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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.

The purpose of this paper is to analyze previous models of the concept of ranking accuracy within the weighted aggregated sum product assessment (WASPAS) method and make necessary refinements.

This paper presents a correct combination of the weighted sum model (WSM) and weighted product model (WPM), which is usually performed on an ad hoc basis in the literature.

One of the reasons of rarely conducting ranking accuracy analysis might be that some of the reported equations in the literature are confusing, and hence, accurate partial derivatives cannot be calculated. In this study, all of the necessary formulations are re-derived and necessary modifications are proposed.

A corrected WASPAS equation for optimal combination parameters is derived. Two examples are used to validate the formulations, and software implementation is provided. Because multiple attribute decision-making (MADM) has gained widespread attention from both the academia and industry, the findings of this paper help decision makers fully capitalize the concept of ranking accuracy and avoid possible confusions regarding the equations reported in the literature.

WASPAS is a relatively new MADM method and has enjoyed a visible position in the MADM literature. In addition to its simplicity, the WASPAS method utilizes the concept of ranking accuracy by combining the well-known WSM and WPM. This combination realized via an optimization criterion brings unique opportunities for decision makers such as evaluating confidence intervals for relative significance of alternatives and reducing estimated variance of ranking results. Despite its crucial importance, the combination of WSM and WPM is usually performed on an ad hoc basis in the literature. In this study, all of the necessary formulations are re-derived and necessary modifications are proposed along with clarifying examples.

Pharmaceutical supply chains (PSCs) need a well-operating and faultless logistics system to successfully store and distribute their medicines. Hospitals, health institutes, and pharmacies must maintain extra stock to respond requirements of the patients. Nevertheless, there is an inverse correlation between the level of medicine stock and logistics service level. The high stock level held by health institutions indicates that we have not sufficiently excellent logistics systems presently. As such, selecting appropriate logistics service providers (drug distributors) is crucial and strategic for PSCs. However, this is difficult for decision-makers, as highly complex situations and conflicting criteria influence such evaluation processes. So, a robust, applicable, and strong methodological frame is required to solve these decision-making problems.

To achieve this challenging issue, the authors develop and apply an integrated entropy-WASPAS methodology with Fermatean fuzzy sets for the first time in the literature. The evaluation process takes place in two stages, as in traditional multi-criteria problems. In the first stage, the importance levels of the criteria are determined by the FF-entropy method. Afterwards, the FF-WASPAS approach ranks the alternatives.

The feasibility of the proposed model is also supported by a case study where six companies are evaluated comprehensively regarding ten criteria. Herewith, total warehouse capacity, number of refrigerated vehicles, and personnel are the top three criteria that significantly influence the evaluation of pharmaceutical distribution and warehousing companies. Further, a comprehensive sensitivity analysis proves the robustness and effectiveness of the proposed approach.

The proposed multi-attribute decision model quantitatively aids managers in selecting logistics service providers considering imprecisions in the multi-criteria decision-making process.

A new model has been developed to present a sound mathematical model for selecting logistics service providers consisting of Fermatean fuzzy entropy and WASPAS methods. The paper's main contribution is presenting a comprehensive and more robust model for the ex ante evaluation and ranking of providers.

World Cup tournament is one of the most popular international organizations in football. The purpose of this paper is to investigate the overall performance of World Cup 2018 teams via multi-criteria decision-making (MCDM) approaches.

The presented approach adopts entropy integrated Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Weighted Aggregated Sum Product Assessment (WASPAS) approaches to weight the criteria and evaluate the performance of World Cup 2018 teams. Initially, the authors compute weight criteria via Shannon Entropy method. Then, the authors compute and compare the results of TOPSIS and WASPAS methods so as to evaluate the performance of World Cup 2018 teams.

According to TOPSIS and WASPAS results, Belgium has demonstrated better performance in comparison to other teams. As per to the empirical results, both methods have shown a significant harmony in terms of performance figures. There is also strong positive correlation between TOPSIS and WASPAS method results. This result confirms the stability of the analysis.

This paper contributes to sport performance management literature by using MCDM methods in FIFA World Cup 2018 teams. To the best of the authors’ knowledge, this is the first paper to measure performance of an international football organization via MCDM methods.

Low cost carriers (LCCs) have become one of the most significantly growing parts of the airline servicing market. This new player has redesigned the whole airline industry, which was previously led by the national/international full-service airline companies. Considering such advancements, the hub locations of LCCs became an important issue than ever before. Within this concept, a guiding framework is developed for an LCC company, which is in search of a new hub airport location within Turkey to satisfy the demand and attract new passengers.

An interval-valued intuitionistic fuzzy (IVIF) sets based weighted aggregated sum product assessment (WASPAS) and multi-objective optimization by ratio analysis (MULTIMOORA) methods are developed for decision-making processes.

Five airport locations are evaluated using the developed method. Results showed that in determining hub locations for LCCs, potential number of passengers of the city, airport quality and the number of hotels within the city are obtained as the three most important criteria among 12 evaluation criteria. The best location for the LCC company is determined as Antalya Airport.

To apply the proposed method to a different set of alternatives, data gathered on comparing location of alternatives from experts should be updated.

