Vishal Ashok Wankhede and S. Vinodh
The purpose is to assess Industry 4.0 (I4.0) readiness index using fuzzy logic and multi-grade fuzzy approaches in an automotive component manufacturing organization.
Abstract
Purpose
The purpose is to assess Industry 4.0 (I4.0) readiness index using fuzzy logic and multi-grade fuzzy approaches in an automotive component manufacturing organization.
Design/methodology/approach
I4.0 implies fourth industrial revolution that necessitates vital challenges to be dealt with. In this viewpoint, this article presents the evaluation of I4.0 Readiness Index. The evaluation includes two levels with appropriate criteria and factors. Fuzzy logic approach is used for assessment. Furthermore, the results obtained from fuzzy logic have been benchmarked with multi-grade fuzzy approach.
Findings
The proposed assessment model has successfully utilized fuzzy logic approach for assessment of I4.0 readiness index of automotive component manufacturing organization. Based on fuzzy logic approach, readiness index of I4.0 has been found to be (4.74, 6.26, 7.80) which is further benchmarked using multi-grade fuzzy approach. Industry 4.0 readiness index obtained from multi-grade fuzzy approach is 6.258 and thus, validated. Furthermore, 20 weaker areas have been identified and improvement suggestions are provided.
Research limitations/implications
The assessment module include two levels (Six Criteria and 50 Factors). The assessment model could be expanded based on advancements in industrial developments. Therefore, future researchers could utilize findings of the readiness model to further develop multi-level assessment module for Industry 4.0 readiness in organization. The developed readiness model helped researchers in understanding the methodology to assess I4.0 readiness of organization.
Practical implications
The model has been tested with reference to automotive component manufacturing organization and hence the inferences derived have practical relevance. Furthermore, the benchmarking strategy adopted in the present study is simple to understand that makes the model unique and could be applied to other organizations. The results obtained from the study reveal that fuzzy logic-based readiness model is efficient to assess I4.0 readiness of industry.
Originality/value
The development of model for I4.0 readiness assessment and further analysis is the original contribution of the authors. The developed fuzzy logic based I4.0 readiness model indicated the readiness level of an organization using I4RI. Also, the model provided weaker areas based on FPII values which is essential to improve the readiness of organization that already began with the adoption of I4.0 concepts. Further modification in the readiness model would help in enhancing I4.0 readiness of organization. Moreover, the benchmarking strategy adopted in the study i.e. MGF would help to validate the computed I4.0 readiness.
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Lean Six Sigma (LSS) is a widely accepted business improvement methodology in healthcare, which aims to improve operations and quality and reduce cost, medical errors and waiting…
Abstract
Purpose
Lean Six Sigma (LSS) is a widely accepted business improvement methodology in healthcare, which aims to improve operations and quality and reduce cost, medical errors and waiting time by combing the principles of lean thinking with Six Sigma methodologies. To implement LSS successfully in healthcare organizations it is necessary to know the readiness level before starting the change process. Thus, the purpose of this paper is to assess the readiness level for the implementation of LSS in healthcare using a fuzzy logic approach.
Design/methodology/approach
The current study uses a fuzzy logic approach to develop an assessment model for readiness to implement LSS. The conceptual model for readiness is developed with 5 enablers, 16 criteria and 48 attributes identified from the literature review. The current study does the study in a medium-size hospital from India.
Findings
The fuzzy readiness for implementation of LSS index (FRLSSI) and fuzzy performance importance index (FPII) are calculated to identify the readiness level for the implementation of LSS in the case hospital. The FRLSSI is computed as average ready with (3.30, 5.06 and 6.83) and the FPII computed helps to identify 15 weaker attributes from 48 attributes.
Research limitations/implications
The current study uses only one hospital for study. In the future, the model can be tested in many hospitals.
Practical implications
The current study would be used by the managers of a healthcare organization to identify the readiness level of their organization to implement LSS. The proposed model is based on the identification of enablers, criteria and attributes to assess the readiness level of a healthcare organization and it helps to improve the readiness level to implement LSS effectively.
Originality/value
The present study contributes to the knowledge of readiness for the implementation of LSS in a healthcare organization. The conceptual model is developed for assessing the readiness level of a healthcare organization and it helps to improve the readiness level for successful implementation of LSS. Weaker attributes are identified and necessary corrective actions should be taken by the management to improve the readiness. The continuation of the assessment readiness model over a period of time would help to improve the readiness level of healthcare for the implementation of LSS.
