Purpose In the era of Industry 4.0, conventional manufacturing supply chains are evolving into data-centric supply chains. Apart from it, organizations are facing a highly complex and dynamic market environment. Consequently, data analytics is emerging as a crucial tool for effective decision-making. Therefore, this study aims to explore the antecedents for the adoption of Big Data Analytics (BDA) in manufacturing firms and to examine its effect on organizational flexibility and supply chain resilience. Design/methodology/approach Drawing from a comprehensive review of existing literature, a research framework has been created to explore the antecedents for BDA adoption and its impact on flexibility and resilience. Study is based on Technology-Organization-Environment (TOE) framework. Hypotheses are framed considering different constructs of the proposed research framework. Questionnaire-based survey was used to collect responses from organizations based in India. Partial least squares structural equation modeling (PLS-SEM) is used to test the proposed hypotheses. Findings Findings show that technological, organizational and environmental factors significantly influence the adoption of BDA. Further, it is also observed that the successful adoption of BDA helps in improving the flexibility and resilience of organizations during disruptions. Research limitations/implications Survey data was obtained from organizations based in India, which restricts the generalizability of results. Therefore, future studies may be carried out across different sectors and countries to compare and validate findings for robust managerial implications. Practical implications Study has explored major antecedents for BDA adoption in organizations. Management may formulate a strategy based on insights for successful BDA adoption. Originality/value Based on insights from study, organizations may improve their flexibility and resilience to sustain in a dynamic market environment and under various disruptions.

In modern era, digital technologies play an important role for manufacturing firms to improve organizational performance and gain competitive advantages in the global market. There has been a growing interest among researchers to explore the role of modern technologies such as big data analytics in manufacturing operations to overcome risks and disruptions and to achieve goal of higher productivity. With the development of the modern technologies, a massive quantity of data is produced in manufacturing systems. These data may help in better visibility in manufacturing processes that may aid in enhancing the performance. However, there is currently a lack of evidence regarding how big data and its uses are comprehended in manufacturing firms. In this study, functional areas in manufacturing sector for BDA application are prioritized. Initially, based on literature review and expert opinion, the key manufacturing field was identified for big data analytics application in the manufacturing firms. Further, the data was collected through the domain expert, and descriptive analysis was used to investigate the level of BDA application in different functional areas of manufacturing. The results of this study revealed that inventory management is the top priority area for BDA application followed by supply chain management/logistics processes, return processes, planning processes, and operations/productions. This study provides the insight in terms of important manufacturing fields to invest in BDA for manufacturing sector.

Purpose The study presents various barriers to adopt big data analytics (BDA) for sustainable manufacturing operations (SMOs) post-coronavirus disease (COVID-19) pandemics. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the graph theory matrix approach (GTMA) is applied. Design/methodology/approach The study presents various barriers to adopt BDA for the SMOs post-COVID-19 pandemic. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the GTMA is applied. Findings The study identifies barriers to investment in BDA implementation. It categorizes the barriers based on factor analysis and computes the intensity for each category of a barrier for BDA investment for SMOs. It is observed that the organizational barriers have the highest intensity whereas the human barriers have the smallest intensity. Practical implications This study may help organizations to take strategic decisions for investing in BDA applications for achieving one of the sustainable development goals. Organizations should prioritize their efforts first to counter the barriers under the category of organizational barriers followed by barriers in data management and human barriers. Originality/value The novelty of this paper is that barriers to BDA investment for SMOs in the context of Indian manufacturing organizations have been analyzed. The findings of the study will assist the professionals and practitioners in formulating policies based on the actual nature and intensity of the barriers.

In the present era of Industry 4.0, organizations are transforming from traditional production systems to digital production systems. This transformation is in terms of additional deployment of technologies that lead to digitization and integration of products and services, business processes and customers, etc. A high volume of unstructured data is being created across different processes due to digitization. The digitization captures the data that includes text, images, multimedia, etc., due to multiplicity of platforms, e.g., machine-to-machine communications, sensors networks, cyber-physical systems, and Internet of Things. Managing this huge data generated from different sources has become a challenging task. Big data analytics (BDA) may be helpful in managing this unstructured data for effective decision making and sustainable operations. Many organizations are struggling to integrate BDA with their manufacturing processes for sustainable operations. The application of BDA from a sustainability perspective is not extensively researched in the current literature. Therefore, firstly this study explores the contribution of BDA in sustainable manufacturing operations. It further identifies strategic factors for the successful application of BDA in manufacturing for sustainable operations. For a detailed analysis of strategic factors in manufacturing, a hybrid approach comprising the analytic hierarchy process, fuzzy TOPSIS and DEMATEL is used. Results revealed that development of contract agreement among all stakeholders, engagement of top management, capability to handle big data, availability of quality and reliable data, developing team of knowledgeable, and capable decision-makers have emerged as major strategic factors for the application of BDA in the manufacturing sector for sustainable operations. Major contribution of this study is in analyzing BDA benefits for manufacturing sector, identifying major strategic factors in implementation and categorization of these factors into cause and effect group. These findings may be used by managers as guidelines for successful implementation of BDA across different functions in their respective organization to achieve sustainable operations goal. The results of this study will also motivate industry professionals to integrate BDA with their manufacturing functions for effective decision making and sustainable operations.

