Chair of Logistics and Quantitative Methods

Supply Chain Collaboration

Research News

Gründerteam Level3

Two of our researchers – Jan Meller and Fabian Taigel – co-founded Level3, a company dedicated to applying AI in Operations & Logistics Management. With their third co-founder, Dr. Sarah Mehringer, the team secured an EXIST Business Start-up Grant from the Federal Ministry for Economic Affairs and Energy.


Together with our 19 partners from the EU-funded PRACTICE project we derrived potential solutions based on collaborative approaches in supply chains.


Problem & Objective

Supply chain collaboration (SCC) is a joint decision making process for aligning plans of individual supply chain members with the aim of achieving coordination under information asymmetry (Stadtler, 2009). It is a well-acknowledged fact that supply chain collaboration yields a significant potential to increase overall supply chain performance. The benefits of collaborative supply chain planning, such as reducing overall supply chain costs and increasing service levels, have been highlighted in many theoretical and empirical studies. Information sharing is a prerequisite for any collaborative planning approach. Individual members of the supply chain dispose of relevant (private) data regarding their own operations (e.g. cost and capacity data, inventory levels, demand forecasts) that need to be exchanged in order to enable joint decision making. There is, however, substantial evidence that information sharing (as a prerequisite for SCC) constitutes the most significant obstacle for implementation of SCC. Companies are oftentimes very reluctant to share sensitive information with their partners.

After carrying out a very successful project called “Secure SCM” (more), which was funded by the European Union (7th Framework Program), we are now part of a large scale research project “PRACTICE”, which started in November 2013 and is run by consortium of 18 partners. Details on PRACTICE can be found here (more). The role of our team from Würzburg in this project is to develop models an processes for the use cases of different industrial partners which later on are to be implemented as part of a secure cloud-based planning system.

Motivated by the work in this project, our research centers on SCC in after-sales service supply chain where increasingly smarter machines create vast amounts of condition-based data that call for exploitation in order to gain competitive advantages for collaborating supply chain partners. The benefits of collaboration in such service chains promise to be even higher than in manufacturing supply chains while research in this particular field is rather scarce.

We take an integrated view at the entangled processes of secure collaborative forecasting, spare parts management and capacity planning. We investigate the potentials of applying machine learning techniques for forecasting with special regard for intermittent spare parts demand which typically shows intermittent or erratic patterns and is therefore hard to forecast by traditional time series analysis. Furthermore, advanced queuing theory is developed and applied in models that allow to use the addittional information for improved capacity planning and collaborative maintenance scheduling.


  • Richard Pibernik (more)
  • Florian Kerschbaum (SAP)
  • Julian Kurz (more)
  • Fabian Taigel (more)
  • Axel Schröpfer (SAP)

Publications and Working Paper

[ 2018 ] [ 2016 ] [ 2011 ] [ 2009 ] [ 2007 ] [ 2006 ]

2018 [ nach oben ]

  • Privacy-preserving condition-based forecasting using machine learning. Taigel, F; Tueno, A K; Pibernik, R in Journal of Business Economics (2018). 88(5) 563-592.

2016 [ nach oben ]

  • Capacity planning for a maintenance service provider with advanced information. Kurz, Julian in European Journal of Operational Research (2016). 251(2)

2011 [ nach oben ]

  • Secure collaborative supply chain planning and inverse optimization - The JELS model. Pibernik, R; Zhang, Y; Kerschbaum, F; Schröpfer, A in European Journal of Operational Research (2011). 208(1) 75--85.

2009 [ nach oben ]

  • Optimizations for risk-aware secure supply chain master planning. Schröpfer, A; Kerschbaum, F; Schütz, C; Pibernik, R in Journal of Universal Computer Science (2009). 15(15) 3019--3037.

2007 [ nach oben ]

  • An approach to inter domain master planning in supply chains. Pibernik, R; Sucky, E in International Journal of Production Economics (2007). 108(1) 200--212.

2006 [ nach oben ]

  • Centralised and decentralised supply chain planning. Pibernik, R; Sucky, E in International Journal of Integrated Supply Management (2006). 2(1) 6--27.