Measuring Performance of Dynamic and Network Structures by SBM Model

The conventional Data Envelopment Analysis (DEA) model considers Decision Making Units (DMUs) as a black box, meaning that these models do not consider the connection and the inner structures of DMUs. Moreover, these models consider that the activities of DMUs in each time are independent of other times, but in the real world, the inner structures of DMUs are complicated, and the activities of DMUs are dependent on other times. Therefore, in this chapter, the authors consider DMUs with network structure and the activity of each DMU in each time dependent to activity of other times, so they call this structure a dynamic network. To this end, in this chapter, models are suggested to evaluate the dynamic network efficiency based on the SBM model, which is a non-radial model of three types with respect to orientation: input-oriented, output-oriented, and non-oriented.

Efficiency of Turkish Provincial General Hospitals with Mortality as Undesirable Output

The authors present a directional distance model where quality of care is brought in by treating mortality in each hospital as a strongly disposable “bad output.” After deriving pure technical and scale inefficiencies under strong disposability, the authors derive “congestion” inefficiencies via allowing weak disposability. A second stage, “seemingly unrelated” regression of these inefficiencies against hospital level variables like spare capacity, inpatient-to-outpatient ratio, and bed turnover rate allows pinpointing the critical areas for hospital performance improvement. Evidence shows that the smallest hospitals are operating on an inefficient scale. Moreover, allocation of specialists should be done very carefully, as shortage of specialists seems to cause congestion inefficiency, while having too many specialists causes technical inefficiency.

Mismanagement or Mismeasurement

Data Envelopment Analysis (DEA) is a well-known frontier valuation method to assess the performance of set of Decision Making Units (DMUs). It derives an overall performance for each DMU based on its efficiency relative to others. All DMUs use the same production function that transfers multiple-input into multiple-output of qualitative and quantitative values. Such big data necessitates the provision of a general framework to guide both researchers and practitioners in the analytical evaluation process for better insights. This chapter proposes a new roadmap to guide future research to implement rigorous and relevant DEA applications. This roadmap consists of five phases: Understand, Prepare, Analyze, Implement, and Monitor (AIM-UP). This roadmap could be used to evaluate the efficiency of resource utilization and the effectiveness of production by the operating processes. Finally, three case studies are used to illustrate DEA implementation, and an up-to-date review of DEA applications is conducted.

Introduction to Performance Improvement Management Software (PIM-DEA)

This chapter provides information on the use of Performance Improvement Management Software (PIM-DEA). This advanced DEA software enables users to make the best possible analysis of the data, using the latest theoretical developments in Data Envelopment Analysis (DEA). PIM-DEA software gives full capacity to assess efficiency and productivity, set targets, identify benchmarks, and much more, allowing users to truly manage the performance of organizational units. PIM-DEA is easy to use and powerful, and it has an extensive range of the most up-to-date DEA models and which can handle large sets of data.

Performance Benchmarking of the Indian Life Insurance Industry

In the last decade, the life insurance companies operating in India have made significant progress in terms of business consolidation. In view of the same, it is of interest to make an enquiry about the operating performance of these companies. This chapter compares 15 life insurance companies operating in India from the period 2005-06 to 2008-09 using the Hybrid Efficiency Model (Tone, 2004). The Hybrid Model provides a unified framework for the estimation of technical efficiency integrating the radial and non-radial characterisation of inputs and outputs. Out of the 15 in-sample life insurance companies, the number of technically efficient life insurers declined from 9 in 2005-06 to 4 in 2006-07 and further to 3 in 2007-08 and 2008-09. The mean technical efficiency scores of the in-sample life insurers declined sharply between 2005-06 and 2006-07 and improved somewhat thereafter.

