Indian Manufacturing Industries Research Articles

Challenges and Opportunities in the Adoption of Flexible Manufacturing Systems in Indian Manufacturing Industries

The manufacturing sector is increasingly adopting automation technologies to enhance productivity and flexibility. Among these technologies, Flexible Manufacturing Systems (FMS) offer substantial benefits, such as reduced lead times, improved production flexibility, and lowered operational costs. Despite its advantages, FMS adoption in Indian manufacturing industries has been slow due to several barriers, including high capital costs, technological complexity, and inadequate government support. This study investigates the current status of FMS adoption in India, identifies the key barriers hindering its implementation, and proposes a decision-making model to facilitate its wider adoption. The research gap highlights the lack of a comprehensive framework to quantify the barriers and assess the performance of FMS adoption. To address this, a two-phase Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodology was used. This approach allowed for the classification and prioritization of barriers, as well as the development of a model for FMS selection based on statistical analysis. A case study was conducted using this methodology to evaluate various FMS models and provide a systematic decision-making framework for manufacturers. The results revealed that the most significant barriers to FMS adoption in India include high upfront costs, insufficient infrastructure, and the challenges posed by rapidly advancing FMS technology. By applying the AHP-TOPSIS approach, this study offers insights into overcoming these barriers and presents actionable recommendations for manufacturers and policymakers. The findings emphasize the need for increased government support and industry collaboration to accelerate FMS adoption in India. This research contributes to the development of a structured decision-making framework, which could help manufacturers implement FMS more effectively, thereby enhancing competitiveness in an increasingly dynamic global market. Keywords— Flexible Manufacturing Systems, Barriers to FMS, Indian Manufacturing, AHP-TOPSIS, Decision-Making Model

Performance Evaluation and Management of Indian Manufacturing Organizations Through Fuzzy Optimization Techniques

In a context of intense competition, evaluating financial performance is crucial for manufacturing sectors. As such, a precise and fitting performance review is essential. In the process of evaluation, financial performance indicators need to be carefully chosen because they show how competitive a business is. In this paper, the financial performances of the firms in the Indian manufacturing industry are evaluated using financial ratios, specifically accounting-based financial performance (AFP) measures and value-based financial performance (VFP) measures. These financial performances are assessed through multi-criteria decision-making (MCDM) techniques, specifically fuzzy multi-objective and optimization on the basis of ratio analysis (F-MOORA) and fuzzy step-wise weight assessment ratio (F-SWARA). First, the financial performance indicators' weights are determined by the F-SWARA approach, and then the firms' rankings are determined by the F-MOORA approach. By applying SWARA methodology in a fuzzy environment, the implications of the findings demonstrate that the factor named Return on Assets (ROA) contains the maximum weight and the factor named regret contains the lowest weight. By applying F-MOORA technique, it also demonstrates that company C1 is the best and company C3 is the worst.

Analyzing green entrepreneurial orientation drivers: Insights from the Indian manufacturing industry

ABSTRACT Business activities often lead to various environmental issues, like deforestation, waste, and pollution. Hence, integrating environmental concerns into manufacturing operations is essential for ensuring long-term sustainability. In this direction, green entrepreneurial orientation (GEO) represents an internal strategy that integrates environmental concerns into business operations. While much of the discussion around GEO has centered on the manufacturing sector in general, the unique dynamics of the textile industry have often been overlooked. This highlights the need for a more targeted exploration of GEO drivers specific to the textile sector. An exploratory approach has been used in the study, where in the first stage drivers are identified through a literature review strategy, while the second stage involves analysis of the company sustainability reports to validate the drivers. The results indicate that managerial environment concerns, technical orientation, big data analytical capabilities, environmental collaboration, and institutional pressure are the primary drivers that must be operationalized by firms to implement GEO. Furthermore, textile firms are concentrating on five core areas that align with GEO principles: energy efficiency, renewable energy, water and effluents management, waste management, and awareness programs. The study provides implications for managers and policy makers to prioritize the integration of environmental concerns into strategic decision making, foster collaborations, invest in data-driven technologies, and strengthen regulatory frameworks to encourage the adoption of green entrepreneurial practices.

