David Lenze, M.Sc., studied industrial engineering at the TU Dortmund University and works as a research assistant at the IPS as well as the cooperation partner RIF e. V.. His substantive focus is on the design of new and optimization of existing production systems in numerous industrial projects, and his doctoral focus is on the development of a simulation-based methodology for buffer design. Since 2016, he has been leading the Production System Dynamics research area. Mr. Lenze is a Six Sigma Green Belt.

• Development of production planning and control concepts with special focus on variability effects 
• Dynamic bottleneck identification and prediction by application of machine learning methods
• Simulation-based design of buffers in variability-influenced production systems

• Supervision of project and final theses
• Execution of lectures of the IPS
• Execution of tutorials to the lectures of the IPS

• Planning and design of production systems (e.g. value stream analysis and design, buffer design)
• Execution of time studies and software-supported preparation of time data into transferable planning time modules
• Development and implementation of production planning and control concepts (line balancing, sequencing, order release)

• Deuse, Jochen; Lenze, David (2016): Limits of Lean Thinking. Six Sigma & Data Analytics im Kontext wertschöpfender Variabilität. Lean Six Sigma trifft Industrie 4.0 und Big Data. MTS-Consulting. Düsseldorf: 28.04.2016.
• Willats, Peter; Deuse, Jochen; Lenze, David (2016): A learning pathway from Lean Management towards Factory Physics. European Lean Educator Conference (ELEC). Buckingham, England: 14.09.2016.
• Roser, Christoph; Lorentzen, Kai; Lenze, David; Deuse, Jochen; Klenner, Ferdinand; Richter, Ralph et al. (2017): Bottleneck Prediction Using the Active Period Method in Combination with Buffer Inventories. In: Lödding, Hermann, Riedel, Ralph et al. (Hg.): Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing. IFIP International Conference on Advances in Production Management Systems. Hamburg, 3.-7.09.2017, S. 374–381.
• Deuse, Jochen; Heuser, Christoph; Konrad, Benedikt; Lenze, David; Maschek, Thomas; Wiegand, Mario; Willats, Peter (2016): Pushing the Limits of Lean. How Industrial Engineers cope with Value Adding Variability. In: International Joint Conference. Building bridges between researchers and practitioners. International Joint Conference (IJC). San Sebastián, Spanien, 13.-15.07.2016.
• Deuse, Jochen; Heuser, Christoph; Konrad, Benedikt; Lenze, David; Maschek, Thomas; Wiegand, Mario; Willats, Peter (2016): Pushing the Limits of Lean Thinking. Design and Management of Complex Production Systems. In: Viles, Elisabeth, Ormazábal, Marta et al. (Hg.): Closing the Gap Between Practice and Research in Industrial Engineering. International Joint Conference (IJC). San Sebastián, Spanien, 13.-15.07.2016: Springer International Publishing, S. 335–342.
• Deuse, Jochen; Lenze, David; Klenner, Ferdinand; Friedrich, Tilman (2016): Manufacturing Data Analytics zur Identifikation dynamischer Engpässe in Produktionssystemen mit hoher wertschöpfender Variabilität. Christopher Schlick (Hg.): Megatrend Digitalisierung. Potenziale der Arbeits- und Betriebsorganisation. Aachen. In: Schlick, Christopher (Hg.): Megatrend Digitalisierung. Potenziale der Arbeits- und Betriebsorganisation. Aachen, 09.-10.09.2016. Wissenschaftliche Gesellschaft für Arbeits- und Betriebsorganisation (WGAB) e.v., S. 11–26.
• Deuse, Jochen; Achenbach, Marlies; Lenze, David (2015): Hybrides Ausbildungskonzept für den Umgang mit Systemkomplexität. In: Meier, Horst (Hg.): Lehren und Lernen für die moderne Arbeitswelt. 28. HAB-Forschungsseminar. Bochum, 09.-10.10.2015. Berlin: Gito (Schriftenreihe der Hochschulgruppe für Arbeits- und Betriebsorganisation e.V. (HAB)), S. 249–264.
• Klenner, Ferdinand; Lenze, David; Schwarzer, Samuel; Deuse, Jochen; Friedrich, Tilman (2016): Smart Data Analytics zur Identifikation dynamischer Engpässe in Flexiblen Fertigungssystemen. In: at - Automatisierungstechnik 64 (7), S. 540–554.