A system architecture for manufacturing process analysis based on big data and process mining techniques

Yang, Hanna; Park, Minjeong; Cho, Minsu; Song, Minseok; Kim, Seong-Joo

doi:10.1109/bigdata.2014.7004336

Cited by 32 publications

(10 citation statements)

References 16 publications

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“…Within the manufacturing domain, there have been only a handful of studies on applying process mining to manufacturing systems. Yang et al (2014) and Viale, Frydman, and Pinaton (2011) present a method to apply process mining on manufacturing data. While the former uses structured and unstructured data generated from manufacturing systems along with operators or workers to provide domain-level knowledge, the latter works with definitions of the systems provided by domain experts to find inconsistencies between model and process.…”

Section: Process Miningmentioning

confidence: 99%

Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations

Farooqui

Bengtsson

Falkman

et al. 2020

International Journal of Production Research

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The technological advancements of recent years have increased the complexity of manufacturing systems, and the ongoing transformation to Industry 4.0 will further aggravate the situation. This is leading to a point where existing systems on the factory floor get outdated, increasing the gap between existing technologies and state-of-the-art systems, making them incompatible. This paper presents an event-based data pipeline architecture, that can be applied to legacy systems as well as new state-of-the-art systems, to collect data from the factory floor. In the presented architecture, actions executed by the resources are converted to event streams, which are then transformed into an abstraction called operations. These operations correspond to the tasks performed in the manufacturing station. A sequence of these operations recount the task performed by the station. We demonstrate the usability of the collected data by using conformance analysis to detect when the manufacturing system has deviated from its defined model. The described architecture is developed in Sequence Planner -a tool for modelling and analysing production systems -and is currently implemented at an automotive company as a pilot project.

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Section: Process Miningmentioning

confidence: 99%

Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations

Farooqui

Bengtsson

Falkman

et al. 2020

International Journal of Production Research

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“…He noted that the granularity needs to be chosen and the process model customised to the analysis' goal. Yang et al [10] propose the enhancement of such high-level production data with the help of unstructured data like emails. Although the technical approach is presented in detail, it remains unclear what added benefits such approach yields.…”

Section: Related Workmentioning

confidence: 99%

Automatic Generation of Improvement Suggestions for Legacy, PLC Controlled Manufacturing Equipment Utilizing Machine Learning

Köehler

Jing

2020

Machine Learning for Cyber Physical Systems

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The manufacturing industry and, for this research, the automotive manufacturing industry specifically, is always on the lookout for opportunities to improve production throughput with a minimum of investment. Identifying these opportunities often requires the observation of the current production process by experts. This paper is the continuation of the previous work ’Automated, Nomenclature Based Data Point Selection for Industrial Event Log Generation’. One of its aims is to provide strategies that can be used to pre-process an in-depth, slightly flawed industrial equipment log to allow for further analysis. The pre-processing is achieved by identifying the flaws, removing the non-value added events and a heuristic methodology to cluster the log into individual sequences. Expert knowledge then is encoded into engineering features to extend the log matrix and prepare it for machine learning model generation for identification of the complete cases. To derive value from the available data, the sequences are plotted into Gantt charts, and eight hypotheses are introduced that allow for automated annotations within this chart to highlight potential areas of improvement. Application of the framework to real life logs, obtained from stations considered bottlenecks within the evaluated automotive body shop, lead to the discovery of improvement potential between two and twelve seconds per cycle.

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“…Since in an Industry 4.0 factory, machines are connected as a collaborative community, Lee et al addressed the trends of manufacturing service transformation in Big Data environment, as well as the readiness of smart predictive informatics tools to manage Big Data, thereby achieving transparency and productivity [25]. Yang et al suggested a manufacturing data analysis system that collects event logs from so-called Big Data and analyzes the collected logs with process mining [26]. This study considered two kinds of Big Data generated from manufacturing processes, i.e., structured data and unstructured data.…”

Section: B Manufacturing Big Data Analysis and Processingmentioning

confidence: 99%

Big Data Analysis Approach for Real-Time Carbon Efficiency Evaluation of Discrete Manufacturing Workshops

Zhang

2019

IEEE Access

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Due to the huge consumption of materials and energy during machining processes, reduction of manufacturing carbon emission is an essential key to decrease the environmental burden of various manufacturing systems. To achieve this target, one critical step is to calculate and evaluate the carbon emissions of machining processes. However, this step is a little difficult for discrete manufacturing processes, because they are always complex and the data sources are diverse. Considering the complexity of discrete manufacturing workshops, a Big Data analysis approach for real-time carbon efficiency evaluation of discrete manufacturing workshops is proposed in an internet of things-enabled ubiquitous environment. Firstly, the deployment of data acquisition devices is introduced to create a ubiquitous manufacturing workshop, and data modeling of production state and carbon emission is described to realize data acquisition and storage. Then, a data-driven multi-level carbon efficiency evaluation of manufacturing workshop is established based on Big Data analysis approaches. Finally, an auto parts manufacturing workshop is studied to verify the feasibility and applicability of the proposed methods. This method realizes the combination of manufacturing Big Data and low-carbon production. Meanwhile, the evaluation method can be used in other production information systems and then assist the production decision-making. INDEX TERMS Big data analysis, data acquisition network, carbon emission, carbon efficiency evaluation, discrete manufacturing workshops.

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A system architecture for manufacturing process analysis based on big data and process mining techniques

Cited by 32 publications

References 16 publications

Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations

Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations

Automatic Generation of Improvement Suggestions for Legacy, PLC Controlled Manufacturing Equipment Utilizing Machine Learning

Big Data Analysis Approach for Real-Time Carbon Efficiency Evaluation of Discrete Manufacturing Workshops

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