Virtual factory based data analytics

Jain, Sanjay; Shao, Guodong; Shin, Seung‐Jun

doi:10.1109/icca.2016.7505298

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…The lack of awareness of government policies on I 4.0 adoption is also a considerable challenge. Government support for manufacturing organizations is also vital (Jain et al 2016;Pfohl et al 2017). With the lack of dedicated resources, the quality of research is affected.…”

Section: Identification Of Challenges In the Indian Scenariomentioning

confidence: 99%

Deployment of Interpretive Structural Modeling in Barriers to Industry 4.0: A Case of Small and Medium Enterprises

Goel¹,

Kumar

Banga

et al. 2022

JRFM

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Small and medium enterprises (SMEs) are vital contributors and significant drivers of any manufacturing sector. The Industry 4.0 (I 4.0) revolution has made the global economy highly competitive and automated, requiring Indian SMEs to adapt more quickly. Therefore, this study aimed to identify the barriers to implementing I 4.0, simplifying the complex interrelationship among such barriers with the help of a suitable model, categorizing them as independent and dependent ones, and, ultimately, leveling the same drivers, autonomous linkages, and dependent forces. The present investigation thoroughly examined the existing literature and summarized the list of barriers into fifteen significant barriers to the smooth establishment of Industry 4.0 in India. The identified barriers were analyzed with the help of Interpretive Structural Modeling (ISM) Diagraph and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis. This study was able to explore the interrelationship among these barriers. The study has found found a lack of support from stakeholders, and insufficient managerial support emerged as a major factor neglected by Indian SMEs. However, uncertainty in the predicted demand for products, the lack of an alternate solution to the technological breakdown, and doubt about the sustainability of Industry 4.0 (relating to its potential to lead to unemployment in society, etc.) are significant contingent barriers. These barriers can impact the other strategic choices related to the successful implementation of Industry 4.0. This study’s observations can help decision-makers make strategic decisions to manage the barriers affecting Industry 4.0 in Indian SMEs. This research revealed a scope that can be extended to other South Asian and developing nations. The results of the present work can be further studied with structural equation modeling (SEM) and multiple regression analysis (MRA).

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Section: Identification Of Challenges In the Indian Scenariomentioning

confidence: 99%

Deployment of Interpretive Structural Modeling in Barriers to Industry 4.0: A Case of Small and Medium Enterprises

Goel¹,

Kumar

Banga

et al. 2022

JRFM

View full text Add to dashboard Cite

show abstract

“…To use the artifact, control functions are also required to manage the production environment, e.g., to create order data that can be processed (R12) (Chen et al, 2018;Dalstam et al, 2018;Gericke et al, 2019). Furthermore, elements like single sensors or whole machines should be controllable, e.g., to switch them off (R13) (Jain et al, 2016;Kirchhof et al, 2020;Shao & Kibira, 2018). Finally, the user can access the simulation functions to simulate the factory layout in conjunction with the order situation (R10) (Davila Delgado & Oyedele, 2021;Kuehn, 2019;Negri et al, 2017).…”

Section: Objectives Of Solutionmentioning

confidence: 99%

“…Finally, the user can access the simulation functions to simulate the factory layout in conjunction with the order situation (R10) (Davila Delgado & Oyedele, 2021;Kuehn, 2019;Negri et al, 2017). Here, the simulation environment should ensure the adaptation of the factory layout to check potential changes (R11) (Heininger & Stary, 2021;Jain et al, 2016;Rolle et al, 2019).…”

Section: Objectives Of Solutionmentioning

confidence: 99%

Design and Implementation of Hierarchical Digital Twins in Industrial Production Environments

Finke,

Groth,

Schumann

et al. 2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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The increasing requirements for industrial production environments due to customer expectations, the implementation of batch size 1, and further automation of production processes are confronting companies with new challenges. In particular, the emergence of cyber-physical systems is influencing and complicating manufacturing processes by capturing an increasing amount of information within production facilities. Digital twins are an interdisciplinary technology that may solve these issues because they serve to monitor, control, and optimize cyber-physical systems by creating a digital representation of realworld objects. Existing concepts for digital twins usually only consider individual machines without their context. This is of limited use for production environments due to a multitude of different machines and associated sensor types. Therefore, we propose a requirements catalog, concept, and prototypical implementation for the hierarchical structuring of digital twins in this paper.

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“…For this type of problem, merely running what-if scenarios on the simulation models can be considered as a waste. Furthermore, given that one of the goals of Lean is to optimise the overall flow of the value stream and minimise waste through continuous improvements, virtual factories that allow successive changes to be evaluated, guided by the SBO approach (Jain, Shao, and Shin 2016), will likely to be the new, innovative toolset at the disposal of any Lean practitioner.…”

Section: Increased Interest In the Combination Of Lean And Simulation With Optimisationmentioning

confidence: 99%

Bringing together Lean and simulation: a comprehensive review

Uriarte

Moris

2019

International Journal of Production Research

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Lean is and will still be one of the most popular management philosophies in the Industry 4.0 context and simulation is one of its key technologies. Many authors discuss about the benefits of combining Lean and simulation to better support decision makers in system design and improvement. However, there is a lack of reviews in the domain. Therefore, this paper presents a four-stage comprehensive review and analysis of existing literature on their combination. The aim is to identify the state of the art, existing methods and frameworks for combining Lean and simulation, while also identifying key research perspectives and challenges. The main trends identified are the increased interest in the combination of Lean and simulation in the Industry 4.0 context and in their combination with optimisation, Six Sigma, as well as sustainability. The number of articles in these areas is likely to continue to grow. On the other hand, we highlight six gaps found in the literature regarding the combination of Lean and simulation, which may induce new research opportunities. Existing technical, organisational, as well as people and culture related challenges on the combination of Lean and simulation are also discussed.

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Virtual factory based data analytics

Cited by 5 publications

References 11 publications

Deployment of Interpretive Structural Modeling in Barriers to Industry 4.0: A Case of Small and Medium Enterprises

Deployment of Interpretive Structural Modeling in Barriers to Industry 4.0: A Case of Small and Medium Enterprises

Design and Implementation of Hierarchical Digital Twins in Industrial Production Environments

Bringing together Lean and simulation: a comprehensive review

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