Investigating the Measures of Sustainable Business Performance in Implementation of Industry 4.0 in Indian Manufacturing Industries: A PLS-SEM Approach

Investigating the Measures of Sustainable Business Performance in Implementation of Industry 4.0 in Indian Manufacturing Industries: A PLS-SEM Approach

Assessing Drivers Influencing Net-Zero Emission Adoption in Manufacturing Supply Chain: A Hybrid ANN-Fuzzy ISM Approach

Nowadays, there is a constant focus on implementing the net-zero emission (NZE) concept in the manufacturing supply chain (MSC). To reduce emissions and improve organisational efficiency, adopting the net-zero concept is a prevalent trend in today’s highly competitive global business environment. Governments and stakeholders are pressuring the manufacturing sector to use natural resources efficiently and reduce environmental impacts. As a result, the manufacturing industry is focusing on cleaner production using net-zero practices. This study aims to identify and analyse the interaction among the drivers of net-zero adoption in the MSC. Through a systematic literature review (SLR), a list of drivers was recognised. To validate these drivers, we conducted an empirical study with 173 respondents from the Indian manufacturing industry. Further, we employed an artificial neural network (ANN) to weigh the nonlinear effect of drivers. Fuzzy interpretive structural modelling (F-ISM) was used to identify the interaction relationships among the drivers and construct a hierarchical structure among these identified drivers. The fuzzy matrix of cross-impact multiplications applied to the classification (F-MICMAC) method was used to categorise these drivers into driving and dependent categories. The outcomes of ANN show that Environmental predictors (100%) emerged as the most significant drivers, followed by Economic drivers (60.38%) and Technological drivers (59.05%). This study is a valuable resource for academia and industry professionals, providing essential insights into how adopting net zero facilitates the manufacturing industry’s ability to achieve net zero across the supply chain.

Sustainability index development by integrating lean green and Six Sigma tools: a case study of the Indian manufacturing industry

PurposeIndia’s manufacturing sector employs about 12% of the labour force and contributes to about 17% of the nation’s GDP. The Indian government intends to implement several initiatives under the “Make in India” and Atma Nirbhar Bharat banners to increase the manufacturing sector’s share of the nation’s GDP to 25% by 2025. Applying lean manufacturing, green manufacturing and Six Sigma is crucial to ensure that India’s manufacturing sectors grow sustainably in international markets. This study aims to identify sustainability indicators and ascertain their respective weights to evaluate the sustainability performance of the Indian manufacturing industry.Design/methodology/approachThis research identifies 25 sustainability indicators and classifies them into the triple bottom line of sustainability based on an evaluative literature review and expert opinion. The Best Worst Method was utilised to determine the weights of the sustainability indicators. The sustainability index was developed to evaluate economic, social and environmental sustainability.FindingsThe sustainability performance of a foundry in a significant Western Indian State city was assessed by applying the developed sustainability index. After the adoption of integrated lean, green and Six Sigma (LG&SS) strategies and related practices in the foundry, there has been a notable improvement of 68.03% in the economic index, 61.62% in the social index and 13.24% in the environmental index.Research limitations/implicationsThe proposed sustainability index is applied and evaluated specifically for assessing the sustainability performance of Indian manufacturing SMEs. It can be used to substantiate firm’s sustainability performance and also to assess the improvement in firm’s performance in economic, environmental and social dimensions after implementing various operational excellence practices. However, it cannot serve as a benchmark tool across similar companies or organisations.Practical implicationsThe developed sustainable index can be used to analyse the company or organisation’s sustainability performance and see how various strategies have improved things. Practitioners can use this index to assess social, economic and environmental performance and focus on areas that need improvement.Social implicationsThe proposed sustainability index serves as a vital tool for monitoring a firm’s progress in triple bottom line (TBL) dimensions of sustainability, tracking a diverse range of indicators and encouraging sustainable organisational practices.Originality/valueThis study attempts to assess the economic, social and environmental performance of Indian Manufacturing SMEs by proposing a sustainability index.

