The impact of work instruction simplification on operator performance and learning curve efficiency

This study investigates how the simplification of work instructions influences operator performance and learning curve efficiency in repetitive industrial tasks. Modern manufacturing environments remain highly dependent on human labor, yet high employee turnover and increasing product variability place growing demands on training and instructional systems. Work instructions are a fundamental component of production processes; however, they are often overly detailed, which may increase cognitive load and reduce efficiency once operators become familiar with the task. The research addresses this challenge by examining whether instruction abstraction, introduced after an initial learning phase, can improve performance without compromising task accuracy.

Ethical approval number: 2025-028

Importantly, the introduction of simplified instructions after familiarization did not lead to an increase in errors. On the contrary, error rates generally decreased in the second session, suggesting that participants had internalized the task structure and no longer required highly detailed guidance. Participants who initially worked with semi-detailed instructions demonstrated a stronger improvement when switching to the shortened format, highlighting the benefit of building a solid mental model before reducing instructional detail. While some individuals were able to perform efficiently even with minimal instructions from the outset, detailed instructions proved valuable in supporting early learning for others.

Overall, the findings support the hypothesis that work instructions should evolve alongside operator proficiency. Starting with more detailed guidance can facilitate understanding and error prevention during early learning, while later abstraction and step merging can reduce cognitive load and enhance execution speed in repetitive tasks. Although the study is exploratory and limited by a small sample size and laboratory conditions, it demonstrates the potential of adaptive instruction strategies to improve operator performance. The results provide a foundation for future research into adaptive and intelligent work instruction systems that dynamically adjust their level of detail based on operator experience and task context

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