Combining Process Mining with Trace Clustering: Manufacturing Shop Floor Process - An Applied Case

Meincheim, Alex; Garcia, Cleiton dos Santos; Nievola, Júlio César; Scalabrin, Edson Emfilio

doi:10.1109/ictai.2017.00082

Cited by 12 publications

(8 citation statements)

References 21 publications

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“…The common issues found in the resulting discovered process models are a disproportionate amount of task nodes and the formation of a large number of connections leading to the traditional "spaghetti models" [10], [23]. The diversity of an event-log is one significant cause of unstructured mining performance [24], i.e., individual cases are significantly different from each other. In these instances, it is reasonable to assume that one event-log contains multiple implicit process variants, each of which is substantially more structured than the overall process.…”

Section: Problem Formulationmentioning

confidence: 99%

Process Discovery Enhancement with Trace Clustering and Profiling

Faizan¹,

Zuhairi²,

Ismail³

2021

AETiC

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The potential in process mining is progressively growing due to the increasing amount of event-data. Process mining strategies use event-logs to automatically classify process models, recommend improvements, predict processing times, check conformance, and recognize anomalies/deviations and bottlenecks. However, proper handling of event-logs while evaluating and using them as input is crucial to any process mining technique. When process mining techniques are applied to flexible systems with a large number of decisions to take at runtime, the outcome is often unstructured or semi-structured process models that are hard to comprehend. Existing approaches are good at discovering and visualizing structured processes but often struggle with less structured ones. Surprisingly, process mining is most useful in domains where flexibility is desired. A good illustration is the "patient treatment" process in a hospital, where the ability to deviate from dealing with changing conditions is crucial. It is useful to have insights into actual operations. However, there is a significant amount of diversity, which contributes to complicated, difficult-to-understand models. Trace clustering is a method for decreasing the complexity of process models in this context while also increasing their comprehensibility and accuracy. This paper discusses process mining, event-logs, and presenting a clustering approach to pre-process event-logs, i.e., a homogeneous subset of the event-log is created. A process model is generated for each subset. These homogeneous subsets are then evaluated independently from each other, which significantly improving the quality of mining results in flexible environments. The presented approach improves the fitness and precision of a discovered model while reducing its complexity, resulting in well-structured and easily understandable process discovery results.

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Section: Problem Formulationmentioning

confidence: 99%

Process Discovery Enhancement with Trace Clustering and Profiling

Faizan¹,

Zuhairi²,

Ismail³

2021

AETiC

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“…Meincheim et al [57] describe how WEG, a Brazilian energy solutions provider and electric motor producer, applies process mining. The company conducts the production planning for each custom control panel.…”

Section: Applications In Operationsmentioning

confidence: 99%

“…The production operators decide which machines take part in the assembling. Bhogal and Garg [40] Brzychczy et al [41] Celonis AG [42] Celonis AG [43] Denno et al [44] Abonyi and Dörgő [45] Engel and Bose [46] ER et al [47,48] Fleig et al [49] Fluxicon [22,50] Ho and Lau [51] Ho et al [52] Knoll et al [53] Koosawad et al [54] Lee et al [32,55]; Park et al [33,56] Meincheim et al [57] Nagy et al [58,59] Park et al [10] Paszkiewicz [60] Pospíšil et al [34] QPR Software Plc [61] QPR Software Plc [62] Rozinat et al [30,63] Ruschel et al [31] Saravanan and Rama Sree [64] Ulsan Institute [65]; Son et al […”

Section: Applications In Operationsmentioning

confidence: 99%

“…Wafer scanners D, C, E [30,63] No case study found Not feasible Not feasible Ships D, C [10] Maintenance D [31] Cellular production (product moves along specialized cells/groups) Ships D [32,33,55,56] Control panels D, E [57] No case study found Not feasible Doors D [34] Shoe parts D [47,48] Coffee machine D, C [39] Micro-precision parts D [22,50] Electronic products D [51] Sliders D [52] Propellers D [35] Maintenance D [40]…”

Section: Job Shop Productionmentioning

confidence: 99%

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The Status Quo of Process Mining in the Industrial Sector

Dunzer

Zilker

Marx

et al. 2021

Lecture Notes in Information Systems and Organisation

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Since process mining started to reveal the potential of event logs, it has been applied in various process settings ranging from healthcare to production. Every single setting poses its challenges to process analysts who want to apply process mining. The present paper aims at minimizing such challenges for enterprises in the industrial sector by providing a coherent overview of existing cases. Our systematic literature review relates each production form and layout from existing case studies to the applied process mining type. Further, we use Porter's Value Chain to distinguish operations from other primary and support activities in production. We present the application of process mining, particularly for the production process and the primary activities other than the operations. The results indicate that process mining fits best with cellular production layouts with batch or line production processes.

