Process mining: software comparison, trends, and challenges

Loyola‐González, Octavio

doi:10.1007/s41060-022-00379-0

Cited by 7 publications

(5 citation statements)

References 42 publications

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“…We, then, compared the elicited user stories with functionalities existing in commercial process mining tools. We considered the descriptions provided in (Loyola-González, 2022) to understand to which user story they correspond. Then, we marked the degree to which a respective user story is supported in the tools in Table 4.…”

Section: Resultsmentioning

confidence: 99%

“…We only considered new findings when writing the additional user stories as these present new visualisation requirements not identified during the exploratory interviews. We again compared them with existing tools based on Loyola-González (2022). Minor comments, such as the naming of impact, were also considered and incorporated in IRVIN.…”

Section: Resultsmentioning

confidence: 99%

“…We also compared the elicited requirements with functionality that is available in existing process mining tools (based on the review of Loyola-González (2022)). The aim was to understand the extent by which required functionalities, as perceived by interviewed process analysts, are already implemented in the tools (see section 4.1, step 2).…”

Section: Methodsmentioning

confidence: 99%

“…These insights can be used as input for setting up business rules to continuously check how compliant the process is with the defined rules. Finally, in a comparative study, the authors examined 16 process mining tools and compared them using a taxonomy of 55 distinct features (Loyola-Gonz alez, 2022). Their analysis show that visualisation, as a means to facilitate analysts to Visualising improvement opportunities identify improvement opportunities, is partially supported by a minority of tools.…”

Section: Background and Related Workmentioning

confidence: 99%

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A visual approach to support process analysts in working with process improvement opportunities

Kubrak

Milani

Nolte

2023

BPMJ

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PurposeWhen improving business processes, process analysts can use data-driven methods, such as process mining, to identify improvement opportunities. However, despite being supported by data, process analysts decide which changes to implement. Analysts often use process visualisations to assess and determine which changes to pursue. This paper helps explore how process mining visualisations can aid process analysts in their work to identify, prioritise and communicate business process improvement opportunities.Design/methodology/approachThe study follows the design science methodology to create and evaluate an artefact for visualising identified improvement opportunities (IRVIN).FindingsA set of principles to facilitate the visualisation of process mining outputs for analysts to work with improvement opportunities was suggested. Particularly, insights into identifying, prioritising and communicating process improvement opportunities from visual representation are outlined.Originality/valuePrior work focuses on visualisation from the perspectives – among others – of process exploration, process comparison and performance analysis. This study, however, considers process mining visualisation that aids in analysing process improvement opportunities.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A visual approach to support process analysts in working with process improvement opportunities

Kubrak

Milani

Nolte

2023

BPMJ

View full text Add to dashboard Cite

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“…Process mining is a set of analysis techniques that explain the data-based overview of how a business process is executed in the real environment [1]. Typically, people who conduct business work have a obvious overview of how processes are working.…”

Section: Introductionmentioning

confidence: 99%

Application of Process Mining and Sequence Clustering in Recognizing an Industrial Issue

Saad

2024

Artificial Intelligence

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Process mining has become one of the best programs that can outline the event logs of production processes in visualized detail. We have addressed the important problem that easily occurs in the industrial process called Bottleneck. The analysis process was focused on extracting the bottlenecks in the production line to improve the flow of production. Given enough stored history logs, the field of process mining can provide a suitable answer to optimize production flow by mitigating bottlenecks in the production stream. Process mining diagnoses the productivity processes by mining event logs, this can help to expose the opportunities to optimize critical production processes. We found that there is a considerable bottleneck in the process because of the weaving activities. Through discussions with specialists, it was agreed that the main problem in the weaving processes, especially machines that were exhausted in overloading processes. The improvement in the system has been measured by teamwork; the cycle time for processes has improved to 91%. the worker’s performance has improved from 96%, productivity has improved to 91%, product quality has improved by 85%, and lead time has optimized from days and weeks to hours.

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