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.

Mismanagement or Mismeasurement

Operations and Service Management ◽

10.4018/978-1-5225-3909-4.ch035 ◽

2018 ◽

pp. 713-759

Author(s):

Abdel Latef Anouze ◽

Ibrahim H. Osman

Keyword(s):

General Framework ◽

Evaluation Process ◽

Future Research ◽

Data Envelopment ◽

Analytical Evaluation ◽

Valuation Method ◽

Multiple Input ◽

Overall Performance ◽

Five Phases ◽

Integration of Data Envelopment Analysis and Clustering Methods

Journal of Information & Knowledge Management ◽

10.1142/s0219649220400067 ◽

2020 ◽

Vol 19 (01) ◽

pp. 2040006

Author(s):

Hassan Najadat ◽

Ahmad Alaiad ◽

Sanaa Abu Alasal ◽

Ghadeer Anwar Mrayyan ◽

Izzat Alsmadi

Keyword(s):

Evaluation Process ◽

Public Hospitals ◽

Data Envelopment ◽

Efficiency Evaluation ◽

Clustering Methods ◽

Multiple Input ◽

Mining Methods ◽

Crs Model ◽

Data Envelopment Analysis (DEA) has been applied creatively in various study domains to compare and evaluate different Decision Making Units (DMUs) based on multiple input–output attributes. In this paper, the performance of Jordanian public hospitals is assessed via a methodology combining DEA with data mining methods, specifically, clustering. Initially, inputs of inefficient hospitals were altered to check for waste in the allocated resources. Then, the number of inputs–outputs was manipulated to test if the number is strongly influencing the productivity of the DMUs. The number of DMUs used was 27 public hospitals and the applicable efficiency measurements used were constant return to scale (CRS) and variable return to scale (VRS) through the DEAP software. Experiments showed that the efficiency of a hospital might be more meaningfully assessed if it is compared with a group of hospitals that are similar in some factors. More specifically, results of applying the CRS model proved that 77% of the hospitals were efficient. Additionally, we found that the inefficiencies of some hospitals are linked to weak resource utilization. It is concluded that number of inputs–outputs inserted in the efficiency evaluation process impacts the resulted values.

Ranking DMUs by Calculating the Interval Efficiency with a Common Set of Weights in DEA

Journal of Applied Mathematics ◽

10.1155/2014/346763 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Vahideh Rezaie ◽

Tahir Ahmad ◽

Siti-Rahmah Awang ◽

Masumeh Khanmohammadi ◽

Normah Maan

Keyword(s):

Decision Maker ◽

Relative Efficiency ◽

Efficiency Score ◽

Upper Bounds ◽

Data Envelopment ◽

Lower And Upper Bounds ◽

Common Set Of Weights ◽

Overall Performance ◽

Interval Efficiency ◽

To evaluate the performance of decision making units (DMUs), data envelopment analysis (DEA) was introduced. Basically, the traditional DEA scheme calculates the best relative efficiency score (i.e., the “optimistic” efficiency) of each DMU with the most favorable weights. A decision maker may be unable to compare and fully rank the efficiencies of different DMUs that are calculated using these potentially distinct sets of weights on the same basis. Based on the literature, the assignable worst relative efficiency score (i.e., the “pessimistic” efficiency) for each DMU can also be determined. In this paper, the best and the worst relative efficiencies are considered simultaneously. To measure the overall performance of the DMUs, an integration of both the best and the worst relative efficiencies is considered in the form of an interval. The advantage of this efficiency interval is that it provides all of the possible efficiency values and an expanded overview to the decision maker. The proposed method determines the lower- and upper-bounds of the interval efficiency over a common set of weights. To demonstrate the implementation of the introduced method, a numerical example is provided.

Construction of pseudo CRS frontier for a negative data using RTS model of Allahyar & modified multiplier BCC model

RAIRO - Operations Research ◽

10.1051/ro/2020039 ◽

2020 ◽

Author(s):

Subhadip Sarkar

Keyword(s):

Decision Making ◽

Returns To Scale ◽

Data Envelopment ◽

Negative Data ◽

Data Set ◽

Decision Making Unit ◽

Multiple Input ◽

Data Problem ◽

Positive Dataset ◽

Performance measurement of Decision Making Units (DMU) possessing an array of positive and negative type of data has been an extensively researched topic in Data Envelopment Analysis. However, assessment of Returns to Scale (RTS) under negative data problem is rarely witnessed without the steps referred by Allahyar, M. (2015). Authors purported a solution around the vicinity of the Decision Making Unit under examination to predict the nature of the Return to Scale of a firm. The extant investigation is aimed to extend the research of Allahyar, M. (2015) to identify a Pseudo Frontier for a negative data problem under Constant Return to Scale. In addition to it, a new origin based on the provided data is also computed with a view to convert the entire data set into a positive dataset. However, this approach seems to be ineffective to create a frontier under the multiple input output scenario. In this regard, a new variation of the Multiplier form of BCC model is proposed here to detect the new origin for the sake of designing the Pseudo CRS Frontier. Small examples are added for the elaboration of the CRS efficient DMUs using methods described by Allahyar, M. (2015) and identification of the New Origin from the Multiplier form of BCC model.

