A collaborative method for simultaneous operations: case of an eye clinic

Singh, Sudhanshu; Verma, Rakesh; Koul, Saroj

doi:10.1007/s12597-021-00513-9

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…This paper collaborates with a method to integrate Six Sigma, i.e. the DMAIC cycle, with PM A collaborative method for simultaneous operations: a case of an eye clinic (Singh et al, 2022) It was learned that there exists a gap between the application of Lean Six Sigma using PM techniques with continuous improvement methods (Plan, Do, Study, and Act -PDSA) And this paper provides a collaborative method to integrate both operations Process mining and lean six Sigma: a novel approach to analyze the supply chain quality of a hospital (Ramires and Sampaio, 2022) This paper shows how it is possible to use PM discovery techniques to improve and accelerate the DMAIC cycle. The paper relates that this blend of techniques can bring visibility to operations and accelerate process improvement initiatives Source: Authors relevant, time-based goals, defining the scope and objectives of the project.…”

Section: Presentation Of Agile Define-measure-analyze-improve-control...mentioning

confidence: 99%

“…After all, the usage of PM merged with Six Sigma methods, i.e. DMAIC cycle is shown in literature as an exciting way to improve both speed and assertiveness during process improvements (Peters et al , 2013; Park and Kang, 2016; Shin et al , 2019; Kregel et al , 2021; Singh et al , 2022; Ramires and Sampaio, 2022; Graafmans et al , 2021; Antony et al , 2019; Gupta et al , 2020), or even can be used to improve quality measurements (Camolesi, 2020) and be used to achieve an excellent operational environment (Lameijer et al , 2021). The central idea is to use the event log resulting from the execution of the analyzed processes.…”

Section: Introductionmentioning

confidence: 99%

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Agile DMAIC cycle: incorporating process mining and support decision

Prado,

Boareto,

Chaves

et al. 2023

IJLSS

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Purpose The aim of this paper is to explore the possibility of using the Define-Measure-Analyze-Improve-Control (DMAIC) cycle, process mining (PM) and multi-criteria decision methods in an integrated way so that these three elements combined result in a methodology called the Agile DMAIC cycle, which brings more agility and reliability in the execution of the Six Sigma process. Design/methodology/approach The approach taken by the authors in this study was to analyze the studies arising from this union of concepts and to focus on using PM tools where appropriate to accelerate the DMAIC cycle by improving the first two steps, and to test using the AHP as a decision-making process, to bring more excellent reliability in the definition of indicators. Findings It was indicated that there was a gain with acquiring indicators and process maps generated by PM. And through the AHP, there was a greater accuracy in determining the importance of the indicators. Practical implications Through the results and findings of this study, more organizations can understand the potential of integrating Six Sigma and PM. It was just developed for the first two steps of the DMAIC cycle, and it is also a replicable method for any Six Sigma project where data acquisition through mining is possible. Originality/value The authors develop a fully applicable and understandable methodology which can be replicated in other settings and expanded in future research.

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Section: Presentation Of Agile Define-measure-analyze-improve-control...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Agile DMAIC cycle: incorporating process mining and support decision

Prado,

Boareto,

Chaves

et al. 2023

IJLSS

View full text Add to dashboard Cite

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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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Measurement of the efficiency of a Multi–Input–Multi–Output (MIMO) production process using transfer function and fuzzy logic

Obidike

Nwobi-Okoye

2023

OPSEARCH

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A collaborative method for simultaneous operations: case of an eye clinic

Cited by 7 publications

References 37 publications

Agile DMAIC cycle: incorporating process mining and support decision

Agile DMAIC cycle: incorporating process mining and support decision

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

Measurement of the efficiency of a Multi–Input–Multi–Output (MIMO) production process using transfer function and fuzzy logic

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