A digital twin based framework for detection, diagnosis, and improvement of throughput bottlenecks
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Digitalization through Industry 4.0 technologies is one of the essential steps for the complete collaboration, communication, and integration of heterogeneous resources in a manufacturing organization towards improving manufacturing performance. One of the ways is to measure the effective utilization of critical resources, also known as bottlenecks. Finding such critical resources in a manufacturing system has been a significant focus of manufacturing research for several decades. However, finding a bottleneck in a complex manufacturing system is difficult due to the interdependencies and interactions of many resources. In this work, a digital twin framework is developed to detect, diagnose, and improve bottleneck resources using utilization-based bottleneck analysis, process mining, and diagnostic analytics. Unlike existing bottleneck detection methods, this novel approach is capable of directly utilizing enterprise data from multiple levels, namely production planning, process execution, and asset monitoring, to generate event-log which can be fed into a digital twin. This enables not only the detection and diagnosis of bottleneck resources, but also validation of various what-if improvement scenarios. The digital twin itself is generated through process mining techniques, which can extract the main process map from a complex system. The results show that the utilization can detect both sole and shifting bottlenecks in a complex manufacturing system. Diagnosing and managing bottleneck resources through the proposed approach yielded a minimum throughput improvement of 10% in a real factory setting. The concept of a custom digital twin for a specific context and goal opens many new possibilities for studying the strong interaction of multi-source data and decision-making in a manufacturing system. This methodology also has the potential to be exploited for multi-objective optimization of bottleneck resources.Keywords:
Factory (object-oriented programming)
Process mining
Process mining
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Business process discovery
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Business process in an organization consists of numerous activities performed by different actors. A process model is a representation of process executions. In practices, a process model is typically created through meetings and interviews with various stakeholders in the organization. This traditional approach usually takes up to several years to complete. On the other hand, process mining offers an automatic means to develop a process model. The process model discovered by process mining is based on actual process behavior recorded in the event log. However, process mining is a relatively young field, and there is a lack of attention about how to perform a process mining project. In this thesis, we proposed a three-phase process analysis approach using process mining techniques involving process owner and process analyst. The application of the proposed approach is demonstrated using real-life data sets. The approach elaborations and result of the demonstration is combined into a “guideline” document in the form of a white-paper. For evaluation purpose, the guideline is presented to potential process mining users.
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Solving bottlenecks can increase the performance of processes.
One way to detect bottlenecks is by using process mining
techniques. This research focuses on bottleneck analysis using
process mining. The goal is to provide a way to analyze process
mining bottleneck analysis techniques. This is done by
presenting a conceptual framework that classifies the state-ofthe-art based on how mature a bottleneck analysis by using
process mining techniquesis conducted. The proposed maturity
levels are Detect, Predict, and Recommend. The results
indicated that most research is about detecting bottlenecks
only, while limited attention is given to prediction and
recommendation techniques. Therefore, researching prediction
and recommendation techniques is a possible future research
direction. The presented framework is validated through a
demonstration that shows how process mining bottleneck
analysis techniques can be applied in practice. The framework
can be used to check for a case which maturity level suits.
Process mining
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Process-orientation has gained significant momentum in manufacturing as enabler for the integration of machines, sensors, systems, and human workers across all levels of the automation pyramid. With process orientation comes the opportunity to collect manufacturing data in a contextualized and integrated way in the form of process event logs (no data silos) and with that data, in turn, the opportunity to exploit the full range of process mining techniques. Process mining techniques serve three tasks, i.e., (i) the discovery of process models based on process event logs, (ii) checking the conformance between a process model and process event logs, and (iii) enhancing process models. Recent studies show that particularly, (ii) and (iii) have become increasingly important. Conformance checking during run-time can help to detect deviations and errors in manufacturing processes and related data (e.g., sensor data) when they actually happen. This facilitates an instant reaction to these deviations and errors, e.g., by adapting the processes accordingly (process enhancement), and can be taken as input for predicting deviations and errorsfor future process executions. This chapter discusses process mining in the context of manufacturing processes along the phases of an analysis project, i.e., preparation and analysis of manufacturing data during design and run-time and the visualization and interpretation of process mining results. In particular, this chapter features recommendations on how to employ which process mining technique for different analysis goals in manufacturing.
