Intelligent Multi-Attribute Decision Making Applications: Decision Support Systems for Performance Measurement, Evaluation and Benchmarking

Efficiency has been and continues to be an important attribute of competitive business environments where limited resources exist. Owing to growing complexity of organizations and more broadly, to global economic growth, efficiency considerations are expected to remain a top priority for organizations. Continuous performance evaluations play a significant role in sustaining efficient and effective business processes. Consequently, the literature offers a wide range of performance evaluation methodologies to assess the operational efficiency of various industries. Majority of these models focus solely on quantitative criteria omitting qualitative data. However, a thorough performance measurement and benchmarking require consideration of all available information since accurately describing and defining complex systems require utilization of both data types. Most evaluation models also function under the unrealistic assumption of evaluation criteria being dependent on one another. Furthermore, majority of these methodologies tend to utilize discrete and contemporary information eliminating historical performance data from the model environment. These shortcomings hinder the reliability of evaluation outcomes leading to inadequate performance evaluations for many businesses. This problem gains more significance for business where performance evaluations are tied in to important decisions relating to business expansion, investment, promotion and compensation. The primary purpose of this research is to present a thorough, equitable and accurate evaluation framework for operations management while filling the existing gaps in the literature. Service industry offers a more suitable platform for this study since the industry tend to accommodate both qualitative and quantitative performance evaluation factors relatively with more ease compared to manufacturing due to the intensity of customer (consumer) interaction. Accordingly, a U.S. based food franchise company is utilized for data acquisition and as a case study to demonstrate the applications of the proposed models. Compatible with their multiple criteria nature, performance measurement, evaluation and benchmarking systems require heavy utilization of Multi-Attribute Decision Making (MADM) approaches which constitute the core of this research. In order to be able to accommodate the vagueness in decision making, fuzzy values are also utilized in all proposed models. In the first phase of the study, the main and sub-criteria in the evaluation are considered independently in a hierarchical order and contemporary data is utilized in a holistic approach combining three different multi-criteria decision making methods. The cross-efficiency approach is also introduced in this phase. Building on this approach, the second phase considered the influence of the main and sub-criteria over one another. That is, in the proposed models, the main and sub-criteria form a network with dependencies rather than having a hierarchical relationship. The decision making model is built to extract the influential weights for the evaluation criteria. Furthermore, Group Decision Making (GDM) is introduced to integrate different perspectives and preferences of multiple decision makers who are responsible for different functions in the organization with varying levels of impact on decisions. Finally, an artificial intelligence method is applied to utilize the historical data and to obtain the final performance ranking. Owing to large volumes of data emanating from digital sources, current literature offers a variety of artificial intelligence and machine learning methods for big data analytics applications. Comparing the results generated by the ANNs, three additional well-established methods, viz., Adaptive Neuro Fuzzy Inference System (ANFIS), Least Squares Support Vector Machine (LSSVM) and Extreme Learning Machine (ELM), are also employed for the same problem. In order to test the prediction capability of these methods, the most influencing criteria are obtained from the data set via Pearson Correlation Analysis and grey relational analysis. Subsequently, the corresponding parameters in each method are optimized via Particle Swarm Optimization to improve the prediction accuracy. The accuracy of artificial intelligence and machine learning methods are heavily reliant on large volumes of data. Despite the fact that several businesses, especially business that utilize social media data or on-line real-time operational data, there are organizations which lack adequate amount of data required for their performance evaluations simply due to the nature of their business. Grey Modeling (GM) technique addresses this issue and provides higher forecasting accuracy in presence of uncertain and limited data. With this motivation, a traditional multi-variate grey model is applied to predict the performance scores. Improved grey models are also applied to compare the results. Finally, the integration of the fractional order accumulation along with the background value coefficient optimization are proposed to improve accuracy

DEMATEL Technique: A Systematic Review of the State-of-the-Art Literature on Methodologies and Applications

