Digital Lean - The Crossroads-Model for Controlling Material Flows in Production and Logistics Systems

Erklärung und Auswahl von Steuerungsansätzen für Produktions- und Logistiksysteme in Zeiten der Digitalisierung

JournalIndustrie 4.0 Management
Issue Volume 34, 2018, Edition 5, Pages 33-38
Open Accesshttps://doi.org/10.30844/I40M18-5_33-38
Bibliography Share Cite Download

Abstract

Methods for monitoring and controlling material flows in a production or logistics system should support objectives like costs and throughput-time. Lean focuses on decentral, demand-driven steering of activities. Advanced manufacturing concepts for Smart Factories rely on innovative digital technologies. Which method is the best fit for steering the material flow? The Crossroads-Model explains different approaches and supports the selection of a suitable method for corporate practice.

Keywords


Bibliography

[1] Ohno, T.: Das Toyota-Produktionssystem: Das Standardwerk zur Lean Production, 3. Auflage. Frankfurt am Main New York 2013.
[2] Womack, J. P.; Jones, D. T.: Lean Thinking: Ballast abwerfen, Unternehmensgewinne steigern, 3. Auflage. Frankfurt New York 2013.
[3] Erlach, K.: Wertstromdesign: Der Weg zur schlanken Fabrik, 2. Auflage. Wiesbaden 2010.
[4] Ziegenbein, R.: Die Grundprinzipien der schlanken Fertigung. In: Ziegenbein,R (Hrsg): Handbuch Lean-Konzepte für den Mittelstand. Münster 2014, S. 1-12.
[5] Heise online: Telekom-Chef: „Alles wird vernetzt“. URL: https://www.heise./newstik-

ker/meldung/Telekom-Chef-Alles-wird-vernetzt-2661572.html, Abrufdatum 07.02.2018.
[6] Arbeitskreis Industrie 4.0: Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0 – Abschlussbericht des Arbeitskreises Industrie 4.0. 2013.
[7] Schuh, G.; Anderl, R.; Gausemeier, J.; ten Hompel, M.; Wahlster, W.: Industrie 4.0 Maturity Index – Die digitale Transformation von Unternehmen gestalten, Studie der acatech – Deutsche Akademie der Technikwissenschaften. München Berlin Brüssel 2017.
[8] Vogel-Heuser, B.; Bauernhansel, T.; ten Hompel, M.: Handbuch Industrie 4.0 Bd. 1: Produktion. Wiesbaden 2017.
[9] Appelfeller, W.; Feldmann, C.: Die digitale Transformation des Unternehmens – Ein systematischer Leitfaden: Zehn Elemente zur Strukturierung und Reifegradmessung auf dem Weg zum digitalen Unternehmen. Wiesbaden 2018.
[10] Roy, D.; Mittag, P.; Baumeister, M.: Industrie 4.0 – Einfluss der Digitalisierung auf die fünf Lean-Prinzipien: Schlank vs. Intelligent. In: productivity. URL: https://productivity-management.de/node/523, Abrufdatum 19.02.2018.
[11] Porter, M. E.; Heppelmann, J. E.: How Smart, Connected Products Are Transforming Companies. In: Harvard Business Review 93 10 (2015) 10, S. 97-114.
[12] Hanschke, I.: Lean Management – Erfolgsfaktor für das IT-Management. In: Lang, M.

(Hrsg): CIO-Handbuch – Band 4: Strategien für die digitale Transformation. Düsseldorf 2016, S. 19-38.
[13] Soder, J.: Use Case Production – Von CIM über Lean Production zu Industrie 4.0. In: Vogel-Heuser, B.; Bauernhansel, T.; ten Hompel, M. (Hrsg): Handbuch Industrie 4.0 Bd. 1 – Produktion. Berlin 2016, S. 3-26.
[14] Feldmann, C.; Gorj, A.: 3D-Druck und Lean Production – Schlanke Produktionssysteme mit additiver Fertigung. Wiesbaden 2017.
[15] Weinreich, U.: Lean Digitization – Digitale Transformation durch agiles Management. Berlin Heidelberg 2016.
[16] Ketteler, D.; König, C.: Lean 4.0 – Schlank durch Digitalisierung. BearingPoint Studie. URL: https://www.bearingpoint.com/de-de/unsere-expertise/insights/lean-40 schlank-durch-digitalisierung, Abrufdatum 06.02.2018.
[17] Adam, D.: Planung und Entscheidung. Modelle – Ziele – Methoden; mit Fallstudien und Lösungen, 4. Auflage. Wiesbaden 1996.
[18] Emiliani, B.: Digital Transformation & Lean Transformation.URL: http://www.bobemiliani.com/digital-transformation-lean-transformation, Abrufdatum 06.02.2018.

