Criteria Clustering and Supplier Segmentation Based on Sustainable Shared Value Using BWM and PROMETHEE

With the advent of healthy visions, two of the trends that have become extremely important in the supply chain in recent decades are corporate social responsibility (CSR) and sustainability, which have affected the activities of buyers and suppliers. The next trend that is emerging is the vision of creating shared value (CSV), which wants to move the supply chain toward solving social problems in a completely strategic way. This research intends to develop a step-by-step framework for evaluating and segmenting suppliers based on CSV criteria in the supply chain. In the first stage, the criteria for creating sustainable shared value (CSSV) are obtained through existing activities in the field of CSR. The obtained criteria are then divided into two categories, strategic and critical, and then the weight of each criterion is obtained using the best–worst method (BWM). In the next step, based on the Kraljic model, the suppliers are divided into four clusters using the preference ranking organization method for enrichment evaluation (PROMETHEE) technique. This framework helps the buyer to conclude and select purchasing decisions and relationships with suppliers through the lenses of CSV and sustainability.

An Integrated Approach to Evaluating Eco-Innovation Strategies from the Perspective of Strategic Green Transformation: A Case of the Lithuanian Furniture Industry

Evaluation and selection of eco-innovation strategies is a significant and complex strategic decision, and despite the relevance and interest in the field of eco-innovation, the area of eco-innovation strategies has not been explored in depth in the scientific literature. Therefore, in this study, we propose an integrated approach to evaluating eco-innovation strategies from the perspective of strategic green transformation that helps decision-makers evaluate and select eco-innovation strategy aiming to achieve a competitive advantage. For this study, we adopted a validated multi-criteria decision-making methodology (MCDM) by combining Analytical Hierarchy Process (AHP) and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The reliability of the proposed framework was tested and applied in the context of the Lithuanian furniture industry. This study offers three contributions and provides a comprehensive and profound insights into eco-innovation strategies. First, this study conceptualizes eco-innovation strategy from the perspective of strategic green transformation and proposed a novel definition and classification of eco-innovation strategies leading to competitive advantage. Second, this study proposes a novel approach to the evaluation of eco-innovation strategies taking into account micro-, meso-, and macro-level environmental factors. Third, the findings of this study provide implications for scholars and decision-makers in the field of eco-innovation strategy and set an agenda for future research.

The analysis of risk assessment for the transmission of COVID-19 by using PROMETHEE and ELECTRE methods

Multi-Criteria Decision Making (MCDM) methods help researchers in solving many prob-lems in terms of numerical analysis. However, MCDM methods have not been very popular in the health sector. In this study, five ones of Turkey's most intense and highly populated cities were selected and the risk of the spread of Covid-19 disease was evaluated on the basis of seven criteria. The PROMETHEE and the ELECTRE methods were conducted to rank the cities in terms of the spread of Covid-19. The PROMETHEE method correctly ranked the most risky city as Istanbul, but ELECTRE ranked Istanbul the second most risky. The results of the meth-ods are compared with real data. PROMETHEE gave more convenient results than ELECTRE. Also, this paper offers a new field of study to the literature.Cite this article as: Pekel ozmen E, Demir B. The analysis of risk assessment for the trans-mission of COVID-19 by using PROMETHEE and ELECTRE methods. Sigma J Eng Nat Sci 2023;41:2:232-242.