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The purpose of this paper is to report a research on the development of axiomatic modeling of a lean manufacturing system.
Abstract
Purpose
The purpose of this paper is to report a research on the development of axiomatic modeling of a lean manufacturing system.
Design/methodology/approach
A conceptual model for lean manufacturing has been adopted. A hierarchical structure has been developed to model the design process of a lean manufacturing system composed of functional requirements, design parameters and process variables.
Findings
The theory of axiomatic design advocates the creation of process variables by mapping the design parameters in the process domain. This article serves as an efficient guideline for the design process to clarify the tools, methods and resources of designing lean manufacturing system.
Research limitations/implications
The implications of the axiomatic model has been derived based on the experiences gained from a single manufacturing organization. Yet, the findings and contributions of this research work would be useful to the captains of thee majority of the manufacturing companies situated in the world.
Practical implications
The axiomatic modeling approach serves as an efficient guideline for the design process to clarify the tools, methods and resources of designing a lean manufacturing system of an Indian rotary switches manufacturing organization.
Originality/value
The conceptual model of lean manufacturing has been developed from literature. Based on the conceptual model, the hierarchical model for axiomatic approach has been developed. The contributions and the inferences are original and add value to the state of the art approaches.
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Organisations are looking for a concept that can solve traditional as well upgraded production problems with current resources and technology, and this can be addressed by…
Abstract
Purpose
Organisations are looking for a concept that can solve traditional as well upgraded production problems with current resources and technology, and this can be addressed by integration of lean six sigma (LSS) with Industry 4.0 (I4.0) technologies. This reduces complexity in the manufacturing process through digital technologies. Cyber physical system (CPS) is considered as primary I4.0 technology with which all other technologies are associated to extend. CPS can integrate with other prevailing manufacturing approaches like lean, LSS and so on. LSS, on the other hand, is a team-focussed performance improvement strategy which is widely used by the industries to identify problems, eliminate waste to meet customer requirements. The study aims at analysis of challenges for LSS and CPS integration.
Design/methodology/approach
Integrating LSS and CPS will solve both traditional and modern manufacturing problems. To integrate these technologies, organisational requirements need to be assessed. These requirements are posed as challenges in this study. Their priority weights are analysed, and challenges are prioritised using fuzzy Combinative Distance-based Assessment (CODAS) method. Sensitivity analysis is employed to assess the robustness of the results.
Findings
The result of this study enables top management to integrate LSS and CPS. In this study, 20 challenges were identified, and they are assessed to compute their relative assessment score. Requirement of new tools and methods with 0.6 score ranks first followed by interplay with big data and requirement of new communication protocol. The result highlighted the need for integration of LSS and CPS, proper utilisation of information and communication technologies, and cyber security management as the main impediments that need to be addressed to implement CPS in an LSS environment.
Originality/value
The analysis of challenges of LSS and CPS integration using MCDM tool is the original contribution of the authors.
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Rohit, S. Vinodh and R. Vigneshvaran
This study aims to provide an analysis of cyber-physical system (CPS)-based lean tools. This study focuses on the identification of lean tools for integration with CPS and…
Abstract
Purpose
This study aims to provide an analysis of cyber-physical system (CPS)-based lean tools. This study focuses on the identification of lean tools for integration with CPS and analyzes those tools using MCDM (multi-criteria decision-making) approaches.
Design/methodology/approach
There exists a need to integrate lean manufacturing with Industry 4.0 technologies. According to literature analysis, CPS is the first stage to implement Industry 4.0 technologies. Based on the extensive study, six CPS-based lean tools, i.e. CPS-based Jidoka system, CPS-based Kanban, CPS-based Andon support system, CPS-based Just-in-time delivery system, CPS-based poka-yoke cell and CPS-based value stream mapping have been considered; then Grey TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) MCDM technique has been applied to rank those tools. These CPS-based lean tools are ranked based on seven performance measures as recognized by academic and industry experts.
Findings
The top three CPS-based lean tools are CPS-based Kanban 4.0, CPS-based value stream mapping and CPS-based Just-in-time delivery system have been selected based on the above ranking. The study results have been validated using grey-based approach.