Relative Efficiency of Decision Making Units Producing Both Desirable and Undesirable Outputs: A Case of Textile Processing Units in Pakistan.

The Pakistan Development Review ◽

10.30541/v50i4iipp.685-698 ◽

2011 ◽

Vol 50 (4II) ◽

pp. 685-698

Author(s):

Samina Khalil

Keyword(s):

Decision Making ◽

Relative Efficiency ◽

Value Added ◽

Data Envelopment ◽

Undesirable Outputs ◽

Textile Processing ◽

Decision Making Unit ◽

Desirable And Undesirable Outputs ◽

This paper aims at measuring the relative efficiency of the most polluting industry in terms of water pollution in Pakistan. The textile processing is country‘s leading sub sector in textile manufacturing with regard to value added production, export, employment, and foreign exchange earnings. The data envelopment analysis technique is employed to estimate the relative efficiency of decision making units that uses several inputs to produce desirable and undesirable outputs. The efficiency scores of all manufacturing units exhibit the environmental consciousness of few producers is which may be due to state regulations to control pollution but overall the situation is far from satisfactory. Effective measures and instruments are still needed to check the rising pollution levels in water resources discharged by textile processing industry of the country. JEL classification: L67, Q53 Keywords: Data Envelopment Analysis (DEA), Decision Making Unit (DMU), Relative Efficiency, Undesirable Output

A benchmarking study of Indian seaports

Journal of Global Operations and Strategic Sourcing ◽

10.1108/jgoss-05-2019-0037 ◽

2019 ◽

Vol 13 (1) ◽

pp. 88-102

Author(s):

Sajeev Abraham George ◽

Anurag C. Tumma

Keyword(s):

Financial Performance ◽

Future Research ◽

Data Envelopment ◽

Operational Parameters ◽

Two Stage ◽

Content Type ◽

The Public ◽

Port Authorities ◽

Financial Performance Measures ◽

The Government

Purpose The purpose of this paper is to benchmark the operational and financial performances of the major Indian seaports to help derive useful insights to improve their performance. Design/methodology/approach A two-stage data envelopment analysis (DEA) methodology has been used with the help of data collected on the 13 major seaports of India. The first stage of the DEA captured the operational efficiencies, while the second stage the financial performance. Findings A window analysis over a period of three years revealed that no port was able to score an overall average efficiency of 100 per cent. The study identified the better performing units among their peers in both the stages. The contrasting results of the study with the traditional operational and financial performance measures used by the ports helped to derive useful insights. Research limitations/implications The data used in the study were majorly limited to the available sources in the public domain. Also, the study was limited to the major seaports which are under the Government of India and no comparisons were carried out with other local or international ports. Practical implications There is a need to prioritize investments and improvement efforts where they are most needed, instead of following a generalized approach. Once the benchmark ports are identified, the port authorities and other relevant stakeholders should work in detail on the factors causing inefficiencies, for possible improvements in performance. Originality/value This paper carried out a two-stage DEA that helped to derive useful insights on operational efficiency and financial performance of the India seaports. A combination of the financial and operational parameters, along with a comparison of the DEA results with the traditional measures, provided a different perspective on the Indian seaport performance. Considering the scarcity of research papers reported in the literature on DEA-based benchmarking studies of seaports in the Indian context, it has the potential to attract future research in this field.

Performance Evaluation of Fishery Enterprises Using Data Envelopment Analysis—A Malmquist Model

Mathematics ◽

10.3390/math9050469 ◽

2021 ◽

Vol 9 (5) ◽

pp. 469

Author(s):

Chia-Nan Wang ◽

Thi-Ly Nguyen ◽

Thanh-Tuan Dang ◽

Thi-Hong Bui

Keyword(s):

Performance Evaluation ◽

Technological Progress ◽

Data Envelopment ◽

Domestic Economy ◽

Factor Productivity ◽

Sales Revenue ◽

Business Efficiency ◽

Using Data ◽

In Vietnam, fishing is a crucial source of nutrition and employment, which not only affects the development of the domestic economy but is also closely related to exports, heavily influencing the economy and foreign exchange. However, the Vietnamese fishery sector has been facing many challenges in innovating production technology, improving product quality, and expanding markets. Hence, the fishery enterprises need to find solutions to increase labor productivity and enhance competitiveness while minimizing difficulties. This study implemented a performance evaluation from 2015 to 2018 of 17 fishery businesses, in decision making units (DMUs), in Vietnam by applying data envelopment analysis, namely the Malmquist model. The objective of the paper is to provide a general overview of the fishery sector in Vietnam through technical efficiency, technological progress, and the total factor productivity in the four-year period. The variables used in the model include total assets, equity, total liabilities, cost of sales, revenue, and profit. The results of the paper show that Investment Commerce Fisheries Corporation (DMU10) and Hoang Long Group (DMU8) exhibited the best performances. This paper offers a valuable reference to improve the business efficiency of Vietnamese fishery enterprises and could be a useful reference for related industries.

