches, online learning and pandemic modeling, to dramatically improve forecast accuracy. Both the MIT Data Science Lab team and AB Inbev independently tested the new method in various business scenarios. When compared with other methods--previuos approach used by AB Inbev and a different approach based on online linear regression--the online non-parametric regression method has reduced the forecast error by over 30% in forecasting sales volumes. For more details, see D. Simchi-Levi, R. Sun, M. X. Wu, and R. Zhu (2020), Calibrating Sales Forecast in a Pandemic Using Online Non-Parametric Regression Model. Available at SSRN.

Model Misspecifications for Oracle technology: In 2017-2018, Simchi-Levi and his PhD students collaborated with Oracle Retail to develop and test a dynamic pricing algorithm for fashion retail. The goal was to integrate the pricing algorithm into Oracle Retail's price optimization products, which mainly optimize markdown pricing for fashion retailers. The proposed algorithm, referred to as the Random Price Shock (RPS) algorithm, includes features such as the ability to correct for mis-specified model; feature-based pricing; learning and earning on the fly; and a variety of business constraints. For details, see Nambiar M., D. Simchi-Levi and H. Wang (2019), Dynamic Learning and Pricing with Model Misspecification. Management Science, Vol. 65, No. 11, November 2019, pp. 4980–5000.

Pricing at Groupon and Rue La La: In 2017, Simchi-Levi and his PhD students collaborated with the two companies and developed new models that integrate machine learning and optimization techniques to optimize these online retailer pricing decisions. The impact on the companies was significant increasing Rue La La’s revenue by about 11 percent and Groupon’s by about 20 percent. More on this in Simchi-Levi, D. (2017), The New Frontier of Price Optimization. Sloan Management Review, Fall 2017, pp. 22–26.

Customized Offerings in the Airline and Insurance industries: In 2015, Simchi-Levi and his PhD students developed a new method, the Contextual Treatment Selection (CTS) algorithm, for optimizing customizing offering based on individual characteristics. Initially, the algorithm, which combines Machine Learning and Optimization techniques, was implemented in the airline industry to offer online customers ancillary products such as priority boarding, seat upgrades or car rentals. Later, the same algorithm has been applied in the Insurance industry to offer car, home and life insurance to maximize customer lifetime value. For details, see Zhao Y., X. Fang and D. Simchi-Levi (2017), Uplift Modeling with Multiple Treatments and General Response Types. SIAM Data Mining 2017, pp. 588-596.

Supply Chain Risk Management at Ford: In 2014, Simchi-Levi and his Phd students developed a new way for companies to identify hidden risk in their supply chain by employing new concepts, refer to as Time-to-Recover and Time-to-Survive. The new approach, which was initially implemented at Ford Motor Company allows management to understand the impact of a disruption originating anywhere in the firm's supply chain and quantify it using operational and financial performance metrics. See more in Simchi-Levi, D., W. Schmidt, and Y. Wei (2014), From Superstorms to Factory Fires: Managing Unpredictable Supply Chain Disruptions. Harvard Business Review, January–February 2014, pp. 96–101.

Supply Chain Segmentation at Dell: Simchi-Levi’s 2013 paper with colleagues at Dell developed and implemented the first supply chain segmentation strategy that was able to transform Dell business. The impact, as described in the paper was dramatic, see Simchi-Levi, D., A. Clayton and B. Raven (2013), When One Size Does Not Fit All. Sloan Management Review, Volume 54, No. 2, pp. 14–17.