Rooftop solar micro-power plants mitigate pollution and transmission problems in crowded Indian cities. The Indian government is exploring alternate solutions, and the micro, small, and medium enterprises (MSME) sector is also coming forward to opt for sustainable solutions. This study is focused on the MSME segment in India, which constitutes approximately 63 million entities. Installing a solar system for which a rooftop could be the best place to tap solar energy from these entities is required. Checking the rooftop solar potential of any MSME entity requires a suitable technique to decide which sector to target first and the further order. A unique methodology based on multi-criteria decision-making (MCDM) methods is presented to rank 10 MSME alternatives with seven criteria. It includes the measurement of alternatives and ranking according to compromise solution (MARCOS) to rank MSME sectors in India with rooftop solar potential. Weights of importance are assigned with three objective techniques and combined with the Bonferroni operator. The ranks attained are compared with different MCDM methods, and sensitivity analysis is performed while changing weights. Kendall and Spearman rank correlation coefficients are utilized to access the correlation levels between various strategies. Finally, Garrett’s ranking technique is used to finalize the different ranks. The results reveal that the MARCOS method performed well, and “textiles” ranks first, followed by “auto and engineering products.” As a result, the leading industrial sectors will help improve energy utilization while concentrating on renewable energy resources.

The design performance of real-time operating gas turbine power plants (GTPPs) deteriorates in terms of efficiency, reliability, and commercial availability due to aging, rubbing, contamination, and other similar problems. A manager minimizes operation and maintenance (O&M) expenses, consociating about the function of its basic structure (i.e., layout and design), availability (maintenance aspects), operation efficiency (trained workforce), safety and security, and other regulatory elements. Understanding plant structure improves performance, economical design, maintenance planning, etc. For a better understanding of design, a technique comprising graph theory, matrix method, and combinatorics is developed to determine the performance of GTPP. Detailed methodology for developing a system structure graph, various system structure matrices, and their permanent functions are described for the GTPP. For accessible and appropriate performance analysis, GTPP is divided into seven sub-systems. Structural interconnections between seven sub-systems of GTPP are as per real-time GTPP. The gas turbine system structure is developed in the form of a digraph. Matrix representation corresponding to digraph representation is developed, which is processed with the help of combinatorics. With the help of a computer programming tool developed in C++ for calculating the permanent of a matrix, the result comes out as a numerical value called the GTPP index. The methodology applied in the present work can be incorporated with standard computer programming tools developed for the performance assessment. In this way, it is easy to assess the dynamic behavior of the gas turbine system, as the methodology of the present work can incorporate tangible and intangible factors. Results obtained from the present methods agree with the available information in the literature.

Advances in developed and developing countries are more attributable to growth in industrial activities that directly impact increasing energy demand. Energy availability has been inconsistent globally, necessitating energy storage (ES) for use as per requirement. Various energy storage technologies (ESTs) are available in mechanical, electrochemical, electrical, chemical, and thermal forms to fulfil the energy demand of a user as and when required. The factors responsible for making a commercially viable energy storage product are further being researched for an eco-friendly and optimal solution to store energy for a longer duration. Researchers are employing different strategies to evaluate the energy efficiency of storage technologies. This paper uses the VIKOR technique to analyze ESTs while assigning objective weights with the entropy weights method based on identified energy performance indicators and ranking them according to their commercialization viability. The method helps a consumer choose better ESTs as per their requirement while manufacturers compete with each other to enhance the commercial value of their energy storage products. Sensitivity analysis has been performed to understand the uncertainties, pros, and cons with the limitations and scope of using the decision model and thus taking an informed decision. The analysis of different energy storage technologies has indicated Hydrogen Fuel Cells (HFC) to be impressive and promising for the future.