Measuring the Productivity of Energy Consumption of Major Industries in China: A DEA-Based Method

Mathematical Problems in Engineering ◽

10.1155/2014/121804 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 6

Author(s):

Xishuang Han ◽

Xiaolong Xue ◽

Jiaoju Ge ◽

Hengqin Wu ◽

Chang Su

Keyword(s):

Decision Making ◽

Energy Consumption ◽

Malmquist Productivity Index ◽

Productivity Index ◽

Productivity Change ◽

Data Envelopment ◽

Policy Makers ◽

Time Periods ◽

Data envelopment analysis can be applied to measure the productivity of multiple input and output decision-making units. In addition, the data envelopment analysis-based Malmquist productivity index can be used as a tool for measuring the productivity change during different time periods. In this paper, we use an input-oriented model to measure the energy consumption productivity change from 1999 to 2008 of fourteen industry sectors in China as decision-making units. The results show that there are only four sectors that experienced effective energy consumption throughout the whole reference period. It also shows that these sectors always lie on the efficiency frontier of energy consumption as benchmarks. The other ten sectors experienced inefficiency in some two-year time periods and the productivity changes were not steady. The data envelopment analysis-based Malmquist productivity index provides a good way to measure the energy consumption and can give China's policy makers the information to promote their strategy of sustainable development.

The creation and development of learning organizations: a review

Journal of Knowledge Management ◽

10.1108/jkm-10-2020-0795 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Mohit Kumar ◽

Justin Paul ◽

Madhvendra Misra ◽

Rubina Romanello

Keyword(s):

Knowledge Management ◽

Future Research ◽

Business Organization ◽

It Infrastructure ◽

Content Type ◽

Modern Business ◽

Managerial Implications ◽

Overall Performance ◽

Stored Information ◽

And Storage

Purpose In this paper, using the antecedents, decisions and outcomes (ADO) framework, the factors/key performance indicators (KPIs) most relevant for creating or building a learning organization (LO) are identified. This study aims to contribute to the field of knowledge management (KM) in terms of introducing KPIs to foster a business organization with a continuous learning process, mechanisms of knowledge creation and memorization. Design/methodology/approach In total, 57 papers were selected for this systematic literature review (SLR) from Web of Science and Scopus covering the period 1985–2019. Findings The 12 most relevant KPIs are identified based on the literature survey conducted in the field of LO. Research limitations/implications The managerial implications of this review paper will be an added advantage to the modern business organization worldwide that have adopted KM practices to foster knowledge management with information technology (IT) infrastructure. As IT infrastructure focuses on knowledge acquisition, dissemination and storage but the KPIs revealed through this review will help in transforming stored information as learning for the organization to improve its overall performance. Originality/value This review synthesizes prior studies and provides directions for future research.

Forecast of the efficiency confidence interval of decision-making units in data envelopment analysis

Nexo Revista Científica ◽

10.5377/nexo.v33i02.10782 ◽

2020 ◽

Vol 33 (02) ◽

pp. 431-445

Author(s):

Azarnoosh Kafi ◽

Behrouz Daneshian ◽

Mohsen Rostamy-Malkhalifeh ◽

Mohsen Rostamy-Malkhalifeh

Keyword(s):

Decision Making ◽

Confidence Interval ◽

Data Envelopment ◽

Data Set ◽

Interval Model ◽

Time Period ◽

Proposed Model ◽

The Future ◽

Data Envelopment Analysis (DEA) is a well-known method for calculating the efficiency of Decision-Making Units (DMUs) based on their inputs and outputs. When the data is known and in the form of an interval in a given time period, this method can calculate the efficiency interval. Unfortunately, DEA is not capable of forecasting and estimating the efficiency confidence interval of the units in the future. This article, proposes a efficiency forecasting algorithm along with 95% confidence interval to generate interval data set for the next time period. What’s more, the manager’s opinion inserts and plays its role in the proposed forecasting model. Equipped with forecasted data set and with respect to data set from previous periods, the efficiency for the future period can be forecasted. This is done by proposing a proposed model and solving it by the confidence interval method. The proposed method is then implemented on the data of an automotive industry and, it is compared with the Monte Carlo simulation methods and the interval model. Using the results, it is shown that the proposed method works better to forecast the efficiency confidence interval. Finally, the efficiency and confidence interval of 95% is calculated for the upcoming period using the proposed model.