A GROUP DECISION-MAKING METHOD FOR RANKING PROBLEMS UNDER SUSTAINABILITY, RISKS, AND INTUITIONISTIC FUZZY ENVIRONMENT

This paper presents a group decision-making mechanism to properly manage ranking problems in an intuitionistic fuzzy environment. TOPSIS ranking multi-criteria decision-making (MCDM) methods is utilized under the intuitionistic fuzzy set theory. This solution technique examines the sets of criteria employed in decision-making problems, the preferences of a group of decision-makers, and the importance levels of decision-makers. Managers use the ranking methods as a reliable technique for making supplier evaluation decisions. Furthermore, the supply chain suffers from the shortage of materials, transportation problems, etc. In the post COVID-19 era, the need for a practical and exhaustive tool is explicit. An illustrative case on a supplier selection problem considering sustainability and risks in the post-COVID-19 era is used to demonstrate the applicability of the proposed technique by detailing the procedure step by step. A comparative analysis of the results is carried out. The results are compared with the results of the MARCOS method. The results show that the presented methodology is applicable to the other areas as well. (English) [ FROM AUTHOR] Bu makale, sezgisel bulanık bir ortamda sıralama problemlerini düzgün bir şekilde yönetmek için bir grup karar verme mekanizması sunmaktadır. Sezgisel bulanık küme teorisi kapsamında çok kriterli karar verme (ÇKKV) yöntemi olan TOPSIS kullanılmaktadır. Bu çözüm tekniğinde karar verme problemlerinde kullanılan birtakım kriterler, karar vericiler grubunun tercihleri ve karar vericilerin önem düzeyleri incelenmektedir. Yöneticiler, sıralama yöntemlerini tedarikçi değerlendirme kararlarını vermek için güvenilir bir teknik olarak kullanır. Ayrıca, COVID-19 döneminden sonra tedarik zinciri malzeme sıkıntısı, ulaşım sorunları vb. sıkıntılardan muzdariptir, pratik ve kapsamlı bir araca olan ihtiyaç açıktır. Prosedürü adım adım detaylandırarak önerilen tekniğin uygulanabilirliğini göstermek için, COVID-19 sonrası dönemde sürdürülebilirliği ve riskleri dikkate alan bir tedarikçi seçimi sorununa ilişkin örnek bir vaka kullanılmıştır. Sonuçların karşılaştırmalı analizi gerçekleştirilmiştir. Sonuçlar, MARCOS yönteminin sonuçları ile karşılaştırılmıştır. Sonuçlar, sunulan metodolojinin diğer alanlara da uygulanabilir olduğunu göstermektedir. (Turkish) [ FROM AUTHOR] Copyright of Pamukkale University Journal of Social Sciences Institute / Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi is the property of Pamukkale University, Social Sciences Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Novel pythagorean fuzzy entropy-distance measures using MCDM in the selection of face masks

Wearing a face mask can help reduce the spread of infection and contamination from airborne harmful germs. The requirement to wear a face mask is perhaps one of the most noticeable lifestyle changes brought on by the COVID-19 pandemic. COVID-19 transmission can be slowed down by wearing a mask, especially while in close contact with others. Choosing the best face mask is a cumbersome task from the available alternatives in India. Several multi-criteria decision-making (MCDM) techniques and approaches have been suggested to choose the optimally probable options. The purpose of this article is to deliver an entropy-distance measure for Pythagorean fuzzy sets. To validate these measures, some of the properties were also proved. A multi-criteria decision-making approach is used to rank and hence select the best face mask for wearing. The proposed research allows the ranking of face masks based on specified criteria in a Pythagorean fuzzy environment to aid in the selection process. The results suggest that the proposed model provides a realistic way to select the best mask in the pool of considered brands. A case study on the selection process and its experimental results using Pythagorean fuzzy sets are discussed. © 2023 River Publishers. All rights reserved.

Co-evolution of neural architectures and features for stock market forecasting: A multi-objective decision perspective

