Evaluating the performance of Colombian banks by hybrid multicriteria decision making methods

The aim of the study in this paper is to show how the performance of banks can be evaluated by ranking them based on Balanced Scorecard (BSC) and Multicriteria Decision Making (MCDM) methods. Nowadays, assessing the performance of companies is a vital work for finding their weaknesses and strengths. The banking sector is an important area in the service sector. Many people want to know which bank performs best when entrusting their money to them. For assessing the performance of banks, BSC can be used. This method helps to translate strategic issues to meaningful insights for the respective financial institutions. After that, the banks will be ranked based on performance indicators by the Weighted Aggregated Sum Product Assessment (WASPAS) method. Because this method is based on a decision matrix, weights are required. To find such weights, the Step-wise Weight Assessment Ratio Analysis (SWARA) method is applied. The results show that the International Bank of Colombia has a much better performance than other Colombian banks. Besides, further insights regarding the evaluation process based on BSC, SWARA, and WASPAS are obtained

Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises

APPLICATION OF HYBRID DIBR-FUCOM-LMAW-BONFERRONI-GREY-EDAS MODEL IN MULTICRITERIA DECISION-MAKING

The selection of unmanned aerial vehicles for different purposes is a frequent topic of research. This paper presents a hybrid model of an unmanned aerial vehicle (UAV) selection using the Defining Interrelationships Between Ranked criteria (DIBR), Full Consistency Method (FUCOM), Logarithm Methodology of Additive Weights (LMAW) and grey - Evaluation based on Distance from Average Solution (G-EDAS) methods. The above-mentioned model is tested and confirmed in a case study. First of all, in the paper are defined the criteria conditioning the selection, and then with the help of experts and by applying the DIBR, FUCOM and LMAW methods, the weight coefficients of the criteria are determined. The final values of the weight coefficients are obtained by aggregating the values of the criteria weights from all the three methods using the Bonferroni aggregator. Ranking and selection of the optimal UAV from twenty-three defined alternatives is carried out using the G-EDAS method. Sensitivity analysis confirmed a high degree of consistency of the solutions obtained using other MCDM methods, as well as changing the criteria weight coefficients. The proposed model has proved to be stable; its application is also possible in other areas and it is a reliable tool for decision-makers during the selection process

Un enfoque de toma de decisiones multicriterio aplicado a la estrategia de transformación digital de las organizaciones por medio de la inteligencia artificial responsable en la nube de las organizaciones. Estudio de caso en el sector de salud

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Estudios Estadísticos, leída el 08-02-2023Organisations are committed to understanding both the needs of their customers and the capabilities and plans of their competitors and partners, through the processes of acquiring and evaluating market information in a systematic and anticipatory manner. On the other hand, most organisations in the last few years have defined that one of their main strategic objectives for the next few years is to become a truly data-driven organisation in the current Big Data and Artificial Intelligence (AI) context (Moreno et al., 2019). They are willing to invest heavily in Data and AI Strategy and build enterprise data and AI platforms that will enable this Market-Oriented vision (Moreno et al., 2019). In this thesis, it is presented a Multicriteria Decision Making (MCDM) model (Saaty, 1988), an AI Digital Cloud Transformation Strategy and a cloud conceptual architecture to help AI leaders and organisations with their Responsible AI journey, capable of helping global organisations to move from the use of data from descriptive to prescriptive and leveraging existing cloud services to deliver true Market-Oriented in a much shorter time (compared with traditional approaches)...Las organizaciones se comprometen a comprender tanto las necesidades de sus clientes como las capacidades y planes de sus competidores y socios, a través de procesos de adquisición y evaluación de información de mercado de manera sistemática y anticipatoria. Por otro lado, la mayoría de las organizaciones en los últimos años han definido que uno de sus principales objetivos estratégicos para los próximos años es convertirse en una organización verdaderamente orientada a los datos (data-driven) en el contexto actual de Big Data e Inteligencia Artificial (IA) (Moreno et al. al., 2019). Están dispuestos a invertir fuertemente en datos y estrategia de inteligencia artificial y construir plataformas de datos empresariales e inteligencia artificial que permitan esta visión orientada al mercado (Moreno et al., 2019). En esta tesis, se presenta un modelo de toma de decisiones multicriterio (MCDM) (Saaty, 1988), una estrategia de transformación digital de IA de la nube y una arquitectura conceptual de nube para ayudar a los líderes y organizaciones de IA en su viaje de IA responsable, capaz de ayudar a las organizaciones globales a pasar del uso de datos descriptivos a prescriptivos y aprovechar los servicios en la nube existentes para ofrecer una verdadera orientación al mercado en un tiempo mucho más corto (en comparación con los enfoques tradicionales)...Fac. de Estudios EstadísticosTRUEunpu