Practical implications
Appropriate criteria to evaluate significant lean tools for integration with CPS are identified, which facilitates managers to assess their current tools and technologies that could be integrated with I4.0, and the implementation of CPS-based lean tools would improve organizational performance.
Originality/value
In the emerging Industry 4.0, integration with advanced technologies provides high degrees of optimization. But there exist challenges for industries to integrate CPS with lean tools; hence, this study attempts to identify and analyze CPS-based lean tools. The lean tools are ranked for integration with CPS, the problem is modeled as MCDM problem, and the obtained results are again validated using grey approach. Prioritizing lean tools for integration with CPS is the original contribution of this study.
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Vishal Ashok Wankhede and S. Vinodh
The present study aimed to assess performance of Industry 4.0 (I4.0) in case organization by considering potential performance measures and analysis using scoring approach.
Abstract
Purpose
The present study aimed to assess performance of Industry 4.0 (I4.0) in case organization by considering potential performance measures and analysis using scoring approach.
Design/methodology/approach
50 performance measures grouped into five dimensions namely manufacturing management, manufacturing economics, manufacturing strategy, manufacturing technology and workforce were considered for the analysis. The study had been done with relevance to automotive component manufacturing organization. Further, questionnaire for each performance measure was developed to gather expert inputs regarding different performance aspects of I4.0 in case organization. Reliability of the expert responses towards questionnaire was assessed by computing Cronbach's alpha (a) using Statistical Package for the Social Sciences (SPSS) software.
Findings
Findings of the study revealed overall I4.0 performance index (OIPI) of 0.71, i.e. 71% signifying improvement scope of 29% pertaining to I4.0 adoption. Gap analysis was performed across dimensions and performance measures to realize the weaker areas. Gap analysis revealed workforce dimension with highest gap and manufacturing management with lowest gap. The gaps that obstruct performance of I4.0 are being recognized and proposals for improvement were provided to the industrial practitioners. Based on further analysis, dimensions and performance measures found to be weaker.
Practical implications
The study helped industrial practitioners and managers to create the foundation for evaluating performance of I4.0-focused organization. Industry practitioners can employ the study to understand different performance measures with respect to different dimensions and realize the significance of I4.0 adoption.
Originality/value
The identification of performance dimensions and measures for I4.0 performance measurement and assessment using scoring approach is the original contribution of the authors.
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Vishal Ashok Wankhede and S. Vinodh
The manufacturing domain presently focusing on Industry 4.0 (I4.0). One such domain is the automotive sector. The purpose of this study is to analyse the I4.0 research studies…
Abstract
Purpose
The manufacturing domain presently focusing on Industry 4.0 (I4.0). One such domain is the automotive sector. The purpose of this study is to analyse the I4.0 research studies with a focus on the automotive sector using a systematic literature review (SLR).
Design/methodology/approach
This paper presents a SLR of previous studies on I4.0 characteristics from its inception to performance measures focusing on the automotive sector. A total of 90 papers published in reputed journals during 2014–2020 were collected from major publishers, namely, Elsevier, Springer, Taylor and Francis, Emerald, Institute of Electrical and Electronics, MDPI, etc.
Findings
The findings of the study provided vital insights on various perspectives of I4.0 in an automotive organization. Moreover, this systematic analysis would help the automotive industry policymakers in implementing I4.0 in an organization. Based on the SLR, a conceptual framework is established to guide industry practitioners towards I4.0 implementation. The review findings could be used to carry out future studies in assessing the readiness of I4.0 in the organization with the help of a survey.
Research limitations/implications
The limitation of the study is in the adoption of the sampling approach. In the present study, conference papers and refereed journals have been considered based on the relevance of I4.0 in the automotive industry. As I4.0 is a growing concept, non-refereed articles, book chapters and white papers may cover practical aspects regarding I4.0 implementation that need to be considered for depth analysis. Moreover, the framework needs to be validated with various automotive industries for ensuring practical validity.
Originality/value
The unique contribution of the study is the SLR of I4.0 in manufacturing with a focus on the automotive sector.
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Anilkumar Malaga and S. Vinodh
The purpose of the article is to report a study on evaluation of smart manufacturing (SM) performance using a grey theory-based approach.
Abstract
Purpose
The purpose of the article is to report a study on evaluation of smart manufacturing (SM) performance using a grey theory-based approach.
