{"title":"Uncovering the potential and pitfalls of Process Mining in manufacturing","authors":"Júlia Villwock Gomes de Oliveira, Eduardo Alves Portela Santos, Silvana Pereira Detro","doi":"10.1016/j.procir.2025.01.004","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"132 ","pages":"Pages 19-24"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Procedia CIRP","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212827125000046","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.