There are various parameters on which material removal rate depends. These parameters categorized as input parameters and output parameters. In turning operation speed, feed depth of cut and nose radius considered as input parameters and the output parameters are material removal rate and texture of a surface. In this work author analysis on the effect of these parameters on machining by using TOPSIS approach to find out the best result.

A new concept of developing welding materials has been proposed. Physical-chemical processes that define the formation of the weld metal chemical composition, structures and properties, have been analyzed. The concept is taking into account the influence of the composition and properties of the welding materials on the process. Factors that define the performance of weld metal have been examined. Methods of mathematical modeling for development and optimization of electrodes, flux cored wires and other materials have been proposed. A general algorithm and a welding materials’ computer-aided design system have been created.

To survive on today’s global market; enterprises can no longer optimise their businesses isolated from their business partners, because a sum of sub optimal solutions does not have to present global optimal solution. An optimal supply chain is one that can respond with the shortest order-lead time and the minimal overall cost. In this paper the potential of Taguchi method is used in a supply chain design process.

The automotive vehicles have been a significant contributor to air pollution on total mass basis. According to one survey it has been shown that transportation contributes about 44% (by wt.) of the major pollutants. Motor vehicles alone contribute about 40%. Transportation contributes about 55% of the man made hydrocarbons (HC), a little over 64% of the total carbon mono-oxide (CO) emission and about 40% of the nitrogen oxide (NOx).Major pollutants from automobiles are unburned hydrocarbon (UBHC), oxides of nitrogen, carbon monoxide, lead compounds, and oxides of sulphur. The air pollution due to these pollutants affects adversely materials, animals and human life. It reduces visibility to a large extent causing traffic hazards. The vegetarian and plants are affected badly by sulphur dioxide, photo chemical smog and lead. To evaluate the performance and design of catalytic converter in this work a mathematical model for catalytic converter is made which predicts the catalytic converter performance. We can optimize catalytic converter design by adjusting different parameters.

The air pollution is serious problem in present scenario if this continues to rise at such a rapid rate it would make human generation extinct. Emissions produced in internal combustion engine comprises of Carbon monoxide (CO)- a poisonous gas that is colorless and odorless Hydrocarbons or Volatile organic compounds (VOCs)- Produced mostly from unburned fuels that evaporates. Sunlight breaks down to form oxidants which react with oxides of nitrogen. Nitrogen Oxides together Contribute to smog and acid rain and also cause irritation to human mucus membranes. Catalytic converter generally used in modern automobile vehicles is based on noble metal (platinum, rhodium and palladium). Catalytic converter based on noble metal has certain demerits. Automobiles are responsible for large scale air pollution hence clean air act 1970 is applied to all automobiles. According to this act the exhaust from automobiles must be within permissible limit. To carry out this process we induce catalytic converter which is fitted to exhaust of engine along with muffler to reduce toxic & harmful exhaust gases into harmless and non polluting gases. The paper is mainly concentrated on study of catalytic converter and to find out new innovative design to bring about best efficiency of this extremely useful device. Advance Exhaust System reduces CO level and HC level by 68% & 60% respectively.

The work and study presented in this paper aims to investigate the effect of the cutting speed, feed rate and depth of cut on surface roughness, in CNC milling of Aluminum (6060) in dry condition. The effect of cutting condition (cutting speed and feed rate) on surface roughness were studied and analyzed. Design of experiments were conducted for the analysis of the influence of the machining parameters on the surface roughness by using Taguchi design and then followed by optimization of the results using Analysis of Variance to find minimum surface roughness. The speed was identified as the most influential process parameter on surface roughness. The optimum surface roughness was reached when the feed rate and depth of cut were set as low as possible.

Interest in supply chain management has steadily increased since the 1980s when firms saw the benefits of collaborative relationships within and beyond their own organization. Firms are finding that they can no longer compete effectively in isolation of their suppliers or other entities in the supply chain. A number of definitions of supply chain management have been proposed in the literature and in practice. This paper defines the concept of supply chain management and discusses its historical evolution. The term does not replace supplier partnerships, nor is it a description of the logistics function. The competitive importance of linking a firm's supply chain strategy to its overall business strategy and some practical guidelines are offered for successful supply chain management.