J. Zhu and V. Charles, Data-Enabled Analytics: DEA for Big Data, Springer-Nature, New York, to be published
ISBN:
ISBN-13:


Data envelopment analysis (DEA) has been and continues to be a widely-used technique both in performance and productivity measurement, having covered a plethora of challenges and debates within the modelling framework. Over the past four decades, DEA models have been applied in almost every major field of study. Despite this, however, DEA has not been used to its fullest extent. As the inter- and intra-disciplinary research grows, DEA could be used in potentially many other ways. DEA could be viewed as a data-oriented data science tool for data-enabled analytics, benchmarking, performance evaluation, and developing composite indexes, among other new uses, in addition to the traditional uses, such as production efficiency and productivity measurement. One opportunity is brought by the existence of big data. Although big data have existed for a while now, gaining its popularity among insight seekers, we are still in incipient stages when it comes to taking full advantage of its potential. As the amount of (big) data keeps growing in an exponential manner, so does its complexity; in this sense, various types of data are surfacing, whose study and examination could shed new light on phenomena of interest.

A quick review of existing literature shows that big data is a new entrant within the DEA framework. Recently, there has been an increasing interest in bringing the two concepts together, with research studies aiming to integrate DEA and big data concepts within a single framework. Despite this, however, more work is needed to fully explore the value of their intersection. It is thus time to view DEA considering its potential usage in new fields or new usage within the existing fields, under the big data umbrella. Otherwise stated, it is time to view DEA models beyond their present scope to mine new insights for better data-driven decision-making. This book seeks new DEA developments that are tailored for big data research and data-enabled analytics.

The following topics are of particular interest.
1) DEA models for big data
2) Linkage between DEA and the Vs in big data
3) Network DEA is viewed as mining “value” from the big data. Therefore, any papers on network DEA are welcome.
4) Combine DEA with other data science tools


The expected publication date of the book is January 2021.

Read the full paper: Zhu, Joe, DEA under big data: data enabled analytics and network data envelopment analysis, Annals of Operations Research, (in press)

For further information or clarifications about this book, please do not hesitate to contact the Editors directly, via email. (jzhu@wpi.edu; editor.profvc@gmail.com)

V. Charles, J. Aparicio, and J. Zhu, Data Science and Productivity Analytics, Springer, New York, 2020.
ISBN: 978-3-030-43384-0
About this Book

This book includes a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of ‘productivity analysis/data envelopment analysis’ and ‘data science/big data’. Data science is defined as the collection of scientific methods, processes, and systems dedicated to extracting knowledge or insights from data and it develops on concepts from various domains, containing mathematics and statistical methods, operations research, machine learning, computer programming, pattern recognition, and data visualisation, among others.

Examples of data science techniques include linear and logistic regressions, decision trees, Naïve Bayesian classifier, principal component analysis, neural networks, predictive modelling, deep learning, text analysis, survival analysis, and so on, all of which allow using the data to make more intelligent decisions. On the other hand, it is without a doubt that nowadays the amount of data is exponentially increasing, and analysing large data sets has become a key basis of competition and innovation, underpinning new waves of productivity growth. This book aims to bring a fresh look onto the various ways that data science techniques could unleash value and drive productivity from these mountains of data.

Researchers working in productivity analysis/data envelopment analysis will benefit from learning about the tools available in data science/big data that can be used in their current research analyses and endeavours. The data scientists, on the other hand, will also get benefit from learning about the plethora of applications available in productivity analysis/data envelopment analysis.

J. Aparicio, C.A.K. Lovell, J.T. Pastor and J. Zhu, Advances in Efficiency and Productivity II, Springer-Nature, New York, 2020.
ISBN-13: 978-3030416171
ISBN: 978-3-030-41618-8

About this Book This book surveys the state-of-the-art in efficiency and productivity analysis, examining advances in the analytical foundations and empirical applications. The analytical techniques developed in this book for efficiency provide alternative ways of defining optimum outcome sets, typically as a (technical) production frontier or as an (economic) cost, revenue or profit frontier, and alternative ways of measuring efficiency relative to an appropriate frontier. Simultaneously, the analytical techniques developed for efficiency analysis extend directly to productivity analysis, thereby providing alternative methods for estimating productivity levels, and productivity change through time or productivity variation across producers.
This book includes chapters using data envelopment analysis (DEA) or stochastic frontier analysis (SFA) as quantitative techniques capable of measuring efficiency and productivity. Across the book’s 15 chapters, it broadly extends into popular application areas including agriculture, banking and finance, and municipal performance, and relatively new application areas including corporate social responsibility, the value of intangible assets, land consolidation, and the measurement of economic well-being. The chapters also cover topics such as permutation tests for production frontier shifts, new indices of total factor productivity, and also randomized controlled trials and production frontiers.

J. Zhu and W.D. Cook, Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, Springer, Boston, 2007
ISBN: 0387716068
ISBN-13: 978-0-387-71606-0

About this Book


TABLE OF CONTENTS
Data Irregularities And Structural Complexities In Dea.- Rank Order Data In Dea.- Interval And Ordinal Data. - Variables With Negative Values In Dea.- Non-Discretionary Inputs. - DEA with Undesirable Factors. - European Nitrate Pollution Regulation and French Pig Farms’ Performance. - PCA-DEA.- Mining Nonparametric Frontiers. - DEA Presented Graphically Using Multi-Dimensional Scaling. - DEA Models For Supply Chain or Multi-Stage Structure. - Network DEA.- Context-Dependent Data Envelopment Analysis and its Use. - Flexible Measures–Classifying Inputs and Outputs.- Integer Dea Models. - Data Envelopment Analysis With Missing Data.- Preparing Your Data for DEA.

G. Gregoriou and J. Zhu, Evaluating Hedge Funds and CTA Performance: Data Envelopment Analysis Approach John Wiley & Sons, New York, 2005,
ISBN 0-471-68185-7
About this Book

See a book review (pdf, 125kb)

The software included in the book only works under Excel 2003/XP.

D. Sherman and Joe Zhu, Service Productivity Management: Improving Service Performance Using Data Envelopment Analysis (DEA)
Springer, Boston, 2006,
ISBN  0-387-33211-1

About this Book

TABLE OF CONTENTS
Management of Service Organization Productivity.- Data Envelopment Analysis Explained. - DEA Concepts for Managers.- Solving DEA Using DEAFrontier Software. - DEA Model - Extensions.- Managing Bank Productivity.- Quality-Adjusted DEA (Q-DEA). - Applying DEA to Health Care Organizations.- Government Productivity Management. - Multidimensional Quality-of-Life Measure.- Hedge Fund Performance Evaluation.

The included DEA software works under Excel 97, 2000 and 2003.

The DEA software includes the following DEA models: Envelopment Model, Multiplier Model (with Epsilon), Restricted Multipliers (AR/Cone Ratio Model), Slack-based Model, Measure Specific Model (Uncontrollable factors), and Returns to Scale Estimation.

W.D. Cook and Joe Zhu, Modeling Performance Measurement: Applications and Implementation Issues in DEA