A study on the machinability of some metal alloys using grey TOPSIS method

The machinability of a material can be defined as the ease with which it can be machined. Materials with good machinability property require less power to cut, can be cut quickly, and easily obtain a good finish without wearing the tooling much. Therefore, to manufacture components economically, production engineers are challenged to discover ways to determine machinability of materials which mainly depends on their mechanical properties, as well as on other cutting conditions. In this paper, the machinability characteristics of alloys of three materials, i.e. aluminium, copper and steel are studied applying grey TOPSIS (technique for order preference by similarity to ideal solution) method. For each case, eight different alloys are considered whose machinability is evaluated based on different mechanical properties which are expressed in grey numbers. Using the adopted methodology, it now becomes easier for the manufacturers to select a particular alloy that can be easily machined. It is observed that A357RC, CuCr1Zr and AISI 5140 are the best machinable aluminium, copper and steel alloys respectively. It is also found that the ranking performance of grey TOPSIS method remains unaffected with the variation in greyness of the considered mechanical property values

Serial-integrated multi-criteria decision-making technique for resilient supplier selection in the manufacturing industry

A supply chain is an entire system of producing and delivering a product or service, from the very beginning stage of sourcing raw material to the final stage of delivering a product or service to end-users. Several global risks and disruptions brought massive and devastating impacts on the world economy including the Small and Medium Enterprises (SMEs). Since the supplier is one of the important elements in a supply chain, economic resilience can be implemented by selecting a resilient supplier. However, the literature shows that previous supplier selections only focused on traditional, green and sustainable suppliers’ criteria but resilience was rare to be discussed. Thus, the first objective of the study is to identify the generic criteria for selecting resilient suppliers. At the same time, there are problems in dealing with uncertainties and incomplete information while selecting suppliers. The second objective is to develop a new integrated Multi-Criteria Decision-Making (MCDM) model that considers incomplete data and uncertainties in selecting resilient suppliers. In this study, the proposed criteria were quality, lead time, cost, flexibility, visibility, responsiveness and financial stability. A serial-integrated MCDM technique was proposed by combining Grey Relational Analysis (GRA) from the grey theory and the Best Worst Method-Technique for Order Preference by Similarity to an Ideal Solution (BMW-TOPSIS) technique in serial to assess the suppliers and select the best alternative. The proposed criteria and technique were applied in the metal manufacturing company (Case 1) and the food manufacturing company (Case 2) which were facing economic problems to demonstrate its effectiveness. The result was generated using MATLAB. The result for Case 1 shows that Financial Stability has the largest weight and Supplier 1 is the best supplier for the company. For Case 2, Cost shows the largest weight, and the best supplier is Supplier 4. Then, the result was verified through manual calculation and validated with Analytic Hierarchy Process-VlseKriterijumska Optimizacija I Kompromisno Resenje (AHP-VIKOR). Through the identification of the generic resilience criteria and the suitable MCDM model, the managers can focus on resilience with the consideration of uncertainties and incomplete information to improve the supplier selection process. This can help to raise the supply chain performance of the companies

Supply Chain Performance Appraisement and Benchmarking for Manufacturing Industries: Emphasis on Traditional, Green, Flexible and Resilient Supply Chain along with Supplier Selection

