Uncovering the potential and pitfalls of Process Mining in manufacturing

Procedia CIRP Pub Date : 2025-01-01 Epub Date: 2025-02-28 DOI:10.1016/j.procir.2025.01.004
Júlia Villwock Gomes de Oliveira, Eduardo Alves Portela Santos, Silvana Pereira Detro
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Abstract

Process Mining (PM) is emerging as a crucial technique for analyzing and improving manufacturing processes within the Industry 4.0 landscape. However, the diverse mix of legacy and state-of-the-art technologies in modern manufacturing poses significant challenges for PM applications. This paper maps the current state of PM in manufacturing by analyzing 34 papers from the past five years and identifies six thematic groups: Production, Planning and Control, Quality, Industry 4.0, Digital Twin, Logistics, and Maintenance. These groups highlight specific challenges that can be addressed with comprehensive PM solutions. Two major categories of challenges are identified: Information Technology, which relates to data complexity and quality, and Governance, which pertains to data accountability and regulations. Object-Centric Process Mining (OCPM) extends traditional PM by focusing on multiple interacting objects, providing a more comprehensive view of manufacturing processes.
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揭示过程挖掘在制造业中的潜力和缺陷
过程挖掘(PM)正在成为工业4.0环境中分析和改进制造过程的关键技术。然而,现代制造业中遗留技术和最先进技术的多样化组合为PM应用程序带来了重大挑战。本文通过分析过去五年的34篇论文,描绘了制造业中PM的现状,并确定了六个主题组:生产、计划和控制、质量、工业4.0、数字孪生、物流和维护。这些小组强调了可以用全面的项目管理解决方案来解决的具体挑战。确定了两类主要挑战:信息技术(与数据复杂性和质量有关)和治理(与数据问责制和法规有关)。以对象为中心的过程挖掘(OCPM)通过关注多个交互对象扩展了传统的项目管理,提供了制造过程的更全面的视图。
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