Profiles

Fredrik Eng Larsson

Fredrik Eng Larsson

Universitetslektor

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Works at Stockholm Business School
Telephone 08-674 72 03
Email fredrik.englarsson@sbs.su.se
Visiting address Kräftriket, hus 3, 7, 15 och 24
Room 24:155a
Postal address Företagsekonomiska institutionen 106 91 Stockholm

About me

Fredrik Eng-Larsson is Assistant 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 create financially and environmentally sustainable flows of products or people. In his research he has collaborated with firms such as McKinsey & Co., BASF, 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 the European Journal of Operational Research, Transport Policy, and the International Journal of Physical Distribution and Logistics Management.

Publications

A selection from Stockholm University publication database
  • 2016. Daniel Steeneck, Fredrik Eng-Larsson, Francisco Jauffred.

    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.

Show all publications by Fredrik Eng Larsson at Stockholm University

Last updated: September 5, 2018

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