Supply chain represents a network of interconnected activities starting from raw material extraction to delivery of the finished product to the end-user. The main constituents of supply chain are supplying/purchasing, inbound logistics, manufacturing, outbound logistics, marketing and sales. In recent times, the traditional supply chain construct is being modified to embrace various challenges of present business needs. Today’s global market has become highly volatile; customers’ expectations are ever-changing. Fierce competition amongst business sectors necessitates adapting modern supply chain management philosophies. Agility, greenness, flexibility as well as resilience have become the key success factors in satisfying global business needs. In order to remain competitive in the turbulent marketplace, industries should focus on improving overall performance of the supply chain network. In this dissertation, supply chain performance assessment has been considered as a decision making problem involving various measures and metrics (performance indicators). Since most of the performance indices are subjective in nature; decisionmaking relies on active participation of a group of decision-makers (DMs). Subjective human judgment often bears some sort of ambiguity as well as vagueness in the decision making; to overcome uncertainty in decision making, adaptation of grey/fuzzy set theory seems to be fruitful. To this end, present work deals with a variety of decision support tools to facilitate supply chain performance appraisement as well as benchmarking in fuzzy/grey context. Starting from the traditional supply chain, this work extends appraisement and benchmarking of green supply chain performance for a set of candidate case companies (under the same industry) operating under similar supply chain construct. Exploration of grey-MOORA, fuzzy-MOORA, IVFN-TOPSIS, fuzzy-grey relation method has been illustrated in this part of work. Apart from aforementioned empirical studies, two real case studies have been reported in order to estimate a quantitative performance metric reflecting the extent of supply chain flexibility and resilience, respectively, in relation to the case company under consideration. Performance benchmarking helps in identifying best practices in perspectives of supply chain networking; it can easily be transmitted to other industries. Organizations can follow their peers in order to improve overall performance of the supply chain. vi Supplier selection is considered as an important aspect in supply chain management. Effective supplier selection must be a key strategic consideration towards improving supply chain performance. However, the task of supplier selection seems difficult due to subjectivity of supplier performance indices. Apart from considering traditional supplier selection criteria (cost, quality and service); global business scenario encourages emphasizing various issues like environmental performance (green concerns), resiliency etc. into evaluation and selection of an appropriate supplier. In this context, the present work also attempts to explore fuzzy based decision support systems towards evaluation and selection of potential suppliers in green supply chain as well as resilient supply chain, respectively. Fuzzy based Multi-Level Multi-Criteria Decision Making (MLMCDM) approach, fuzzy-TOPSIS and fuzzy-VIKOR have been utilized to facilitate the said decision making

Supply Chain Performance Appraisement and Benchmarking for Manufacturing Industries: Emphasis on Traditional, Green, Flexible and Resilient Supply Chain along with Supplier Selection

Supply chain represents a network of interconnected activities starting from raw material extraction to delivery of the finished product to the end-user. The main constituents of supply chain are supplying/purchasing, inbound logistics, manufacturing, outbound logistics, marketing and sales. In recent times, the traditional supply chain construct is being modified to embrace various challenges of present business needs. Today’s global market has become highly volatile; customers’ expectations are ever-changing. Fierce competition amongst business sectors necessitates adapting modern supply chain management philosophies. Agility, greenness, flexibility as well as resilience have become the key success factors in satisfying global business needs. In order to remain competitive in the turbulent marketplace, industries should focus on improving overall performance of the supply chain network. In this dissertation, supply chain performance assessment has been considered as a decision making problem involving various measures and metrics (performance indicators). Since most of the performance indices are subjective in nature; decisionmaking relies on active participation of a group of decision-makers (DMs). Subjective human judgment often bears some sort of ambiguity as well as vagueness in the decision making; to overcome uncertainty in decision making, adaptation of grey/fuzzy set theory seems to be fruitful. To this end, present work deals with a variety of decision support tools to facilitate supply chain performance appraisement as well as benchmarking in fuzzy/grey context. Starting from the traditional supply chain, this work extends appraisement and benchmarking of green supply chain performance for a set of candidate case companies (under the same industry) operating under similar supply chain construct. Exploration of grey-MOORA, fuzzy-MOORA, IVFN-TOPSIS, fuzzy-grey relation method has been illustrated in this part of work. Apart from aforementioned empirical studies, two real case studies have been reported in order to estimate a quantitative performance metric reflecting the extent of supply chain flexibility and resilience, respectively, in relation to the case company under consideration. Performance benchmarking helps in identifying best practices in perspectives of supply chain networking; it can easily be transmitted to other industries. Organizations can follow their peers in order to improve overall performance of the supply chain. vi Supplier selection is considered as an important aspect in supply chain management. Effective supplier selection must be a key strategic consideration towards improving supply chain performance. However, the task of supplier selection seems difficult due to subjectivity of supplier performance indices. Apart from considering traditional supplier selection criteria (cost, quality and service); global business scenario encourages emphasizing various issues like environmental performance (green concerns), resiliency etc. into evaluation and selection of an appropriate supplier. In this context, the present work also attempts to explore fuzzy based decision support systems towards evaluation and selection of potential suppliers in green supply chain as well as resilient supply chain, respectively. Fuzzy based Multi-Level Multi-Criteria Decision Making (MLMCDM) approach, fuzzy-TOPSIS and fuzzy-VIKOR have been utilized to facilitate the said decision making

