Impact on Practice: Examples

The Resiliency of the Global Semiconductor Supply Chain: In 2021-2022, Professor Simchi-Levi and his PhD student Feng Zhu, collaborated with Denso Auto Parts, the world’s 2nd largest automotive parts manufacturing company, to understand risk exposure in semiconductor supply chains. In this research, we analyzed rich data sets from the semiconductor industry and applied the supply chain resilience and stress-tests technology developed by the MIT Data Science Lab. Because of the unique challenges of this industry, we introduced a new concept, refers to as Time-to-Recover Inventory (or, TTRInv), that measures how long it takes the supply chain to return to normal (target) inventory levels after a disruption. Our analysis shows, for example, that a short disruption of a semiconductor fabrication facility, or “fab,” in Taiwan for 10 days, could cause a flurry of additional disruptions and shortages across the entire supply chain that would last almost a year. Our research also reveals that expanding the number of semiconductor fabrication facilities in the United States will not alone suffice to prevent such shortages from occurring again. Equally important, our analysis, insights and recommendations could also be applied to supply chains for other products, such as the one for batteries and magnets used in electric vehicles (EVs). For more detail, see David Simchi-Levi, with Feng Zhu and Matthew Loy, Fixing the U.S. Semiconductor Supply Chain (

Trading Inventory Cost for Online Sales Revenue: In 2021, Professor Simchi-Levi and researchers in the MIT Data Science Lab collaborated with Accenture and Home Depot to study an inventory placement optimization problem, where demand is sensitive to service response time, under the online retail setting. To predict the sensitivity of demand to response time, the team developed a novel demand prediction and elasticity model for different product categories. Such a model may suggest products that need to be positioned closer to market demand. Unfortunately, such a strategy will increase inventory cost. To address this challenge, a new method based on data-driven stochastic programming was devised that optimally trades safety stock for service response time. The efficiency of the approach was demonstrated through data provided by one of the largest e-commerce retailers in North America. The new approach led to more than 10% total profit increment. Our approach offers supply chain managers a general-purpose decision support tool that optimally position inventory in the supply chain and generates recommended stocking levels for stores, distribution centers and warehouses on a daily basis. For more details, see H. Qin, D. Simchi-Levi, R. Ferer, J. Mays, K. Merriam, M. Forrester, A. Hamrick (2022), Trading Safety Stock for Service Response Time in Inventory Positioning. Available at SSRN.

Smart Supply Chain Digitization: In 2020, Professor Simchi-Levi, together with Accenture Executives and PhD students from the MIT Data Science Lab developed a practical supply chain digitization strategy that includes three important components: a unified, single, view of demand; supply chain segmentation; and smart planning and execution, all of which are powered by Digitization, Analytics and Automation. This strategy was implemented in a variety of industries including fashion retail, Consumer Packaged Goods manufacturers and high-tech. For more information, see D. Simchi-Levi and K. Timmermans (Accenture), “Deep Transformation with Smart Supply Chain Digitization.” See more here.

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.