Process Mining Research in Management Science and Engineering Fields: The Period of 2010–2019

Kulaklı, Atik; Birgün, Semra

doi:10.1007/978-3-030-62784-3_35

Cited by 2 publications

(3 citation statements)

References 27 publications

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“…A 2010-2019 review by Kulakli and Birgun [19] showed that PM in the mining industry is severely understudied despite the expansion of PM in various industries. In general, there has been an increased application of data mining research in underground mining, especially in the last five years, leveraged by the information boom.…”

Section: Process Mining In the Mining Industrymentioning

confidence: 99%

Towards the Application of Process Mining in the Mining Industry—An LHD Maintenance Process Optimization Case Study

et al. 2023

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Inefficiencies in mine equipment maintenance processes result in high operation costs and reduce mine sustainability. However, current methods for process optimization are limited due to a lack of access to structured data. This research aims to test the hypothesis that process mining techniques can be used to optimize workflow for mine equipment maintenance processes using low-level data. This is achieved through a process-oriented analysis where low-level data are processed as an event log and used as input for a developed process model. We present a Discrete-Event Simulation of the maintenance process to generate an event log from low-level data and analyze the process with process mining. A case study of the maintenance process in an underground block caving mine is used to gain operational insight. The diagnosis of the mine’s maintenance process showed a loss of 23,800 equipment operating hours per year, with a non-production cost of about 1.12 MUSD/year. Process mining obtained a non-biased representation of the maintenance process and aided in identifying bottlenecks and inefficiencies in the equipment maintenance processes.

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Section: Process Mining In the Mining Industrymentioning

confidence: 99%

Towards the Application of Process Mining in the Mining Industry—An LHD Maintenance Process Optimization Case Study

et al. 2023

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“…Process Mining is an analytical, evidence-based, and data-driven method to detect, monitor, and improve business or project processes [7]. As a theme of Business Project Management, Process Mining is a combination of data-mining and traditional model-driven Business Project Management that analyses processes simply and systematically by exploiting current and historical process data stored as event logs in the information systems.…”

Section: Process Mining For Risk Managementmentioning

confidence: 99%

“…Based on Kulakli and Birgun (2021) [7] comprehensive literature review, there has been a significant increase in publication numbers of Process Mining starting from 2015. It is studied for 1101 (2022) 032023 IOP Publishing doi:10.1088/1755-1315/1101/3/032023 3 various business applications with multidimensional techniques, like transaction verification level, accident emergency rescue, fraud detection, information system audits and reengineering the export process, information navigation, production planning in ERP environment, assessing the life cycle phase of the business process redesign, and accounting control flow [11].…”

Section: Process Mining For Risk Managementmentioning

confidence: 99%

Data-Driven Process Mining Framework for Risk Management in Construction Projects

Khodabakhshian

Cecconi²

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Construction Projects are exposed to numerous risks due to their complex and uncertain nature, threatening the realization of the project objectives. However, Risk Management (RM) is a less efficient realm in the industry than other knowledge areas given the manual and time-consuming nature of its processes and reliance on experience-based subjective judgments. This research proposes a Process Mining-based framework for detecting, monitoring, and analysing risks, improving the RM processes using evidence-based event logs, such as Risk Registers and Change-Logs within previous projects’ documents. Process Mining (PM) is a data-driven methodology, well established in other industries, that benefits from Artificial Intelligence(AI) to identify trends and complex patterns among event logs. It performs well while intaking large amounts of data and predicting future outputs based on historical data. Therefore, this research proposes a Bayesian Network (BN)-based Process Mining framework for graphical representation of the RM processes, intaking the conditional dependence structure between Risk variables, and continuous and automated risk identification and management. A systematic literature review on RM, PM, and AI forms the framework theoretical basis and delineates the integration areas for practical implementation. The proposed framework is applied to a small database of 20 projects as the case study, the scope of which can be tailored to the enterprise requirements. It contributes to creating a holistic theoretical foundation and practical workflow applicable to construction projects and filling the knowledge gap in inefficient and discrete conventional RM methods, which ignore the interdependencies between risk variables and assess each risk isolated.

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Process Mining Research in Management Science and Engineering Fields: The Period of 2010–2019

Cited by 2 publications

References 27 publications

Towards the Application of Process Mining in the Mining Industry—An LHD Maintenance Process Optimization Case Study

Towards the Application of Process Mining in the Mining Industry—An LHD Maintenance Process Optimization Case Study

Data-Driven Process Mining Framework for Risk Management in Construction Projects

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