In a multi-objective setting, a portfolio manager's highly consequential decisions can benefit from assessing alternative forecasting models of stock index movement. The present investigation proposes a new approach to identify a set of non-dominated neural network models for further selection by the decision-maker. A new co-evolution approach is proposed to simultaneously select the features and topology of neural networks (collectively referred to as neural architecture), where the features are viewed from a topological perspective as input neurons. Further, the co-evolution is posed as a multi-criteria problem to evolve sparse and efficacious neural architectures. The well-known dominance and decomposition based multi-objective evolutionary algorithms are augmented with a non-geometric crossover operator to diversify and balance the search for neural architectures across conflicting criteria. Moreover, the co-evolution is augmented to accommodate the data-based implications of distinct market behaviors prior to and during the ongoing COVID-19 pandemic. A detailed comparative evaluation is carried out with the conventional sequential approach of feature selection followed by neural topology design, as well as a scalarized co-evolution approach. The results on three market indices (NASDAQ, NYSE, and S&P500) in pre- and peri-COVID time windows convincingly demonstrate that the proposed co-evolution approach can evolve a set of non-dominated neural forecasting models with better generalization capabilities.

Multi-Criteria Evaluation of eLearning Attributes using the Fuzzy TOPSIS Method

Various models of evaluating eLearning system success have been identified in the past and the need for effective evaluation of eLearning systems has been highlighted during the COVID-19 pandemic. The purpose of the present work is to elicit how both academic staff and students (the evaluators) view the performance of eLearning attributes when being taught using an eLearning system. The attributes are ranked using a multi-criteria evaluation algorithm called the Fuzzy Technique for Order Preference by Similarity to the Ideal Solution (Fuzzy TOPSIS). Here, using linguistic-response expert questionnaires, a set of eLearning system attributes and a set of eLearning system criteria are evaluated. The Fuzzy TOPSIS algorithm yields weightings for each of the attributes which can then be ranked to arrive at the optimal solution in terms of how well they contributed to the success of the current eLearning system. IT service quality is found to rank highest, followed by technical system quality, information quality and finally the consideration of different learning styles. Large agreement is seen between academic staff and student evaluators, with minor disagreement between students of two different disciplines. As regards practical implications, it is shown from the rankings that the eLearning system must be reorganized and consideration of different learning styles must be improved. The Fuzzy TOPSIS method has been found to be a reliable and economic evaluation approach of eLearning systems, since it does not require large numbers of evaluators and provides a ranking of attributes which translate directly into priorities for improvement.

An Integrated Neutrosophic SNA-MABAC Approach for Blockchain Applicability Evaluation in Sustainable Supply Chains

The outbreak of the novel coronavirus pneumonia and the turbulent international situation in recent years have seriously disrupted the normal operation of the entire supply chain (SC). As an emerging technology, blockchain is characterized by decentralization, reliability, transparency and traceability, which can be effectively applied to solve social, environmental and economic concerns and achieve sustainability of supply chain. However, whether blockchain is suitable for every function of a sustainable supply chain (SSC), or what function is best suited for the application of a set of blockchain criteria, can be viewed as a multi-criteria group decision-making (MCGDM) problem. This paper presents a combined MCGDM technique utilizing the social network analysis (SNA) and Multi-Attributive Border Approximation Area Comparison (MABAC), for selecting an appropriate function of SSCs to implement blockchain technology with Neutrosophic information. The framework gives quantitative consideration to the weight of relevant blockchain criteria and decision makers under high uncertainty. This study can also facilitate the effective allocation of resources and enhance the competitiveness of SSCs in the coordinated planning of various blockchain deployments. © 2022 IEEE.

Research on the Service Quality Index and Alternatives Evaluation and Ranking for Online Yue Kiln Celadon Art Education in Post COVID-19 Era

Online education has been still a common way for teaching and learning in the post epidemic era. However, the related research on service quality for the online Yue kiln celadon art education industry is still a vital research gap during this period. Thus, a hybrid method of FANP and GRA is proposed in this study to analyse and evaluate the key factors for providing and maintaining high service quality of online Yue kiln celadon art education industry in the post coronavirus era. In this research, whether in the model of FANP and GRA, factors such as safety mechanism of transaction and education, personnel quality, and the ability of customer need handling are essential conditions for providing excellent service quality in the post-COVID-19 era. The main contribution of this study is to propose an integrated method of FANP and GRA to calculate and rank potential solutions of online Yue kiln celadon art education service quality in the post-COVID-19 era under fuzzy environment and discrete conditions. Finally, the research findings of this study have a guiding role, thereby becoming a guide for the industries related to online Yue kiln celadon art education to maintain good service quality in similar scenarios in the future. © 2023 by the authors.

