Indian Manufacturing Sector Research Articles

Attitudes and Perceptions of Leadership on Succession Planning in the Indian Manufacturing Sector

This study explores the critical role of organisational leadership in the implementation of succession planning within Indian manufacturing organisations, given the increasing importance of seamless leadership transitions for maintaining operational stability and organisational resilience. The research investigates the attitudes and perceptions of senior leaders towards succession planning, focusing on how their views influence the execution of these strategies. Employing a mixed-methods approach with quantitative and qualitative interviews, the study gathers insights from a representative sample of organisational leaders. The main objectives are assessing leadership attitudes towards succession planning and understanding their perceptions of its implementation. Two key hypotheses are proposed: H01 asserts that leadership attitudes have no significant impact on succession planning, while H02 posits that leadership perceptions do not significantly influence its execution. Analysis of the collected data aims to reveal the extent to which leadership perspectives shape succession planning practices, providing valuable insights and recommendations for strengthening leadership approaches and succession frameworks. The findings are expected to contribute to a broader understanding of how effective leadership can foster a strong succession planning system, driving sustainable growth and resilience in the manufacturing sector.

Corporate social responsibility and energy efficiency: Empirical evidence from the manufacturing sector of India

AbstractThe strategic business firm manages today's profitability and tomorrow's sustainability to achieve a comparative advantage in the emerging market economy. Considering India's ambitious emission reduction target and rapid economic development, this study explores the potential relationship between corporate social responsibility (CSR) and the energy efficiency of manufacturing firms in India. Using firm‐level data, the panel fixed effects model shows that CSR negatively explains energy intensity (EI). We observed a consistent negative relationship between CSR and EI for all models, with and without controlled firm‐specific variables. Firms that manufacture chemical and chemical products, pharmaceuticals, medical chemicals and botanical products are energy intensive without CSR; however, CSR activities helped these firms become energy efficient. Our result confirms that voluntary CSR is less efficient than mandatory CSR in contributing to environmental and energy‐related indicators at the firm level. Our empirical results indicate several policy implications: (1) CSR should also focus on sustainable business models related to energy efficiency and reduction in carbon emissions; (2) firms should be encouraged to use CSR related to environmentally friendly innovations. We conclude that CSR spending and environmental expenses will increase energy efficiency at the firm level, which will help achieve net‐zero targets and sustainable industrial development.

An expert system based decision-making framework for benchmarking industry in sustainable manufacturing

PurposeThe purpose of this study is to evaluate and enhance sustainable manufacturing practices across various industries, focusing on environmental, economic and social dimensions, to promote a comprehensive understanding and implementation of sustainability, thereby improving overall industry performance and fostering long-term ecological and economic health.Design/methodology/approachThe study uses multi-criteria decision-making-multivariate analysis technique to examine sustainable manufacturing practices (SMPs) in the Indian manufacturing sector. It identifies 11 SMP criteria through literature review and expert recommendations. Data are collected through questionnaires, expert committees and interviews. The study focuses on four key industries: automobile, steel, textile and plastic. Techniques like principal component analysis (PCA), technique for order preference by similarity to ideal solution (TOPSIS) and complex proportional assessment (COPRAS) are used to rank and assess performance.FindingsThe Indian automobile industry has shown the most effective SMPs compared to steel, textile and plastic sectors. The automobile sector is the benchmark for sustainable measures, emphasizing the importance of green practices for environmental, social and economic performance. Recommendations extend beyond the automobile sector to cement, electronics and construction.Practical implicationsThe research emphasizes the importance of SMPs across various industries, focusing on economic, environmental and social considerations. It advocates for a holistic approach that enhances resource efficiency and minimizes ecological footprint.Originality/valueThe study uses ranking methods like PCA-integrated TOPSIS and COPRAS to evaluate performance in different industries, focusing on the benchmarked automobile sector. The research offers valuable insights and advocates for the widespread adoption of sustainable policies beyond the studied sectors.

Firm Heterogeneity and India’s Manufacturing Exports: Lessons on Protection and Trade Theory

ABSTRACT Empirical examinations of heterogeneity among Indian manufacturing firms provide helpful lessons for trade theories and policies. Using descriptive statistics of cross-section data for 2011, 2015, and 2019, the asymptotic Wilcoxon-Mann-Whitney test, and regression analysis, the study reveals that firms are heterogeneous within each 4-digit National Industrial Classification (NIC) category, exporting firms are more productive than non-exporters, and exporter premia decline with trading activities. The study concludes that recent protectionist policies reduced extensive trade margins, firm heterogeneity, and productivity in the Indian manufacturing sector.

