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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.

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 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.

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.

The identification of performance dimensions and measures for I4.0 performance measurement and assessment using scoring approach is the original contribution of the authors.

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.

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.

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.

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.

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.

The authors' original contribution is the empirical investigation of operational characteristics responsible for I4.0 and LSS integration.

This research aims to identify lean tools and techniques that are needed to be implemented to improve the performance in the fabrication industry. The objective is to find the wastes in manufacturing processes using value stream mapping (VSM) and prioritize the lean tools suitable to enable the attainment of leanness and streamline the processes.

VSM tool is applied in the industry to construct the current state map, identify improvement proposals and implement in future state. Fuzzy technique for order performance by similarity to ideal solution (TOPSIS), a multi-criteria decision-making technique (MCDM), is used to prioritize the identified improvement proposals. This study observed that mistake-proof processing and layout organization are the two techniques with the top priority that needs further improvement to enhance the leanness of the organization.

Upon successful implementation, the cycle time is reduced by 14.97%, and total inventory is reduced by 45.67% which leads to the improvement of value addition from 5.88 to 9.21%. Although lean has been adopted for many years, implementation of lean in the fabrication industry has been limited.

This study addresses the challenges in terms of implementing lean in fabrication industries and practical implications of lean tools and techniques and the prioritization of lean concepts against various lean criteria to enable leanness.

The deployment of improvement prioritization tool integrated with VSM in the context of a fabrication industry is the original contribution of the authors.

The purpose of this study is to discuss the construction of a structural measurement model utilizing structural equation modelling (SEM) to confirm the link between Industry 4.0 technologies, sustainable manufacturing practices and organizational sustainable performance. Relationship among the paradigm has yet to be fully investigated, necessitating a more conceptual and empirical examination on what impact they have on organizational sustainable performance when used together.

Industry 4.0 and sustainable production practices aim to progress a company's business competitiveness, forming sustainable development that benefits manufacturing companies. The aim of the study is to analyze the relationship between constructs that lead to operational excellence in firms that use Industry 4.0 technologies and sustainable manufacturing techniques. Experts from diverse automotive industries, who are applying both Industry 4.0 and sustainable manufacturing practices, provided data for the study.

Statistical estimations (hypotheses) are created to substantiate the measurement model that has been developed. The structural model was analysed, and the findings were discussed. The statistical estimate is either approved or rejected based on the findings. According to the conclusions of this study, strong link exists between Industry 4.0 technologies and sustainable manufacturing practices that affect organizational sustainable performance environmentally, economically and socially.

The research was conducted in the framework of automobile component manufacturing companies in India. The outcomes of the study are practically feasible.

The authors' novel contribution is the construction of a structural model with Industry 4.0 technologies and sustainable manufacturing practices into account.

The purpose of this paper is to report a review of various perspectives of lean in heavy engineering and fabrication industries and to propose a framework.

Lean manufacturing (LM) earlier known as Toyota Production System (TPS) was mainly used by automobile industries where the operations occur in sequence and the adoption of LM was easier. When it comes to heavy engineering industries, the concept of lean can be adopted in a versatile operating environment to have more advantages by waste elimination thereby having higher profit. Thus, an effort is made to consolidate various lean perspectives, namely, barriers, principles, tools and performance measures and a framework was proposed on how various lean perspectives can be adopted in a systematic way.

A comprehensive analysis of lean from various perspectives from viewpoint of the fabrication industry are done and the review is presented. Also, the framework linking barriers, principles, tools and performance measures is being contributed as an outcome of the review.

The review of lean from various perspectives for the fabrication industry and the development of a framework is the original contribution of the authors.

The study's goal was to identify the factors contributing to the practical completion of Kaizen events (KEs). The effect of the work-study man's characteristics, the supervisor's conduct and the autonomy of the Kaizen team are analysed in this study.

Data were collected from 249 respondents working in the manufacturing sector in India, mainly those who had been involved in Kaizen projects. Three-step procedures, namely, exploratory factor analysis, confirmatory factor analysis and partial least squares, have been applied to test the research hypotheses through structural equational modelling.

The exploratory factor analysis extracted in-role performance, creative performance and human aspect as latent variables explaining work-study man's performance (eigenvalue = 1). The study's findings indicate that the performance of work-study man (in-role, creative and human) and supervisors' conduct is directly related to the success of KEs. It was shown that supervisors might influence the outcomes of KEs only by moderating the human aspects. Additionally, the degree of autonomy of the Kaizen team was found having a significant positive relationship with the success of KEs.

The current study suggests that in-role and creative performance are prime assets of a work-study man. At the same time, the human aspect is a delicate issue that can affect the supervisor's behaviour. Therefore, the study implies that work-study men have the tactics and abilities to work with other co-workers to make a Kaizen project successful.

Although the significance of Kaizen projects has been widely emphasised, past research has failed to establish what factors contribute to the success of Kaizen efforts. Similarly, the supervisor's critical role has been highlighted several times. However, it is unclear how their conduct influences the relationship between work-study man's performance and the effectiveness of Kaizen projects. This study contributes significantly to organisational culture and human resource management by answering these questions.

This paper aims to propose a new framework on prioritizing and deployment of design for additive manufacturing (DfAM) strategies to an industrial component using Fuzzy TOPSIS multiple criteria decision-making (MCDM) techniques. The proposed framework is then applied to an automotive component, and the results are discussed and compared with existing design.

