Influencing factors on worker task sequencing decisions in a medium-sized hydraulic cylinder manufacturer

• Published in: Procedia computer science Vol. 253; pp. 1820 - 1829
• Main Authors: Herrmann, Jan-Phillip, Tackenberg, Sven, Burgert, Florens, Nitsch, Verena
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2025
• Subjects: Case Study; Human Factors; Learning To Rank; Manufacturing; Scheduling; Task Sequencing; Learning To Rank; Scheduling; Manufacturing; Human Factors; Task Sequencing; Case Study
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2025.01.244

In weakly-structured work processes, workers are free to decide in which sequence to process their tasks. Predicting their decision-making helps plan production more accurately while preserving workers’ autonomy. The factors that influence workers’ decision-making depend on the manufacturing process and person considered, and they must be newly collected for each use case. This paper identifies the factors influencing workers when deciding in which sequence to process manufacturing tasks in a medium-sized hydraulic cylinder manufacturer. Five workers and two lead workers were observed and interviewed during several work shifts about influencing factors. The authors propose a new interview technique called indifference testing to overcome subjects’ difficulty articulating their decision-making process. Collected factors were categorized using inductive category formation and context analysis. The analyses identified 75 influencing factors comprising 37 decision attributes and 38 decision rules. The identified decision attributes indicate that worker preferences are influenced by attributes from the classical scheduling literature and attributes related to worker well-being, circadian rhythms, and ergonomics. The identified decision rules are useful constituents of more complex preference functions. The decision attributes and rules enable the construction of machine learning models to predict workers’ task sequencing decisions in job shops. Potential applications include systematically eliminating or controlling influencing factors through workplace design measures to increase worker well-being and optimality of their decisions.