The dharma of work: Cracking the code to social sustainability puzzle for the future of human resources

AbstractThe growing awareness of social sustainability and employee experiences has gained significant global organizational attention toward socially sustainable issues, mainly due to the sudden surge in resignations, strikes, elevated job stress, and depression in the Indian manufacturing sector. Despite organizational efforts to address these challenges, a notable gap persists in understanding the influence of socially sustainable strategies on employee well‐being and organizational sustainability. Hence, this research aims to fill that void by identifying and ranking strategies for achieving sustainable development from a holistic angle. The triangulation method is employed in this study as a validation strategy, reinforcing the credibility of the findings. This study utilizes a hybrid approach that integrates the Fuzzy Delphi Method (FDM) and the Fuzzy Analytic Hierarchy Process (FAHP) to enhance accuracy and dependability in identifying and prioritizing critical social strategies. A literature review and FDM are used to create initial strategies, which are subsequently prioritized through the FAHP method to determine their relative significance. Eight strategies aligned with sustainable development goals are identified, with “improving employee well‐being” deemed most crucial, followed by “improving diversity, equity, and inclusion” and “equipping leaders with empathy.” The study illustrates how these social practices advance the development of sustainable business models by improving organizational culture, fostering future competencies, and promoting human‐centered leadership. This study offers managerial insights for managers striving to align organizational objectives with global sustainability aims. Organizations may leverage the identified factors for strategic planning to establish a resilient foundation capable of enduring the challenges of a dynamic market environment.

Comparison of Cobb-Douglas Production Function with Deep Learning Models to Estimate the Productivity of Organised Manufacturing Sector - Indian Perspective

The production function defines the output of a firm, industry, or economy based on various combinations of inputs. This study focuses on estimating the production function of India's manufacturing sector and determining the relationship between its inputs and outputs. To predict the output, measured as Gross Value Added (GVA), the study applies the Cobb Douglas Production Function (CDPF) along with several deep learning techniques, including Feedforward, Recurrent, Long Short-Term Memory (LSTM), and Bidirectional LSTM networks. Model performance is evaluated using metrics like Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that the Cobb Douglas Production Function outperforms the deep learning models in predicting GVA for the organized manufacturing sector in India.

Revisiting the financial challenges faced by women-led microenterprises: some evidence from the Indian manufacturing sector

Revisiting the financial challenges faced by women-led microenterprises: some evidence from the Indian manufacturing sector

Examining the impact of green entrepreneurship drivers on sustainable development in the context of emerging country

PurposeGreen entrepreneurship (GE) is a novel concept in business and enhances environmentally friendly production and operation activities for “sustainable development” (SD). The aim of this study is to determine the drivers that contribute to the growth and success of “micro, small, and medium enterprises” (MSMEs) in the manufacturing sector in India. The study also examines the mutual and cause-and-effect relationships among these identified drivers.Design/methodology/approachThe study used integrated research methodology and identified nine key drivers of GE (GEDs) through extensive literature reviews, theoretical perspectives (i.e. “resource-based view” (RBV), “natural resource-based view” (NRBV) and “critical success factor theory” (CSFT)), and expert opinions. Further, “total interpretive structural modeling” (TISM) and “matrice d'impacts croisés multiplication appliquée á un classment” (MICMAC) analysis are used here to develop a hierarchical model and cluster the drivers, and fuzzy “decision-making trial and evaluation laboratory” (fuzzy-DEMATEL) is used to develop causal relationships among the drivers. Further, a sensitivity analysis is conducted to ensure the robustness of the results.FindingsResults indicated that green manufacturing and operation capability development, green business process management and attitudes toward developing sustainable business models significantly impacted GE and SD. The findings of this study help managers, policymakers, and practitioners gain an in-depth understanding of the drivers of GE.Research limitations/implicationsThe study considers a limited number of drivers and is specific to Indian manufacturing MSMEs only. Further, a limited number of experts from different enterprises are considered for data analysis. This study is also based on interrelationships and their relative importance based on multicriteria decision-making techniques. This study aids government decision-making, policy formulation and strategic decision-making for manufacturing businesses in achieving SD goals. In addition, this research also encourages green entrepreneurs to start eco-driven companies and facilitate the use of environmentally friendly goods to offset environmental challenges and accomplish sustainable development goals.Originality/valueThis study proposes an integrated methodology that will benefit managers, practitioners and others in developing strategies and innovations to improve and develop green practices. This study further helps with responsive, sustainable business development in various manufacturing MSMEs.

