Josef Svoboda, M.Sc.



  • Inventory Management
  • Machine Learning
  • Data-Driven Optimization

Academic Career and Positions held to date

  • 10/2017 - 09/2021: Research assistant at the Department of Logistics and Supply Chain Management (Prof. Dr. Minner); TU Munich

Professional Experience

  • 2014 - 2017: Supply Chain Management at BSH Home Appliances GmbH, Munich

Academic Education

  • 2013: Graduation as Master of Science in Business Engineering (M.Sc.) at Karlsruhe Institute of Technology
  • 2011 - 2013: Studies of Business Engineering at Karlsruhe Institute of Technology
  • 2011: Graduation as Bachelor of Science in Business Engineering (B.Sc.) at Karlsruhe Institute of Technology
  • 2007 - 2011: Undergraduate studies of Business Engineering at Karlsruhe Institute of Technology

Master Theses:

  • Data-Driven Inventory Optimization in Multi-Echelon Supply Chains (with Celonis)
  • Supply Chain Segmentation with Machine Learning
  • Data-Driven Forecasting in Inventory Management
  • Influence of supply chain visibility on parts availability in the spare parts business (in cooperation with Barkawi Management Consulting)
  • Optimal inventory management and replenishment strategies of a retailer for perishable goods considering seasonalities of fruits and vegetables
  • Roll-Container vs. Euro-Palette: Analyse der Auswirkung einer Einführung eines neuen Ladungsträgers auf die Filialdistribution unter Berücksichtigung der zukünftigen Sortimentsentwicklung (in cooperation with ALDI SÜD)
  • Simulation Optimization of Inventory Management Policies in Multi-Echelon Distribution Systems Customer Order Patterns and Optimal Inventory Management (with Load Fox)
  • Clustering and Inventory Control in Large-Scale Supply Chain Networks
  • Value of Information in Inventory Control
  • Data-Driven Optimization for Improving Supply Chain Resilience (with Procter & Gamble)
  • Inventory Control Parameter Optimization within DD-MRP (with BSH Home Appliances)

Bachelor Theses:

  • MIP-Based Backorder Allocation Rules in Distribution Systems
  • Demand Driven vs. Consumption Driven Inventory Management (with Voith)
  • Machine Learning in Material Planning (with Faurecia)
  • Multiple Sourcing Policies in Inventory Management
  • Machine Learning in Inventory Management (with Keller&Kalmbach)
  • Supply Chain Segmentation and Inventory Management (in cooperation with Thorlabs)
  • Key Performance Indicators in Retail Logistics
  • Simulating Demand in Multi Echelon Inventory Systems
  • Additive Manufacturing in Multi Echvelon Spare Parts Supply Chains
  • Data-Driven Optimization in Multi-Echelon Supply Chains (with Barkawi Management Consulting)
  • Data-and feature-driven inventory control policies
  • Sampling Representative Training Data for Machine Learning Models in Inventory Control
  • Inventory Managenemt of perishable, seasonal products

Project Studies:

  • Blockchain Technology: Application in Logistics (in cooperation with Wassermann AG)
  • Inventory Management of perishable goods considering seasonal supply and supplier change
  • Spare Parts Management for Munichs Subway Operations (in cooperation with SWM)
  • Plattform-basiertes ad Hoc Pricing in der luftfracht (in cooperation with Panalpina)
  • 3D Printing in Spare Part Supply Chains (with EOS)