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Process mining is originated form the fact that the modern information systems systematically record and maintain history of the process which they monitor and support. Systematic study of the recorded information in process centric manner will help to understand the process in a better way. Process mining acts as enabling technology by facilitating process centric analysis of data, which other available data science like data mining etc. fails to provide. Process mining algorithms are able to provide excellent insights on the process which they analyze, but they fail to handle the change in the process. Concept drift is a phenomenon of change in the process while it is being analyzed and it is a non-stationary learning problem. As the process changes while it is being analyzed, end result of the analysis becomes obsolete. Process mining algorithms are static biased, they assume that process at the beginning of analysis period will remain as same at the end of analysis period. There is at most requirement to effectively deal with the change in process to conduct optimal analysis. The main focus of this paper is to identify different factors to be considered while designing the solution for the problem of concept drift and explain each of the identified factors briefly. As the phenomenon of concept drift is extensively under consideration for research in other scientific research disciplines, this article considers restricting the content strictly concerning to the context of process mining.
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Process mining has matured as analysis instrument for process-oriented data in recent years. Manufacturing is a challenging domain that craves for process-oriented technologies to address digitalization challenges. We found that process mining creates high expectations, but its implementation and usage by manufacturing experts such as process supervisors and shopfloor workers remain unclear to a certain extent. Reason (1) is that even though manufacturing allows for well-structured processes, the actual workflow is rarely captured in a process model. Even if a model is available, a software for orchestrating and logging the execution is often missing. Reason (2) refers to the work reality in manufacturing: a process instance is started by a shopfloor worker who then turns to work on other things. Hence continuous monitoring of the process instances does not happen, i.e., process monitoring is merely a secondary task, and the shopfloor worker can only react to problems/errors that have already occurred. (1) and (2) motivate the goals of this study that is driven by Technical Action Research (TAR). Based on the experimental artifact TIDATE -- a lightweight process execution and mining framework -- it is studied how the correct execution of process instances can be ensured and how a data set suitable for process mining can be generated at run time in a real-world setting. Secondly, it is investigated whether and how process mining supports domain experts during process monitoring as a secondary task. The findings emphasize the importance of online conformance checking in manufacturing and show how appropriate data sets can be identified and generated.
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Process mining
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Process mining is a paradigm shift from traditional process understanding methodologies like interviews and surveys to a data-driven understanding of the actual digital processes. It analyzes business processes by applying algorithms to the event data generated by digital systems. The chapter provides insight into various uses of process mining in different social and economic processes, with examples from past works demonstrating how practical process mining is in detecting and mitigating bottlenecks in these sectors. Then the chapter further delves into the details of process mining algorithms, key features, and metrics that can help practitioners and researchers evaluate process mining for their work. It also highlights some data quality issues in the event log that can inhibit obtaining fair results from process models. Additionally, some current limitations and concerns are described for creating awareness and building over the body of knowledge in the process and sequential mining techniques.
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Process mining is an emerging technique that can discover the real sequence of various activities from an event log, compare different processes and ultimately find the bottleneck of an existing process and hence improve it. Curriculum data is the history of the courses effectively taken by students. It is essentially process-centric. Applying process mining on curriculum data provides a means to compare cohorts of students, successful and less successful, and presents an opportunity to adjust the requirements for the curriculum by applying enhancement of process mining. This can lead to building recommenders for courses to students based on expected outcome. In this paper we first discover a process model of students taking courses, then, compare the paths that successful and less successful students tend to take and highlight discrepancies between them. The conclusion we reached is that process mining indeed has a great potential to assist teachers and administrators to understand students behavior, to recommend the correct path to students, and at last to enhance the design of a curriculum.
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In this paper, we investigate the problem of the availability of complete process execution event logs in order to offer automatic process model generation (process discovery) possibility by process mining techniques. Therefore, we present the Process Observer project that generates manual logs and guides process participants through process execution. Like this, our project offers the possibility for the automatic generation of process models within organizations, without the availability of any information system. Process participants are encouraged to work with the Process Observer by various process execution support functions, like an auto-suggestion of process data and dynamic recommendations of following processes.
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