A Classification of Process Mining Bottleneck Analysis Techniques for Operational Support

Bemthuis, Rob; Slooten, Niels van; Arachchige, Jeewanie Jayasinghe; Piest, Jean Paul Sebastian; Bukhsh, Faiza Allah

doi:10.5220/0010578601270135

Cited by 5 publications

(26 citation statements)

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“…This paper extends earlier work related to bottleneck classification using process mining [7] and interoperability challenges [23]. Bottlenecks can occur in many processes and disrupt their functioning.…”

Section: Introductionsupporting

confidence: 63%

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A Method for Bottleneck Detection, Prediction, and Recommendation Using Process Mining Techniques

Piest

Bemthuis

Cutinha

et al. 2023

Communications in Computer and Information Science

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Bottlenecks arise in many processes, often negatively impacting performance. Process mining can facilitate bottleneck analysis, but research has primarily focused on bottleneck detection and resolution, with limited attention given to the prediction of bottlenecks and recommendations for improving process performance. As a result, operational support for bottleneck resolution is often partially or not realized. The aim of this paper is to propose a method for Bottleneck Detection, Prediction, and Recommendation (BDPR) using process mining techniques to achieve operational support. A design science research methodology is adopted to design, develop, and demonstrate the BDPR method. A systematic literature review and a developed classification model provide theoretical support for the BDPR method and offer scholarly in the field of process mining a starting point for research. The BDPR method extends the utility of the classification model and aims to provide guidance to scholars and practitioners for assessing, selecting, evaluating, and implementing process mining techniques to realize operational support. A case study at a logistics service provider demonstrates the use of the proposed BDPR method.

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Section: Introductionsupporting

confidence: 63%

“…Process mining enables process discovery from event logs and offers techniques for conformance checking and process enhancement [1]. Earlier work reveals that bottleneck analysis is applied in various domains, but mainly focused on bottleneck detection [7] (see Sect. 4).…”

Section: Introductionmentioning

confidence: 99%

A Method for Bottleneck Detection, Prediction, and Recommendation Using Process Mining Techniques

Piest

Bemthuis

Cutinha

et al. 2023

Communications in Computer and Information Science

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show abstract

“…Although the search included studies from 2004, all studies that were relevant to the topic of process mining and logistics of manufacturing companies began from the year 2017. As shown in Table 2, two studies of [4,13] were conducted in 2017, three studies of [2,8,16] were published in 2018, five studies of [1,3,11,14,17] were conducted in 2019, two studies of [6,18] were published in 2020, and three studies of [12,15,19] were published in 2021. Figure 2 is a chart representing the years searched and the number of publications in each of these years.…”

Section: Resultsmentioning

confidence: 99%

“…Most proposed methodologies were implemented and their effectiveness was justified through case studies such as in [11] where the MDPM methodology was found to be useful for steady recording, assessment, and waste analysis of each process within internal logistics. Moreover, the classification model proposed in [19] showed that the application of process mining bottleneck analysis techniques is validated in a logistics case study. All of the selected studies used real case studies except for [1,8].…”

Section: Bemthuis Et Al 2021mentioning

confidence: 99%

Application of Process Mining in Logistic Processes of Manufacturing Organizations: A Systematic Review

Alnahas

2023

Sustainability

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With the continuous development in technology and changes in the logistic systems, organizations should review their logistic processes that evolve over time. To attain a good insight into the conformance of these processes with the designed process model, constant detection and monitoring is required. The main objective of this systematic review is to investigate the state of the art in process mining applications in logistics specifically related to manufacturing organizations. The review aims to analyze and assess the use of process mining techniques and models in the logistics domain, based on the selected studies. In this review, literature was searched between the years 2004 and 2022 using several inclusion and exclusion criteria. Fifteen published studies were selected and analyzed on the use of process mining in the logistics domain based on the process mining techniques and models used. All of the selected studies used models and thirteen of them used real case studies. All of the fifteen studies used one or more algorithms. Only three of these studies did not mention the modeling language used to represent the process. Moreover, seven studies focused on discussing the process discovery alone and five more addressed process discovery in addition to other types of process mining. Eight studies mentioned the process mining tools used that included DISCO and several versions of ProM.

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“…Process mining techniques can offer insights into real-world data, including performance evaluations, root-cause analysis, process prediction, and optimization [1]. Another application of process mining concerns identifying and eliminating bottlenecks [11].…”

Section: Introductionmentioning

confidence: 99%

Balancing Simplicity and Complexity in Modeling Mined Business Processes: A User Perspective

Maneschijn¹,

Bemthuis²,

Arachchige³

et al. 2023

Lecture Notes in Business Information Processing

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Process mining techniques use event logs to discover process models, check conformance, and aid business analysts in improving business processes. A proper interplay between the complexity of discovered process models and the expertise of human interpreters, such as stakeholders, domain experts, and process analysts, is key for the success of process mining efforts. However, current practice often suffers from a misalignment between aligning the technical or business background of a human subject and the results obtained through process mining discovery techniques. This results in either overly complex or overly simplistic discovered process models for particular users. In this study, we propose and validate a methodology for aligning users of process models with different abstractions of discovered process models. We present two case studies to illustrate the usability of our six-phase methodology and demonstrate its potential. We hope that these findings will encourage systematic approaches for better alignment between users' willingness to employ process models and process model abstraction.

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A Classification of Process Mining Bottleneck Analysis Techniques for Operational Support

Cited by 5 publications

References 0 publications

A Method for Bottleneck Detection, Prediction, and Recommendation Using Process Mining Techniques

A Method for Bottleneck Detection, Prediction, and Recommendation Using Process Mining Techniques

Application of Process Mining in Logistic Processes of Manufacturing Organizations: A Systematic Review

Balancing Simplicity and Complexity in Modeling Mined Business Processes: A User Perspective

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