Establishment of maintenance inspection intervals: an application of process mining techniques in manufacturing

Ruschel, Edson; Santos, Eduardo Alves Portela; Loures, Eduardo de Freitas Rocha

doi:10.1007/s10845-018-1434-7

Cited by 39 publications

(27 citation statements)

References 45 publications

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“…Therefore, they deploy performance-and timeperspective conformance checking in shipbuilding. Ruschel et al [31] discover Bayesian Networks in a Brazilian automotive company. The Bayesian Networks can estimate the process cycle time based on a defined availability and cost functions.…”

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%

See 2 more Smart Citations

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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Section: Applications In Operationsmentioning

confidence: 99%

Section: Applications In Operationsmentioning

confidence: 99%

Section: Job Shop Productionmentioning

confidence: 99%

See 1 more Smart Citation

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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show abstract

“…Prior knowledge of delays in the production time, cycle time and process behavior are extremely important for manufacturing companies. Since increasing amounts of data are being generated, turning them into useful information becomes a major challenge [2]. Most of the modern tools are able to process big data and to provide appropriate results.…”

Section: Introductionmentioning

confidence: 99%

Screening Process Mining and Value Stream Techniques on Industrial Manufacturing Processes: Process Modelling and Bottleneck Analysis

et al. 2022

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One major result of the Industrial Digitalization is the access to a large set of digitalized data and information, i.e. Big Data. The market of analytic tools offers a huge variety of algorithms and software to exploit big datasets.Implementing their advantages into one approach brings better results and empower possibilities for process analysis. Its application in the manufacturing industry requires a high level of effort and remains to be challenging due to product complexity, human-centric processes, and data quality. In this manuscript, the authors combine process mining and value streams methods for analyzing the data from the information management system, applying the approach to the data delivered by one specific manufacturing system. The manufacturing process to be examined is the process of assembling gas meters in the manufacture. This specific and important part of the whole supply-chain process was taken as suitable for the study due to almost full-automated line with data about each process activity of the value-stream in the information system. The paper applies process mining algorithms in discovering a descriptive process model that plays the main role as a basis for further analysis. At the same time, modern techniques of the bottleneck analysis are described, and two new comprehensible methods of bottlenecks detection (TimeLag and Confidence intervals methods), as well as their advantages, will be discussed. Achieved results can be subsequently used for other sources of big data and industrial-compliant Information Management Systems.

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“…In the research literature on production engineering, an increasing number of DM applications has been observed for several years. Examples include: scheduling and production planning [2], customer needs research [3], machine failure prediction [4], improvement of the assembly process [5], supply chain management [6] or product design [7].…”

Section: Introductionmentioning

confidence: 99%

Methods of Data Mining for Quality Assurance in Glassworks

Paśko¹,

Litwin²

2019

Collaborative Networks and Digital Transformation

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In manufacturing enterprises implementing the idea of Industry 4.0, devices that generate data are increasingly used. Over time, huge data sets are created. These collections, known as Big Data, are very important to the company because they can contain valuable information. One of the goals of today's enterprises is to discover this information and transform it into knowledge. The aim of the article is to present the methodology of exploration of large data sets from the manufacturing process in glassworks. The result of the research is knowledge about the parameters of the manufacturing process causing defects in the products.

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Establishment of maintenance inspection intervals: an application of process mining techniques in manufacturing

Cited by 39 publications

References 45 publications

The Status Quo of Process Mining in the Industrial Sector

The Status Quo of Process Mining in the Industrial Sector

Screening Process Mining and Value Stream Techniques on Industrial Manufacturing Processes: Process Modelling and Bottleneck Analysis

Methods of Data Mining for Quality Assurance in Glassworks

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