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<p>Obtaining efficient and precise production planning has become a significant challenge in today’s industrial environment, particularly for companies with production systems characterized by a large number of mildly differentiated SKUs. Finding tools that can offer viable results and address demand uncertainties, without oversimplifying the process, can become problematic, sometimes even impossible for many companies, causing issues across the entire supply chain and potentially endangering some industrial sectors in their entirety. This paper aims to present a new Planning Bill of Material (PBOM) approach, leveraging mathematical tools and algorithms from graph theory to address the need for an easy-to-implement and ready-to-use production planning tool, able to identify sub-optimal solutions with reduced computational complexity. To validate the proposed approach, it was also applied in a real production planning situation, where it was used to revise, update, and improve the company’s older Planning Bill of Materials system.</p>

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Enhancing production efficiency with an optimized PBOM structure: a clustering-based methodology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月10日

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  • Italo Cesidio Fantozzi,
  • Luca Delfini,
  • Luca Martuscelli,
  • Dario Antonelli,
  • Massimiliano M. Schiraldi

摘要

Obtaining efficient and precise production planning has become a significant challenge in today’s industrial environment, particularly for companies with production systems characterized by a large number of mildly differentiated SKUs. Finding tools that can offer viable results and address demand uncertainties, without oversimplifying the process, can become problematic, sometimes even impossible for many companies, causing issues across the entire supply chain and potentially endangering some industrial sectors in their entirety. This paper aims to present a new Planning Bill of Material (PBOM) approach, leveraging mathematical tools and algorithms from graph theory to address the need for an easy-to-implement and ready-to-use production planning tool, able to identify sub-optimal solutions with reduced computational complexity. To validate the proposed approach, it was also applied in a real production planning situation, where it was used to revise, update, and improve the company’s older Planning Bill of Materials system.