A fuel cell, an energy conversion system, needs analysis for its performance at the design and off-design point conditions during its real-time operation. System performance evaluation with logical methodology is helpful in decision-making while considering efficiency and cross-correlated parameters in fuel cells. This work presents an overview and categorization of different fuel cells, leading to the developing of a method combining graph theory and matrix method for analyzing fuel cell system structure to make more informed decisions. The fuel cell system is divided into four interdependent sub-systems. The methodology developed in this work consists of a series of steps comprised of digraph representation, matrix representation, and permanent function representation. A mathematical model is evaluated quantitatively to produce a performance index numerical value. With the aid of case studies, the proposed methodology is explained, and the advantages of the proposed method are corroborated.

Despite significant technological advancements, the surface quality of SLM produced components remains a concern. Surface quality determines not only the features of the created product, but the entire process is sometimes called into question. Additive manufacturing offers several advantages and applications; however this approach cannot yet create high precision objects. As a result, post-processing of produced components aimed at improving total surface quality of manufactured parts becomes more important and crucial. The study examines the chemical polishing of AlSi10Mg components made with SLM technology, as well as the possibility of increasing overall surface quality by chemical treatment. SLM's chemical cure of the surface of AlSi10Mg was successful in improving the surface finish. The sample's original surface roughness was 19.37 microns, while the surface roughness after chemical polishing was reduced to 3.39 microns.

Advancement in futuristic technologies is growing exponentially catching entities unaware rendering their product in obsolescence. Existence of a current entity in the near future entirely depends on its adaptability to changes in an Industry 4.0 environment. Adoption of the Industry 4.0 system by entrepreneurs will eventually help to sustain themselves in future. Industry 4.0 environments entail smart objects using collaborative technologies to make informed decisions themselves and serve the entire value-chain. This system may look awesome but is eventually full of challenges as integrating each of the technologies with one another to create a unique system is a huge task. One of the major challenges is the standardization of the various technologies in order to integrate them seamlessly and help in interoperability of connected machines. Connectivity is another major issue as Industry 4.0 envisages data flow, analysis, and prediction/prescription in real time. A disruption or delay in data flow may be reflected as a delay or even stoppage at multiple points in the entire value chain. However, by overcoming challenges, a system gets created which is unique and capable of producing quality products at a much faster pace. Entrepreneurs have a plethora of manufacturing business models based on Industry 4.0 at their disposal to implement. Opportunities galore exist after the successful implementation of Industry 4.0 in creating a smart factory and entrepreneurship for long term sustenance. As the time from conception to final product shortens, it eventually translates into a better perspective for all stakeholders.

Process parameters significantly influence the properties of the specimen in any manufacturing system. Laser sintering is no exception when it comes to properties like surface roughness; density or mechanical properties. The present study is focused on evaluating the influence of process parameters specifically laser power, scan speed, hatch spacing and orientation on part properties namely density and surface roughness of the specimens sintered on selective laser melting of AlSi10Mg alloy. To delineate the parameters, the design of experiments was created using RSM. The data so received by conducting the experiments and observing the readings of the outcomes under study were analysed using ANOVA analysis. The optimisation was carried out using MATLAB software with optimisation toolbox. Hatch spacing proved to be the major contributor for influencing both density as well as surface roughness when the final outcomes were investigated.

The demand for energy in the modern world is increasing exponentially with further industrialization and the advancement in technologies. As such, non-renewable sources of energy are undoubtedly most sought after. Solar energy sources have been in use for a long time, though their overall contribution to energy has only increased in the last decade. Research has provided breakthroughs resulting in efficient solar panels used in solar power plants. Due to the high quantity of solar panels required, the selection of vendors of the solar panels should be decided by taking identified quantitative and qualitative criteria into consideration. The Fuzzy Analytic Hierarchy Process (FAHP), one of the Multi-Criteria Decision Modelling (MCDM) techniques, can be used for the ranking of vendors. The objective of this work is the selection of vendors of solar panels based on identified quantitative and qualitative criteria. A hierarchical structure of criteria has been drawn based on MCDM. The vendor list qualifying for the final round of selection are the alternatives of FAHP. Solar panel expert opinion is used for pairwise comparison. However, the subjectivity issue arises in pairwise comparison and is dealt with with the fuzzy approach. The application based on Multi-Criteria Decision-Making will serve as a guideline for other similar selection processes.