Fuzzy AHP Approach for Supply Chain Strategy Selection: A Post-Pandemic Scenario

Supply chains have been severely disrupted globally due to the COVID-19 pandemic. The paper examined the strategic responses of automobile firms for meeting supply chain challenges they face post-pandemic. Data were collected using a specifically designed structured questionnaire from supply chain experts working with leading automobile manufacturing firms in India. The fuzzy analytic hierarchy process (FAHP), as a part of a multi-criteria decision-making model using R programming, was applied to identify and rank the choice of supply strategies using various criteria, such as lead time, logistics cost (holding cost, carrying cost, warehousing cost, handling cost), and the need of products. Two-wheeler and four-wheeler manufacturing firms were selected for the study. Logistics cost was found to be a dominant criterion, followed by a demand for products and lead time, which helped select an appropriate supply chain strategy. Buffering was observed to be the best strategic choice, and automation and robotics applications were the least preferred ones both for two-wheelers and four-wheeler manufacturing companies. The findings would be helpful to both practitioners and researchers in evaluating diverse strategic choices, especially under the risk and disruptions faced by business firms in the supply chain. © 2023, Associated Management Consultants Pvt. Ltd.. All rights reserved.

Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach.

Environmental pollution has been a major concern for researchers and policymakers. A number of studies have been conducted to enquire the causes of environmental pollution which suggested numerous policies and techniques as remedial measures. One such major source of environmental pollution, as reported by previous studies, has been the garbage resulting from disposed hospital wastes. The recent outbreak of the COVID-19 pandemic has resulted into mass generation of medical waste which seems to have further deteriorated the issue of environmental pollution. This necessitates active attention from both the researchers and policymakers for effective management of medical waste to prevent the harm to environment and human health. The issue of medical waste management is more important for countries lacking sophisticated medical infrastructure. Accordingly, the purpose of this study is to propose a novel application for identification and classification of 10 hospitals in Iraq which generated more medical waste during the COVID-19 pandemic than others in order to address the issue more effectively. We used the Multi-Criteria Decision Making (MCDM) method to this end. We integrated MCDM with other techniques including the Analytic Hierarchy Process (AHP), linear Diophantine fuzzy set decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis to generate more robust results. We classified medical waste into five categories, i.e., general waste, sharp waste, pharmaceutical waste, infectious waste, and pathological waste. We consulted 313 experts to help in identifying the best and the worst medical waste management technique within the perspectives of circular economy using the neural network approach. The findings revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporized hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most effective methods to dispose of medical waste during the pandemic. Additionally, ozone was identified as the most suitable technique among all to serve the purpose of circular economy of medical waste. We conclude by discussing the practical implications to guide governments and policy makers to benefit from the circular economy of medical waste to turn pollutant hospitals into sustainable ones.

A Multi-Criteria Decision-Making Model with Interval-Valued Intuitionistic Fuzzy Sets for Evaluating Digital Technology Strategies in COVID-19 Pandemic Under Uncertainty.

