Design of networked manufacturing systems for Industry 4.0

Milisavljevic-Syed, Jelena; Allen, Janet K.; Commuri, Sesh; Mistree, Farrokh

doi:10.1016/j.procir.2019.03.244

Cited by 23 publications

(8 citation statements)

References 7 publications

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“…Milisavljevic-Syed et al (2019) remarked that the I4.0 enterprises must support large-scale flexible automation by allocating interconnections for data exchange between various components. An I4.0 manufacturing system should be adaptable to dynamic market demands, robust to network/connectivity failures and capable of mass customization.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Technologies like Artificial Intelligence (AI), Big Data Analytics, Cobotics, Cloud Computing, Internet of Everything (IoE), Internet of Things (IoT), Human Machine Interface and Robotics will be integrated with machines (Schlechtendahl et al, 2015). Milisavljevic-Syed et al (2019) remarked that the I4.0 enterprises must support large-scale flexible automation by allocating interconnections for data exchange between various components. An I4.0 manufacturing system should be adaptable to dynamic market demands, robust to network/ connectivity failures and capable of mass customization.…”

Section: Machinerymentioning

confidence: 99%

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Development of an industry 4.0 transformability index for manufacturing systems

Kumar

Asjad

James³

et al. 2022

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Purpose Evaluation of the extent of transformability of an existing system into an industry 4.0 (I4.0) compatible system is indispensable for both the technical and economic planning for implementing I4.0. This paper aims to propose a procedure to evaluate the transformability of an existing manufacturing system into an I4.0 system. Design/methodology/approach Six significant components of a manufacturing system and their five levels of modifications essential for the decision of transformation are identified. Based on expert opinion on facilitation and the impact of the transformation of one component on the transformation of others, a graph theory-based procedure for estimation of transformability index (TI) along with its relative and threshold values is proposed. Findings The paper introduced the concept of transformability into manufacturing systems. It proposed a simple procedure for calculating the ideal, relative and threshold value for TI to assess the suitability of the up-gradation of any manufacturing system into the I4.0 system. Research limitations/implications Though the proposed procedure is based on six system components and their five levels of facilitation, it is quite versatile and able to integrate new components and different facilitation levels according to system requirements for their impact analysis in the transformation process. It can be extended to other domains like services and health care. Further, it can be used to estimate and establish the transformability criteria of a factory/service unit/industry from its current state to any regime. Practical implications The proposed method for deducing the TI, relative transformability index (RTI) and their threshold values would be a handy tool for decision-makers to assess the upgrading suitability of the entire manufacturing system and its component for use in the new regime or scrapping. It would provide mathematical and scientific support to the transformability decisions by assessing the influence of transforming one component to others and the system. This study would pave the way for further explorations in the domain of transformability. Originality/value In the light of available literature and best of the author’s knowledge, this study is the first of its kind that has applied the concept of transformability of existing manufacturing systems toward I4.0 compatible systems and proposed a procedure to estimate TI, RTI and their threshold values.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Machinerymentioning

confidence: 99%

Development of an industry 4.0 transformability index for manufacturing systems

Kumar

Asjad

James³

et al. 2022

View full text Add to dashboard Cite

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“…With the advent of new technologies brought about by Industry 4.0 [1], [2], the complexity of production systems continues to increase to ensure a high degree of adaptability [3], flexibility [4], reconfigurability [5], [6] and robustness against the increased level of uncertainty [7]. The challenges brought about by increased complexity are inherent in Industry 4.0 solutions [8].…”

Section: Introductionmentioning

confidence: 99%

Scheduling Under Uncertainty for Industry 4.0 and 5.0

et al. 2022

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This article provides a review about how uncertainties in increasingly complex production and supply chains should be addressed in scheduling tasks. Uncertainty management will be particularly important in Industry 5.0 solutions that will require the close integration of operators and technical systems. To prepare for these challenging developments, this work reviews the sources of uncertainty and the scheduling algorithms that deal with the different types of models developed to handle the uncertain nature of the elements and the environment of complex technological systems. The paper not only identifies the challenges, but also the main building blocks that can help to manage and reduce uncertainties based on the I4.0 and I5.0 solutions. We hope that this study will serve as a starting point for R&D projects and algorithm developments, which will be needed primarily in the field of multi-agent, multistage and inverse optimizations.

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“…Despite of many attempts to develop theoretical and implementation bases for the "Industry 4.0" concept, there are relatively few ideas to realise the intention of digital integration of e2e in DM, e.g. (Milisavljevic-Syed et al, 2019;Zheng et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Cooperation platform for distributed manufacturing

Pietroń

2021

DMMS

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The aim of the paper is to analyse contemporary trends in distributed manufacturing (DM) research and to present a concept to develop and test some task allocation, planning and scheduling algorithms for DM network organisations. Some concepts to identify key factor criteria and reasoning policies and rules for production/manufacturing decision support system are also undertaken. And finally, an aim is to draw a proposal for a development of a prototype decision support system with necessary communication and knowledge oriented modules to be implemented in an example of dynamic, DM and logistics network structure, particularly for very popular dynamic cluster forms in Poland. The developed concept of the organization of a multi-entity DM network will enable business-effective use of the system, supporting manufacturing decision making, consulting and offering information services in the control centre (the so-called Competence Centre) by constructing virtual reality and access to services in a distributed network of cloud computing type. Integration of the whole system into one information system will enable analysis and network resource optimization of manufacturing and logistics processes, new analytical functions, reduction of delays in the manufacturing system, management of changes and risks, and visualization of the current state of the DM system.

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Design of networked manufacturing systems for Industry 4.0

Cited by 23 publications

References 7 publications

Development of an industry 4.0 transformability index for manufacturing systems

Development of an industry 4.0 transformability index for manufacturing systems

Scheduling Under Uncertainty for Industry 4.0 and 5.0

Cooperation platform for distributed manufacturing

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