Decision making trial and evaluation laboratory (DEMATEL) is considered as an effective method for the identification of cause-effect chain components of a complex system. It deals with evaluating interdependent relationships among factors and finding the critical ones through a visual structural model. Over the recent decade, a large number of studies have been done on the application of DEMATEL and many different variants have been put forward in the literature. The objective of this study is to review systematically the methodologies and applications of the DEMATEL technique. We reviewed a total of 346 papers published from 2006 to 2016 in the international journals. According to the approaches used, these publications are grouped into five categories: classical DEMATEL, fuzzy DEMATEL, grey DEMATEL, analytical network process- (ANP-) DEMATEL, and other DEMATEL. All papers with respect to each category are summarized and analyzed, pointing out their implementing procedures, real applications, and crucial findings. This systematic and comprehensive review holds valuable insights for researchers and practitioners into using the DEMATEL in terms of indicating current research trends and potential directions for further research.Peer Reviewe

Evaluating drivers and barriers for reverse logistics implementation under a multiple stakeholders' perpective analysis using grey-DEMATEL approach

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Produção, Florianópolis, 2015.Abstract : In the past few decades, the amount of waste produced by companies and businesses has grown dramatically while, at the same time, industrial production and consumer demand has also grown. Specifically, more raw materials are being used and available landfills are filling up. In order to solve this increasing problem, recently, an interest in product recovery, reverse logistics, and closed-loop supply chains has attracted not only the attention of companies and professionals but also has become a subject of interest for researchers. Additionally, both the emergence of stricter environmental regulations and a greater environmental consciousness of customers have pushed industries to think about environmental management by means of reverse logistics (RL) and by the implementation of End-Of-Life (EOL) products. However, while RL is becoming a mandatory component of the Supply Chain (SC) in developed countries particularly due to legislation issues, RL is still in a state of infancy in emerging economies such as Brazil. Furthermore, RL might be considered as the most difficult initiative of Green Supply Chain Management (GSCM) to implement, when compared to green purchasing or eco-design. In these connections, influential factors, such as drivers and barriers, have to be considered and analyzed previously, as well as the many different perspectives from the key stakeholders for RL development. To tackle this problem, the primary objective of this research is to evaluate the interrelationship among RL drivers and barriers under the perspectives of the most important RL stakeholders in the Brazilian context. To accomplish that, at first, a research design is proposed, and each adopted step is presented in the course of this study. Thereafter, this work begins with a general description of RL and its practices, some insights on RL in developing countries, and a detailed picture of RL in the Brazilian context through a systematic literature review process. Next, two exploratory case-based studies performed in Brazil are presented, including a manufacturing company and a third party reverse logistics service provider (3PRL) with the objective of gathering practical knowledge on RL in Brazil. As a result of the two case studies, this manuscript shifts focus to provide a thorough literature review on RL drivers, barriers, and stakeholders. For that, the theoretical lenses of Stakeholder and Resource-Based View theories are used, and a multi-perspective framework for RL drivers and barriers is created, including organizational, customers?, societal, and governmental perspectives. The following step is the evaluation of these influential factors (drivers and barriers) from the multi-perspective framework with experts by means of a Multi-Criteria Decision Making (MCDM) tool named grey-based DEMATEL (Decision Making Trial and Evaluation Laboratory). Four respondents - one expert from each stakeholder ? have been consulted to obtain the pair-wise comparison of RL drivers and barriers. Thus, both the net effect and the importance level of each factor is provided, as well as the similarities and differences among stakeholders? opinions. The contributions of research are significant. Most of the key barriers from the RL multiple perspective framework are from the organizational point of view. That is, external pressures may harm RL implementation, but firms may first focus on overcoming the internal barriers, such