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In general, Indian Classical Music (ICM) is classified into two: Carnatic and Hindustani. Even though, both the music formats have a similar foundation, the way of presentation is varied in many manners. The fundamental components of ICM are raga and taala. Taala basically represents the rhythmic patterns or beats (Dandawate et al., 2015; Kirthika and Chattamvelli, 2012). Raga is determined from the flow of swaras (notes), which is denoted as the wider terminology. The raga is defined based on some vital factors such as swaras, aarohana-avarohna and typical phrases. Technically, the fundamental frequency is swara, which is definite through duration. Moreover, there are many other problems for automatic raga recognition model. Thus, in this work, raga is recognized without utilizing explicit note series information and necessary to adopt an efficient classification model.

This paper proposes an efficient raga identification system through which music of Carnatic genre can be effectively recognized. This paper also proposes an adaptive classifier based on NN in which the feature set is used for learning. The adaptive classifier exploits advanced metaheuristic-based learning algorithm to get the knowledge of the extracted feature set. Since the learning algorithm plays a crucial role in defining the precision of the raga recognition, this model prefers to use the GWO.

Through the performance analysis, it is witnessed that the accuracy of proposed model is 16.6% better than NN with LM, NN with GD and NN with FF respectively, 14.7% better than NN with PSO. Specificity measure of the proposed model is 19.6, 24.0, 13.5 and 17.5% superior to NN with LM, NN with GD, NN with FF and NN with PSO, respectively. NPV of the proposed model is 19.6, 24, 13.5 and 17.5% better than NN with LM, NN with GD, NN with FF and NN with PSO, respectively. Thus it has proven that the proposed model has provided the best result than other conventional classification methods.

This paper intends to propose an efficient raga identification system through which music of Carnatic genre can be effectively recognized. This paper also proposes an adaptive classifier based on NN.

This paper aims to evaluate and prioritize the key supplier selection indicators and to establish the relationship between available alternatives and selected indicators by using step-wise weight assessment ratio analysis (SWARA) and weighted aggregated sum product assessment (WASPAS).

Authors have extracted the supplier selection criteria from literature and used a combined SWARA-WASPAS method to evaluate and rank the criteria’s. SWARA is applied for evaluating and weighting selection criteria, whereas WASPAS helped in evaluating different available alternatives based on supplier selection indicators.

Finding from SWARA suggests that supplier management is the high weighted criteria followed by information sharing and joint actions. WASPAS was used to evaluate the available alternatives and supplier A1 got the highest priority. Additionally, sensitivity analysis indicates the different scenarios for the best supplier selection.

Working executives can use the SWARA for assessment of weights of finalized indicators for their firm in the cement industry. Further, the calculated weights can be used for product and sum weightage through WASPAS to finalize the best supplier.

The originality of the manuscript lies in the sector and methodology. Author(s) applied the SWARA and WASPAS method for supplier selection in the Indian cement industry that will help working executives to evaluate their supply chain partners.

Effective educational methods are critical for successfully training future supply chain talent. The paper proposes a fuzzy multi-criteria decision-making model to evaluate and select the best educational method for tomorrow's supply chain leaders integrating skill development priorities in an uncertain environment.

The Grounded theory scheme is used to identify SC leaders' skillsets criteria and educational method alternatives. Fuzzy step-wise weight assessment ratio analysis sets the priority and determines the weight of 17 criteria. Eight decision-makers evaluate 13 alternatives using fuzzy linguistic terms. Fuzzy technique for order preference by similarity to ideal solution ranks and shows the most effective educational method. Sensitivity analysis presents the applicability of this study.

Its implementation in a university-industry collaboration case in Brazil, Mentored learning from industry experts is the best educational method. The skill development priorities are data analytics ability, end-to-end supply chain vision and problem-solving. Technical skills are the most important criteria that influence the selection of the optimal option and educational methods related to learning from others rank in the top teaching pool, including multidisciplinary cross-cultural training.

This paper is among the first to evaluate educational methods with skill development priorities integration for supply chain students using fuzzy SWARA–fuzzy TOPSIS. It provides actionable insights: a decision-making procedure for educational method selection, a broad skills profile for supply chain professional success and educational methods that professors can bring to in classroom/virtual environment.

