Subproject D1 Modelling of regeneration processes

The individual composition and optimisation of regeneration processes presents a promising chance for regeneration service providers since a high logistics performance and concurrent low costs are significant for achieving the logistics goals successfully. Therefore, the superior objective of sub-project D1 is the increase of the logistics performance in regeneration processes by supporting the configuration of regeneration supply chains.

Motivation and objectives

The regeneration supply chain describes the process of product regeneration starting with the disassembly of components. Additional pool stages can be added along the regeneration supply chain to open up further room for improvement.

Suppliers of regeneration services face a variety of options for configuring their regeneration supply chains. Even if the configuration is defined to a certain extent by the capital good itself as well as by the technologies required for regeneration, a large number of degrees of freedom remain on the production logistics side. These lie, for example, in capacity deployment, the choice of suitable planning and control procedures, and spare parts management and the associated inventory dimensioning. Depending on the chosen configuration of the supply chain, regeneration service providers are able to offer a specific logistics service on the market. This is perceived by the customer mainly through the delivery date reliability as well as the offered delivery time. A central success factor here is the consideration of the industry-typical great uncertainty of information with regard to expected future capacity and material demands. However, so far no approach exists that allows the evaluation of different configuration measures in regeneration supply chains under the influence of various uncertainties. Therefore, a quantitative evaluation of possible and in common configurations of regeneration supply chains with respect to the achievable logistics performance as well as the resulting logistics costs shall be made possible. Therefore, among other things, evaluation models are developed on the basis of a comprehensive simulation model, which allow an evaluation of quantitative effects of different supply chain configurations on the realizable logistic as well as monetary target values


In the first funding period, optimization approaches for capacity planning with short-, medium- and long-term time horizons were developed on the basis of Bayesian networks. On the basis of, for example, usage data of the capital goods, these enable more precise forecasts with regard to expected regeneration orders in the future and the resulting workload. Based on this, basic mathematical models for describing the interdependencies between design options, which can be attributed to capacity and workload coordination, and the resulting supply situation in reassembly have already been derived in the second funding period. The focus was placed on the material supply of the reassembly from repair processes, a pool stage and new parts warehouses. Consequently, a model was developed that allows an evaluation of the missing parts situation in reassembly depending on the selection and quantity of pooling components used. In the third funding period, further models were developed for the description and evaluation of configuration decisions (e.g. in the disassembly and a further pooling stage) as well as the planning behavior under the influence of information uncertainty.

Over all three funding periods, subproject D1 focused on the logistical optimisation of regeneration processes within different scopes.

Current research and outlook

Currently, the research is being brought together in an evaluation model which, in addition to statements on process capability (supply chain structure and planning) and process reliability (control), also allows the impact of design or control changes on logistics performance and logistics costs to be evaluated without having to rely on exhaustive, use case-specific simulation studies. In addition, special focus is on the development of a model that allows the configuration and combination of multi-stage pool component inventories to design regeneration supply chains that are as robust as possible, while considering the achievable information acuity. In this context, it is also being investigated how in the future material requirements in regeneration can be better predicted and as a result further uncertainty can be removed from the regeneration process.

Subproject leader

Prof. Dr.-Ing. habil. Peter Nyhuis



International Scientific Journal Paper, peer-reviewed

  • Lucht, Torben; Mütze, Alexander; Kämpfer, Tim; Nyhuis, Peter (2021): Model-Based Approach for Assessing Planning Quality in Production LogisticsIEEE Access 9, S. 115077–115089
    DOI: 10.1109/ACCESS.2021.3104717
  • Heuer, Tammo; Lucht, Torben; Nyhuis, Peter (2020): Material Disposition and Scheduling in Regeneration Processes using Prognostic Data MiningProcedia Manufacturing 43 (2020) 208–214
    DOI: 10.1016/j.promfg.2020.02.138
  • Seitz, Melissa; Lucht, Torben; Keller, Christian; Ludwig, Christian; Strobelt, Rainer; Nyhuis, Peter (2020): Improving MRO order processing by means of advanced technological diagnostics and data mining approachesProcedia Manufacturing 43, S. 688–695
    DOI: 10.1016/j.promfg.2020.02.121
  • Denkena, B.; Nyhuis, P.; Bergmann, B.; Nübel, N.; Lucht, T. (2019): Towards an autonomous maintenance, repair and overhaul processProcedia Manufacturing 40, S. 77–82
    DOI: 10.1016/j.promfg.2020.02.014
  • Kuprat, T.; Nyhuis, P. (2016): Designing Capacity Synchronization within the Regeneration of Complex Capital GoodsIn: Universal Journal of Management 2016 (4(10)), S. 581
    DOI: 10.13189/ujm.2016.041008
  • Eickemeyer, S. C.; Herde, F.; Irudayaraj, P.; Nyhuis, P. (2014): Decision models for capacity planning in a regeneration environmentInternational Journal of Production Research 52 (23), S. 7007–7026
    DOI: 10.1080/00207543.2014.923122
  • Eickemeyer, S. C.; Borcherding, T.; Schäfer, S.; Nyhuis, P. (2013): Validation of data fusion as a method for forecasting the regeneration workload for complex capital goodsProd. Eng. Res. Devel. 7 (2-3), S. 131–139
    DOI: 10.1007/s11740-013-0444-8

