Extended Topics in the Integration of Data Envelopment Analysis and the Analytic Hierarchy Process in Decision Making.

The Analytic Hierarchy Process (AHP) is a procedure, which can only consider relative priorities as estimated by decision-makers. A Data Envelopment Analysis (DEA) model is a data-oriented approach for evaluating the relative efficiency of a group of entities referred to as Decision Making Units (DMUs). This research work integrates and combines positive aspects of AHP\u27s estimated qualitative data and DEA\u27s quantitative data. This combination is accomplished by specifying two variants of the DEA methodology for selection of the best DMU. Initially the priority weights of AHP are integrated with the DEA methodology to provide results that are logic based. Next, a method is developed to work backwards through the DEA model to provide values that would be the required results from an AHP formulation to give the same result in DEA. The objective of the research is to propose variants of DEA that would possibly improve the results and also integrate subjective data. Through the application of the methods developed in this research, it is believed that the acceptability of the results obtained from DEA analysis can be improved

An integrated fuzzy AHP/DEA approach for performance evaluation of territorial units in Turkey

Due to the differences between regions and sub-regions in the countries, some problems come out especially in economic and social life. The issue of differences of regions has been widely implemented to evaluate the economic performance of Turkey in many disciplines. The objective of this paper is to evaluate the efficiency of 26 sub-regions of NUTS-2 classification using integration Fuzzy Analytic Hierarchy Process (FAHP) with Data Envelopment Analysis (DEA). The integrated FAHP/DEA method comprises two stages. In the first stage, linguistic terms are used to determine the decision makers’ opinion and are converted to quantitative forms by using FAHP methods. Subsequently, in the second stage, DEA method is applied to obtain relative efficiency of sub-regions in Turkey. The integrated FAHP/DEA method is illustrated with a real case study

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

Comparing total productivity through effiency and effectiveness in two Iranian banks

The objective of this study is to compare the productivity level of both private and government banks in Iran based on their effectiveness and efficiency as well as their total productivity and partial productivity. The subjects used in this research were Mellat Bank (private) and Melli Bank (government). In order to obtain substantial findings, a sequential mixed method was applied for this research. Firstly, the Modified Delphi Method; a qualitative approach was conducted upon 20 top managers from each bank. In this phase, the determinants of productivity consist of input, output and outcome as the main criteria. This is further divided into: labour, capital, and deposit as the sub-criteria of input; investment and partnership, as well as loans and advances as the sub-criteria of output; and customer satisfaction, job satisfaction and profits as the sub-criteria of outcome. On a micro level, labour is further sub-categorised into the number of employees, education level, experience and personnel cost; capital is further divided into IT capital and Non IT capital; and deposit is further sub-categorised into several accounts namely current, saving, and investment. As such, a three-stage Productivity Estimation Model for Mellat Bank and Melli Bank was formed. The second phase of the research is the quantitative approach in which the Fuzzy AHP method was used. In order to obtain the weights from Fuzzy AHP method, questionnaires were distributed to the same 20 top managers from each bank. Furthermore, the secondary data from annual reports were used to derive these determinants: effectiveness, efficiency, total productivity and partial productivity. Finally, it appears that both banks (Melli Bank and Mellat Bank) are located in the Golden Quadrant where the efficiency level of Mellat Bank is more than that of Melli Bank (1.17 as compared to 1.02) but the effectiveness level of Mellat Bank (0.77) is less than that of Melli Bank (0.84). In addition, total productivity level of Mellat Bank has been computed to be 0.9 while for Melli Bank it is 0.86. With regards to the partial productivity level the capital productivity of Melli Bank is slightly more thant that of Mellat Bank (0.77 as compared to 0.74) but the labor and deposit productivity of Mellat Bank are higher than that of Melli Bank with1.17 and 0.77 respectively. Suggestions have been made for managers and also academic researchers to further this study

Risk management and risk control for state-owned firms of China

As global economic integration deepens and enterprises scale up their business, the enterprise groups have become the mainstream of the company's development form. Subsidiaries of the Company have grown in size and increasingly diversified. Thus how does the parent Company control its subsidiaries effectively has become an urgent challenge, especially for the state-owned enterprises in China. This thesis studies the management and control of state-owned enterprises in China, carrying certain theoretical and practical significance. The research examined the theory and mechanism of management of SOEs, and evaluation on employee performance. It also analyzed performance evaluation, coordination and risk control strategies of SOEs' subsidiaries. The same studies were repeated on state-owned enterprise groups and extended to the strategies of risk management and risk control. The thesis first examined the conundrum of effective cooperation between subsidiaries of different departments and the parent company for efficient allocation of resources. To tackle this headache, the IAHP and DEA model were adopted to help group decision makers better measure the performance of employees and organizations. The thesis used the Balanced Scorecard (BSC) tool as the main principle and the combination of fuzzy mathematics and Delphi and entropy weight methods as the main methodology to assess the performance. In addition, a novel method of using multi-reasoning, multi-dimensional and dynamic factors was developed to assess the performance of SOE employees, and this method was proven to be effective. Moreover, the super-efficiency DEA model which takes into account work performance, work ability, work attitude, job potential and other factors in the evaluation on employee performance was developed and tested. Finally, risk map for SOEs was proposed and evaluated

