Process Mining Applied to the Test Process of Wafer Scanners in ASML

Rozinat, A Anne; Jong, I.S.M. de; Günther, Christoph; Aalst, Wil M. P. van der

doi:10.1109/tsmcc.2009.2014169

Cited by 104 publications

(61 citation statements)

References 14 publications

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“…Many existing precision metrics (e.g., [25,30,34]) do not explicitly take into account possible deviations between the behavior observed in the event log with the behavior modeled in the models, while many case studies show that such deviations often occur in practice (e.g., [11,16,19,20,23,31,35,36]). Thus, these metrics might be biased due to unfitting logs and models.…”

Section: Introductionmentioning

confidence: 99%

Measuring precision of modeled behavior

Adriansyah

Muñoz-Gama

Carmona

et al. 2014

Inf Syst E-Bus Manage

125

100

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Conformance checking techniques compare observed behavior (i.e., event logs) with modeled behavior for a variety of reasons. For example, discrepancies between a normative process model and recorded behavior may point to fraud or inefficiencies. The resulting diagnostics can be used for auditing and compliance management. Conformance checking can also be used to judge a process model automatically discovered from an event log. Models discovered using different process discovery techniques need to be compared objectively. These examples illustrate just a few of the many use cases for aligning observed and modeled behavior. Thus far, most conformance checking techniques focused on replay fitness, i.e., the ability to reproduce the event log. However, it is easy to construct models that allow for lots of behavior (including the observed behavior) without being precise.In this paper, we propose a method to measure precision of process models, given their event logs by first aligning the logs to the models. This way, the measurement is not sensitive to non-fitting executions and more accurate values can be obtained for non-fitting logs. Furthermore, we introduce several variants of the technique to deal better with incomplete logs and reduce possible bias due to behavioral property of process models. The approach has been implemented in the ProM 6 framework and tested against both artificial and real-life cases. Experiments show that the approach is robust to noise and applicable to handle logs and models of real-life complexity.

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Section: Introductionmentioning

confidence: 99%

Measuring precision of modeled behavior

Adriansyah

Muñoz-Gama

Carmona

et al. 2014

Inf Syst E-Bus Manage

125

100

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show abstract

“…[4,7,9]) assume that the event log perfectly fits the model, while many case studies show that this assumption does not hold (e.g. [5,8,14]). In this paper, we do not use such assumptions and propose a robust approach to measure the precision between an event log and a model.…”

Section: Introductionmentioning

confidence: 99%

Alignment Based Precision Checking

Adriansyah

Muñoz-Gama

Carmona

et al. 2013

Business Process Management Workshops

108

100

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Abstract. Most organizations have process models describing how cases need to be handled. In fact, legislation and standardization (cf. the Sarbanes-Oxley Act, the Basel II Accord, and the ISO 9000 family of standards) are forcing organizations to document their processes. These processes are often not enforced by information systems. However, torrents of event data are recorded by today's information systems. These recorded events reflect how processes are really executed. Often reality deviates from the modeled behavior. Therefore, measuring the extent process executions conform to a predefined process model is increasingly important. In this paper, we propose an approach to measure the precision of a process model with respect to an event log. Unlike earlier approaches, we first align model and log thus making our approach more robust, even in case of deviations. The approach has been implemented in the ProM 6 tool and evaluated using both artificial and real life cases.

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“…Rozinat et al [1] introduced the application of process mining in the test process of ASML's wafer scanner. ASML, is the world's leading manufacturer of chip-making equipment and a key supplier to the chip industry [1].…”

Section: Discussionmentioning

confidence: 99%

“…Currently, MPA is mostly used to monitor the current production process and measure the performance of overall processes. Some of previous works on MPA using process mining showed a comprehensive results (Rozinat et al [1], Son et al [2,3]). However, there are corresponding discussions on more foundational questions related to the importance of discovering process model and the usefulness of process mining in the manufacturing domain (Aalst et al [4]).…”

Section: Introductionmentioning

confidence: 99%

The Development of Manufacturing Process Analysis: Lesson Learned from Process Mining

Yahya

2014

jti

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Process analysis is recognized as a major stage in business process reengineering that has developed over the last two decades. In the field of manufacturing, manufacturing process analysis (MPA) is defined as performance analysis of the production process. The performance analysis is an outline from data and knowledge into useful forms that can be broadly applied in manufacturing sectors. Process mining, an emerge tool focusing on process perspective and resource perspective, is a way to analyze system based on the event log. The objective of this study is to extend the existing process analysis framework by considering attribute perspective. This study also aims to learn the lessons from some experiences on process mining in manufacture industries. The result of this study will help manufacturing organizations to utilize the process mining approach for analyzing their respective processes.

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Process Mining Applied to the Test Process of Wafer Scanners in ASML

Cited by 104 publications

References 14 publications

Measuring precision of modeled behavior

Measuring precision of modeled behavior

Alignment Based Precision Checking

The Development of Manufacturing Process Analysis: Lesson Learned from Process Mining

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