Productivity and Public Funds

This chapter aims at evaluating the effect of Italian regional policies in the agricultural field. Performances of regional systems have been evaluated through an extension of Data Envelopment Analysis (DEA), the Directional Distance Function (DDF), which allows one to consider emissions of ammonia as undesirable output. Productivity and efficiency of agricultural systems are based not only on agri-production but also on the contraction of emissions deriving from the fertilizer's usage. Results show that a convergence path between productivity and public funds exists and that there are differences among Italian macro-areas considering both efficiency and productivity dynamics. In particular, if efficiency scores are interpreted with the amount of public funds distributed by the Rural Development Programs over the period 2000-2006, empirical evidence suggests that more resources are received by disadvantaged areas. Findings underline that the most disadvantaged areas, in terms of productivity, are those receiving more structural public funds.

A New Integrative Approach Based on Balanced Scorecard, Data Envelopment Analysis, and Management Performance to Prioritize Research and Development Projects

The objective of this chapter is to propose a new approach for evaluating Research and Development (R&D) projects at different stages of their life cycle. The approach is based on the integration of the balanced scorecard, Data Envelopment Analysis (DEA), and Multiple Objective (MO) linear programming. An interactive MO-DEA model is presented to incorporate Decision Maker's (DM) preference to effectively establish a common basis for fully ranking projects. The approach is illustrated on 50 R&D projects from the literature to highlight the effectiveness of the approach to fully rank all competing projects, hence increasing the discrimination power of DEA approach.

A Cognitive Analytics Management Framework (CAM-Part 3)

The main objectives of the chapter are to evaluate the impact of the tsunami of big data, business analytics, and technology on the delivery and diffusion of knowledge around the world through the use of Internet-of-things and to design future academic education and training programs. Global and local trends are analyzed to evaluate the impact of the digital tsunami on the delivery and diffusion of knowledge; to identify the shortage of critical skills, drivers of challenges, hot skills in demand, and salaries in big data/business analytics; to highlight obstacles to make informed decisions. CAM education framework is proposed to design customized higher education and training programs to meet current shortage and future generation with the relevant and rigorous skills to boost productivity growth and to impact society and professional domains in the digital economy. Finally, new ideas on how governments, academic institutions, technology companies, and professional employers can work together to reform the traditional education value chain and integrate the “massive open online courses” to achieve mass diffusion of knowledge, to transform people from loyalty to parties, clergies, and dictatorships to society's loyalty, and to develop a culture of shared-value in a move towards a smarter and fairer planet in the 21st century.

A New Framework for Industrial Benchmarking

The authors present a benchmarking study on the companies in the Turkish food industry based on their financial data. The aim is to develop a comprehensive benchmarking framework using Data Envelopment Analysis (DEA) and information visualization. Besides DEA, a traditional tool for financial benchmarking based on financial ratios is also incorporated. The consistency/inconsistency between the two methodologies is investigated using information visualization tools. In addition, k-means clustering, a fundamental method from machine learning, is applied. Finally, other relevant data, apart from the financial data, is introduced to the analysis through information visualization to discover new insights into DEA results. The results show that the framework developed is a comprehensive and effective strategy for benchmarking; it can be applied in other industries as well. The study contributes to the literature with a novel methodology that integrates the various benchmarking methods from the fields of operations research, machine learning, and financial analysis.

The Performance of Printed Circuit Boards in the Presence of Production Errors

The Printed Circuit Board (PCB) assembling production process is generally optimized to ensure very low levels of production errors (defects) so as to assure a higher quality product. In view of the number of components and solder joints in the products, and the very high demands placed on quality, the operation of this process is critical to the success of the products that are manufactured. A special class of the efficiency identification problem considered in this case relates to the occurrence of different kinds of production errors during the assembling process of the PCBs. However, the process of assembling often gets influenced by certain factors, which make some of the assembled PCBs to be defective. This chapter addresses the efficiency identification problem of a teleprinter-manufacturing company that assembles PCBs. The technique of Data Envelopment Analysis (DEA) is used to assess the efficiency of different types of PCBs.