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Area of Science:

  • Operations Research
  • Industrial Engineering
  • Performance Management

Background:

  • Existing production system evaluation methods often use linear normalization, which may not accurately reflect real-world stakeholder preferences.
  • A three-level hierarchy (Evaluation Groups, Key Performance Indicators, Performance Indicators) is common in performance evaluation systems.

Purpose of the Study:

  • To develop an improved performance evaluation method that addresses the limitations of linear normalization.
  • To introduce a more realistic way to scale performance indicators (PIs) that aligns with evaluator preferences.

Main Methods:

  • The study adapts a hierarchical evaluation structure.
  • It replaces linear normalization of PIs with an S-shaped curve derived from the Weibull distribution.
  • Detailed steps for structuring the evaluation system and calibrating S-curve parameters are provided.

Main Results:

  • The proposed S-curve normalization method provides a more accurate representation of evaluator preferences compared to linear scaling.
  • Evaluation scales are demonstrated to be non-linear in reflecting performance.

Conclusions:

  • The S-curve, calibrated to evaluator preferences, offers a superior approach to performance evaluation.
  • This method enhances the fidelity of production system performance assessment.