Setting targets with interval data envelopment analysis models via wang method

Data envelopment analysis (DEA) is a mathematical programming for evaluating the relative efficiency of decision making units (DMUs). The first DEA model (CCR model) assumed for exact data, later some authors introduced the applications of DEA which the data was imprecise. In imprecise data envelopment analysis (IDEA) the data can be ordinal, interval and fuzzy. Data envelopment analysis also can be used for the future programming of organizations and the response of the different policies, which is related to the target setting and resource allocation. The existing target model that conveys performance based targets in line with the policy making scenarios was defined for exact data. In this paper we improved the model for imprecise data such as fuzzy, ordinal and interval data. To deal with imprecise data we first established an interval DEA model. We used one of the methods to convert fuzzy and ordinal data into the interval data. A numerical experiment is used to illustrate the application to our interval model

Confident-DEA: A Unified Approach For Efficiency Analysis With Cardinal, Bounded And Ordinal Data

This paper proposes an extension to the existing literature in DEA, the authors call Confident-DEA approach. The proposed new approach involves a bi-level convex optimization model, and hence NP-hard, to which a solution method is suggested. Confident-DEA constitutes a generalization of DEA for dealing with imprecise data and hence a potential method for forecasting efficiency. Imprecision in data is defined as two forms, one is bounded data and the second is cardinal data. Complementing the methodology proposed by Cooper et al (1999) which provides single valued efficiency measures, Confident-DEA provides a range of values for the efficiency measures, e.g. an efficiency confidence interval, reflecting the imprecision in data. For the case of bounded data, a theorem defining the bounds of the efficiency confidence interval is provided. For the general case of imprecise data, that is a mixture of ordinal and cardinal data, a Genetic-Algorithm-based meta-heuristic is used to determine the upper and lower bounds defining the efficiency confidence interval. To the best knowledge of the authors, this is the first work combining Genetic algorithms with DEA. In both cases of imprecision, a Monte-Carlo type simulation is used to determine the distribution of the efficiency measures, taking into account the distribution of the bounded imprecise data over their corresponding intervals. Most of previous DEA works dealing with imprecise data implicitly assumed a uniform distribution. Confident-DEA, on the other hand, allows for any type of distribution and hence expands the scope of the analysis. The bounded data used in the illustrative examples are assumed to have truncated normal distributions. However, the methodology suggested here allows for any other distribution for the data

Robust approach to DEA technique for supplier selection problem: A case study at Supplying Automotive Parts Company (SAPCO)

Abstract In many industries such as automotive industry, there are a lot of suppliers dealing with the final products manufacturer. With growing numbers of suppliers, the suppliers' efficiency measurement often becomes the most significant concern for manufacturers. Therefore, various performance measurement models such as DEA, AHP, TOPSIS, are developed to support supplier selection decisions. After an exhaustive review of the supplier selection methods, we employ data envelopment analysis (DEA) for computing the relative efficiency of the suppliers and introducing the most efficient supplier as a benchmark. In reality, there are large amounts of uncertainty regarding the suppliers' measurements; therefore, we propose the robust optimization approach to the real application of DEA (RDEA). In this approach, uncertainties about incomes and outcomes of decision making units (DMUs) are involved in the relative suppliers' efficiencies. The proposed RDEA approach is utilized for the selection of suppliers which manufacture the automotive safety components in Supplying Automotive Parts Company (SAPCO), an Iranian leading automotive enterprise. Numerical example will illustrate how our proposed approach can be used in the real supplier selection problem when considerable uncertainty exists regarding the suppliers' input and output dat

Sustainability, Digital Transformation and Fintech: The New Challenges of the Banking Industry