Proposed hybrid framework is presented as the first time in the literature as a decision-making tool. In order to validate framework's applicability, efficiency and effectiveness, a comparison and a sensitivity analysis are conducted at the end of the study.

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.

The construction industry is a key driver of economic growth. However, the adverse impacts of construction and demolition waste (CDW) resulted from the active construction projects on the economy, environment, public health and social life necessitates an appropriate control and management of this waste stream. Developing and promoting the construction and demolition waste management (CDWM) hierarchy program at the strategic level is essential.

This study aims to propose a hybrid decision model that hybridizes the Integrated Determination of Objective Criteria Weights (IDOCRIW) and weighted aggregated sum product assessment (WASPAS) under a fuzzy environment.

The proposed method ranks the potential strategic alternatives by the sustainable development criteria to improve the performance of CDWM. As indicated in the results, the fuzzy approach in the decision-making process enables the transformation of linguistic variables into fuzzy numbers that show uncertainty and ambiguity in real-world systems. Moreover, the close correlation between the final ranking of the proposed methodology and the average priority order of the strategic alternatives obtained by five different multi-criteria decision-making (MCDM) methods implies the validity of the model performance.

This proposed model is an appropriate tool to effectively decide on the development of CDWM from a strategic point of view. It aims to establish an MCDM framework for the evaluation of effective strategies for CDWM according to the indices of sustainable development. Implementing proper operational plans and conducting research in CDWM has the highest priority, and enacting new and more stringent laws, rules and regulations against the production of CDW has secondary priority. This study contributes to the field by optimizing the CDWM by applying the top-priority strategies resulted from the proposed fuzzy hybrid MCDM methodology by the decision-makers or policy-makers to reach the best managerial strategic plan.

In the proposed methodology, the IDOCRIW technique is utilized and updated with the triangular fuzzy numbers for the first time in the literature to derive the weights of sustainable development criteria. The fuzzy WASPAS method is utilized for evaluation and providing a final ranking of the strategic alternatives.

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.

The choice of best machining parameters is an extremely basic factor in handling of any machined parts. The purpose of this paper is to exhibit a multi-objective optimization technique; in view of weighted aggregate sum product assessment (WASPAS) technique toward upgrade the machining parameters in modified air abrasive jet machining (MAAJM) process: injecting pressure, stand-off distance (SOD), and abrasive mesh size measure with 100 rpm rotatable worktable on Nickel 233 alloy material. Three conflicting destinations, material removal rate (MRR), surface roughness (SR) and taper angles (Ta), respectively, are considered at the same time. The proposed procedure uses WASPAS, which is the examination of parametric optimization of the abrasive jet machining (AJM) process. The results was used any scopes of reactions in MAAJM process is the ideal setting of parameters are resolved through investigations represented. There is wide utilization of Nickel 233 in aviation enterprises; machining information on producing a hole utilizing MAAJM for the first time is given in this work, which will be helpful different industries.

This paper exhibits a multi-objective optimization technique; in view of WASPAS technique toward upgrade the machining parameters in MAAJM process: injecting pressure, SOD, and abrasive mesh size measure with 100 rpm rotatable worktable on Nickel 233 alloy material.

As an outcome of using the tool in any ranges of responses in the AJM process, the optimal setting of parameters is determined through experiments illustrated. The machining data of generating a hole using AJM are studied for the first time in this work, which will be useful for aerospace industries, where Nickel 233 is used broadly.

A new material in unconventional machining process and also a multi-objective optimization technique are adopted.

This study aims to detect the influential factors and their respective variables that affect the effectiveness or demand-driven level of the industrial parks in Iran. A hybrid procedure is sought to be developed, which embraces both qualitative and quantitative methodologies to identify the aforementioned factors and variables.

This study is incorporated with both qualitative and quantitative methodologies. To implement the qualitative approach, the researchers used focus groups and the related literature. The quantitative methodology has been carried out via a reliable questionnaire that obtained the viewpoints of 700 experts. The reliability of the designed questionnaire has been investigated through Cronbach’s alpha coefficient. By conducting several one-sample t-tests, it was confirmed that the identified factors and variables are significantly influential on the effectiveness of Iran’s industrial parks. The Kruskal–Wallis statistical test was used to determine the priorities of the factors. This research also used a multi-criteria decision-making method, namely, the weighted aggregates sum product assessment (WASPAS) to rank 15 industrial parks of Khorasan province in Iran according to the identified factors.

Comprehensive analyses have been conducted on the identified factors. Results indicate that the infrastructural facilities factor has the highest priority when it comes to affecting the effectiveness of the industrial parks. After that, industrial land and internal factors take the second and the third positions in terms of importance. A total of 15 industrial parks of the aforementioned province have been ranked by the WASPAS. The ranking offered by the WASPAS has been approved by the experts.

Based on the literature investigations, the authors were convinced that there is a scarcity of studies investigating the influential factors that affect the effectiveness or demand-driven level of industrial parks (especially in Iran). Hence, this research has been conducted to propose a procedure equipped with quantitative and qualitative techniques that detect these important factors and their subordinate variables. By means of the developed procedure of this research, it is possible to locate future industrial parks, plan for establishment of future industrial areas and plan for development of current industrial parks.