Maritime accident prevention strategy formulation from a human factor perspective using Bayesian Networks and TOPSIS

Human factors contribute to majority of maritime accidents. This study proposes an advanced methodology for maritime accident prevention strategy formulation from a human factor perspective. It is conducted by incorporating Bayesian network (BN) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in a multi-criteria decision-making system. In order to develop rational accident prevention strategies, this work integrates Multiple Correspondence Analysis (MCA), Hierarchical Clustering (HC) and Classification Tree (CT) to generate strategies and describes accident types as criteria for a new multi-criteria risk-based decision-making system. Specifically, MCA is performed to detect patterns of contributory factors explaining maritime accident types. It is complemented by HC and a CT, aiming at creating different classes of vessels. Next, a Bayesian-based TOPSIS model is built to illustrate the features of multiple criteria and the relations among alternatives (i.e. strategies), so as to select the best-fit strategies for accident prevention. The results show that the information, clear order, and safety culture are the three most effective recommendations for maritime accident prevention considering human errors, which presents new insights for accident prevention practice for maritime authorities. © 2020 Elsevier Lt

Development of decision support systems towards supply chain performance appraisement

Purpose: The aim of this research is to develop various Decision Support Systems (DSS) towards supply chain (SC) performance appraisement as well as benchmarking. The purpose of this work is to understand multi-level (measures and metrics) performance appraisement index system to evaluate overall supply chain performance extent, monitor ongoing performance level and to identify ill-performing areas of the supply chain network. Design/methodology/approach: Fuzzy logic as well as grey theory has been explored in developing a variety of SC performance appraisement modules (evaluation index systems). Generalized fuzzy numbers, generalized intervalvalued fuzzy numbers theory have been utilized in order to tackle decision-makers’ linguistic evaluation information towards meaningful and logical interpretation of procedural hierarchy embedded to the said appraisement modules. Fuzzy-grey relation theory, MULTIMOORA method coupled with fuzzy logic as well as grey theory have also been adapted to facilitate overall SC performance assessment, performance benchmarking and related decision making. Findings: Supply chain performance index has been computed in terms of fuzzy as well as grey context, suggesting the present performance status of the said organizational supply chain. Ill-performing areas of the SC have been identified too. Fuzzy as well as grey based MULTIMOORA (MOORA: Multi-Objective Optimization by Ratio Analysis), fuzzy-grey relation analysis, thus adapted, appeared helpful in evaluating performance ranking order (and selecting the best) of various candidate alternatives (industries/enterprises) operating under similar supply chain architecture according to the ongoing SC performance. Empirical illustrations exhibited the fruitful application potential of the developed decision support tools. Practical implications: The decision support tools thus proposed may be proved fruitful for companies that are trying to identify key business performance measures for their supply chains. Ill-performing areas can easily be identified; companies can seek for possible means in order to improve those SC aspects so as to improve/enhance overall SC performance extent. Benchmarking may help in identifying best practices in relation to the SC which is performing as ideal (benchmarked practices). Best practices of the ideal organization need to be transmitted to the others. Companies can follow their peers in order to improve overall performance level of the entire supply chain. In view of this, the work reported in this dissertation may be proved as a good contributor for effective management of organizational SC. Research limitations: The methodology and presentation is conceptual, yet the tool can provide very useful interpretations for both researchers as well as management practitioners. Accessibility and availability of data are the main limitations affecting which model will be applied. Procedural steps towards implementing the said decision support tools have been demonstrated through empirical research. The decision support tools tools have neither been validated by practical case study nor have these been tested for assessing their reliability. Originality/value: This work articulates various approaches for supply chain performance evaluation considering multiple evaluation criteria (subjective evaluation indices), with a flexibility to modify and analyze using the available data sets collected from a group of experts (decision-makers). The approaches of performance evaluation index system are attempted due to structure and fuzzy (as well as grey) sets. The work is aimed at operational researchers, engineers and special managers