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“…Um log de eventos consiste em um conjunto de traces eé a fonte de dados para a tarefa de agrupamento de traces (LU, 2018), que beneficia diferentes problemas daárea de mineração de processos. Alguns exemplos são: descoberta de processos (WEERDT et al, 2013;APPICE;MALERBA, 2016;GRECO et al, 2006;KONINCK et al, 2017), melhoria de qualidade em log de eventos (LU, 2018), busca por variantes de processos em "chão de fábrica" (MEINCHEIM et al, 2017), sistemas de recomendação em processos de negócio (YANG et al, 2017), concept drift em processos de negócio (HOMPES et al, 2017).…”

Section: Introductionunclassified

Agrupamento interativo aplicado à mineração de processos de negócio

Neubauer¹

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Dedico este trabalhoà minha família, que possibilitou que eu vencesse mais essa etapa. Agradecimentos Agradeçoà toda minha família, que valoriza a educação, que me incentivou e me deu possibilidades financeiras de estudar e que sempre me apoiou psicologicamente com muitas horas de conversas sobre todos meus anseios (que nunca foram poucos). Agradeçoà minha mãe, Andréa Rodrigues, por tudo que me proporcionou, mas, principalmente, pelos valores morais e exemplos de conduta, por ter sempre me feito enxergar as belezas da carreira acadêmica e por ter me inspirado, por tantas vezes, no desenvolvimento de habilidades de discurso. Agradeçoà minha queridíssima avó, Maria Aparecida Coutinho Cardoso, ou Maricy para osíntimos (e os não tãoíntimos assim também), por ter me ensinado e proporcionado tantas coisas, entre elas o exemplo e o gosto por estudar e por ensinar, tão essenciais na carreira que escolhi. Obrigada, vó, por todas as fichas de estudo que fizemos e por todas as horas de estudo juntas. Ao meu companheiro de vida, meu amor, meu primeiro eúnico namorado, meu Alexandre Rossi Alvares: muitíssimo obrigada pelo apoio sempre gigantesco em todas as minhas empreitadas, mas especialmente nesses doisúltimos anos de muita ansiedade e pouco dinheiro, muitas madrugadas que acabei não dormindo junto com você por ficar trabalhando neste projeto e muitas conversas sobre ele. A minha orientadora, Profa. Dra. Sarajane Marques Peres, muito obrigada por ser essa profissional que inspira. Sou imensamente grata por termos podido trabalhar juntas todos esses anos. Obrigada por todos os ensinamentos técnicos e não-técnios, por todas as conversas formais e informais, por toda a paciência e dedicação. Com toda certeza, todo esse mestrado, desde decisão de ingressar, aconteceu em grande devido ao seu envolvimento. Agradeço ainda ao meu co-orientador, Prof. Dr. Marcelo Fantinato, por toda a dedicação e envolvimento no trabalho, por todas as opiniões e sugestões e por ter sido nosso guia daárea de mineração de processos. Obrigada também aos colegas Claudio e Alexandre, pelo tempo dedicado para auxiliar no aprimoramente deste trabalho, e a minha grande amiga Vivian pela paciência de ajudar na revisão deste documento. Por fim, mas não menos importante, ressalto que o presente trabalho foi realizado com apoio da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)-Código de Financiamento 001. Palavras-chaves: Agrupamento de traços. Agrupamento interativo. Agrupamento de traços interativo. Mineração de processos. Mineração de dados. Gerenciamento de processos de negócio.

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Combining Process Mining with Trace Clustering: Manufacturing Shop Floor Process - An Applied Case

Cited by 12 publications

References 21 publications

Process Discovery Enhancement with Trace Clustering and Profiling

Process Discovery Enhancement with Trace Clustering and Profiling

The Status Quo of Process Mining in the Industrial Sector

Agrupamento interativo aplicado à mineração de processos de negócio

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