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Assessment of Product Variety Complexity.

Vladimir Modrak1, Zuzana Soltysova1

  • 1Faculty of Manufacturing Technologies, Technical University of Kosice, Bayerova 1, 080 01 Presov, Slovakia.

Entropy (Basel, Switzerland)
|January 21, 2023
PubMed
Summary

Product variety complexity assessment is crucial for manufacturing. This study introduces an information theory-based metric to better measure product variety complexity beyond just variant count, aiding mass customization efforts.

Keywords:
complexityentropymass customizationproduct configurationsproduct variety

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Area of Science:

  • Industrial Engineering
  • Operations Management
  • Systems Engineering

Background:

  • Product variety complexity significantly impacts manufacturing and supply chain operations.
  • Current complexity measures, often based solely on variant numbers, are insufficient.
  • Accurate complexity assessment is vital for effective system design and mass customization.

Purpose of the Study:

  • To develop a novel measurement method for product variety complexity.
  • To create a metric that accurately reflects the relationship between optional components and product variants.
  • To provide a tool for better understanding and reducing variety-induced manufacturing complexity.

Main Methods:

  • Application of information theory concepts to quantify complexity.
  • Development of a new complexity metric based on component-variant relationships.
  • Validation of the metric using a realistic design case study.

Main Results:

  • The proposed information theory-based metric effectively measures product variety complexity.
  • The metric provides a more nuanced understanding than simply counting product variants.
  • The developed metric aids in identifying and reducing complexity in mass customization scenarios.

Conclusions:

  • Information theory offers a robust framework for assessing product variety complexity.
  • The new metric enhances the ability to manage complexity in assembly systems and supply chains.
  • This approach serves as a valuable complementary tool for optimizing mass customization strategies.