Design/methodology/approach
In total, 30 criteria and 79 attributes for SM performance have been developed. A grey theory-based approach has been used for SM performance evaluation. The grey index has been calculated, and weaker areas have been derived. Performance level of SM has been evaluated using the Euclidean distance approach.
Findings
The SM performance index is found to be (3.036, 12.296). The ideal grey performance importance index (GPII) is obtained as (3.025, 4.875). The level of visibility and traceability, vertical integration, lead time and configuration data espionage and control ability are strong performing attributes. Integration abilities of services and manufacturing systems, ability of self-control, worker and raw material productivity, collaboration among buyers and suppliers and dynamic scheduling are identified as weaker areas, and suggestions for improvement have been derived. SM performance level has been identified as “Good.”
Research limitations/implications
Additional performance measures could be included as a part of evaluation. Practitioners can overcome weaker areas in the early phase. Management achieves confidence and practitioners attain success in implementation of SM in industry through the developed SM performance indexing system.
Originality/value
Identification of SM performance measures and analysis of SM performance is the original contribution of the authors. The developed approach assists practitioners and managers to focus more on specific areas for performance improvement.
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Sakthivel Aravindraj and S. Vinodh
– The purpose of this study was to develop a 40-criteria agility assessment model and explore its practical feasibility in an industrial scenario.
Abstract
Purpose
The purpose of this study was to develop a 40-criteria agility assessment model and explore its practical feasibility in an industrial scenario.
Design/methodology/approach
Agile manufacturing (AM) principles enable organizations to understand customer needs and incorporate the necessary changes in product- and processes-oriented approaches. In this research study, a 40-criteria agility assessment model was developed. The agility assessment model was subjected to investigation in an Indian relays manufacturing organization.
Findings
The research study indicates that the organization is agile. Besides computing agility level, the gaps across agile criteria have been identified and actions for agility improvement were subjected to implementation in the case organization.
Research limitations/implications
The 40-criteria agility assessment model was subjected to investigation in a single manufacturing organization. In future, more number of studies could be conducted.
Practical implications
To acquire agile characteristics, modern organizations should assess the agility level at which they operate. In this context, the agility assessment model was developed.
Originality/value
The agility assessment tool presented in this paper consists of 40 agile criteria, which are well supported by the research findings reported in literature. Hence, the developed 40-criteria agile model is original and novel.
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Vishal Ashok Wankhede, S. Vinodh and Jiju Antony
To achieve changing customer demands, organizations are striving hard to embrace cutting-edge technologies facilitating a high level of customization. Industry 4.0 (I4.0…
Abstract
Purpose
To achieve changing customer demands, organizations are striving hard to embrace cutting-edge technologies facilitating a high level of customization. Industry 4.0 (I4.0) implementation aids in handling big data that could help generate customized products. Lean six sigma (LSS) depends on data analysis to execute complex problems. Hence, the present study aims to empirically examine the key operational characteristics of LSS and I4.0 integration such as principles, workforce skills, critical success factors, challenges, LSS tools, I4.0 technologies and performance measures.
Design/methodology/approach
To stay competitive in the market and quickly respond to market demands, industries need to go ahead with digital transformation. I4.0 enables building intelligent factories by creating smart manufacturing systems comprising machines, operators and information and communication technologies through the complete value chain. This study utilizes an online survey on Operational Excellence professionals (Lean/Six Sigma), Managers/Consultants, Managing Directors/Executive Directors, Specialists/Analysts/Engineers, CEO/COO/CIO, SVP/VP/AVP, Industry 4.0 professionals and others working in the field of I4.0 and LSS. In total, 83 respondents participated in the study.
Findings
Based on the responses received, reliability, exploratory factor analysis and non-response bias analysis were carried out to understand the biasness of the responses. Further, the top five operational characteristics were reported for LSS and I4.0 integration.
Research limitations/implications
One of the limitations of the study is the sample size. Since I4.0 is a new concept and its integration with LSS is not yet explored; it was difficult to achieve a large sample size.
Practical implications
Organizations can utilize the study findings to realize the top principles, workforce skills, critical success factors, challenges, LSS tools, I4.0 tools and performance measures with respect to LSS and I4.0 integration. Moreover, these operational characteristics will help to assess the organization's readiness before and after the implementation of this integration.
Originality/value
The authors' original contribution is the empirical investigation of operational characteristics responsible for I4.0 and LSS integration.