Lean Practices in Tractor Manufacturing Industries in India

Lean is one of the most popular concepts of an Industry and being utilized to improve efficiency by reducing or eliminating waste. This study investigates the impact of implementing lean practices in Indian Tractor Industry, specifically the 5S System, Kanban Framework, Worth Stream Planning (VSM), and Kaizen (Nonstop Improvement), based on hierarchical execution in conditions of conveyance and adaptability improvement, business result upgrade, and efficiency improvement. The examination philosophy includes relapse investigation of information gathered from businesses that have embraced lean practices. This research paper will focus on the existing research gap on impact of Lean Practices in the manufacturing operations in Tractor Manufacturing Industry in India. Keywords: Lean Manufacturing, Shop Floor Management, Qualitative Research, Tractor Industry

Implementation of Low Carbon Supply Chain Management Practices (LCSCMP) in Indian Manufacturing Industries using ISM-DEMATEL

Low-carbon supply chain management (LCSCM) strategies are required in the Indian manufacturing industries due to rapidly growing energy usage for financial and environmental reasons. LCSCM technologies face several challenges that limit their adoption. Eleven hurdles were selected from the literature and the recommendations of one business expert and one academic expert. These barriers are defined by several factors, including a lack of information, financial worries, environmental concerns, and governmental limits, among others. In addition, efforts are being made to quantify the most optimal relevant barriers using multi-criteria decision-making (MCDM) techniques such as interpretive structural modeling (ISM) and the decision-making trial and evaluation laboratory (DEMATEL). The MICMAC analysis estimates the sensitivity and priority of barriers based on dependency and driving power. One of these barriers is a needed factor; three are driving elements; four barriers are linked factors; and three are self-sufficient factors. The findings of this study can help academic specialists, industry representatives, and practitioners in their ongoing attempts to adopt “LCSCM techniques” in Indian manufacturing industries. The model is appropriately explained and applies the link between the barriers. The objective of this paper is to evaluate and select the most influential barriers to adopting LCSCM in the Indian manufacturing sector that are interrelated.

Impact of Digital Transformation on Indian Manufacturing Industry

Impact of Digital Transformation on Indian Manufacturing Industry

Enablers Impacting the Circular Economy in Indian Manufacturing Industries: Modeling through a SmartPLS4-SEM Approach

Enablers Impacting the Circular Economy in Indian Manufacturing Industries: Modeling through a SmartPLS4-SEM Approach

Achieving energy efficiency and sustainability in galvanization process: A case study on Indian tower manufacturing industry

Abstract There is a significant gap between India’s energy production and its demand. Dependency on energy imports to meet the energy requirement affects the economic growth & overall development. The manufacturing sector consumes a substantial portion of the country’s energy but also holds considerable potential for reducing consumption. This paper presents a case study on an Indian tower manufacturing industry, aiming to achieve energy efficiency and sustainability in the galvanization process through the application of the statistical tool Design of Experiments (DOE). By systematic implementation of 22 factorial model of DOE, the study identified that the excessive zinc coating thickness is the major cause of energy consumption in hot-dip batch galvanization process (HDBGP). It was found that average LPG consumption is minimized at moderate coating thickness. Applying DOE resulted in a 38.67% reduction in LPG consumption when the zinc coating thickness was maintained at a moderate level in HDBGP. DOE provides a systematic mechanism based on experimental data to identify potential causes of problems and offers statistical validation of results. This research focuses on creating an energy-efficient production system and demonstrates the practical application of statistical tools and techniques to achieve sustainable competitive advantages. Highlights Experimental work using the Design of Experiments tool identified excessive zinc coating thickness as the primary cause of high energy consumption in the hot-dip batch galvanization process. Validation of optimized parameters showed that average energy consumption is minimized at moderate coating thickness, resulting in a 38.67% reduction in energy usage. Design of experiments tool provides a systematic mechanism based on experimental data to identify potential causes of problems and offers successful validation of results. Discussion While the significant reduction in LPG consumption enhances energy efficiency and sustainability, excess reduction in coating thickness may compromise the quality and durability of galvanized products, potentially leading to higher long-term costs for maintenance and replacements. Additionally, the focus on statistical tools like DOE alone could divert attention from exploring alternative potential energy-saving technologies and methods that might offer greater benefits. Therefore, it is also essential to consider the broader implications and potential limitations of various approaches in the quest for sustainability and competitive advantage suitable for the existing manufacturing setups. Graphical abstract

