Rakesh Kumar Phanden

Net zero is a promising approach for small and medium-sized enterprises (SMEs) in the manufacturing sector. It focuses on adopting carbon–neutral practices and reducing the organization’s carbon footprint to zero. The use of recyclable materials, renewable energy, and circular-economy practices helps SMEs adopt a net-zero approach to manufacturing. SMEs, due to their significant contribution, are considered the backbone of the manufacturing sector. The current study examines roadblocks and enablers to achieving net zero in Indian SMEs, providing insights for planning strategies to adopt Net Zero and the SDGs. In the current study, the authors have identified eleven roadblocks and an equal number of enablers of net zero from the literature and analyzed them by fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques. This technique also helps in the categorization of these factors based on their causation and influencing effects. The analysis indicates that the lack of regulations and norms on net zero, the limited supply of carbon–neutral materials, and the high cost of net zero technologies are the top three roadblocks. Furthermore, analysis reveals that top management’s supportive policies, awareness of Reducing Carbon emissions, Incentives, and foreign direct investment in net zero are the top three enablers. The findings of the current study suggest that understanding the roadblocks and enablers is crucial for Indian SMEs to implement net-zero practices and contribute to sustainable development effectively. Categorization of roadblocks and enablers also helps in forming strategies for handling them. The findings of the current study can help SMEs in developing economies, especially those in the manufacturing sector, formulate strategies to adopt net zero and fulfil the Sustainable Development Goals (SDGs).

Purpose: The United Arab Emirates (UAE) cement industry is a vital contributor to national development, but faces significant sustainability challenges due to high energy consumption, carbon emissions, and resource depletion. This study aims to identify and prioritise critical success factors (CSF) that drive sustainability adoption in the sector using the fuzzy analytic hierarchy process (FAHP). Design/methodology/approach: Through a mixed-method approach integrating literature review, expert opinions and stakeholder inputs, CSF are categorised into environmental, social and economic dimensions. The study employs advanced statistical tools, including exploratory factor analysis (EFA) for factor identification, Kaiser–Meyer–Olkin (KMO) and Bartlett’s tests for data adequacy, Cronbach’s alpha for reliability assessment, the relative importance index (RII) for ranking CSF, and the Kruskal–Wallis test for hypothesis validation. Findings: Key findings highlight several important sustainability enablers, such as waste recycling technology used (ENV3), demand for green/sustainable products (SOC1), Lean Six Sigma (LSS) tools and methods that are assigned to solve problems that arise (ECO4). The research provides a strategic roadmap for industry leaders and policymakers, aligning sustainability efforts with the UAE’s green economy vision. While the study focuses on the UAE cement industry, findings offer broader implications for similar industries globally. However, reliance on expert inputs may introduce subjectivity, and future research should incorporate real-time industry data for enhanced validation. By leveraging FAHP and robust statistical analysis, this study delivers a comprehensive framework to facilitate sustainability adoption, enhance decision-making and contribute to long-term environmental and economic resilience in cement manufacturing. Practical implications: This article offers valuable, real-world insights to guide the UAE cement industry towards a more sustainable future. First, by identifying and prioritising CSFs such as waste recycling technologies and LSS tools, it provides a guide for regulators to create focused policies and incentives that encourage the most impactful sustainability practices. Second, the clear ranking of these factors gives UAE cement companies a benchmark for assessing their progress across all three pillars of sustainability. Finally, the study highlights the necessity for UAE cement industry practitioners to prioritise operational improvements using LSS tools and robust, real-time resource monitoring. Originality/value: This study advances sustainability literature by integrating the FAHP with empirical statistical validation, offering a rigorous method for prioritising CSF in industrial sustainability. It contributes to a structured framework that bridges multi-criteria decision-making theory with practical implementation strategies, particularly within resource-intensive sectors like cement manufacturing.