Coronavirus diseases 2019 (COVID-19) pandemic is an essential challenge to the health and safety of people, medical members, and treatment systems worldwide. Digital technologies (DTs) have been universally introduced to improve the treatment of patients during the pandemic. Nevertheless, only a few governments have been partly successful in executing the DT strategies. In this regard, it is critical to demonstrate a suitable strategy for the governments. This problem is built based on the experts' opinions with some conflicting criteria to evaluate various types of alternatives. Hence, this research presents a new multi-criteria decision-making (MCDM) model under uncertain conditions. For this reason, interval-valued intuitionistic fuzzy sets (IVIFSs) are employed to help decision-makers (DMs) evaluate in a broader area and cope with uncertain information. Moreover, a new extended weighting method based on weighted distance-based approximation (WDBA) and a new combined ranking approach are proposed to determine the DMs' weights and rank the alternatives under IVIF conditions. The developed weighting method is constructed based on computing the DMs' weights with objective criteria weights. Furthermore, a new ranking approach is proposed by obtaining two ranking indexes separately: The first and second ranking indexes are calculated according to the positive and negative ideal solutions distances and the nature of criteria weights, respectively. Afterward, the final values of rankings are computed by considering a new aggregating procedure. The results of the proposed model represent the first alternative as the best strategy. Comparisons between the IVIF-TOPSIS and IVIF-VIKOR methods are also provided to investigate the proposed model to determine the rankings of main alternatives. Sensitivity analyses are conducted to check the reliability and the robustness of the model. For this purpose, criteria weights are analyzed to compute the dependencies' degree of the new extended weighting method. The dependencies of the ranking model are discussed on the criteria weights as well.

An integrated CRITIC-VIKOR model for overtourism assessment in Spain: post-COVID-19 sustainable actions

The tourism industry has developed rapidly in recent decades. This boom has led to overtourism in some tourist areas of Spain. The COVID-19, on the other hand, has paralysed the sector and must be reactivated without reproducing past errors. Overtourism remains open to subjective interpretations due to the paucity of precise measurements. This emanates from the fact that overtourism lacks a realistic set of indicators to quantitatively measure its negative impact. The main objective of this paper is to provide a set of indicators that allow reviewing and redirecting decision-making at the business and political levels. An integrated MCDM approach has been applied namely the CRITIC method for calculating the weights of the indicators, and the VIKOR method for assessing the alternatives. The case study of Spain provides useful insights, and the obtained results demonstrated that the economic indicators hinder sustainable development and contribute to worsening the tourist attitude. Copyright © 2022 Inderscience Enterprises Ltd.

Ranking of Service Quality Solution for Blended Design Teaching Using Fuzzy ANP and TOPSIS in the Post-COVID-19 Era

The blended educational method has become a common way of teaching and learning in the post-COVID-19 era. However, the related research on the selection model for the blended design teaching service quality solution is still an important research gap during this period. Therefore, this study proposed a hybrid method of fuzzy analytic network process (FANP) and technique for order preference by similarity to ideal solution (TOPSIS) to analyse the dimensions, indicators and alternatives of blended design teaching service quality. As for the findings of this research, the dimension of assurance is the most vital factor, followed by responsiveness, reliability and empathy. Meanwhile, this research discovered that the top three significant alternatives are "Employees are trustworthy”, "Safe transaction mechanism and environment” and "Personalised needs of customers”. Also, we found that dimensions utilised to evaluate the quality of education service are similar whether in the post COVID-19 era, in the COVID-19 epidemic or prior to the COVID-19 pandemic. The main contribution of this study is to establish a multi-criteria decision-making (MCDM) model for the ranking of the blended design teaching service quality index and solution under a fuzzy environment. Finally, the research findings of this study have a guiding role, thereby becoming a guide for the industries related to hybrid design education to maintain good service quality in similar scenarios in the future. © 2023 by the authors.

Improving supplier performance and strategic sourcing decisions by integrating jobshop scheduling, inventory management and agile new product development

Purpose: 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. Design/methodology/approach: 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. Findings: 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”. Research limitations/implications: 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. Practical implications: 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. Originality/value: 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. © 2023, Emerald Publishing Limited.

