Integrated Order Planning for Multi-Variant Products

Generation, network allocation and customer matching of planned orders

Jens Bürgin, Han Hao, Gisela Lanza ORCID Icon, Hansjörg Tutsch
JournalIndustrie 4.0 Management
Issue Volume 33, 2017, Edition 5, Pages 29-32
Share Cite Download

Abstract

Medium-term order planning for the serial production of multi-variant products such as automobiles enables an integrated planning of procurement, production and sales as well as robust and optimized plans. In addition, continuity between medium-term and short-term planning can be achieved through a medium-term order basis. This article presents a planning methodology for the generation of planned orders to create a medium-term order basis, the assignment of planned orders to plants in a production network, and the assignment of incoming customer orders to planned orders.

Keywords

, ,

Your downloads

You might also be interested in

Learning Factories for the Future of Manufacturing in Brazil

Learning Factories for the Future of Manufacturing in Brazil

Advancing manufacturing through technology and skills development
Manufacturing firms in developing countries face challenges in closing productivity gaps while adopting Industry 4.0 technologies. Learning factories are one helpful approach to countering these challenges. One such example is the learning factory Fábrica do Futuroin São Paulo, Brazil, which has engaged students, supported competence development, and collaborated with industry in applied research, functioning as a hub for advanced manufacturing initiatives.
Serious Gaming and the Energy Transition

Serious Gaming and the Energy Transition

Collaborative knowledge generation and interactive understanding of complex interrelationships
Janine Gondolf ORCID Icon, Gert Mehlmann, Jörn Hartung, Bernd Schweinshaut, Anne Bauer
Conveying the complexity and multifaceted nature of the energy transition to a broad audience is a challenge. This article demonstrates how interactive serious games on a multitouch table can help make connections tangible and comprehensible. The games and the table were used in various conversational contexts. These are presented here in three case vignettes based on participant observation of the different applications, as well as situated and shared reflection. The vignettes demonstrate how interaction can trigger epistemic processes, enable shifts in perspective, and foster collective thinking, all of which are necessary for shaping the future of society as a whole.
Industry 4.0 Science | Volume 42 | 2026 | Edition 2 | Pages 62-69
Experiencing Digital Twins in Production and Logistics

Experiencing Digital Twins in Production and Logistics

The fischertechnik® Learning Factory 4.0 as a development platform for possible expansion stages
Deike Gliem ORCID Icon, Sigrid Wenzel ORCID Icon, Jan Schickram, Tareq Albeesh
The fischertechnik® Learning Factory 4.0 has proven to be a suitable experimental environment for testing digital twins. Depending on the targeted maturity stage, the functions of a digital twin range from status monitoring and forecasting to the operational control of production and logistics systems. To systematically classify these functions, this article presents a maturity model that serves as a framework for the development of a digital twin. Building on this, selected use cases are implemented in a test and development environment based on a system architecture with multi-layered logic structure. These initial implementations serve to highlight application purposes, relevant methods, and typical challenges and potentials in the transfer to real factory environments.
Industry 4.0 Science | Volume 42 | Edition 2 | Pages 30-37 | DOI 10.30844/I4SE.26.2.30
Collaborative Robots in Production Environments

Collaborative Robots in Production Environments

Employee qualification and acceptance for human-machine interaction
Tobias Wienzek, Mathias Cuypers ORCID Icon
The introduction of new technologies poses a major challenge, especially for small and medium-sized enterprises (SMEs). At the same time, SMEs must rise to this challenge in order to keep pace technologically and economically. Employee acceptance is an important factor in ensuring that both the introduction and the long-term use of a technology are successful. At the same time, the introduction process also has a central influence on acceptance in the long term. This article uses the implementation of collaborative robotics as an example for examining such an introduction process, identifying the key factors that influence employee acceptance and the important role played by advanced employee training. It serves to highlight how the introduction process and employee training are seamlessly interlinked.
Industry 4.0 Science | Volume 42 | 2026 | Edition 2 | Pages 14-21 | DOI 10.30844/I4SE.26.2.14
XAI for Predicting and Nudging Worker Decision-Making

XAI for Predicting and Nudging Worker Decision-Making

Feasibility and perceived ethical issues
Jan-Phillip Herrmann ORCID Icon, Catharina Baier, Sven Tackenberg ORCID Icon, Verena Nitsch ORCID Icon
Explainable artificial intelligence (XAI)-based nudging, while ethically complex, may offer a favorable alternative to rigid, algorithmically generated schedules that simultaneously respects worker autonomy and improves overall scheduling performance on the shop floor. This paper presents a controlled laboratory study demonstrating the successful nudging of 28 industrial engineering students in a job shop simulation. The study shows that the observed concordance between students’ sequencing decisions and a predefined target sequence increases by 9% through nudging. This is done by using XAI to analyze students’ preferences and adjusting task deadlines and priorities in the simulation. The paper discusses the ethical issues of nudging, including potential manipulation, illusory autonomy, and reducing people to numbers. To mitigate these issues, it offers recommendations for implementing the XAI-based nudging approach in practice and highlights its strengths relative to rigid, ...
Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 70-78
Applied AI for Human-Centric Assembly Workplace Design

Applied AI for Human-Centric Assembly Workplace Design

An ethics-informed approach
Tadele Belay Tuli ORCID Icon, Michael Jonek ORCID Icon, Sascha Niethammer, Henning Vogler, Martin Manns ORCID Icon
Artificial intelligence (AI) can enhance smart assembly by predicting human motion and adapting workplace design. Using probabilistic models such as Gaussian Mixture Models (GMMs), AI systems anticipate operator actions to improve coordination with robots. However, these predictive systems raise ethical concerns related to safety, fairness, and privacy under the EU AI Act, which classifies them as high-risk. This paper presents a conceptual method integrating probabilistic motion modeling with ethical evaluation via Z-Inspection®. An industrial case study using the Smart Work Assistant (SWA) demonstrates how multimodal sensing (motion, gaze) and interpretable models enable anticipatory assistance. The approach moves from ethics evaluation to ethics-informed work design, yielding transferable principles and a configurable assessment matrix that supports compliance-by-design in collaborative assembly.
Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 60-68 | DOI 10.30844/I4SE.26.1.58
Christoph Brocks