In the current competitive scenario, the banking industry must contend with multiple challenges tied to regulations, legacy systems, disruptive models/technologies, new competitors, and a restive customer base, while simultaneously pursuing new strategies for sustainable growth. Banking institutions that can address these emerging challenges and opportunities to effectively balance long-term goals with short-term performance pressures could be aptly rewarded. This book comprises a selection of papers addressing some of these relevant issues concerning the current challenges and opportunities for international banking institutions. Papers in this collection focus on the digital transformation of the banking industry and its effect on sustainability, the emergence of new competitors such as FinTech companies, the role of mobile banking in the industry, the connections between sustainability and financial performance, and other general sustainability and corporate social responsibility (CSR) topics related to the banking industry. The book is a Special Issue of the MDPI journal Sustainability, which has been sponsored by the Santander Financial Institute (SANFI), a Spanish research and training institution created as a collaboration between Santander Bank and the University of Cantabria. SANFI works to identify, develop, support, and promote knowledge, study, talent, and innovation in the financial sector

A model to promote entrepreneurial competitiveness in the South African telecommunications sector

The fast pace of technological advancements is a driver of change in the world. In telecommunications, advancements as well as sector transformation pose challenges to entrepreneurs to remain competitive. The purpose of this study is to contribute to the promotion of entrepreneurial competitiveness in the telecommunications sector in South Africa. In order to achieve this purpose, the objective was to develop and test a theoretical model to promote entrepreneurial competitiveness in this sector. The purpose of the study was that if the factors that influence entrepreneurial businesses in this sector can be identified and recommendations applied, the competitiveness of these businesses can be improved. The approach was as follows: 1. Identify the factors, in a literature review, in three areas related to this study, namely, Entrepreneurial Orientation, Telecommunications and Benchmarking; 2. Develop a conceptual theoretical model comprising these identified factors which formed the base for the data collection; 3. Develop a measuring instrument to empirically test the relationships described in the conceptual model; 4. Empirically test the proposed model and suggested hypotheses by means of sourcing data from entrepreneurs in the telecommunications sector in South Africa and thereafter statistically analyse the sourced data; 5. Formulate the final theoretical model to support the research objective and 6. Propose recommendations based on the results of the statistical analysis. The three areas of literature study analysed were Entrepreneurial Orientation which focused on the entrepreneur, the entrepreneurial process and the positioning of technological entrepreneurs in the sector. The telecommunications section included an overview of telecommunications from a global perspective followed by specific focus on the South African sector. The section on benchmarking covered business performance aspects together with measurement techniques and benchmarking institutions relevant to entrepreneurship and telecommunications businesses. Initially, the literature study delivered four intervening variables (Entrepreneurial Orientation, Opportunity Recognition, Resource Allocation and Strategic Positioning) which influence entrepreneurial competitiveness. Within these four intervening variables, twelve underlying independent variables were identified. All the variables were hypothesised as they were perceived significantly to influence the dependent variable, perceived to be entrepreneurial competitiveness in the telecommunications sector in South Africa. These factors, clearly defined and operationalised, were structured in a questionnaire which was sent to entrepreneurs in the telecommunications sector. A response rate of 37 percent was achieved. Data collected from 301 questionnaires were subjected to various statistical analysis techniques. Cronbach-alpha coefficients were calculated to confirm the validity and reliability of the measuring instrument that was tested whilst the latent variables were confirmed by exploratory factor analysis. Structural Equation Modelling (SEM) was used to test the hypothesised significance of the relationships between the variables. Due to the sample size limitation, the conceptual model could not be subjected to SEM as a whole and consequently two sub-models were identified and subjected to further analysis. The SEM results presented the factors influencing entrepreneurial competitiveness whereafter the final model was presented for this study. This study contributed to this specific field of knowledge as follows: 1. New literature contributions are made in the field of entrepreneurial competitiveness in a specific sector; 2. It is the first known research conducted into the promotion of entrepreneurial competitiveness in the telecommunications sector in South Africa; 3. A theoretical model was developed that can be used to promote entrepreneurial competitiveness in the sector and 4. It suggests recommendations on empirically tested factors that significantly influence entrepreneurial competitiveness. Additional knowledge has been gained through the identification and description of how the following individual factors significantly influence entrepreneurial competitiveness in this sector: Benchmarking; Entrepreneurial Mindset; Entrepreneurial Management; Entrepreneurial Orientation; Financial Resources; Infrastructural Change; Regulatory Alignment and Technological Entrepreneurship. The present study was conducted in a time frame where sector transformation is prevalent in South Africa. The current circumstances relating to sector transformation and infrastructural changes will not last forever. The theoretical model therefore is limited to the specific sector conditions in a specific time cycle. In conclusion, the model and managerial recommendations that are presented can act as a guideline for entrepreneurs to adopt in order to improve the competitiveness of their businesses