Comparative Analysis of Pitch Ratings in All Formats of Cricket

The article identifies measurable attributes to find the consistency in pitch ratings which are otherwise subjective decisions made by referees for international test match cricket. To do so, the article uses statistics related to test matches, one-days and T20s played among all test playing nations between March 2017 and March 2019 (53 tests, 142 T20s and 172 one-day matches: the next two seasons ending May 2020/2021 were hit by COVID-19 and hence excluded). To measure the consistency of pitch ratings (very good, good, above average, average, below average and poor), measurable attributes like runs/day, wickets/day, runs/over, runs/wicket and overs/wicket were identified. To rank pitch ratings using these attributes, the multi-criteria decision-making technique—PROMETHEE II was used. We found that the referee pitch ratings are largely consistent and the attributes developed can be utilized to further analyse future judgements regarding pitch ratings. Further, six-pitch ratings can be clustered into two distinct groups that are significantly different from each other. The article is among the first to analyse sports pitch ratings by using team performance-based statistics. This study paves the way for similar studies and development of newer statistical flow-based attributes. © 2023 XLRI Jamshedpur, School of Business Management & Human Resources.

Structural transformation of fuzzy analytical hierarchy process: a relevant case for Covid-19

Covid-19 has posed difficult and challenging situations to the supply chains and companies are in fix how to choose the vendors under the uncertainty and complexity in recent years. Therefore, this research aims to incorporate structural transformation of the fuzzy analytical hierarchy process (FAHP) that is most appropriate for the uncertainty and disruption caused by Covid-19 like situation for ensuring supplies from vendors. The conventional approaches for vendor selection and evaluation use numerous multi-criteria decision-making tools that may not ensure reliability in a dynamic situation caused due to Covid-19. In this research, Fleiss' Kappa method ensures the reliability of responses from eight respondents by using pairwise comparisons and assigning weights as envisaged in FAHP. In addition to determine the reliability of responses, a step under FAHP has been altered. This alteration is demonstrated in the vendor selection case in the Covid-19 scenario. The research suggests a plausible system required to address the uncertainties associated with Covid-19 to select and evaluate vendors by modifying a FAHP. The proposed altered mechanism can be incorporated in a similar type of other decision-making circumstances such as Covid-19, where the decision-makers are more than one, and the situation is very dynamic. The study is likely to facilitate information management, algorithmic development in decision making, or machine-driven decisions in uncertain conditions. The study offers managerial implications to purchase managers to accommodate and combine multiple factors and responses concerning the vendor performances for their evaluation, thus making a process more reliable.

The effects of the Covid-19 pandemic on the performance of academic journals: The case of Turkey

Since the beginning of 2020, "Covid-19” has affected the whole world in an unprecedented way in modern times. It is inevitable that this pandemic, which has negatively affected many fields, will also have an impact on academic journals. The aim of this study is to determine the effect of the Covid-19 pandemic on the performance of academic journals. In our study, a "Data Envelopment Analysis” methodology with 3 inputs and 3 outputs was used to determine the relative "performance of the journals”. Within the scope of the study, 109 journals published in "Turkey” and scanned in "Web of Science” indexes were examined. Results show that eleven journals were efficient in 2019, while in 2020 this number decreased to seven. Four fields have been positively affected by the pandemic and journals publishing in these fields have increased their efficiencies. Eighteen fields were adversely affected by the pandemic and the efficiency of journal publishing in these fields decreased. Eleven fields and journals publishing in these fields maintained their efficiency both before and during the pandemic. As the Covid-19 pandemic is not over yet, our data is limited. In the coming years, more detailed and comprehensive studies can be carried out with more extensive data and a further number of journals from different countries. © 2022 Pontificia Universidade Catolica de Campinas. All rights reserved.

