Analysis of barriers of cyber-physical system adoption in small and medium enterprises using interpretive ranking process

Wankhede, Vishal Ashok; Vinodh, S.

doi:10.1108/ijqrm-06-2021-0174

Cited by 24 publications

(9 citation statements)

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“…These techniques only consider one type of variable and establish the interrelationship by doing the comparison among the variables with each other. Wankhede and Vinodh (2021) observed that the IRP has been used as an alternative ranking approach, specifically in lean manufacturing, supply chain management, manufacturing practices, etc. In this study, authors provide a comparative interpretation of ISM and IRP approaches and shows that the IRP gives more detailed hierarchical representation than the ISM, since it goes one step further to include the relationship of lean practices with respect to performance dimensions.…”

Section: Resultsmentioning

confidence: 99%

Prioritization and ranking of lean practices: a case study

Sangwa

Sangwan

2022

IJPPM

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PurposeThe paper aims to identify, prioritize and rank lean practices in the context of an Indian automotive component manufacturing organization using interpretive ranking process (IRP) and interpretive structural modeling (ISM) approaches.Design/methodology/approachLean practices are identified from the literature. Then, two hierarchical models were are developed using two distinct modeling approaches – ISM and IRP with expert opinions from an Indian automotive component manufacturing organization to analyze the contextual relationships among the various lean practices and to prioritize and rank them with respect to performance dimensions.FindingsIn the study, the hierarchical structural models are developed using ISM and IRP approaches for an Indian automotive component manufacturing organization. In ISM-based modeling, lean practices can be categorized into five levels. Top priority should be given to the motivators followed by value chain, system/technology and organization centric practices. IRP model shows the dominance relationship among the various lean practices with respect to performance dimensions.Practical implicationsThe models are constructed from the organizational standpoint to evaluate their impact to the implementation of lean manufacturing. The study leverages the organizations to prioritize limited resources as per the hierarchy. Managers get the inter-linkages and ranking of various lean practices, which leads to a better perspective for the effective implementation of lean. The structural models also assist management to assign proper roles to employees/departments for effective lean implementation.Originality/valueThere is hardly any structural model of lean practices in the literature for clustering, prioritizing and ranking of lean practices. The study fills this gap and develops the hierarchical models of lean practices through IRP and ISM approaches for an Indian automotive component manufacturing organization. The results from both approaches are compared for illustrating the benefits of one over the other.

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Section: Resultsmentioning

confidence: 99%

Prioritization and ranking of lean practices: a case study

Sangwa

Sangwan

2022

IJPPM

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“…The approach has been applied in the literature for the analysis of factors and barriers. Wankhede and Vinodh (2021) analyzed barriers to cyber-physical system (CPS) adoption in small and medium enterprises using the IRP method and developed a model showing a hierarchy of CPS barriers. Bhadu et al (2021) analyzed lean implementation barriers in Indian ceramic industries.…”

Section: Methodsmentioning

confidence: 99%

Investigating and modeling interactions among manufacturing barriers due to Covid-19 pandemic: an interpretive ranking process

Badhotiya

Sachdeva

Soni

2022

IJIEOM

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PurposeThe manufacturing industry is one of the most disrupted systems as a result of the global spread of the Covid-19 pandemic. Manufacturing firms are looking for strategies and policies to deal with the situation while also meeting customer demands. This study aims to discuss and analyze the barriers that have impacted manufacturing systems during this period.Design/methodology/approachThe barriers and performance measures were extracted from the extant literature and further discussed with academic and industry experts. Based on the response of experts, a list of ten barriers and five performance measures were selected for further analysis. The interpretive ranking process (IRP) is applied to analyze the inter-relationship among the barriers with respect to performance variables. The cross-interaction matrices and the dominance profile are created to prioritize the barriers. Based on dominance value, an IRP-based manufacturing barrier evaluation model is developed for validation.FindingsThe impact of the pandemic on the manufacturing industry is analyzed through the list of barriers and a structured ranking model is proposed. The research findings of the study indicate that “Financial constraints” is the most influential barrier to manufacturing due to the outbreak of Covid-19, followed by “Government imposed restrictions” and “Setbacks in logistics services.”Practical implicationsThe ranking of barriers and developed interpretive ranking process model would be helpful for practitioners and policymakers to formulate strategies for manufacturing organizations to deal with the pandemic situation. The finding can be beneficial as it promotes similar studies in other sectors.Originality/valueThis study contributes to the manufacturing sector by developing a contextual relationship among the set of identified barriers against various performance measures. As per the author's knowledge, this is the first study that provides a relationship and ranking of manufacturing barriers due to the outbreak of Covid-19.

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“…Sustainability 2022, 13, x FOR PEER REVIEW 25 of 36 13. Managers expect these technologies to evolve and/or enhance real-time "Capture/measure" of information relating to the status and performance of the production system.…”

Section: % 42%mentioning

confidence: 99%

“…Sustainability 2022, 13, x FOR PEER REVIEW 29 of 36 13. At the level of the "Capture/measure" step of the decision-making process, the use of artificial intelligence is less relevant or more restrictive to implement than big data analysis.…”

Section: %mentioning

confidence: 99%

“…Although different views and distinctions coexist as to which technologies are included in Industry 4.0, the Boston Consulting Group [10] identifies nine main pillars, namely, autonomous robots, simulation, horizontal and vertical integration of information systems, the Internet of Things, cyber security, the cloud, additive manufacturing, augmented reality, and big data and analytics. Furthermore, cyber-physical systems (CPS) are presented as one of the most significant 2 of 35 directions in the development of computer science and information and communication technologies by many authors [11][12][13]. CPSs integrate other 4.0 technologies and interact with humans and other autonomous subsystems at all production levels through many new modalities [11].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing the Decision-Making Process through Industry 4.0 Technologies

et al. 2022

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In order to meet the increasingly complex expectations of customers, many companies must increase efficiency and agility. In this sense, Industry 4.0 technologies offer significant opportunities for improving both operational and decision-making processes. These developments make it possible to consider an increase in the level of operational systems and teams’ autonomy. However, the potential for strengthening the decision-making process by means of these new technologies remains unclear in the current literature. To fill this gap, a Delphi study using the Régnier Abacus technique was conducted with a representative panel of 24 experts. The novelty of this study was to identify and characterize the potential for enhancing the overall decision-making process with the main Industry 4.0 groups of technologies. Our results show that cloud computing appears as a backbone to enhance the entire decision-making process. However, certain technologies, such as IoT and simulation, have a strong potential for only specific steps within the decision-making process. This research also provides a first vision of the manager’s perspectives, expectations, and risks associated with implementing new modes of decision-making and cyber-autonomy supported by Industry 4.0 technologies.

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Analysis of barriers of cyber-physical system adoption in small and medium enterprises using interpretive ranking process

Cited by 24 publications

References 50 publications

Prioritization and ranking of lean practices: a case study

Prioritization and ranking of lean practices: a case study

Investigating and modeling interactions among manufacturing barriers due to Covid-19 pandemic: an interpretive ranking process

Enhancing the Decision-Making Process through Industry 4.0 Technologies

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