Lean manufacturing systems in the area of Industry 4.0: a lean automation plan of AGVs/IoT integration

Ilias Vlachos, Rodrigo Martinez Pascazzi, George Zobolas, Panagiotis Repoussis and Mihalis Giannakis ()
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Mihalis Giannakis: Audencia Business School

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Abstract: Industry 4.0 represents a new industrial paradigm ignited by disruptive technologies that can transform manufacturing into a cyber-physical system that integrates products, people and processes. However, there is little guidance concerning how to implement and integrate Industry 4.0 technologies by existing lean manufacturing (LM) systems. We select autonomous guided vehicles (AGVs) and internet of things (IoT) to develop an action plan that helps managers integrate Industry 4.0 technologies into their manufacturing systems and achieve lean automation. We conducted a case study of a large manufacturing company that introduced AGVs and IoT to automate its lean operations. We used socio-technical systems (STSs) design logic to integrate the two distinct domains (lean and automation) into an action plan that successfully meets six lean automation objectives. The findings demonstrate that AGVs implementation should include three phases: design, integration and continuous improvement. The lean automation objectives are: cost, reusability, reliability, simplicity, compactness, fit, engage and culture. The lean automation plan successfully manages the interactions and interplay between social factors (people and culture), technical factors (infrastructure and technology) and operational factors (routines and processes). The lean automation plan has significant managerial implications helping companies integrate lean philosophy, which is people-centric, with Industry 4.0 technologies, which promote efficiency via automation.

Keywords: ean manufacturing; Industry 4.0; lean automation; AGV; internet of things; socio-technical systems (search for similar items in EconPapers)
Date: 2023-03-12
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Citations: View citations in EconPapers (3)

Published in Production Planning and Control, 2023, 34 (4), pp.345-358. ⟨10.1080/09537287.2021.1917720⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04004536

DOI: 10.1080/09537287.2021.1917720

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