Purpose: The aim of this research is to assess the key enablers of Industry 4.0 (I4.0) in the context of the Indian automobile industry. It is done to apprehend their comparative effect on executing I4.0 concepts and technology in manufacturing industries, in a developing country context. The progression to I4.0 grants the opportunity for manufacturers to harness the benefits of this industry generation.Design/methodology/approach: The literature related to I4.0 has been reviewed for the identification of key enablers of I4.0. The enablers were further verified by academic professionals. Additionally, key executive insights had been revealed by using interpretive structural modelling (ISM) model for the vital enablers unique to the Indian scenario. The authors have also applied MICMAC analysis to group the enablers of I4.0.Findings: The analysis of this study’s data from respondents using ISM provided us with seven levels of enabler framework. This study adds to the existing literature on I4.0 enablers and findings highlight the specificities of the territories in India context. The results show that top management is the major enabler to I4.0 implementation. Infact, it occupies the 7th layer of the ISM framework. Subsequently, government policies enable substantial support to develop smart factories in India.Practical implications: The findings of this work provide implementers of I4.0 in the automobile industry in the form of a robust framework. This framework can be followed by the automobile sector in enhancing their competency in the competitive market and ultimately provide a positive outcome for the Indian economic development led by these businesses. Furthermore, this work will guide decision-makers in enabling strategic integration of I4.0, opening doors for the development of new business opportunities as well.Originality/value: The study proposes a framework for Indian automobile industries. The automobile sector was chosen for this study as it covers a large percentage of the market share of the manufacturing industry in India. The existing literature does not address the broader picture of I4.0 and most papers do not provide validation of the data collected. This study thus addresses this research gap.

Lean six sigma (LSS) is a well-structured process improvement methodology aimed at removing wastes and defects to increase the operational efficiency of a process. In this article, the authors were tasked to identify the root causes of the reworks and rejections occurring in the polycrystalline diamond (PCD) reamer line of a tool manufacturing industry and provide solutions and implement methods to overcome the rejections. Therefore, the author constructed a hybrid approach comprising the select, define, measure, map, analyse, improve, control, sustain (SDMMAICS) model to enhance the define, measure, analyse, improve, control (DMAIC) approach. Further, decision-making trial and evaluation laboratory (DEMATEL) was incorporated in the improve phase of the LSS model to ensure effective decision making. The model is backed by a KAIZEN framework to ensure the sustenance of the improvements implemented. This hybrid LSS framework was successfully applied and resulted in an increased sigma level from 3.3 to 4.3, improving the cycle time of PCD reamer production by 12%. The hybrid SDMMAICS-DEMATEL approach used is easy to follow and can be adopted in any LSS project. Moreover, the relevance of the model in a part manufacturing setup has been effectively tested and will serve as a reference for researchers in process improvement studies.