An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies

To stay competitive in a business environment, continuous performance evaluation based on the triple bottom line standard of sustainability is necessary. There is a gap in addressing the computational expense caused by increased decision units due to increasing the performance evaluation indices to more accuracy in the evaluation. We successfully addressed these two gaps through (1) using principal component analysis (PCA) to cut the number of evaluation indices, and (2) since PCA itself has the problem of merely using the data distribution without considering the domain-related knowledge, we utilized Analytic Hierarchy Process (AHP) to rank the indices through the expert’s domain-related knowledge. We propose an integrated approach for sustainability performance assessment in qualitative and quantitative perspectives. Fourteen insurance companies were evaluated using eight economic, three environmental, and four social indices. The indices were ranked by expert judgment though an analytical hierarchy process as subjective weighting, and then principal component analysis as objective weighting was used to reduce the number of indices. The obtained principal components were then used as variables in the data envelopment analysis model. So, subjective and objective evaluations were integrated. Finally, for validating the results, Spearman and Kendall’s Tau correlation tests were used. The results show that Dana, Razi, and Dey had the best sustainability performance.This article belongs to the Special Issue Sustainability Assessmen

Building a Digital Transformation Maturity Evaluation Model for Construction Enterprises Based on the Analytic Hierarchy Process and Decision-Making Trial and Evaluation Laboratory Method

With digital transformation underway in various Chinese construction enterprises, each enterprise has progressed differently, and a clear direction for future digital transformation and upgrading is lacking. As such, the importance of measuring the level of digitization among Chinese construction enterprises is increasing. This paper presents a model for evaluating digital transformation maturity within construction enterprises. The model considers six aspects: digital strategy, digital business applications, digital technology capabilities, and so on. The digital maturity of enterprises is determined using the Analysis of Hierarchy (AHP)-Decision Making Experiment and Evaluation Laboratory (DEMATEL) method. Technical abbreviations are explained when first used. This study demonstrates that digital business applications are the most significant primary indicator, with a weight of 29.53%. The success of digital transformation in the construction industry is strongly influenced by the interconnection between digital technology and construction sites, as well as other factors such as new technical personnel, digital infrastructure, digital innovation, and innovation iteration ability. It is crucial to understand how digital technology and the construction industry can effectively connect in order to achieve success in this realm. This paper aims to enhance the digital transformation capabilities and efficiency of construction companies and boost their core competitiveness through targeted measures

A FRAMEWORK FOR STRATEGIC PROJECT ANALYSIS AND PRIORITIZATION

Projects that support the long-term strategic intent and alignment are considered strategic projects. Therefore, these projects must consider their alignment with the organization’s current strategy and focus on the risk, organizational capability, resources availability, political influence, and socio-cultural factors. Quantitative and qualitative methods prioritize the projects; however, they are usually suitable for specific industries. Although prioritization models are used in the private sector, the same in the public sector is not widely seen in the literature. The lack of models in the public sector has happened because of the projects’ social implications, the value perception of different projects in the public sector, and potentially differing value perceptions attached to the types of projects in different decision-making environments in the public sector. The thesis proposes a generic framework to develop a priority list of the available basket of projects and decide on projects for the next undertaking. The focus of the thesis is on public projects. The analysis in the framework considers the critical factors for prioritization obtained from the literature clustered through the agglomerative text clustering technique. In the proposed framework, 13 critical clusters are identified and weighted using the Criteria Importance Through Intercriteria Correlation (CRITIC) method to develop their ranking using the Technique for Order of Preference Similarity Ideal Solution (TOPSIS) method. In addition, the proposed framework uses vector weighting to prioritize projects across industries. The applicability of the framework is demonstrated through Qatar’s real estate and transportation projects. The outcome obtained from the framework is compared with those obtained through the experts using the System Usability Scale (SUS). The comparison shows that the framework provides good predictability of the projects for implementation

Antecedents of salesperson effectiveness and efficiency performance: A data envelopment analysis

The objective of this dissertation was to (1) measure salesperson efficiency; (2) investigate both personal and organizational factors that determine salesperson efficiency; and (3) investigate both personal and organizational factors that determine salesperson effectiveness. Salesperson efficiency was assessed by data envelopment analysis (DEA). Two different DEA models were employed in order to increase the reliability of the efficiency results. Antecedents of salesperson efficiency and effectiveness were tested using Tobit regression analysis and ordinary least square regression analysis, respectively. These antecedents include not only personal level variables such as working smart, working hard, learning goal orientation, and performance goal orientation, but also organizational variables such as organizational culture, sales force control systems, and training. The sample frame consisted of a national sample of insurance agents who subscribed to Life Insurance Selling magazine. A self-report questionnaire was mailed to a stratified random sample of 1,000 potential respondents. The life insurance professionals were sent the study questionnaire three times. The resulting response rate was 23.00% in the present study. At the individual level of analysis, this study provides evidence that engaging in working smart behaviors enhances salesperson efficiency. While working hard was found to positively influence salesperson effectiveness, working smart was found to make salespeople more efficient and effective in selling. These results are a distinct contribution to the personal selling research literature. The results also indicate that a learning goal orientation enhances salesperson efficiency and effectiveness. In addition, the relationship between performance goal orientation and effectiveness was found to be moderated by salesperson self-efficacy. At the organizational level, this study found that the clan organizational culture type negatively influences salesperson effectiveness, while the market culture type positively influences efficiency. While past studies have found that organizational culture directly influenced organizational performance, the current study was the first to find a direct influence on individual performance. Additionally, behavior control systems were found to enhance salesperson efficiency and positively influence, although marginally, salesperson effectiveness. Finally, the application of data envelopment analysis in sales research was extended. This study showed how DEA can be used to measure individual salesperson efficiency and subsequently identify those variables that influence this important measure of salesperson performance