Increasing concerns about climate change and waste management pressure manufacturers to innovate their practices, focusing on the environment and society without impacting the economic returns. Such concerns brought a new realm to manufacturing, sustainable manufacturing, which is a combination of economy, environment and society. Though several studies have attempted to integrate sustainability in manufacturing successfully, very few studies have been reported on sustainable manufacturing practices. Even these existing studies failed to explore the difference between implementing sustainable manufacturing in different geographical contexts. To fill this gap, this study considers four different geographical context Saudi Arabia, India, Peru and China to understand their preferences of achieving sustainable development in manufacturing through sustainable manufacturing practices. At first, common sustainable manufacturing practices were collected from literature and the same has been evaluated with case industries from Saudi Arabia, India, Peru and China. Totally 15 practices under three different dimensions have been considered in this study. From the obtained results, it can be confounded that “Integrate efficient technologies into core manufacturing system” (SP4) has been identified as key influential practices irrespective of the considered geographies. With the enlighten of these obtained results, practitioners and researchers can understood various in-depth insights on how different nations following different strategies for implementing sustainable development in manufacturing sector. Such clarity improves inhouse policy making which leads to better decision making for faster transition towards sustainable manufacturing.

The present work centers on aluminum-based metal matrix composites (AMCs), synthesized via stir casting and then processed by electrical discharge machining (EDM) in the case of Al7075 as a matrix and 6 wt.% boron carbide (B4C) as reinforcement. A design of experiment (DoE) approach, powered by hybrid optimization techniques (such as the entropy weight method (EWM), grey relational analysis (GRA) incorporated Taguchi method) was used to investigate the relationship between current (I), pulse ON time (Ton), pulse OFF time (Toff), and electrode gap (Gap) as input parameters and the material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR) as response parameters. The results showed that an I = 140 A, Ton = 120 ms, Toff = 50 ms, and Gap = 0.4 mm combination gives the best response parameters of MRR = 0.5628 mm3/min, TWR = 0.0048 mm3/min, and SR = 4.4034 μs.

This study explores how machining parameters affect Surface Roughness (SR), Tool Wear Rate (TWR), and Material Removal Rate (MRR) during Electrical Discharge Machining (EDM) of a hybrid aluminum metal matrix composite (AMMC). The composite includes 6 % Silicon carbide (SiC) and 6 % Boron carbide (B4C) in an Aluminum 7075 (Al7075) matrix. A combined optimization approach was used to balance these factors, evaluating Pulse ON time, Current, Voltage, and Pulse OFF time. Response Surface Methodology (RSM) optimized single responses, while multi-response optimization employed a hybrid method combining the Entropy Weight Method (EWM), Taguchi approach, TOPSIS, and GRA. Analysis of Variance (ANOVA) assessed parameter significance, revealing substantial impacts on SR, MRR, and EWR. Based on TOPSIS and GRA, optimized parameters achieved a desirable balance: high MRR (0.4172, 0.5240 mm³/min), minimal EWR (0.0068, 0.0103 mm³/min), and acceptable SR (10.3877, 9.1924 μm) based on EWM-weighted priorities. Confirmation experiments validated a 15 % improvement in the closeness coefficient, and a 16 % improvement in the Grey relational grade, which considers combined SR, MRR, and EWR performance. Scanning Electron Microscope (SEM) analysis of surfaces machined with optimal parameters showed minimal debris, cracks, and no recast layer, indicating high surface integrity. This research enhances EDM optimization for AMMC, achieving efficiency in machining, minimizing tool wear, and meeting surface quality requirements.

Achieving sustainability has become an antecedent motive for numerous organizations since the last decade, and it has even accelerated with the introduction of sustainable development goals. Researchers and practitioners are trying different things to integrate sustainability principles in various fields of application, including manufacturing, which is one of the largest contributors to global GDP. This study investigates to improve the effectiveness of sustainable manufacturing implementation through customer participation. Existing literature in sustainable manufacturing is largely lacking with a stakeholder perspective, particularly with regard to customer participation. To bridge the gap, this study analyzes and evaluates the common enabling factors of customer participation for sustainable manufacturing implementation. Common factors have been collected and validated and the same has been analyzed with DEMATEL. Input for the study is taken from a case industry located in Saudi Arabia. Results revealed that among the 15 possible factors considered, ‘cost implications of sustainability' (E1) has the top position and ‘government regulations' (E14) has the least position in influencing customer participation in the implementation of sustainable manufacture. Finally, this study concludes by highlighting important contributions and opportunities for future expansion of customer-led sustainable manufacturing.