The Analytic Hierarchy Process (AHP) has simplified the subjective decision-making process to a great extent, thereby contributing to its popularity. The process primarily involves the development of the hierarchical structure of the given system to be analyzed based on multiple criteria affecting it. The goal to be achieved by the system is indicated on top, while criteria and sub-criteria affecting it are placed beneath it in order, forming a hierarchical structure validation. A list of available alternatives, out of which the most optimum needs to be selected based on analysis, is placed last in the structure. AHP has been widely used in diverse decision-making situations including, but not limited to, ranking, resource allocation, benchmarking, choice selection, conflict resolution, forecasting, quality improvement, etc. Subjectivity issues during the comparison of criteria have come under criticism due to separate values provided by different evaluators. The problem has been mitigated to a large extent by using the fuzzy approach, thereby significantly removing the inaccuracies and uncertainties of the input data. Furthermore, different sigmoid functions and quantification of different factors are also versed. A brief discussion on the commonly used defuzzification methods, i.e. Centre of Sums (CoS), Centroid of Area (CoA), is also presented.

Benchmarking is a versatile tool to improve the existing supply chain performance of the manufacturing industries through continuous use of plan do check action (PDCA) cycle. The regular practice of benchmarking provides a decision support in the performance analysis of internal supply chain of manufacturing Industry. The seven PMs have been identified through the literature review. The brainstorming activity of expert's team from different manufacturing industries is used to identify the importance/weightage/effect of one performance measure as compare to other performance measure. The survey questionnaire tool is used to collect the opinion of experts about benchmarking of internal supply chain management (ISCM). The present work includes, weighted interpretive structural modelling (WISM) approach to develop a structural model, which will represents the interrelationships among identified PMs. WISM model allocates the key PMs at three levels. Such type of model not only helps in understanding the conceptual relationship among the PMs, but it may also use in determining their interdependence to assess the ISCM performance of industries.

Abstract AlSi10Mg is one of the most promising alloys in SLM technology due to its low weight and competitive mechanical properties. It gives a good alternative to the conventional manufacturing systems in terms of design flexibility, cost and production time. But the parts made from laser sintering of powder are bound to have rough surface texture. The present study is focused on investigating the impact of process parameters namely laser power, scan speed, hatch spacing and orientation on density and surface roughness of the specimens sintered on selective laser melting of AlSi10Mg. To characterize the parameters, design of experiments was created using RSM and ANOVA analysis was employed to find the factors affecting the responses. The optimization was carried out in MATLAB using optimization toolbox. As per the study, hatch spacing proved to be the major contributor for influencing both density as well as surface roughness.

Competing globally with world class industries, Indian manufacturing industries need to brace itself with benchmarking models and continuous self-improvement. Internal supply chain management (ISCM) performance may be focused in particular to create a niche over others. Proper understanding of key performance indicators of ISCM is necessary to analyse the gaps. A suitable methodology is also required to interpret the loopholes in order to remove them. A combined approach of fuzzy logic and analytic hierarchy process (AHP) technique is used for better result of theoretical benchmarking model. Comparison control bar charts are also used to view the performance gap between internal supply chains of selected manufacturing competitors. The primary purpose of this paper is to identify worst performance measures by implementation of benchmarking model, fuzzy logic and AHP methodology.

Several qualitative and quantitative techniques are available for factors analysis of benchmarking of supply chain management. Fifteen variable factors of benchmarking of internal supply chain management (ISCM) has been identified and derived theoretically from various literature sources and opinions of expert's from 300 manufacturing industries. Mean score and an interpretive structural modelling (ISM) approach is applied to assign the rank of factors. In ISM approach, influence between factors is determined by considering the opinions of experts from relevant field. An industrial questionnaire method is used to collect the opinion of experts. Firstly, analysis of the interactions among factors for benchmarking of ISCM by ISM approach and matriced impacts croises multiplication appliqueeaun classement (MICMAC) analysis. Secondly, to develop the relationship among identified rank of factors. Finally, to do the classification of variable factors into clusters based on their driving power and dependence power. According to social implication and managerial point of view, this research provides help to researchers and managers to understand the mutual influence of factors. This research work is also helpful to identify those factors which support in benchmarking of ISCM of any business organisation.