Aim: This research aims to mitigate defects in the turning operation of thrust plates used in fighter jet fuel tank assemblies, thereby reducing the rejection rate and improving overall quality. This aligns with the aerospace industry's reliability goals. Background: The thrust plate is a critical component in fighter jet fuel tank assembly, transmitting engine thrust to the airframe. Quality compromises in this component can impair jet performance. It was observed that the thrust plate had a rejection rate of about 2.9% due to various defects. This real- world scenario underscores the importance of our study on the thrust plate and its potential impact on the aerospace industry. The rejection rate underscores its significance and potential for patent by quality improvement in turning of the thrust plate. Objective: The objective is to mitigate turning operation defects on the thrust plate to reduce rejection rates, aligning with aerospace industry reliability goals. Methods: Experimentation encompassed four pivotal factors: turning speed, feed rate, cutting depth, and tool inserts, implemented through Taguchi's Orthogonal Array technique. Grey Relational Analysis was utilized to optimize parameters in thrust plate turning. Specifically, this paper targeted the enhancement of its diameter, surface roughness, and tool life. Results: A single coefficient for the multiple responses, i.e., grey relational grade, has been determined, and optimum levels for the parameters have been identified. Confirmation experiments with the optimal factor level combination were carried out on a sample of thrust plates, and no rejections were observed. Conclusion: An experimental design based on Taguchi’s orthogonal array approach was used to conduct the experiments. The Grey Relational Analysis has been applied to analyze the experimental results and optimize the turning operation process parameters for the responses thrust plate diameter, tool life, and surface roughness. With this, the rejection of the thrust plate has been considerably reduced.

Purpose: This article seeks to examine the interrelationship between the NSE and other prominent global markets. Design/Methodology/Approach:An empirical study has been undertaken to assess the cointegration and causation across stock market indexes using Johansen’s Cointegration and VAR Test. Daily data points covering one financial year (FY22-23) have been used for empirical analysis. Findings: The VAR test findings indicate that NIFTY50 is influenced only by Merval. Moreover, the regression model findings indicate that only the FTSE100 and KOSPI are statistically significant. The findings indicate the lack of multicollinearity and autocorrelation. Research Limitations: The data period taken for this study is less and can be increased. Managerial Implications: The findings of this article may be very beneficial to investment firms, portfolio managers, and foreign institutional investors. Originality/Value:The study highlighted the importance of studying cointegration between the markets.

Nanofluids represent a promising frontier in materials science and engineering, offering unprecedented opportunities to enhance heat transfer efficiency, mechanical properties, and biomedical applications. Nanofluids, which are suspensions of nanomaterials in base fluids, have garnered significant interest due to their unique properties and potential applications across various fields. This chapter overview of recent advancements, preparation methods, stability evaluation, stability enhancement and current applications for nanofluids. Continued research and innovation in nanotechnology will play a crucial role in unlocking the full potential of nanofluids across diverse fields, paving the way for more efficient and sustainable technological advancements in the future.

In recent years, significant strides in technological advancement have revolutionized our lifestyles, driving a surge in demand for multifunctional and intelligent materials. Among these materials, Shape Memory Alloy (SMA) stands out for its unique ability to memorize and revert to its original shape through phase transformation. Despite its remarkable properties, SMAs exhibit a minor limitation in accurately retaining their original shape or length. Furthermore, there is a notable dearth of information regarding the modelling of SMA behaviour with high precision. This study endeavors to address these challenges by integrating experimental data with neural network modelling to comprehensively examine SMA behaviour for mechanical applications. Leveraging an experimental dataset, this research employs feedforward backpropagation neural network (BPNN) modelling to forecast the strain recovery of SMA Nitinol alloy. The model aims to predict the recovery strain of SMA by utilizing three input parameters: temperature conditional, number of coils, and initial length. Remarkably, the achieved error rates of 0.29%, 0.80%, and 9.20% for various strain measurements significantly undercut the commonly accepted error threshold of 10% for nonlinear data predictions in SMA behaviour. The final results underscore the high prediction accuracy of the Artificial Neural Network (ANN), offering promising prospects for SMA applications involving temperature-strain interactions and enhancing engineering design.

Cutting flying materials with minimum deviations and metallurgical damages is always preferred for airframe work in aircraft industries. In the present scenario, the laser cutting process has become the preferred choice for fine cutting of sheet materials with close tolerances. This paper reports the investigation of excellence in pulsed laser cutting of Al-alloy (Al6061-T6) sheet. Five quality characteristics for quantifying the dimensional accuracy (i.e., kerf width and deviation at the top and bottom side) and metallurgical damage (i.e., heat-affected zone) have been considered for the proposed work. These characteristics depend on the lamp current, laser parameters (pulse width and frequency), and relative motion between the sheetmetal and laser source. The experiments were performed using the design of the experiments technique. Further, the results of laser cutting have been optimized by using grey relational analysis (GRA) coupled with principal component analysis (PCA). The application of GRA-PCA is found capable of optimizing all five process responses at a time. The unevenness within the kerf is reduced by 22% and 32% at the top and bottom sides, respectively.