Modelling for working capital efficiency: integrating SBM-DEA and artificial neural networks in Indian manufacturing

Purpose This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN). Design/methodology/approach A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME. Findings Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME. Originality/value The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

Inflated energy prices and capital structure: firm level evidence from Indian manufacturing sector

Purpose The purpose of this paper is to study the influence of inflated energy prices on the capital structure of Indian manufacturing corporations and to investigate whether the capital structure of Indian firms is driven by demand shocks or supply shocks during the study period. Design/methodology/approach After conducting a thorough review of the capital structure and inflation-based research studies, panel data-based regression model and correlation matrix have been used as statistical tools for Indian manufacturing sector available with the Centre for Monitoring Indian Economy Prowess database. Findings The results suggest that variables like the presence of inflated energy prices had adversely influenced the capital structure of Indian corporations. Not only this, the study also highlights that factors pertaining to the demand shock had induced Indian corporations to have higher debt levels in the capital structure. Practical implications This study has laid some ground work to explore the influence of inflation on capital structure of Indian firms upon which a more detailed evaluation could be based. Originality/value To the best of the authors’ knowledge, this study is the first that explores the influence of inflated energy prices on the capital structure of manufacturing firms in India by using the most recent data.

A conceptual framework-based architecture strategy for implementing industry 4.0 enabling technologies in Indian commercial vehicle manufacturing plant

ABSTRACT From the first industrial revolution to the era of Industry 4.0 (I4.0), Indian manufacturing sectors have undergone significant transformations. The global market’s competitiveness has surged due to the rising demand for customized consumer goods, prompting industries to adopt advanced manufacturing models to maintain their edge. This paper introduces a novel Conceptual Framework-Based Architecture (CFA) using a real-life case study of an Indian commercial vehicle manufacturing plant. The proposed CFA aims to minimize barriers to implementing I4.0 technologies by developing a standardized architecture customized to the Indian manufacturing context. It addresses existing technological requirements and implementation challenges in three phases, considering existing production systems, current technologies, and system needs. The CFA enhances productivity, and transparency through real-time data monitoring, automated system communication, and responsiveness. It also tackles I4.0 implementation challenges between physical and cyber systems, employee adaptability to new technologies, the absence of global standards, and the need for uniform standards in tools, equipment, systems, and data services. The CFA elements were validated for system readiness in an existing manufacturing plant. This framework serves as a reference tool for industrial strategists, decision-makers, policymakers, and researchers, aiding in the integration and configuration of I4.0 technologies in the Indian manufacturing sector.

FDI and GVC Participation of Firms: An Empirical Investigation of Indian Manufacturing Sector

FDI and GVC Participation of Firms: An Empirical Investigation of Indian Manufacturing Sector

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.

External shocks and dynamics of resource use patterns: empirical evidence from the Indian manufacturing sector

While promoting manufacturing growth is imperative to boost economic growth, potential adverse consequences on natural resources and ecology have created sustainability-related concerns and challenges. In India, with the reform processes deepening further and the business environment becoming more market-oriented over the years, the adoption of appropriate measures for sustainable manufacturing growth under such conditions would require a deeper investigation of the underlying dynamics, particularly to address the impacts of external shocks. This is especially true when external shocks alter market dynamics and, hence, input is used significantly in the manufacturing sector. Given this backdrop, the present paper examines how the major external shocks to the Indian economy during in the post-reform era have altered the resource use patterns in selected manufacturing industries. The paper uses secondary data collected from the KLEMS database of the Reserve Bank of India for the period from 1980–81 to 2019–20 and applies descriptive statistics and econometric techniques to address the research objective. It is found that energy intensity, vertical integration, capital intensity, and rate of growth of total factor productivity decreased over the decades, whereas labour productivity experienced an increasing trend in most industries. However, the trends are mixed in the case of material intensity. Further, the regression results indicate significant effects of external shocks on resource use, factor productivity, and structural changes. There are also industry-specific differences in the nature and extent of such changes. Hence, a deeper investigation of the underlying factors and impacts at a disaggregate industry level is necessary to draw more robust insights.

