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Industry 4.0, based on increasingly progressive digitalization, is a global phenomenon that affects every part of our work. The Internet of Things (IoT) is pushing the process of automation, culminating in the total autonomy of cyber-physical systems. This process is accompanied by a massive amount of data, information, and new dimensions of flexibility. As the amount of available data increases, their specific timeliness decreases. Mastering Industry 4.0 requires humans to master the new dimensions of information and to adapt to relevant ongoing changes. Intentional forgetting can make a difference in this context, as it discards nonprevailing information and actions in favor of prevailing ones. Intentional forgetting is the basis of any adaptation to change, as it ensures that nonprevailing memory items are not retrieved while prevailing ones are retained. This study presents a novel experimental approach that was introduced in a learning factory (the Research and Application Center Industry 4.0) to investigate intentional forgetting as it applies to production routines. In the first experiment (N = 18), in which the participants collectively performed 3046 routine related actions (t1 = 1402, t2 = 1644), the results showed that highly proceduralized actions were more difficult to forget than actions that were less well-learned. Additionally, we found that the quality of cues that trigger the execution of routine actions had no effect on the extent of intentional forgetting.
Business processes are regularly modified either to capture requirements from the organization’s environment or due to internal optimization and restructuring. Implementing the changes into the individual work routines is aided by change management tools. These tools aim at the acceptance of the process by and empowerment of the process executor. They cover a wide range of general factors and seldom accurately address the changes in task execution and sequence. Furthermore, change is only framed as a learning activity, while most obstacles to change arise from the inability to unlearn or forget behavioural patterns one is acquainted with. Therefore, this paper aims to develop and demonstrate a notation to capture changes in business processes and identify elements that are likely to present obstacles during change. It connects existing research from changes in work routines and psychological insights from unlearning and intentional forgetting to the BPM domain. The results contribute to more transparency in business process models regarding knowledge changes. They provide better means to understand the dynamics and barriers of change processes.
Consumer behaviour changes and strategic management decisions are driving adaptations in manufacturing routines. Based on the theory of situational strength, we investigated how contextual and person-related factors influence workers’ adaptation in a two-worker position routine. Contextual factors, like retrieval cues (Study 1), time pressure (Study 2), and convenience (Study 3), were varied. Person-related factors included retentivity, general and routine-specific self-efficacy, and perceived adaptation costs. Dependent variables included various error types and production time before and after adaptation. In each study, 148 participants were trained in a production routine at t1 and executed an adapted routine at t2, one week later. Repeated measures ANOVA for performance at t1 and t2, and MANOVA for performance at t2, revealed that time increased for all groups at t2. For participants in Studies 1 & 2, error rates remained consistent. Retentivity significantly impacted errors at both t1 and t2, emphasising that routine changes in a ‘running business’ take time, regardless of contextual factors. Workers with lower retentivity may require additional support.