Mining sector always comes under severe scrutiny due to their negative impacts towards society and environment. Several studies contributed to reduce these impacts exists in the mining operations, in the development, studies also started to explore various assessment mechanism to understand the mining firm's sustainability impact. Among such assessment strategies, sustainability indicators gained huge momentum in recent years specifically with mining operations. This study considers one such area to focus, sustainability indicator analysis in mining. There are several sustainability indicators were introduced in the literature, which makes the consideration of sustainability indicators as a chaotic process for practitioners. Considering the fact, this study sought to explore the influential sustainability indicator and their corresponding sustainability dimension with the case context of China. As a major global manufacturer, China explores different ways to do a sustainable mining business for their long-term growth and this study could impact on their sustainable development goals roadmap. Different sustainability indicators considering mining were collected from the existing studies and further validated and categorized with expert opinions under their respective dimensions of sustainability (economy, environment, and society). The validated sustainability indicators were evaluated through a multi criteria decision making tool, DEMATEL. The inputs for the analysis were collected from a Chinese mining case company. The results revealed the influential sustainability indicator for Chinese mining sector. By understanding the most and least influential indicators, the Chinese mining practitioners can eliminate the strategies to motivate the least influential indicator and improve the strategies to motivate most influential indicator.

This paper assesses the interrelationships between hazards/risks (considered as barriers) for the effective implementation of Occupational Health and Safety (OHS) measures using Interpretive Structural Modeling (ISM). We investigate within and among hazard/risk classes and formulate a structural model that identifies and prioritizes their levels. Mitigation strategies for the identified barriers make the research outcome more useful and applicable. Results reveal that insulation failure, barrier (B7), is the most influential hazard; hence, it must be addressed first for the effective implementation of OHS measures in coal-fired thermal power plants. The second barrier in the ISM hierarchy, (B6) fire (mill/pool/jet), and additional hazards and risks require further assessment.

Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies' capabilities in collecting End-of-Life (EOL) products, customers' interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network.

Due to the increased attention in recent years toward sustainability issues, supplier selection is a fundamental component of ensuring an efficient supply chain. Selecting a supplier is a burden for any firm, but the fireworks industry faces sustainability issues that are particularly important because of their environmental impact. All these concepts make the purchasing manager's job difficult. Selecting the best supplier based on its level of CSR is too difficult because no single study clearly explains the whole concept. A few research studies exist, but they typically address automobile, textile, or other manufacturing industries. Because CSR is an unstable issue and these issues underscore the supplier selection process that is vital to the fireworks industry, and we propose a model that is validated using an interpretive structural modeling (ISM) approach. Firework industries in south Asian countries generally have not properly employed CSR issues. Therefore, the proposed model addresses firework industry situated in the southern part of India. This study assists purchasing managers to identify the prime issue that underscores the selection of a supplier based on CSR. Based on that prime, managers can more responsibly and confidently make their supplier selection. This study can be extended by diversifying the CSR issues and by including other MCDM tools and it provides a manner to consider other supplier selection issues by implementing a hybrid approach with ISM.

Due to the rising importance of our global interdependence, corporate social responsibility (CSR) is primarily concerned with social, ethical, and economic decisions, particularly as they relate to the selection of suppliers in contemporary supply chains. In this paper, we analyze the importance of seven criteria as a means of selecting the best supplier under CSR environment. Those seven criteria are human rights issues, underage labor, female gender labor, long working hours, pollution, safeguarding mechanisms, and organizational legal responsibilities. We, however, include some relevant sub-criteria as well. These issues are compared and ranked with the combined assistance of several existing literature reviews and by managerial interviews conducted from leaders in the southern region of India. The importance of the criteria and sub-criteria used in the CSR-based supplier selection is identified by the analytical hierarchy process. This paper concludes with a presentation of the rankings and priorities of the proposed seven criteria and establishes a useful method for selecting the best supplier based on CSR.

IJAR - Indian Journal of Applied Research (IJAR) IJAR is a double reviewed monthly print journal that accepts research works from scholars, academicians, professors, doctorates, lecturers, and corporate in their respective expertise of studies.

Recently Oil Hardened Non Shrinkable (OHNS) steel finding many applications in Manufacturing parts like Shaft, Gears and Tooling due to their excellent Mechanical properties. The continuous development of carbide milling cutter and its coating technology are great concern with manufacturing Environment. Titanium Aluminum Nitrite coating play an important role in milling cutter to produce better surface finish and tool life with minimum cost. In this Experimental investigation of face milling operation of OHNS steel plates with different process parameters like spindle speed, feed rate and depth of cut and to find optimal machining conditions of minimum surface roughness (Ra).The experiments are designed and conducted based on Taguchi’s design of experiments using L9 orthogonal array and analyzed by ANOVA.