International Conference Paper, peer-reviewed

  • Heuer, Tammo; Wildmann, Benedict; Lucht, Torben; Nyhuis, Peter (2020): Regeneration Supply Chain Model and Pool Stock DimensioningNyhuis, P.; Herberger, D.; Hübner, M. (Eds.): Proceedings of the 1st Conference on Production Systems and Logistics (CPSL 2020)
  • Lucht, Torben; Heuer, Tammo; Nyhuis, Peter (2020): Disassembly sequencing in the regeneration of complex capital goodsNyhuis, P.; Herberger, D.; Hübner, M. (Eds.): Proceedings of the 1st Conference on Production Systems and Logistics (CPSL 2020)
    DOI: 10.15488/9642
  • Lucht, T.; Kämpfer, T.; Nyhuis, P. (2019): Characterization of supply chains in the regeneration of complex capital goods.Dimitrov, D., Hagedorn-Hansen, D. und Leipzig, K. von (Hg.): International Conference on Competitive Manufacturing (COMA 19), 31 January-2 February 2019, Stellenbosch, South Africa. Knowledge valorization in the age of digitalization. S. 444–449.
  • Hoffmann, L.-S.; Kuprat, T.; Kellenbrink, C.; Schmidt, M.; Nyhuis, P. (2017): Priority based planning approaches for regeneration processesProcedia CIRP 2017 (59), 89-94
  • Kuprat, T.; Schmidt, M.; Nyhuis, P. (2016): Model-based analysis of reassembly processes within the regeneration of complex capital goodsIn: Procedia CIRP 2016 (55), S. 206
  • Eickemeyer, S. C.; Steinkamp, S.; Schuster, B.; Schäfer, S. (2014): From Fuzzy Maintenance, Repair and Overhaul Data to Reliable Capacity PlanningNew Production Technologies in Aerospace Industry Proceedings of the 4th Machining Innovations Conference, Hannover, September 2013: Springer International Publishing Switzerland 2014, S. 181–186
    DOI: 10.1007/978-3-319-01964-2_24
  • Kellenbrink, C.; Herde, F.; Eickemeyer, S. C.; Kuprat, T.; Nyhuis, P. (2014): Planning the Regeneration Processes of Complex Capital GoodsProcedia CIRP 24, S. 140–145
    DOI: 10.1016/j.procir.2014.08.001
  • Eickemeyer, S. C.; Nyhuis, P. (2010): Capacity Planning and Coordination with Fuzzy Load InformationThe Business Review, Cambridge 16 (1), S. 259–264

National Scientific Journal Paper, peer-reviewed

  • Eickemeyer, S. C.; Herde, F. (2012): Regeneration komplexer Investitionsgüter - Potenziale für Kapazitätsplanung und -steuerung sowie AuftragsannahmeZeitschrift für wirtschaftlichen Fabrikbetrieb 2012 (10), S. 761–765

National Scientific Journal Paper, not peer-reviewed

  • Lucht T.; Schäfers, P.; Nyhuis, P. (2018): Durchgängige modellbasierte Bewertung von RegenerationslieferkettenZWF - Zeitschrift für wirtschaftlichen Fabrikbetrieb 113 (4), S. 220–224
    DOI: 10.3139/104.111893
  • Kuprat, T.; Burmeister, T.; Nyhuis, P. (2017): Bewertung von Gestaltungsoptionen in der RegenerationZWF - Zeitschrift für wirtschaftlichen Fabrikbetrieb 2017 (112) (6), 396-400
  • Kuprat, T.; Schmidt, M.; Nyhuis, P. (2016): Gestaltung von Regenerationslieferketten - Bewertung der Synchronität mithilfe des BereitstellungsdiagrammesIn: ZWF - Zeitschrift für wirtschaftlichen Fabrikbetrieb 2016 (12), 809-812
  • Kuprat, T.; Nyhuis, P. (2015): Konfiguration von regenerationsspezifischen GestaltungsoptionenZeitschrift für den wirtschaftlichen Fabrikbetrieb 2015 (5) (110), S. 277–280
  • Eickemeyer, S. C.; Kruse, S.; Hübner, M.; Schäfer, S. (2013): Mathematische Modelle zur bedarfsgerechten KapazitätsplanungZWF 108 (07-08/2013), S. 552–555
  • Eickemeyer, S. C.; Busch, J.; Heinke, Y.; Goßmann, D. (2012): Verfügbarkeitsoptimierung in der KapazitätsplanungZWF 107 (12), S. 903–907
  • Eickemeyer, S. C.; Goßmann, D.; Wesebaum, S.; Nyhuis, P. (2012): Entwicklung einer Schadensbibliothek für die Regeneration komplexer InvestitionsgüterIndustrie Management 28 (2), S. 58–61
  • Eickemeyer, S. C.; Doroudian, S.; Schäfer, S.; Nyhuis, P. (2011): Ein generisches Prozessmodell für die Regeneration komplexer InvestitionsgüterZWF 106 (11), S. 861–865
  • Eickemeyer, S. C.; Krüger, C.; Nyhuis, P. (2010): Kapazitätsplanung und -abstimmung bei unscharfen BelastungsinformationenZWF 105 (4), S. 323–327


  • Kuprat T. (2018): Modellgestütztes Ersatzteilmanagement in der Regeneration komplexer InvstitionsgüterGarbsen: TEWISS Verlag
  • Eickemeyer, S.C. (2014): Kapazitätsplanung und -abstimmung für die Regeneration komplexer InvestitionsgüterLeibniz Universität Hannover, Garbsen: Berichte aus dem IFA
    ISBN: 978-3-944586-77-9
  • Berkholz, D. A. (2012): Grundmodell zur Kapazitäts- und Belastungsabstimmung eines Arbeitssystems in der RegenerationBerichte aus dem IFA, Garbsen 2012
    ISBN: 978-3-943104-60-8
All publications of the Collaborative Research Centre