The purpose of this study is to improve supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development. During the COVID-19 pandemic, the organizations have struggled a lot to maintain the supplier performance and strategic sourcing decisions in the organizational benefit. However, in this context, the organization’s agile new product development (ANPD) process must be aligned with this requirement by maintaining the inventory and jobshop scheduling. As a result, identifying ANPD indicators, performance metrics and developing a structural framework to guide practitioners at various stages for smooth adoption is essential to improve the overall performance.

A comprehensive literature review is conducted to identify jobshop scheduling, inventory management and ANPD indicators along with the performance metrics, and the hierarchical structure is developed with the help of expert opinion. The modified stepwise weight assessment ratio analysis (SWARA) and weighted aggregated sum product assurance (WASPAS) techniques, along with expert judgement, are used in this study to calculate the weights of the indicators and the ranking of the performance metrics.

As per the weight computation by SWARA method, the strategy indicators have the highest relative weight, followed by the product design indicators, management indicators, technical indicators, supply chain indicators and organization culture indicators. According to the ranking of performance metrics obtained through WASPAS, the “frequency of new product development is at the top”, followed by “advances in product design and development” and “estimated versus actual time to market”.

It is believed that the framework developed will help industrial practitioners to plan effectively to improve supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison.

The outcomes of the present study will be extremely beneficial for the industry practitioners to improve the supplier performance. The indicators identified may guide the ANPD penetration, and performance metrics may be useful for evaluation and comparison.

A unique combination of modified SWARA–WASPAS technique has been used in this study which would be beneficial for organizations willing to adopt the jobshop scheduling and inventory management and ANPD for improving supply chain performance.

In the recent scenario, there has been an increasing trend toward lean practices and implementation in production systems for the improvement of an organization’s performance as its basic nature is to eliminate the wastes. The increasing interest of customers in customized products and the fulfillment of customers’ demand with good productivity and efficiency within time are the challenges for the manufacturing organization; that is why adopting lean manufacturing concept is very crucial in the current scenario.

In this paper, the authors considered three different methodologies for fulfilling the objective of our research. The analytical hierarchy process, best–worst method and fuzzy step-wise weight assessment ratio analysis are the three methods employed for weighting all the enablers and finding the priority among them and their final rankings.

Further, the best results among these methodologies could be used to analyze their interrelationships for successful lean supply chain management implementation in an organization. In this paper, 35 key enablers were identified after the rigorous analysis of literature review and the opinion of a group of experts consisting of academicians, practitioners and consultants. Thereafter, the brainstorming sessions were conducted to finalize 28 lean supply chain enablers (LSCEs).

For lean manufacturing practitioners, the result of this study can be beneficial where the manufacturer is required to increase efficiency and reduce cost and wastage of resources in the lean manufacturing process.

This paper is the first of the research papers that considered deep literature review of identified LSCEs as the initial step, followed by finding the best priority weightage and developing the ranking of various lean enablers of supply chain with the help of various methodologies.

The purpose of this paper is to propose a new framework for assessing the risks of turnaround projects in upstream oil process plants.

This study represents a new hybrid framework for turnaround project risk assessment. First, according to experts’ opinions, the project risks were identified using interviews and brainstorming. The most important risks selected by experts and a hybrid multiple-attribute decision-making (MADM) method used to assess and prioritize them. The proposed MADM method uses fuzzy step-wise weight assessment ratio analysis (SWARA) and fuzzy evaluation based on distance from average solution (EDAS) methods based on trapezoidal fuzzy numbers.

In this research, 28 usual risks of turnaround projects are identified and 10 risks are then selected as the most important ones. The findings show, that among the risks of upstream oil industry turnaround projects from the perspective of experts, the risk of timely financing by the employer, with an appraisal score of 0.83, has the highest rank among the risks and the risk of machine and equipment failure during operation, with an appraisal score of 0.04, has the lowest rank.

The risk analysis based on inputs collected from the experts in the Iranian upstream oil industry, and so the generalization of the results is limited to the context of developing countries, especially oil producer ones. However, the proposed risk analysis methodology and key insights developed can be useful for researchers and practitioners in any other process industry everywhere.

