David Lenze

TU Dortmund University

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i-10 index

Research Trends

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Document Type

Co-Authors

Jochen Deuse

University of Technology Sydney

Peter Willats

University of Buckingham

Marius Syberg

TU Dortmund University

Ralph Richter

TU Dortmund University

Jacqueline Schmitt

National Heart Foundation of Australia

Thomas Maschek

TU Dortmund University

Benedikt Konrad

TU Dortmund University

Laura Tomidei

University of Technology Sydney

Nathalie Sick

University of Technology Sydney

Christoph Roser

Karlsruhe University of Applied Sciences

Cooperative Institutions

TU Dortmund University

University of Technology Sydney

Institut für Forschung und Transfer

Amberg (Czechia)

Feuerwehr Dortmund

Australian Centre for Robotic Vision

Robert Bosch (Germany)

National Heart Foundation of Australia

University of South Australia

Georgia Institute of Technology

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Pushing the Limits of Lean Thinking–Design and Management of Complex Production Systems

Lecture notes in management and industrial engineering (2017)

Jochen Deuse Christoph Heuser Benedikt Konrad David Lenze Thomas Maschek

Factory (object-oriented programming)

Value (mathematics)

Production manager

10.1007/978-3-319-58409-6_37

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Citations (5)

Framework for predictive sales and demand planning in customer-oriented manufacturing systems using data enrichment and machine learning

Procedia CIRP (2023)

Marius Syberg Nikolai West David Lenze Jochen Deuse

Companies with Make-to-Order (MTO) manufacturing have always faced the conflict of meeting a large volume of individual customer orders on time while remaining as flexible as possible. Unlike Make-to-Stock (MTS) manufacturing, planning the production requirements for MTO is a challenging task since incoming orders may vary in time and quantity, while also being subject to a number of variables. In some cases, the delivery time allocated by the customer can be less than the required Order Lead Time to fulfil the order. Manufacturers can respond to this with either with approached from production management or from data science. This paper presents a framework to leverage the benefit of both domains. We conduct a literature review and present the results of an expert workshop. We propose criteria for a suitable data enrichment and the application of Machine Learning (ML) methods in sales and demand forecasting. In conclusion, the proposed framework helps to equip manufacturing companies with a structured strategy for data management and to utilize the benefit of ML for sales and demand forecasting.

Leverage (statistics)

Lead time

Build to order

10.1016/j.procir.2023.09.133

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Citations (1)

Bottleneck Prediction Using the Active Period Method in Combination with Buffer Inventories

IFIP advances in information and communication technology (2017)

Christoph Roser Kai Lorentzen David Lenze Jochen Deuse Ferdinand Klenner

Buffer (optical fiber)

10.1007/978-3-319-66926-7_43

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Citations (11)

Creating lean value streams through proactive variability management

International Journal of Production Research (2022)

Ralph Richter Marius Syberg Jochen Deuse Peter Willats David Lenze

Complex product and production systems often result in high variability in the production flow, prohibiting the sustainable implementation of lean practices. In this paper the authors introduce a PDCA cycle to analyse and reduce variability in value streams. The value stream is divided into zones, which are then qualified as stable or unstable. Lean practices can be applied in stable zones, unstable zones remain expert-driven. Measures are introduced to reduce variability in unstable zones with the ultimate target, to turn them into stable zones, extending sustainable lean activities in the value stream, step by step. An IT system is developed to acquire, process and visualise the vast amount of data to provide structured information for experts and management for the reduction of variability in production.

Value stream mapping

PDCA

Value (mathematics)

10.1080/00207543.2022.2111614

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Citations (3)

Dynamic Value Stream Mapping—How Industry 4.0 Can Help Us to Learn to See Better

Lecture notes in mechanical engineering (2023)

Laura Tomidei Nathalie Sick Matthias R. Guertler Julian Schallow David Lenze

Value stream mapping

10.1007/978-3-031-34821-1_82

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