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Fredrik Eng LarssonAssociate Professor

About me

Fredrik Eng-Larsson is Associate Professor of Operations Management at Stockholm University, specializing in supply chain management. In his research he studies supply chains on both global and local scales, from international transportation to last-mile delivery. With a particular focus on the retail industry, his research uses advanced analytics with large and often imperfect datasets to examine how supply chains can adapt to shifting consumer patterns, macro-level uncertainties and higher sustainability demands.
 
He earned his Ph.D. in Industrial Management and Logistics from Lund University. Before joining Stockholm University, he worked at the Massachusetts Institute of Technology (MIT) in the United States. He has also been a Fulbright researcher at the University of California, Los Angeles, and an Erasmus scholar at ETH Zurich, Switzerland. He has worked with industry leaders such as H&M, McKinsey & Company, and Volvo, as well as public agencies including the Swedish Transport Administration and the Swedish Energy Agency.
 
His work appears in leading journals including Management Science, Manufacturing & Service Operations Management, and the European Journal of Operational Research, as well as in outlets such as Harvard Business Review and The Wall Street Journal. His research informs both academic thinking and industry practice in modern supply chain management.

Research projects

Publications

A selection from Stockholm University publication database

  • Estimating demand for substitutable products when inventory records are unreliable

    2016. Daniel Steeneck, Fredrik Eng-Larsson, Francisco Jauffred.

    Report

    We present a procedure for estimating demand for substitutable products when the inventory record is unreliable and only validated infrequently and irregularly. The procedure uses a structural model of demand and inventory progression, which is estimated using a modied version of the Expectation Maximization-method. The procedure leads to asymptotically unbiased estimates without any restrictive assumptions about substitution patterns or that inventory records are periodically known with certainty. The procedure converges quickly also for large product categories, which makes it suitable for implementation at retailers or manufacturers that need to run the analysis for hundreds of categories or stores at the same time. We use the procedure to highlight the importance of considering inventory reliability problems when estimating demand, rst through simulation and then by applying the procedure to a data set from a major US retailer. The results show that for the product category in consideration, ignoring inventory reliability problems leads to demand estimates that on average underestimate demand by 5%. It also results in total lost sales estimates that account for only a fraction of actual lost sales.

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Associate Professor Operations Management

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