Implementation of Industry 4.0 and Digital Manufacturing Practices in Indian Manufacturing Industries Using Discrete Event Simulation (DES)

Industry 4.0 and digital manufacturing have emerged as significant areas of research in recent years. According to several reputed research papers, Industry 4.0 is a new industrial revolution that involves the integration of cutting-edge technologies such as the Simulation (Discrete Event Simulation), Internet of Things (IoT), artificial intelligence (AI), and big data analytics into the manufacturing process. Smart manufacturing, on the other hand, is the implementation of these technologies in the manufacturing sector to increase efficiency, reduce waste, and improve product quality. The research suggests that Industry 4.0 and smart manufacturing have the potential to revolutionize the manufacturing industry by enabling companies to achieve higher levels of automation, productivity, and agility. The Indian manufacturing industry has witnessed substantial growth in recent years, becoming a significant contributor to the country's economic development. To maintain its competitive edge in the global market and meet the increasing demand for innovative and cost-effective products, the industry has been actively adopting digital manufacturing practices. In this context, Industry 4.0, characterized by the integration of cyber-physical systems, Simulation, the Internet of Things, and data analytics, offers new opportunities for enhancing efficiency and productivity. This research paper presents a comprehensive study on the successful implementation of digital manufacturing practices in the Indian automobile industry through the use of simulation approaches, in alignment with Industry 4.0 principles. The study aims to identify the key challenges faced by the industry and proposes viable solutions to harness the full potential of digitalization. The paper delves into the critical aspects of simulation techniques, such as discrete event simulation and virtual manufacturing, and their role in optimizing manufacturing processes, reducing lead times, and enhancing product quality. The results of this study indicate that the integration of digital manufacturing practices with Industry 4.0 principles has resulted in significant improvements across the entire value chain of the Indian automobile industry. Reduced production costs, increased production flexibility, and enhanced product quality have been observed as the key outcomes of this strategic alignment.

Do firms’ performance act as a catalyst of innovation: empirical evidence from innovative Indian manufacturing firms

PurposeThis paper aims to explore whether firms’ performance determines innovation using a sample of Indian manufacturing firms. The impact of innovation on firms’ performance across specific countries has been discussed in the literature. However, the effect of firms’ performance on innovation output, especially for a developing country like India, remains an open question. Against this backdrop, this paper investigates whether firms’ performance determines innovation in Indian manufacturing firms.Design/methodology/approachThe authors use patent filing information to instrument innovation and total factor productivity to instrument firms’ performance. The patent data are collected from the Patent Search and Analysis Software database and firm-level data from the Centre for Monitoring Indian Economy’s Prowess database. The study uses a sample of 309 Indian manufacturing firms from 2005 to 2021. Given the count nature of the data set used in this study coupled with over-dispersion issues, the authors have used the negative binomial regression to estimate the empirical specification of the models. There could be a possible problem of endogeneity due to the contemporary nature of innovation and firms’ performance. Therefore, to address the possible issues of endogeneity in the model, the authors have used the Generalized Method of Moments (GMM) estimators for more robustness checks of the empirical results.FindingsThe empirical results exhibit a positive and significant impact of firms’ performance on the innovation output, validating that firms’ performance determines innovation in Indian manufacturing firms. The posterior estimation results using GMM estimation also corroborate that firms’ productivity is a determining factor for the innovation output of Indian manufacturing firms. Furthermore, empirical results exhibit that the ex ante innovativeness of the firms substantially affects the current innovation. This validates that the firms’ prior experience, learning by doing and past innovative efforts are more likely to precipitate more innovation in the current period.Originality/valueThis paper’s main contribution is empirically estimating whether firms’ performance determines innovation, which is hardly discussed in the existing innovation literature, specifically using Indian manufacturing industries. Further, it adds to the existing literature in two other prominent ways. First, this paper investigates whether firms require ex ante expertise to innovate or if a firm starting from scratch can innovate significantly without any hindrances. Second, it enriches the literature by instrumenting innovation in output terms with the patent application against input measures of innovation, such as research and development expenditures, acquisition of machinery and equipment, while discussing the relationship between firms’ performance and innovation, specifically in the context of a developing economy like India.

