How Industry 4.0 can enhance Lean practices

Pereira, Ana; Dinis‐Carvalho, José; Alves, Anabela Carvalho; Arezes, Pedro

doi:10.5937/fmet1904810p

Cited by 80 publications

(75 citation statements)

References 65 publications

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“…The vast majority of advantages are associated with the use of Industry 4.0 technology to support Lean principles/tools (Mayr et al, 2018;Mrugalska & Wyrwicka, 2017;Sanders et al, 2016). For example, the paper (Pereira et al, 2019) presents the matrix "Lean tools supported by Industry 4.0 technologies", (Ejsmont & Gladysz, 2020) presents complex review of Industry 4.0 solutions that may support lean techniques, tools and methods, and (Mayr et al, 2018) presents combinations of I4.0 tools and lean methods. Shah and Ward (2007) conducted a comprehensive, multi-step study to identify a multidimensional lean structure.…”

Section: Discussion and Further Researchmentioning

confidence: 99%

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Towards ‘Lean Industry 4.0ʹ – Current trends and future perspectives

Ejsmont

Gładysz

Corti

et al. 2020

Cogent Business & Management

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The enterprises to be competitive are constantly looking for continuous increase of productivity, quality and level of services. With the development of Industry 4.0 concept, manufacturers are more confident about new advantages of automation and systems integration. Lean management is well developed and empirically proven effective managerial approach. Combining Lean and Industry 4.0 practices seems to be necessary evolutionary step for further raise the level of operational excellence (exploitation of finance, workload, materials, machines/ devices). There is an increasing number of Industry 4.0 solutions used to reduce waste (as known from Lean Management). Therefore, the main objective of this article aims at presenting the results of a literature review on the concept of 'Lean Industry 4.0ʹ. Dynamic methodology called "Systematic Literature Network Analysis (SLNA)" was used. It combines the Systematic Literature Review approach with the quantitative analysis of bibliographic networks to detect emerging topics and the dynamic evolution of the topics. The paper is a comprehensive systematization and rationalization of knowledge about the integration of LM and I4.0 concepts, identifies the most important research trends and defines directions for future research. The article contains a framework that presents the current state of knowledge in the area of Lean Industry 4.0.

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Section: Discussion and Further Researchmentioning

confidence: 99%

“…The advantages of combining LM and I4.0 can be demonstrated grounding on the presented above categories of factors (Pereira et al, 2019;Sanders et al, 2016): a. Supplier factors…”

Section: Discussion and Further Researchmentioning

confidence: 99%

Towards ‘Lean Industry 4.0ʹ – Current trends and future perspectives

Ejsmont

Gładysz

Corti

et al. 2020

Cogent Business & Management

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“…It should be pointed out that many recent studies affirm that the implementation of new technologies in Industry 4.0 is compatible with Lean practices [13][14][15].…”

Section: Positioningmentioning

confidence: 96%

Framework for resolving problems resulting from lean integration from the early design phases of production 3D printing machine

Slim¹,

Houssin²,

Coulibaly³

et al. 2021

FME Transactions

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We focus on improving the performance of production machines (timesaving, flexibility, cost-effectiveness, safety) from the early design phase. Lean integration during the design phases might be a more appropriate solution, but this could be a source of some contradictions related to the machine performance and the usage requirements. In this paper, we propose an approach, based on Lean and Inventive Design Methodology (IDM-TRIZ), to identify and resolve these contradictions due to the Lean integration and also to provide innovative technical solutions. The approach is illustrated by the integration of the functionalities of the ''Single Minute of Exchange of Dies'' (SMED) and the ''5S'' method from the early design phase. A case study about the ''3D printer clogged nozzle'' outlines the feasibility of the proposed approach.

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“…Yet, one of the key ideas about Industry 4.0 is the integration of varied technologies due to the limited effect of focusing on a single technology [ 20 ]. In fact, several of the many lean tools have been examined in the context of Industry 4.0 [ 21 , 22 , 23 ], leading to the conclusion that a leaner production is easier to integrate into an Industry 4.0 context and, in many cases, it is possible to combine the ideas and techniques from both frameworks [ 24 , 25 , 26 , 27 , 28 ]. Moreover, it seems to be a necessary evolutionary step for further raising the level of operational excellence (i.e., to coordinate actions, to optimise resource efficiency, to improve work safety, to decrease in cost) [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In–Process Control Visualization

Villalba-Díez

Gutiérrez

Grijalvo

et al. 2021

Sensors

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With the advent of the Industry 4.0 paradigm, the possibilities of controlling manufacturing processes through the information provided by a network of sensors connected to work centers have expanded. Real-time monitoring of each parameter makes it possible to determine whether the values yielded by the corresponding sensor are in their normal operating range. In the interplay of the multitude of parameters, deterministic analysis quickly becomes intractable and one enters the realm of “uncertain knowledge”. Bayesian decision networks are a recognized tool to control the effects of conditional probabilities in such systems. However, determining whether a manufacturing process is out of range requires significant computation time for a decision network, thus delaying the triggering of a malfunction alarm. From its origins, JIDOKA was conceived as a means to provide mechanisms to facilitate real-time identification of malfunctions in any step of the process, so that the production line could be stopped, the cause of the disruption identified for resolution, and ultimately the number of defective parts minimized. Our hypothesis is that we can model the internal sensor network of a computer numerical control (CNC) machine with quantum simulations that show better performance than classical models based on decision networks. We show a successful test of our hypothesis by implementing a quantum digital twin that allows for the integration of quantum computing and Industry 4.0. This quantum digital twin simulates the intricate sensor network within a machine and permits, due to its high computational performance, to apply JIDOKA in real time within manufacturing processes.

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How Industry 4.0 can enhance Lean practices

Cited by 80 publications

References 65 publications

Towards ‘Lean Industry 4.0ʹ – Current trends and future perspectives

Towards ‘Lean Industry 4.0ʹ – Current trends and future perspectives

Framework for resolving problems resulting from lean integration from the early design phases of production 3D printing machine

Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In–Process Control Visualization

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