A model to promote entrepreneurial competitiveness in the South African telecommunications sector

The fast pace of technological advancements is a driver of change in the world. In telecommunications, advancements as well as sector transformation pose challenges to entrepreneurs to remain competitive. The purpose of this study is to contribute to the promotion of entrepreneurial competitiveness in the telecommunications sector in South Africa. In order to achieve this purpose, the objective was to develop and test a theoretical model to promote entrepreneurial competitiveness in this sector. The purpose of the study was that if the factors that influence entrepreneurial businesses in this sector can be identified and recommendations applied, the competitiveness of these businesses can be improved. The approach was as follows: 1. Identify the factors, in a literature review, in three areas related to this study, namely, Entrepreneurial Orientation, Telecommunications and Benchmarking; 2. Develop a conceptual theoretical model comprising these identified factors which formed the base for the data collection; 3. Develop a measuring instrument to empirically test the relationships described in the conceptual model; 4. Empirically test the proposed model and suggested hypotheses by means of sourcing data from entrepreneurs in the telecommunications sector in South Africa and thereafter statistically analyse the sourced data; 5. Formulate the final theoretical model to support the research objective and 6. Propose recommendations based on the results of the statistical analysis. The three areas of literature study analysed were Entrepreneurial Orientation which focused on the entrepreneur, the entrepreneurial process and the positioning of technological entrepreneurs in the sector. The telecommunications section included an overview of telecommunications from a global perspective followed by specific focus on the South African sector. The section on benchmarking covered business performance aspects together with measurement techniques and benchmarking institutions relevant to entrepreneurship and telecommunications businesses. Initially, the literature study delivered four intervening variables (Entrepreneurial Orientation, Opportunity Recognition, Resource Allocation and Strategic Positioning) which influence entrepreneurial competitiveness. Within these four intervening variables, twelve underlying independent variables were identified. All the variables were hypothesised as they were perceived significantly to influence the dependent variable, perceived to be entrepreneurial competitiveness in the telecommunications sector in South Africa. These factors, clearly defined and operationalised, were structured in a questionnaire which was sent to entrepreneurs in the telecommunications sector. A response rate of 37 percent was achieved. Data collected from 301 questionnaires were subjected to various statistical analysis techniques. Cronbach-alpha coefficients were calculated to confirm the validity and reliability of the measuring instrument that was tested whilst the latent variables were confirmed by exploratory factor analysis. Structural Equation Modelling (SEM) was used to test the hypothesised significance of the relationships between the variables. Due to the sample size limitation, the conceptual model could not be subjected to SEM as a whole and consequently two sub-models were identified and subjected to further analysis. The SEM results presented the factors influencing entrepreneurial competitiveness whereafter the final model was presented for this study. This study contributed to this specific field of knowledge as follows: 1. New literature contributions are made in the field of entrepreneurial competitiveness in a specific sector; 2. It is the first known research conducted into the promotion of entrepreneurial competitiveness in the telecommunications sector in South Africa; 3. A theoretical model was developed that can be used to promote entrepreneurial competitiveness in the sector and 4. It suggests recommendations on empirically tested factors that significantly influence entrepreneurial competitiveness. Additional knowledge has been gained through the identification and description of how the following individual factors significantly influence entrepreneurial competitiveness in this sector: Benchmarking; Entrepreneurial Mindset; Entrepreneurial Management; Entrepreneurial Orientation; Financial Resources; Infrastructural Change; Regulatory Alignment and Technological Entrepreneurship. The present study was conducted in a time frame where sector transformation is prevalent in South Africa. The current circumstances relating to sector transformation and infrastructural changes will not last forever. The theoretical model therefore is limited to the specific sector conditions in a specific time cycle. In conclusion, the model and managerial recommendations that are presented can act as a guideline for entrepreneurs to adopt in order to improve the competitiveness of their businesses