A novel framework for risk assessment is introduced for turnaround projects in the oil industry using MADM methods. There is no literature on using MADM methods for turnaround project risk analysis in the oil and gas industries. Furthermore, this paper presents a hybrid fuzzy method based on SWARA and EDAS.

Country and company bankruptcies at international level have put the economies of the world and countries in a difficult position. As a result of these negative developments, the measurement of the effectiveness of internal audits system together with accounting and audits have become important. The unit that plays a key role in measuring the effectiveness of internal control, whether in the private or public sector, is internal audit. In this respect, the purpose of the present study was to outline the criteria that increase the effectiveness of internal audit in public institutions. For this purpose, the SWARA Method was used. The SWARA Method is a multi-criterion decision-making method that is employed by decision-makers to determine the weights of the criteria and to sort them out. The Questionnaire of the study was applied to a participant group that consisted of 11 experts. According to the findings, the criteria that had the highest importance were “the presence of an independent internal audit activity and impartial internal auditors,” “Performing internal audit activities in line with ethical rules, standards, and relevant regulations,” and “Determining the risks regarding the objectives and the purpose of the institution, and measuring the effects of these risks.”

In this research, extracting the innovation drivers of successful crowdfunding from the literature review, screening them for the entrepreneurial small- and medium-sized enterprises (SMEs), analysing the cause-and-effect relationship amongst them and presenting a basic causal conceptual model and eventually determining the importance/weight of each relevant driver were the primary purposes of this research. As a result, the authors have also designed a score function to measure the future innovative crowdfunding score for SMEs.

A multi-layer multi-criteria decision-making (MCDM) approach has been designed and employed to achieve research objectives. After extracting the initial list of drivers, Fuzzy Delphi was applied to screen the relevant innovation drivers of successful crowdfunding for entrepreneurial SMEs. Decision-making trial and evaluation laboratory (DEMATEL) was used to analyse the cause-and-effect relationship amongst the drivers and illustrate a basic conceptual model. Analytical network process (ANP) and Stepwise Weight Assessment Ratio Analysis (SWARA) were applied to determine the importance of the drivers and by aggregating them to measure the innovative crowdfunding score.

Initially, 28 innovation drivers of successful crowdfunding were extracted from the literature. Then by employing the first-round Delphi fuzzy method amongst 15 international entrepreneurs in SMEs, the relevant drivers, including eleven items, were screened and selected. Then by implementing the DEMATEL method, the relationship amongst these screened drivers was identified, and seven drivers were determined as causes and the rest as effects. Subsequently, a conceptual model based on the causal analysis of the drivers from the DEMATEL method was designed. Eventually, by aggregating the weight of drivers emanated from SWARA, DEMATEL and DANP, the score function for measuring the situation of an SME was designed.

According to the crowdfunding scores in this research from entrepreneurs of SMEs, influential factors in developing countries were recognised as two times more prominent in developing countries. This might be rooted in the circumstances of developing countries where many startups and SMEs are emerging in vast areas and different fields due to investment in innovation management. In these countries, the authorities and officials support these companies to empower their capabilities and innovative ideas to (1) deal with the severe competitive market and (2) benefit from them as potential economic engines. Therefore, crowdfunding platforms and public initiatives can be considered one of the most effective government supports, which may involve financial risks.

To the best knowledge of the authors, investigating the innovation drivers of successful crowdfunding via quantitative analysis by multi-layer decision-making approaches has not been considered previously. Moreover, the authors have designed a crowdfunding score function to determine the situation of an entrepreneurial SME in this area. A combination of different MCDM methods, including Fuzzy Delphi, SWARA, DEMATEL, ANP and DANP, to investigate the innovation drivers of successful crowdfunding in SMEs has not been considered previously.

The accelerating business transformation through Industry 4.0 (I4.0) is expected to create significant value in the manufacturing industry by delivering considerably high productivity, superior quality, better efficiency and effectiveness. However, its evolutionary processes have far-reaching challenging for humanity. This has triggered a need to analyze the impact of I4.0 on various people-centric variables (PCVs).

This paper attempts to analyze the interrelationship dynamics between the PCVs in the current digital-industry ecosystem using a focus-group approach and causal loop diagrams. Application of the SWARA (stepwise weight assessment ratio analysis) methodology has provided its prioritized ranking in terms of importance.

