Empowering manufacturing environments with process mining-based Statistical process control

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dc.authoridDOĞAN, ONUR/0000-0003-3543-4012
dc.authoridARETA HIZIROGLU, OURANIA/0000-0001-8607-6089
dc.contributor.authorDogan, Onur
dc.contributor.authorAreta Hiziroglu, Ourania
dc.date.accessioned2025-03-20T09:50:31Z
dc.date.available2025-03-20T09:50:31Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractThe production of high-quality products and efficient manufacturing processes in modern environments, where processes vary widely, is one of the most crucial issues today. Statistical process control (SPC) and process mining (PM) effectively trace and enhance the manufacturing processes. In this direction, this paper proposes an innovative approach involving SPC and PM strategies to empower the manufacturing environment. SPC monitors key performance indicators (KPIs) and identifies out-of-control processes that deviate from specification limits, while PM discovery techniques are applied for those abnormal processes to extract the actual process flow from event logs and model it using Petri nets. Different enhancement techniques in PM, such as decision rules and root cause analysis, are then used to return the process to control and prevent future deviations. The application of the integrated SPC-PM approach is shown through case studies of production processes. SPC charts found that over 6% of processes exceeded specification limits. At the same time, PM methodologies revealed that prolonged times for the 'Quality Control' activity is the fundamental factor increasing the cycle time. Moreover, decision tree analysis provides rules for decreasing the cycle times of unbalanced processes. The absence of a transition from the 'Return from Waiting' activity to 'Packing and Shipment' is a critical factor in decreasing cycle times, as is the shift information. Our newly proposed methodology, which combines process analysis from PM with statistical monitoring from SPC, ensures operational excellence and consistent quality in manufacturing. This study illustrates the application of the proposed methodology through a case study in production processes, highlighting its effectiveness in identifying and addressing process deviations.
dc.identifier.doi10.3390/machines12060411
dc.identifier.issn2075-1702
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85197917602
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/machines12060411
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2235
dc.identifier.volume12
dc.identifier.wosWOS:001256114200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofMachines
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250319
dc.subjectprocess mining
dc.subjectstatistical process control
dc.subjectprocess capability
dc.subjectmanufacturing
dc.titleEmpowering manufacturing environments with process mining-based Statistical process control
dc.typeArticle

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