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Fredrik Eng LarssonUniversitetslektor, docent

Om mig

Fredrik Eng-Larsson is Associate Professor of Operations Management at Stockholm University. He holds a Ph.D. in Industrial Management and Logistics from Lund University. Before joining Stockholm University he was a Postdoctoral Associate at Massachusetts Institute of Technology, U.S.A. He has been a Fulbright researcher at University of California, Los Angeles, and an Erasmus scholar at ETH Zurich, Switzerland.
 
His research focuses on supply chain management, in particular how large and unreliable data can be leveraged to analyze and improve supply chain operations. In his research he has collaborated with firms such as H&M, McKinsey & Co., and Volvo, as well as government agencies such as the Swedish Transport Administration and the Swedish Energy Agency. His work has been published in the Wall Street Journal and in several scientific journals including Management Science, Manufacturing & Service Operations Management, and the European Journal of Operational Research.

Forskningsprojekt

Optimerad butikspåfyllnad

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Forskning

Publikationer

I urval från Stockholms universitets publikationsdatabas

  • Estimating demand for substitutable products when inventory records are unreliable

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

    Rapport

    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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