Leagile is a combination of both lean and agile paradigms; it involves the advantages of both lean as well as agile systems. Through this paper an attempt has been made to study the social implications of this high technology related manufacturing on Indian society. Furthermore, total interpretive structural modelling (TISM) technique has been utilised to identify the driving and dependent social implications of leagile manufacturing. Moreover, TISM technique has been used in finding the mutual relations of these social implications by developing a model. This TISM model may be utilised to identify the key social implications which will be helpful in the area of leagile manufacturing. In the present study, social implication of leagile manufacturing has been identified through literature study and experts review and an TISM model has also been prepared.

The objective of this research work is to develop a benchmarking frame work for internal supply chain management (ISCM). The working steps of benchmarking framework can be explained by a functional activity flow chart. Timely regulation of benchmarking framework should be helpful for optimising the existing internal supply chain process. A case study is used to gather the relevant data information from industrial experts, advisors, supervisors and academics experts. The aim of authors is to evaluate ROI of top two heavy fabrication construction equipment's manufacturing industries. The optimisation of ROI has done by identifying problems of ISCM and taking appropriate corrective measures to rectifying them.

Leagile manufacturing has emerged as an innovative strategy in the last two decades. In this paper, critical success factors (CSFs), affecting leagile manufacturing, have been identified through literature review and discussion with industrialists and academics. Fuzzy decision making trial and evaluation laboratory (DEMATEL) technique have been applied to divide the CSF's into cause and effect categories. It has been found that critical success factors 1 (collaborative relationship), 3 (strategic management), 5 (customer and market sensitiveness), 7 (human resource management), 11 (flexible manufacturing system), 12 (knowledge and IT management), 14 (availability of funds), and 15 (benchmarking) falls under cause group while critical success factors like 2 (management support towards implementation of policy), 4 (training and development programs), 6 (design and engineering), 8 (virtual enterprises), 9 (use of advance manufacturing technologies), 10 (supply chain management), and 13 (rapid reconfiguration) falls under effect group. The research will help the managers to identify the key factors and work accordingly in proper directions to implement leagile system in their organisations. Leagile system results in increased profitability, better quality of products, better customer satisfaction, better sales, increased productivity, increased sales, reduced manufacturing lead time, etc.

Leagile manufacturing strategy has emerged as one of the important strategy adopted by most of manufacturing organizations now a days. It has advantages of both lean as well as agile manufacturing system. Lean manufacturing tries to eliminate all different types of wastages like overproduction, inventory, unnecessary motion etc., while agile manufacturing focus on changing the production system as per the requirements of the customer and provide customized products within short span of time. Lean manufacturing focuses on no inventory and try to implement Just in Time methodology but for the system to be agile, there should be at least some inventory in store so that production can be started as soon as customer order is achieved. In this paper, key performance indicators (KPI) of leagile manufacturing are found by literature review and in consultation of experts and academicians working in the concerned field. Fuzzy TISM approach has been applied to find levels of different KPI’S. MICMAC analysis has been made to analyze the KPI’S and categorize them as autonomous, dependent, linkage, independent etc. on the basis of driving and dependence power. Finally, digraph is drawn to show relationship between various KPI’s.

Leagile manufacturing is one of the recent dominant research areas in the field of operations management in the current scenario. However, there is a lack of research focusing on leagile in manufacturing industries. The leagile system combines the advantages of both lean as well as agile system. The purpose of the paper is to make the industries and academicians aware of the leagile concept and its tools so as to meet the fluctuating needs of customers in short duration of time. It provides industries to remove all different types of wastes and at the same time concurrently meeting the changing needs of customers and hence helps in achieving better customer satisfaction. Both lean and agile systems can be incorporated simultaneously in the system by proper positioning of de-coupling point in the supply chain. In the upstream of the supply chain, lean system is adopted while in downstream, agile system is best suited.