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

Farooqui, Ashfaq; Bengtsson, Kristofer; Falkman, Petter; Fabian, Martin

doi:10.1080/00207543.2020.1735660

Cited by 29 publications

(7 citation statements)

References 36 publications

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“…Manufacturers need to gather real-time data from shop floor machines and further analyze the data and then subsequently use it as an input for enhancing design and manufacturing-related decisions (Dutta et al , 2020). Farooqui et al (2020) proposed architecture to gather data from the shop floor. I4.0 is anticipated to bring new challenges and opportunities for future supply chains (Ghadge et al , 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework

Bag

Pretorius

2020

IJOA

314

179

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Purpose The digital revolution has brought many challenges and opportunities for the manufacturing firms. The impact of Industry 4.0 technology adoption on sustainable manufacturing and circular economy has been under-researched. This paper aims to review the latest articles in the area of Industry 4.0, sustainable manufacturing and circular economy and further developed a research framework showing key paths. Design/methodology/approach Qualitative research is performed in two stages. In the first stage, a review of the extant literature is performed to identify the barriers, drivers, challenges and opportunities. In the second stage, a research framework is proposed to integrate Industry 4.0 technology (big data analytics powered artificial intelligence) adoption, sustainable manufacturing and circular economy capabilities. Findings This research extends the knowledge base by providing a detailed review of Industry 4.0, sustainable manufacturing, and circular economy and proposes a research framework by integrating these three contemporary concepts in the context of supply chain management. Through an exploration of this integrative research framework, the authors propose a future research agenda and seven research propositions. Research limitations/implications It is important to understand the interplay between institutional pressures, tangible resources and human skills for Industry 4.0 technology (big data analytics powered artificial intelligence) adoption. Industry 4.0 technology (big data analytics powered artificial intelligence) adoption can positively influence sustainable manufacturing and circular economy capabilities. Managers must also put more attention to sustainable manufacturing to develop circular economic capabilities. Social implications Factory workers and the local communities generally suffer from various adverse effects resulting from the traditional manufacturing process. The quality of the environment is deteriorating to such an extent that people even staying miles away from the factory are also affected due to environmental pollution that is generated from factory operations. Hence, sustainable manufacturing is the only choice left to manufacturers that can help in the transition to a circular economy. The research framework can help firms to enhance circular economy capabilities. Originality/value This review paper contains the most updated work on Industry 4.0, sustainable manufacturing and circular economy. It also proposes a research framework to integrate these three concepts.

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Section: Literature Reviewmentioning

confidence: 99%

Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework

Bag

Pretorius

2020

IJOA

314

179

View full text Add to dashboard Cite

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“…insurance, banking, healthcare), and studies in manufacturing settings are rare (e.g. Farooqui et al 2020;Park, Lee, and Zhu 2014;Rozinat et al 2009).…”

Section: Process Miningmentioning

confidence: 99%

Using process mining to improve productivity in make-to-stock manufacturing

Lorenz

Senoner

Sihn

et al. 2021

International Journal of Production Research

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This paper proposes a data-driven procedure to improve productivity in make-to-stock manufacturing. By leveraging recent developments in information systems research, the paper addresses manufacturing systems with high process complexity and variety. Specifically, the proposed procedure draws upon process mining to dynamically map and analyse manufacturing processes in an automated manner. This way, manufacturers can leverage data to overcome the limitations of existing process mapping methods, which only provide static snapshots of process flows. By bridging data and process science, process mining can exploit hitherto untapped potential for productivity improvement. The proposed procedure is empirically validated at a leading manufacturer of sanitary products. The field test leads to three concrete improvement suggestions for the company. This research contributes to the literature on production research by demonstrating a novel use of process mining in manufacturing and by guiding practitioners in its implementation.

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“…They conclude that a regression tree is optimal for this task. Machine learning can also be used for detecting disturbances in the performance of a production system (Farooqui et al 2020).…”

Section: Machine Learning For Performance Evaluationmentioning

confidence: 99%

A machine learning approach for implementing data-driven production control policies

Khayyati

Tan

2021

International Journal of Production Research

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Given the extensive data being collected in manufacturing systems, there is a need for developing a systematic method to implement data-driven production control policies. For an e↵ective implementation, first, the relevant information sources must be selected. Then, a control policy that uses the real-time signals collected from these sources must be implemented. We analyze the production control policy implementation problem in three levels: choosing the information sources, forming clusters of information signals to be used by the policy, and determining the optimal policy parameters. Due to the search-space size, a machinelearning-based framework is proposed. Using machine learning speeds up optimization and allows utilizing the collected data with simulation. Through two experiments, we show the e↵ectiveness of this approach.In the first experiment, the problem of selecting the right machines and bu↵ers for controlling the release of materials in a production/inventory system is considered. In the second experiment, the best dispatching policy based on the selected information sources is identified. We show that selecting the right information sources and controlling a production system based on the real-time signals from the selected sources with the right policy improve the system performance significantly. Furthermore, the proposed machine learning framework facilitates this task e↵ectively.

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

Cited by 29 publications

References 36 publications

Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework

Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework

Using process mining to improve productivity in make-to-stock manufacturing

A machine learning approach for implementing data-driven production control policies

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