Does carbon intensity affect technical efficiency? An empirical assessment of manufacturing industries in Maharashtra, Odisha, and India

Technical progress has a tremendous potential to reduce carbon dioxide emissions by reducing energy consumption, a major concern across production units. However, the existing empirical literature concerning technical efficiency and carbon intensity is scanty. Thus, this paper examines the relationship between technical efficiency and carbon intensity for the organized manufacturing sector of two states, Maharashtra and Odisha, and the all-India level from 2001 to 2018. The paper uses data envelopment analysis to estimate technical efficiency scores. It applies the 2006 Intergovernmental Panel on Climate Change Tier 1 methodology for estimating carbon intensity for each 3-digit manufacturing industry in all three sample cases. The study has used static panel regression and fractional logit regression techniques to examine the deterministic relationship between technical efficiency and carbon intensity. The result shows that technical efficiency is highly sensitive to carbon intensity in the Indian manufacturing industries. The findings also addressed that the size of the industries also reduces the technical performance of manufacturing units. This paper also confirmed that increased profit could boost the Indian manufacturing industries’ technical efficiency. Thus, this study addresses that carbon intensity as a proxy for the manufacturing sector’s potential to affect climate change plays a crucial role in explaining the technical efficiency variations across industries. Thus, it calls for better policies aimed at reducing the emissions of industries specifically to achieve sustainable growth for the Indian manufacturing sector.

Investigating smart manufacturing process implementation in the Indian manufacturing industries using tecnomatix and response surface methodology

Investigating smart manufacturing process implementation in the Indian manufacturing industries using tecnomatix and response surface methodology

From intentions to action: how behavioural intentions shape employee performance through digital learning?

PurposeThis study aims to explore the impact of “Performance Expectancy” (PE) on the performance of Indian automotive manufacturing employees through their “Behavioural Intention” (BI) in the context of digital learning within the volatile, uncertain, complex and ambiguous (VUCA) world.Design/methodology/approachA descriptive research design was adopted to gather data from 211 employees of Indian automotive manufacturing companies. Structural equation modelling was applied for data analysis and obtaining results.FindingsLargely, the findings indicate that BI does mediate the relationship between PE and employee performance (EP) in the context of the Indian automotive manufacturing sector. The findings would help in boosting digital learning initiatives, improving EP and fostering organizational success in the VUCA world.Research limitations/implicationsThe current study focused on the Indian manufacturing industry. Extending the research beyond the manufacturing sector to other industries in India could possibly help in generalizing the findings, and thereby enhance knowledge of the broader consequences of digital learning on EP.Practical implicationsDigital learning platforms can enhance sustainable industrialization in the manufacturing sector by providing employees with access to digital learning opportunities. Managers must provide employees with access to digital learning opportunities for making them more creative and innovative, so that they can become real change agents, and can handle real-world problems more efficiently, leading the organization to deal with the complexities and unpredictability in this VUCA world.Originality/valueWithin the ambits of Indian automotive manufacturing organizations, there have been limited research studies that have used two constructs of the UTAUT2 framework to see their impact on EP in terms of digital learning. Thus, based on the limited information available, this research stands as a pioneering effort in the realm of digital learning within the Indian automotive manufacturing industry, where the relationship between PE and EP is examined through the mediational role of BI of employees. Moreover, this research possibly would pave the way for further research studies on exploring the role of potential factors influencing the strength and direction of the relationships between these variables (PE and EP).

