Purpose: Industry 4.0 technologies influence how production is planned, scheduled, and controlled. In literature, different classifications of the tasks and functions of production planning and control (PPC) exist, of which one is the German Aachen PPC model. This research aims to identify and classify current Industry 4.0 approaches for planning and controlling production processes and to reveal researched and unexplored areas of the model.Design/methodology/approach: In an exploratory literature review, we review and classify 48 publications on a full-text basis with the Aachen PPC model's tasks and functions. Two cluster analyses reveal researched and unexplored tasks and functions of the Aachen PPC model. Additionally, we give a summary of each reviewed publication.Findings: We propose a cyber-physical PPC architecture, which incorporates current Industry 4.0 technologies, current optimization methods, optimization objectives, and disturbances relevant for realizing a PPC system in a smart factory. Current approaches focus on the in-house PPC, particularly on the control using real-time information from the shop floor. We propose future research directions for the unexplored tasks and functions of the Aachen PPC model.Research limitations/implications: The selection of search terms and the texts' interpretation is based on an individual assessment. The revelation of unexplored tasks and functions of the Aachen PPC model might have a different outcome if the search term combination is parameterized differently.Originality/value: Using the Aachen PPC model, which holistically models PPC, the findings give comprehensive insights into the current advances of tools, methods, and challenges relevant to planning and controlling production processes under Industry 4.0.

Predicting human decisions is a central challenge for planning and controlling production with weakly structured processes. Thus, workers’ decisions regarding the processing strategies and the temporal sequence of tasks to be processed are to be determined prospectively. Accordingly, there is a need to review methods for preference elicitation to develop individual predictive decision models. This paper presents a systematic literature review and discussion of 42 publications on predictive decision models and decision attributes. Methods for eliciting decision-making knowledge from manufacturing workers as part of the modeling process and decision model validation methods are reviewed and discussed in light of their predictive validity for individual task selection. The article synthesizes the recent literature for predicting human decision-making in manufacturing using artificial intelligence methods. Along with the review results, a future research agenda is proposed for modeling and simulating human decision-making in manufacturing. Knowledge about human preferences and the successful prediction of workers’ decision-making in manufacturing helps companies predict manufacturing objectives and derive organizational and work design measures.

Technological progress, upcoming cyber-physical systems, and limited resources confront small and medium-sized enterprises (SMEs) with the challenge of complexity management in product development projects spanning over the entire product lifecycle. SMEs require a solution for documenting and analyzing the functional relationships between multiple domains such as products, software, and processes. The German research project FuPEP “Funktionsorientiertes Komplexitätsmanagement in allen Phasen der Produktentstehung” aims to address this issue by developing an assistance system that supports product developers by visualizing functional relationships. This paper presents the methodology and results of the assistance system’s requirements elicitation with two SMEs. Conducting the elicitation during a global pandemic, we discuss its application using specific techniques in light of COVID-19. We model problems and their effects regarding complexity management in product development in a system dynamics model. The most important requirements and use cases elicited are presented, and the requirements elicitation methodology and results are discussed. Additionally, we present a multilayer software architecture design of the assistance system. Our case study suggests a relationship between fear of a missing project focus among project participants and the restriction of requirements elicitation techniques to those possible via web conferencing tools.