Your downloads


Solutions: Process Management

You might also be interested in

Serious Games as a Training Tool

Serious Games as a Training Tool

Game mechanics design to promote resilience
Annika Lange ORCID Icon, Thomas Knothe ORCID Icon
Unforeseen events are increasingly challenging manufacturing companies. Being resilient during crises is becoming a key competence. Serious games (SG) can help make resilience-building processes more transparent. This article derives specific requirements for SG from different phases of resilience and shows how these can be implemented in game mechanics in order to effectively support the training of resilience.
Industry 4.0 Science | Volume 42 | 2026 | Edition 2 | Pages 98-104
From Brownfield to Industry 4.0

From Brownfield to Industry 4.0

Learning factories as training and testing environment for digital transformation
Jakob Weber, Sven Völker ORCID Icon
To succeed in their digital transformation, manufacturing companies need engineers with in-depth knowledge of key technologies and concepts, and a profound understanding of the transition from Industry 3.0 to Industry 4.0. This article describes the concept of a learning factory that is continuously subjected to a digital transformation, thereby creating an environment for the development of transformation competencies. The concept of digital transformation is based on digital worker assistance systems and multi-agent systems for production control. These enable the incremental integration of existing resources into the digitalized factory. The learning factory is not presented to students as a completed solution. Instead, it is continuously developed further as part of student projects. This way, it contributes directly to the qualification of personnel for the implementation of Industry 4.0.
Industry 4.0 Science | Volume 42 | 2026 | Edition 2 | Pages 88-96
AI Colleagues?

AI Colleagues?

Competence requirements and training for AI use in industry
Swetlana Franken ORCID Icon
Artificial intelligence is fundamentally changing tasks, roles, and skills in (industrial) companies. Increasingly, it acts as a colleague, preparing decisions, supporting processes, and interacting with people. This article highlights key competence requirements for AI use in industry, presents an integrated competence model, and outlines practical strategies for the transfer of skills. The aim is to prepare companies and employees for humane, competence-oriented AI implementation that combines technological efficiency with human creativity and judgment.
Industry 4.0 Science | Volume 42 | 2026 | Edition 2 | Pages 78-86
Operationalizing Ethical AI with tachAId

Operationalizing Ethical AI with tachAId

Validating an interactive advisory tool in two manufacturing use cases
Pavlos Rath-Manakidis, Henry Huick, Björn Krämer ORCID Icon, Laurenz Wiskott ORCID Icon
Integrating artificial intelligence (AI) into workplace processes promises significant efficiency gains, yet organizations face numerous ethical challenges that stakeholders are often initially unaware of—from opacity in decision-making to algorithmic bias and premature automation risks. This paper presents the design and validation of tachAId, an interactive advisory tool aimed at embedding human-centered ethical considerations into the development of AI solutions. It reports on a validation study conducted across two distinct industrial AI applications with varying AI maturity. tachAId successfully directs attention to critical ethical considerations across the AI solution lifecycle that might be overlooked in technically-focused development. However, the findings also reveal a central tension: while effective in raising awareness, the tool’s non-linear design creates significant usability challenges, indicating a user preference for more structured, linear guidance, especially ...
Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 50-59 | DOI 10.30844/I4SE.26.1.48
AI Implementation in Industrial Quality Control

AI Implementation in Industrial Quality Control

A design science approach bridging technical and human factors
Erdi Ünal ORCID Icon, Kathrin Nauth ORCID Icon, Pavlos Rath-Manakidis, Jens Pöppelbuß ORCID Icon, Felix Hoenig, Christian Meske ORCID Icon
Artificial intelligence (AI) offers significant potential to enhance industrial quality control, yet successful implementation requires careful consideration of ethical and human factors. This article examines how automated surface inspection systems can be deployed to augment human capabilities while ensuring ethical integration into workflows. Through design science research, twelve stakeholders from six organizations across three continents are interviewed and twelve sociotechnical design requirements are derived. These are organized into pre-implementation and implementation/operation phases, addressing human agency, employee participation, and responsible knowledge management. Key findings include the critical importance of meaningful employee participation during pre-implementation, and maintaining human agency through experiential learning, building on existing expertise. This research contributes to ethical AI workplace implementation by providing guidelines that preserve human ...
Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 120-127 | DOI 10.30844/I4SE.26.1.112
Guidelines for the Fair Use of Generative AI

Guidelines for the Fair Use of Generative AI

Practical examples from production management and social welfare
Anja Gerlmaier, Paul-Fiete Kramer ORCID Icon, Dirk Marrenbach ORCID Icon, René Wenzel ORCID Icon
With the rapid spread of assistive AI tools such as ChatGPT, Gemini, and Copilot, companies are being challenged to address the opportunities and challenges of artificial intelligence. Based on two practical examples, this article provides insight into how companies can use company-specific risk and potential analyses to develop guidelines for the fair and responsible use of AI.
Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 22-28 | DOI 10.30844/I4SE.26.1.22