as the low importance of RL relative to other factors and company policies against RL. The most prominent drivers are from inside the organization, namely: Eco-design and Design for ?X? techniques, Long-term sustainability, Economic viability of RL, and Reduction on raw material consumption and waste disposal cost. From a practical and managerial angle, this research is relevant because a critical analysis of RL influential factors, as well as knowing the actors causing them or being affected by them, can be a valuable source of information to decision makers. Knowing the influential forces in their RL environment may help industry managers to better implement and to manage reverse flows and to bridge the gap between existing and future green solutions for reverse logistics.Nas últimas décadas, a quantidade de resíduos aumentou drasticamente enquanto a produção industrial e a demanda dos consumidores cresceu. Isto é, mais matérias-primas são usadas e a capacidade dos aterros sanitários está se extinguindo. A fim de resolver este problema crescente, recentemente, o interesse em recuperação de produtos, logística reversa (LR) e cadeia de suprimentos de circuito fechado tem atraído a atenção não somente das empresas, mas também de pesquisadores. Adicionalmente, o surgimento de leis ambientais mais rigorosas e a consciência ambiental dos clientes impulsionaram as empresas a pensar em gestão ambiental por meio da implementação da LR de produtos em fim de vida útil. Entretanto, enquanto a LR está se tornando um componente obrigatório nas cadeias de suprimento dos países desenvolvidos especialmente por causa de questões legislativas, a LR ainda está imatura nas economias emergentes, como o Brasil. Mais ainda, a LR pode ser considerada como a iniciativa mais difícil de implementar da Gestão de Cadeia de Suprimentos Verde, quando comparada com compras verde e eco-design. Nesse sentido, fatores de influência, como direcionadores e barreiras, devem ser considerados e analisados previamente, assim como as várias perspectivas dos stakeholders chave para o desenvolvimento da LR. Para lidar com esse problema, o principal objetivo desta pesquisa é avaliar as interrelações entre os direcionadores e barreiras da LR sob as perspectivas dos stakeholders mais importantes no contexto Brasileiro. Para tal, primeiramente, um plano de pesquisa é proposto, apresentando cada passo adotado no decorrer deste estudo. Posteriormente, este trabalho começa por uma descrição geral da LR e suas práticas, algumas percepções de LR em países em desenvolvimento, e um retrato detalhado da LR no contexto brasileiro por meio de um processo sistemático de revisão de literatura. Em seguida, dois estudos de caso diferentes realizados no Brasil são apresentados ? uma empresa de manufatura e um operador de logística reversa ? a fim de obter conhecimento prático em LR no Brasil. Na sequência, este manuscrito transfere seu foco para uma detalhada revisão de literatura em direcionadores, barreiras e stakeholders da LR. Para isso, é feito o uso de duas teorias ? Stakeholder e resource-based view theories ? que servem de lentes teóricas para o trabalho, criando-se uma estrutura de múltiplas perspectivas para direcionadores e barreiras da LR. O passo seguinte é a avaliação destes fatores de influência da estrutura de múltiplas perspectivas com experts por meio de uma ferramenta multicritério de apoio à decisão chamada grey-based DEMATEL (Decision Making Trial and Evaluation Laboratory). Um expert de cada stakeholder foi consultado para se obter as comparações par-a-par dos direcionadores e barreiras da LR. Portanto, o efeito de rede e o nível de importância de cada fator é fornecido, assim como as similaridades e diferenças das opiniões dos stakeholders. Com relação às contribuições deste trabalho, a maior parte das barreiras chave da estrutura de múltiplas perspectivas da LR vem da organização. Isto é, pressões externas podem prejudicar a implementação da LR, mas as empresas podem primeiramente focar em superar as barreiras internas, como a baixa importância dada a LR em relação a outras atividades e as políticas da empresa que vão contra à LR. Os direcionadores mais proeminentes vem da organização em si, sendo eles: Eco-design e projeto para técnicas de recuperação (remanufatura, reciclagem, etc.), Sustentabilidade a longo prazo, Viabilidade econômica da LR e Redução do consumo de matérias-primas e custos de despejo de resíduos. De um ângulo prático e gerencial, esta pesquisa mostra-se relevante, uma vez que uma análise crítica dos fatores de influência da LR ? assim como conhecer os atores que os causam ou são afetados por eles ? pode ser uma fonte de informação valiosa para tomadores de decisão. O conhecimento sobre os fatores de influência no ambiente da LR pode auxiliar as indústrias a melhor implementar e gerenciar fluxos reversos e a cobrir a lacuna entre as soluções ambientais existentes e futuras para a LR