The study has highlighted that I4.0 has a significant influence on five of the 13 PCVs – human quality of life, digital dexterity, high-skilled talent, low-skilled employment and creativity which contribute to 80% of the total impact.

The prioritized weights of the human factors from the SWARA approach have facilitated the assessment of the Human Resource Development Index (HRDI). The study is also contributing in enriching the literature on the human impact of the growing I4.0 and triggered the researchers to study further its adverse impact on critical human factors.

1. The paper pertains to debates on a very critical issue of impact of integration of the current intelligent digital technologies in manufacturing and services to transform businesses to be more flexible and agile.

2. This paper features I4.0 as a technology that allows integration of new products in the existing production lines, one-off manufacturing runs and high mix manufacturing.

3. The paper also highlights major adjustments in operational activities, processes, supply chain, and organizational redesign due to I4.0 adoption.

4. The current research study has significantly enriched the literature on the I4.0 impact on people-centric variables (PCVs) using the SWARA method. The use of the Causal Loop Diagram has very aptly brought out the type of causality (polarity) between the different PCVs in the growth of I4.0.

The paper pertains to debates on a very critical issue of impact of integration of the current intelligent digital technologies in manufacturing and services to transform businesses to be more flexible and agile.

This paper features I4.0 as a technology that allows integration of new products in the existing production lines, one-off manufacturing runs and high mix manufacturing.

The paper also highlights major adjustments in operational activities, processes, supply chain, and organizational redesign due to I4.0 adoption.

The current research study has significantly enriched the literature on the I4.0 impact on people-centric variables (PCVs) using the SWARA method. The use of the Causal Loop Diagram has very aptly brought out the type of causality (polarity) between the different PCVs in the growth of I4.0.

The geographical scattering of physical facilities in conventional supply chains enforces firms to shift toward digital supply chains (DSCs). While switching to DSCs, the decision-making becomes more complex with an upsurge in the size of the manufacturing firms. The manufacturing firms need to develop supply chain quality management (SCQM) systems to improvise their processes for delivering advance products and services. For developing SCQM, the role of the digital supplier is significant, as they may recuperate the quality management systems (QMS) for enhancing the firm's performance. The purpose of this paper is to explore the factors that affect the selection of digital suppliers. The other purpose is to evaluate the alternatives for identifying the best supplier that enhances the QMS for DSCs.

The decision-making is complex for digital supplier selection (DSS) and thus, the study has utilized integrated SWARA-WASPAS methods for their critical evaluation. The stepwise weight assessment ratio analysis (SWARA) method has been utilized for identifying the weightage of factors and weighted aggregated sum product assessment (WASPAS) for assessing the digital suppliers to explore the best alternative. The integrated SWARA-WASPAS method is the most advance approach in multi-criteria decision-making (MCDM) for the evaluation of the factors.

The study reveals that supplier competency is the most significant factor in selecting digital supplier in DSC that may improve the product and service quality. The study also explores that manufacturing firms needs an efficient system for developing value for the internal and external partners that help them to cope up with the dynamic world. On the basis of the WASPAS results, supplier S₈ has been ranked as the best supplier who has highest competency in the form of responsiveness, resilience, sustainable practices and digital innovation.

The factors are assessed on the decision team of experts that may be biased and thus, the research may further be validated through empirical studies. The research has to be extended in other nations for exploring how organizations and customers are responding to the DSCs.

The study has given insights to the manufacturing firms to consider the crucial factors for DSS, as it affects the overall performance of the organizations. The decision makers of manufacturing organizations should consider the factors such as supplier competency, digital innovation and information sharing for value creation that may provide them better opportunities for developing their DSCs along with their digital suppliers to connect with stakeholders appropriately.

The improved SCQM aligned with DSS will offer quality products that are sustainable and provide social and economic benefits to the society. The DSS will be able to provide improvisation of the existing products and services for developing a sustainable value chains for the manufacturing organizations. This process will bring more transparency, viability and sustainability in the product and services. As a result, the DSC partners will be more transparent, viable and resilient.

The research on DSS and its importance in enhancing QMS is limited. This research is the novel approach to understand the criteria behind the selection of the digital suppliers’ role and their presence in enhancing the quality of products and services.