Eight DfAM design alternatives associated with eight design criteria have been identified for framing new DfAM strategies. The prioritization order of the design alternatives is identified by Fuzzy TOPSIS MCDM technique through its closeness coefficient. Based on Fuzzy TOPSIS MCDM output, each of the design alternatives is applied sequentially to an automobile component as a case study. Redesign is carried out at each stage of DfAM implementation without affecting the functionality.

On successful implementation of proposed framework to an automotive component, the mass is reduced by 43.84%, from 0.429 kg to 0.241 kg. The redesign is validated by finite element analysis, where von Mises stress is less than the yield stress of the material.

The proposed DfAM framework and strategies will be useful to designers, R&D engineers, industrial practitioners, experts and consultants for implementing DfAM strategies on any industrial component without impacting its functionality.

To the best of the authors’ knowledge, the idea of prioritization and implementation of DfAM strategies to an automotive component is the original contribution.

The purpose of this study is to improve supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development. During the COVID-19 pandemic, the organizations have struggled a lot to maintain the supplier performance and strategic sourcing decisions in the organizational benefit. However, in this context, the organization’s agile new product development (ANPD) process must be aligned with this requirement by maintaining the inventory and jobshop scheduling. As a result, identifying ANPD indicators, performance metrics and developing a structural framework to guide practitioners at various stages for smooth adoption is essential to improve the overall performance.

A comprehensive literature review is conducted to identify jobshop scheduling, inventory management and ANPD indicators along with the performance metrics, and the hierarchical structure is developed with the help of expert opinion. The modified stepwise weight assessment ratio analysis (SWARA) and weighted aggregated sum product assurance (WASPAS) techniques, along with expert judgement, are used in this study to calculate the weights of the indicators and the ranking of the performance metrics.

As per the weight computation by SWARA method, the strategy indicators have the highest relative weight, followed by the product design indicators, management indicators, technical indicators, supply chain indicators and organization culture indicators. According to the ranking of performance metrics obtained through WASPAS, the “frequency of new product development is at the top”, followed by “advances in product design and development” and “estimated versus actual time to market”.

It is believed that the framework developed will help industrial practitioners to plan effectively to improve supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison.

The outcomes of the present study will be extremely beneficial for the industry practitioners to improve the supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison.

A unique combination of modified SWARA–WASPAS technique has been used in this study which would be beneficial for organizations willing to adopt the jobshop scheduling and inventory management and ANPD for improving supply chain performance.

Lean Six Sigma (LSS) is a continuous improvement procedure that helps in improving the performance, decreasing variations, reducing lead time and increasing profit. Industry 4.0 (I4.0) portrays a new phase in the enterprise and aims at digitalization of the enterprise. The basis of I4.0 is a cyber-physical system, leading to product networking and complete coordination of process, value enhancement and further optimization. Integrating LSS methodology with I4.0 will give an edge in competitiveness to an enterprise. The aim of this study is to identify and analyze barriers to have an obstacle free implementation.

LSS and I4.0 barriers are identified through literature review. In this paper 18 barriers of LSS and I4.0 that hinder the implementation of integrated LSS and I4.0 are collected. Analysis of barriers was done using interpretive ranking process (IRP).

From the results, it is observed that lack of top management attitude, commitment and involvement are the most dominant barriers.

The study is done by gathering inputs from industry practitioners and derived inferences have practical relevance.

The study of barriers for integrated LSS and I4.0 is the original contribution of the authors.

The use of technology in 4th industrial revolution is at its peak. Industries are trying to reduce the consumption of resources by effectively utilizing information and technology to connect different functioning agents of the manufacturing industry. Without digitization “Industry 4.0” will be a virtual reality. The present survey-based study explores the factual status of digital manufacturing in the Northern India.

After an extensive literature review, a questionnaire was designed to gather different viewpoints of Indian industrial practitioners. The first half contains questions related to north Indian demographic factors which may affect digitalization of India. The latter half includes the queries concerned with various operational factors (or drivers) driving the digital revolution without ignoring Indian constraints.

The focus of this survey was to understand the current level of digital revolution under the ongoing push by the Indian government focused upon digital movement. The analysis included non-parametric testing of the various demographic and functional factors impacting the digital echoes, specifically in Northern India. Findings such as technological upgradations were independent of type of industry, the turnover or the location. About 10 key operational factors were thoughtfully grouped into three major categories—internal Research and Development (R&D), the capability of the supply chain and the capacity to adapt to the market. These factors were then examined to understand how they contribute to digital manufacturing, utilizing an appropriate ordinal logistic regression. The resulting predictive analysis provides seldom-seen insights and valuable suggestions for the most effective deployment of digitalization in Indian industries.

The country-specific Industry 4.0 literature is quite limited. The survey mainly focuses on the National Capital Region. The number of demographic and functional factors can further be incorporated. Moreover, an addition of factors related to ecology, environment and society can make the study more insightful.

The present work provides valuable insights about the current status of digitization and expects to facilitate public or private policymakers to implement digital technologies in India with less efforts and the least resistance. It empowers India towards Industry 4.0 based tools and techniques and creates new socio-economic dimensions for the sustainable development.

The quantitative nature of the study and its statistical predictions (data-based) are novel. The clubbing of similar success factors to avoid inter-collinearity and complexity is seldom seen. The predictive analytics provided in this study is quite elusive as it provides directions with logic. It will help the Indian Government and industrial strategists to plan and perform their interventions accordingly.