ROLE OF SUPPLY CHAIN MANAGEMENT PRACTICES: INDIAN MANUFACTURING INDUSTRIES PERSPECTIVE

The supply chain of the manufacturing sectors is one of the most important elements that helps any particular organisation maintain a competitive advantage. SCM techniques are used by businesses to manage their supply chains. Organizational performance refers to a company's capacity to make the most use of its distribution and raw material resources while reducing the time it takes to produce finished items, the time it takes to make purchases, the price of its inventory, and the leading to significant. The study's goal was to comprehend the numerous SCM methods and their function in the Indian manufacturing sector's corporate culture. Samples of two hundred employees working in SCM for fifty organisations across five Indian manufacturing sectors were gathered using the questionnaire. To produce findings, descriptive analysis and ANOVA are used. The researcher discovered a variety of SCM methods employed in the industries and discovered that SCM practices play a crucial role and, if effectively implemented, can enhance industry performance.

Analysis of the carbon emissions trend in the Indian manufacturing sector: a decomposition and decoupling approach.

There is a growing emphasis on fostering green growth and lowering carbon emissions in order to achieve sustainable economic development. This study uses the Tapio decoupling model and analyzes the factors influencing changes in carbon emissions from manufacturing in India utilizing the log mean Divisia index (LMDI) techniques. Furthermore, the nexus between carbon emission intensity, information and communication technology (ICT), total factor productivity (TFP), skill, and energy intensity has been analyzed using the system-GMM approach. It is based on the plant-level Annual Survey of Industries (ASI) datasets for the organized manufacturing sector of India from 2001 to 2002 to 2019 2020 for the major 21 Indian states/UT. The findings reflect the presence of weak decoupling in the manufacturing sector both at the aggregate level and in states. This indicates that both output and emissions are increasing; however, output growth surpasses emission growth, which signifies an effort to transition towards more environmentally friendly production methods and enhanced energy efficiency. The output and population effect are found to be leading factors in carbon emissions, while energy intensity is found to be reducing the effect. Further, the system-GMM estimates show that ICT and energy intensity positively affect total factor productivity, while with an increase in carbon emission intensity, productivity declines. The study confirms the existence of an inverted N-shaped Kuznets curve in the sector. This present study will contribute to formulating energy and environmental strategies to reduce emissions and promote adopting cleaner energy sources. These efforts will facilitate the attainment of carbon neutrality and enhance energy efficiency within the sector.

Indian Manufacturing Sector: Miles Travelled, Miles yet to go

Indian Manufacturing Sector: Miles Travelled, Miles yet to go

Interpretive Structural Modelling Approach to Evaluate Knowledge Sharing Enablers in Circular Supply Chain: A Study of The Indian Manufacturing Sector

Interpretive Structural Modelling Approach to Evaluate Knowledge Sharing Enablers in Circular Supply Chain: A Study of The Indian Manufacturing Sector

Impact of Firm-Specific and Macroeconomic Determinants on Environmental Expenditures: Empirical Evidence from Manufacturing Firms

This research aims to examine the association between firm-specific and macroeconomic determinants and environmental expenditures in the Indian manufacturing sector. Furthermore, it seeks to investigate the moderation effect of country-level governance and economic development on the association between macroeconomic, firm-specific, and environmental expenditures. The current study is based on 70 manufacturing firms for the period of 2011 to 2021. The dependent variable is environmental expenditures and the independent variables are firm-specific and microeconomic determinants. The results revealed that market capitalization and firm size have a positive and significant impact on environmental expenditures. On the other hand, inflation and the rule of law negatively and significantly affect environmental expenditures. Regarding the moderation effect, the results revealed that the rule of law and GDP positively moderate the association between inflation and environmental expenditures. Hence, this research has significant implications for corporate executives, financial experts, regulators, and other interested parties.

Is There Any Link Between FDI and Profitability in the Indian Manufacturing Sector?

Although a plethora of studies examine the determinants of profitability, this study adds to existing literature by assessing whether foreign direct investment (FDI) inflows make any significant contribution to profitability. To do so, the article uses disaggregated annual data relating to the Indian manufacturing sector 2012−2019. Results obtained from employing panel data models showed that FDI inflows have a positive and significant impact on profitability. Further, our results indicate that FDI affects profitability only in the long run but not in the short run. Capital intensity and size do not affect the profitability of the Indian manufacturing sector. From a policy perspective, it is imperative for manufacturing industries in India to attract more FDI inflow in order to boost profitability in the long run.