Mediating factors and spillover effects of foreign direct investment : Evidence from Indian manufacturing firms

The present study examines the extent and direction of horizontal, backward and forward spillover effects of FDIs on firms' productivity. It also shows how the mediating factors (firm's age, export and import intensity, R&D and advertisement intensity) contribute to the firms' productivity. Further, the study also uncovers the importance of the ownership patterns of the firms that affect the spillovers. It uses a balanced firm-level panel data set from the Indian manufacturing industries over the available period 2003 to 2016 to examine the inter- and intra-industry spillovers of the FDIs. The estimation methods used in this study are the fixed effects approach and the generalised method of moments. The study also applies the Levinsohn-Petrin method to compute firm-level productivity. It finds a significant positive spillover backward effect and confirms the supportive role of the mediating factors in augmenting the spillover channels. However, the results do not support the existence of horizontal and forward FDI spillover effects for the overall manufacturing industries. They suggest that a comprehensive policy package approach be used, thereby underlining the importance of all channels of the FDI spillover effects and their relations to the downstream sector, particularly by keeping the performance of the firms and their external links in perspective.

What explains exit in Indian manufacturing industries?

PurposeThe purpose of this paper is to investigate the likelihood of firm exit, focusing on firm- and sector-specific factors and other potential constraints that may lead to exit.Design/methodology/approachThe authors address the main research question by using hazard-cox and probit models on plant level data for the period 1998–1999 to 2012–2013, drawn from the Annual Survey of Industries collected by the Central Statistical Organisation.FindingsThe authors find that probability of exit reduces with improved firm performance. Urban firms, proprietary firms and smaller firms are more likely to exit as compared with their respective counterparts. The findings are robust to alternate measures of performance, alternate specifications and different methods.Originality/valueStudies of entry and exit rates at a point in time are useful in examining the turnover of establishments. But to understand the establishment survival, the authors must also examine the probability of firm exit and the possible determinants that aid exit. There are institutional factors that prevent easy exit of firms from an industry. It would be worthwhile to see how the exit rate will be impacted if these barriers ceased to exist. In this study, the authors construct a model of exit, which would help us to predict firm exit.

Validation of Lean–Green–Six Sigma practice model for improving performance and competitiveness in an Indian manufacturing industry

Validation of Lean–Green–Six Sigma practice model for improving performance and competitiveness in an Indian manufacturing industry

Stage-wise green supply chain management and environmental performance: Impact of blockchain technology

In the era of globalisation, the use of technology and concerns for sustainability is eminent in the supply chain management practices. The current study focuses on sustainable and green supply practices in different stages of supply chain management and how they can be facilitated by blockchain technology (BT). The study has addressed the existing gaps in the area namely, the lack of research assessing stage-wise green supply chain for environmental performance focusing on BT. The current study aims to assess the impact of BT on different stages of the green supply chain and a firm's environmental performance. The study also focuses on analyzing the impact of green supply chain stages on environmental performance. The study uses PLS-based structural equation modelling approach to investigate the hypothesised relationships between BT adoption and stage-wise green supply chain practices. The data was collected from individuals from medium-sized enterprises from the manufacturing industry in India. The findings reveal a positive association between blockchain adoption and green supply chain management practices leading to enhancement in environmental performance. Furthermore, the study indicates a positive relationship between blockchain integration and different stages of the green supply chain, underscoring its multi-faceted impact on environmental performance. The findings imply that the BT adoption can facilitate the realization of sustainable supply chain practices and performance improvement.