Applications of XAI for Decision Making in the Manufacturing Domain

This chapter discusses an important topic in factory management, that of improving the understandability of AI applications for group multi-criteria decision making in manufacturing systems. Due to its long-term and cross-functional impact, decision making may be more critical to the competitiveness and sustainability of manufacturing systems than production planning and control. This chapter uses the example of choosing the right smart and automation technologies for factories during the COVID-19 pandemic. This topic is of particular importance as many factories are forced to close or operate on a smaller scale (using a smaller workforce), thus pursuing further automation. Artificial intelligence and Industry 4.0 technologies have many applications in this area, most of which can also be applied for other decision-making purposes in manufacturing systems. First, a systematic procedure was established to guide the group multi-criteria decision-making process. Applications of AI and XAI to identify targets are first reviewed. Subsequently, the application of AI and XAI to selection factors and development of criteria is presented. Artificial intelligence techniques are widely used to derive criteria priorities. Therefore, it is particularly important to explain XAI techniques and tools for such AI applications. Aggregating the judgments of multiple decision makers is the next focus, followed by the introduction of AI and XAI applications to evaluate the overall performance of each alternative. Taking fuzzy ranking preference based on similarity to ideal solution (FTOPSIS) as an example, the application of XAI techniques and tools in explaining comparison results using FTOPSIS is illustrated. Another AI technology used for the same purpose is fuzzy VIKOR. XAI techniques and tools for interpreting fuzzy VIKOR are also presented. Finally, several metrics are proposed to evaluate the effectiveness of XAI techniques or tools for decision making in the manufacturing domain. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Covid-Döneminde ve Öncesinde Firmaların Finansal Performanslarının CRITIC Temelli CoCoSo Yöntemi ile Analizi, BÍST Otomotiv Sektörü Üzerine Bir Uygulama

Covid-19 pandemisinin tüm dünyaya yayılması sonucunda tüm sektörler ekonomik olarak bu krizden etkilenmiştir. Bu krizden etkilen önemli sektörlerden birisi de otomotiv sektörü olmuştur. Küresel ekonominin %4'ünü oluşturan otomotiv sektörü bu süreçte birçok sorunla mücadele etmek durumunda kalmıştır. Bu bağlamda bu çalışma Covid-19 döneminde (2020-2021) ve öncesinde (2018-2019) BÍST'te faaliyet gösteren otomotiv firmalarının finansal performanslarını analiz etmeyi amaçlamıştır. Bu noktadan hareketle firmaların finansal performanslarını analiz etmede çok kriterli karar verme yöntemlerinden birisi olan CRITIC tabanlı CoCoSo yöntemi kullanılmıştır. CRITIC yöntemiyle yapılan analiz sonucunda en fazla önem ağırlığına sahip kriter, özsermaye devir hızı rasyosu olurken;en az önem ağırlığına sahip kriter ise ekonomik rantabilite rasyosu olmuştur. CoCoSo yöntemiyle yapılan analiz sonucunda ise finansal performans sıralamasında FMÍZP ve FROTO firmaları ilk iki sırayı alırken KATMR ve BALAT firmaları ise son sıralarda yer almışlardır. Pandeminin etkisiyle elde edilen analiz sonuçları birlikte incelendiğinde pandemi öncesi dönemle pandemi döneminde finansal performans sıralamasında genel olarak bir değişiklik olmadığı, pandeminin firmaların finansal performans sıralamalarını değiştirmediği görülmektedir.Alternate : As a result of the spread of the Covid-19 pandemic worldwide, all sectors have been economically affected by this crisis. One of these sectors is automotive sector. The automotive sector, which constitutes 4% of the global economy, had to struggle with many problems in this process. In this context, this study aimed to analyse the financial performances of automotive companies operating in the BIST during the Covid-19 period (2020-2021) and before (2018-2019). From this point of view, the CRITIC-based COCOSO method, one of the multi-criteria decision-making methods, was used to analyse the financial performance of companies. As a result of the analysis conducted by the CRITIC method, the most important criterion was the equity turnover ratio. The criterion with the least important was the economic profitability ratio. As a result of the analysis made with the CoCoSo method, FMÍZP and FROTO companies took the first two places in the financial performance ranking, while KATMR and BALAT companies took the last place. When the analysis results obtained with the effect of the pandemic are examined together, it is seen that there was no general change in the financial performance rankings between the pre-pandemic period and the pandemic period, and the pandemic did not change the financial performance rankings of the companies.