Assessment of Socio-Economic Sustainability and Resilience after COVID-19

The pandemic period has caused severe socio-economic damage, but it is accompanied by environmental deterioration that can also affect economic opportunities and social equity. In the face of this double risk, future generations are ready to be resilient and make their contribution not only on the consumption side, but also through their inclusion in all companies by bringing green and circular principles with them. Policy makers can also favor this choice

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

A knowledge base system for overall supply chain performance evaluation : a multi-criteria decision-making approach

Due to the advancement of technology that allows organizations to collect, store, organize and use data information system for efficient decision making (DM), a new horizon of supply chain performance evaluation starts. Today, DM is shifting from “information-driven” to “data-driven” for more precision in overall supply chain performance evaluation. Based on the real-time information, fast decisions are important in order to deliver product more rapidly. Performance evaluation is critical to the success of the supply chain (SC). In managing SC, there are many decisions to be taken at each level of multi-criteria decision making (MCDM) (short-term or long-term) because of many decisions and decision criteria (attributes) that have an impact on overall supply chain performance. Therefore it is essential for decision makers to know the relationship between decisions and decision criteria on overall SC performance. However, existing supply chain performance models (SCPM) are not adequate in establishing a link between decisions and decisions criteria on overall SC performance. Most of the decisions and decision attributes in SC are conflicting in nature and performance measure of different criteria (attributes) at different levels of decisions (long-term and short-term) is different and makes it more intricate for SC performance evaluation. SC performance heavily depends on how well you design your SC. In other words, it is quite difficult to improve overall SC performance if decisions criteria (attributes) are not embedded or considered at the phase of SC design. The connection between the SC design and supply chain management (SCM) is essential for effective SC. Many companies such as Wal-Mart, Dell, etc. are successful companies and they achieve their success because of their effective SC design and management of SC activities. The purpose of this thesis is in two folds: First is to develop an integrated knowledge base system (KBS) based on Fuzzy-AHP that establish a relationship between decisions and decisions criteria (attributes) and evaluate overall SC performance. The proposed KBS assists organizations and decision-makers in evaluating their overall SC performance and helps in identifying under-performed SC function and its associated criteria. In the end, the proposed system has been implemented in a case company, and we developed a SC performance monitoring dashboard of a case company for top managers and operational managers. Second to develop decisions models that will help us in calibrating decisions and improving overall SC performance

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

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

Sustainable Industrial Engineering along Product-Service Life Cycle/Supply Chain

Sustainable industrial engineering addresses the sustainability issue from economic, environmental, and social points of view. Its application fields are the whole value chain and lifecycle of products/services, from the development to the end-of-life stages. This book aims to address many of the challenges faced by industrial organizations and supply chains to become more sustainable through reinventing their processes and practices, by continuously incorporating sustainability guidelines and practices in their decisions, such as circular economy, collaboration with suppliers and customers, using information technologies and systems, tracking their products’ life-cycle, using optimization methods to reduce resource use, and to apply new management paradigms to help mitigate many of the wastes that exist across organizations and supply chains. This book will be of interest to the fast-growing body of academics studying and researching sustainability, as well as to industry managers involved in sustainability management

Artificial intelligence and multiple criteria decision making approach for a cost-effective RFID-enabled tracking management system

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe implementation of RFID technology has been subject to ever-increasing popularity in relation to the traceability of items as one of the most advance technologies. Implementing such a technology leads to an increase in the visibility management of products. Notwithstanding this, RFID communication performance is potentially greatly affected by interference between the RFID devices. It is also subject to auxiliary costs in investment that should be considered. Hence, seeking a cost-effective design with a desired communication performance for RFID-enabled systems has become a key factor in order to be competitive in today‟s markets. This study introduce a cost and performance-effective design for a proposed RFID-enabled passport tracking system through the development of a multi-objective model that takes in account economic, operation and social criteria. The developed model is aimed at solving the design problem by (i) allocating the optimal numbers of related facilities that should be established and (ii) obtaining trade-offs among three objectives: minimising implementation and operational costs; minimising RFID reader interference; and maximising the social impact measured in the number of created jobs. To come closer to the actual design in terms of considering the uncertain parameters, a fuzzy multi-objective model was developed. To solve the multi-objective optimization problem model, two solution methods were used respectively (epsilon constrain and linear programming) to select the best Pareto solution and a decision-making method was developed to select the final trade-off solution. Moreover, this research aims to provide a user-friendly decision making tool for selecting the best vendor from a group which submitted their tenders for implementing a proposed RFID- based passport tracking system. In addition to that a real case study was applied to examine the applicability of the developed model and the proposed solution methods. The research findings indicate that the developed model is capable of presenting a design for an RFID- enabled passport tracking system. Also, the developed decision-making tool can easily be used to solve similar vendor selection problem. Research findings demonstrate that the proposed RFID-enabled monitoring system for the passport tracking system is economically feasible. The study concludes that the developed mathematical models and optimization approaches can be a useful decision-maker for tackling a number of design and optimization problems for RFID system using artificial intelligence mathematical algorithm based techniques