To ensure qualitatively sufficient results for the analysis of complex and diverse production programs, suitable analysis approaches must be utilized. As computer-based cluster algorithms become more widely used in this context, and as the demand for improved communication and coordination with the plant’s stakeholders increases, there is an opportunity to integrate operator experience into clustering algorithms for production programs. This paper investigates whether and when the integration of operator experience is beneficial for this analysis. A single case study approach is utilized for this purpose, gaining insight and deriving general recommendations for integrating operator experience. While the operator’s experience can enhance planning efficiency through tacit knowledge and insights in the form of inputs or feedback loops, it is susceptible to biases and must be checked by statistical analysis.

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Experience-Integrated Product Family Formation Using Clustering Algorithms

  • Tobias Bein,
  • Ulf Bergmann,
  • Oliver Antons,
  • Julia C. Arlinghaus

摘要

To ensure qualitatively sufficient results for the analysis of complex and diverse production programs, suitable analysis approaches must be utilized. As computer-based cluster algorithms become more widely used in this context, and as the demand for improved communication and coordination with the plant’s stakeholders increases, there is an opportunity to integrate operator experience into clustering algorithms for production programs. This paper investigates whether and when the integration of operator experience is beneficial for this analysis. A single case study approach is utilized for this purpose, gaining insight and deriving general recommendations for integrating operator experience. While the operator’s experience can enhance planning efficiency through tacit knowledge and insights in the form of inputs or feedback loops, it is susceptible to biases and must be checked by statistical analysis.