Thursday, November 17, 2016

Productivity Measurement

Productivity management is one of the important functions of industrial engineering departments in companies. Productivity measurement facilitates planning and controlling productivity levels in the companies.

I.  Kendrick – Creamer Model :[1]

Kendrick and Creamer (1965) introduced productivity indexes at the company level in their book, "Measuring Company Productivity". They proposed two types of  indices: total productivity and partial productivity.
Total productivity index for given period = (Measured period output in base period price) / (Measured period input in base period price)
Total factor productivity index = net output/total factor input
Net output – intermediate goods and services
Total factor input = manhour input and total capital
Partial productivity  of  labour, capital or material productivity index can be calculated as: Partial productivity = (Output in base period price) / (Any one Input in base period price)

II. Craig –Harris Model :

Craig and Harris (1972, 1973) [2,3] defined total productivity measure: 

  PT=   OT / ( L+C+R+Q )
PT  = total productivity,
OT= total out put.
L = labor input factor,
C = capital input factor,
R = raw material input factor and
Q = other miscellaneous goods and services input factor
The output is defined as the summation of all units produced times their selling price, plus dividends from securities and interest from bonds and other such sources-all adjusted to base-period values.

III. Hines (1976)[4] proposed some measurement improvements to various individual items in productivity measurement models.

IV. American Productivity Centre Model :[5]

American Productivity Center has measure that expresses profitability as a product of   productivity and price factor. The way it is done is:  
Profitability   = Sales / Cost
                            = (Output quantity) *(Price) / (input quantity)* (unit cost)
                            = (Productivity)* (Price recovery factor)

Where; productivity = output quantity / input quantity

V. Sumanth’s Total Productivity Model (1979) [6]

Total productivity (TPM) = total tangible output/total tangible input
Total tangible output = (value of finished units produced + value of partial units produced
                             + dividends from securities + interest from bonds+ other income)
Total tangible input = value of (human + material + capital + energy + other expenses) inputs used.
Sumanth provided a structure for finding productivity at product level and summing product level productivities to total firm level productivity. The model also has the structure for finding partial productivities at the product level and aggregating them to product level productivities.

Is productivity measurement in practice today?

The answer is yes.
In FY 2006 the study group on the Creation of a Productivity Database on Japanese, Chinese, and South Korean Companies at the Japan Center for Economic Research (JCER) created the East Asian Listed Companies Database 2007 ("EALC 2007") along with the Hitotsubashi University Center for Economic Institutions (CEI), the CENU Center for China and Asian Studies (CCAS; Professor Tomohiko Inui as project representative), and the Center for Corporate Competitiveness of Seoul National University (Professor Keun Lee as project representative). EALC 2007 in principle targets all listed firms in Japan, China, and South Korea (not including the financial sector). It includes data necessary to measure total factor productivity at the company level and the periods covered are 1985 through 2004 for Japanese firms, 1985 through 2005 for South Korean firms, and 1999 through 2004 for Chinese firms.

Based on direct comparison of the total factor productivity of listed firms in Japan, China, and South Korea, the researchers analyzed the following questions. 1) In which industries in particular are South Korean and Chinese companies catching up to Japanese ones? 2) Has productivity growth in Japanese firms stagnated since the 1990s? 3) If it has stagnated, in which industries is this most remarkable? 4) What are the characteristics of disparities in productivity among companies in the same industries in each country? The reports (in Japanese) cover the results of the research on these questions[6].

The Japan Center for Economic Research, the Hitotsubashi University Center for Economic Institutions, the CENU Center for China and Asian Studies, and the Center for Corporate Competitiveness of Seoul National University plan to continue joint research in FY 2007, including revising and updating the EALC database, expanding the countries targeted, and analyzing results.


1. Kendrick, J.W., and D. Creamer, “Measuring Company Productivity: Handbook with Case Studies.” Studies in Business Economics, No. 89, National Industrial Conference Board, New York, 1965.
2. Craig, C.E., and C.R. Harris, “Productivity Concepts and Measurement- A Management Viewpoint,”Unpublished Master’s thesis, M.I.T., Cambridge, Massachusetts, 1972.
3. Craig, C.E., and C.R. Harris,“Total Productivity measurement at the firm level,” Sloan Management Review, Vol 14, No. 3, 1973, pp. 13-29.
4.  Ruch, W.A., “Your Key to Planning Profits”, The Productivity Brief 6, Oct.1981, by American Productivity Cente,r Houston. TX-77024.
5. Sumanth, David J., Productivity Engineering and Management, McGraw Hill Book Company, 1984.

Additional Bibliography

International Applications of Productivity and Efficiency Analysis: A Special Issue of the Journal of Productivity Analysis
Thomas R. Gulledge, C.A. Knox Lovell
Springer Science & Business Media, Nov 11, 2013 - 200 pages

International Applications of Productivity and Efficiency Analysis features a complete range of techniques utilized in frontier analysis, including extensions of existing techniques and the development of new techniques. Another feature is that most of the contributions use panel data in a variety of approaches. Finally, the range of empirical applications is at least as great as the range of techniques, and many of the applications are of considerable policy relevance.

Managerial Issues in Productivity Analysis
Ali Dogramaci, Nabil R. Adam
Springer Science & Business Media, Dec 6, 2012 - 246 pages

A. Dogramaci and N.R. Adam Productivity of a firm is influenced both by economic forces which act at the macro level and impose themselves on the individual firm as well as internal factors that result from decisions and processes which take place within the boundaries of the firm. Efforts towards increasing the produc tivity level of firms need to be based on a sound understanding of how the above processes take place. Our objective in this volume is to present some of the recent research work in this field. The volume consists of three parts. In part I, two macro issues are addressed (taxation and inflation) and their relation to produc tivity is analyzed. The second part of the volume focuses on methods for productivity analysis within the firm. Finally, the third part of the book deals with two additional productivity analysis techniques and their applications to public utilities. The objective of the volume is not to present a unified point of view, but rather to cover a sample of different methodologies and perspectives through original, scholarly papers.
(Chapter 8 is an interesting paper with propositions on technical efficiency of technology in electric utility companies)

Construction Management and Economics
Volume 24, Issue 10, 2006
Construction equipment productivity estimation using artificial neural network model
Seung C. Oka & Sunil K. Sinhaa*

pages 1029-1044

An Introduction to Efficiency and Productivity Analysis
Tim Coelli
Springer Science & Business Media, Jul 22, 2005 - 349 pages
The second edition of this book has been written for the same audience as the first edition. It is designed to be a "first port of call" for people wishing to study efficiency and productivity analysis. The book provides an accessible introduction to the four principal methods involved: econometric estimation of average response models; index numbers; data envelopment analysis (DEA); and stochastic firontier analysis (SFA). For each method, we provide a detailed introduction to the basic concepts, give some simple numerical examples, discuss some of the more important extensions to the basic methods, and provide references for further reading. In addition, we provide a number of detailed empirical applications using real-world data. The book can be used as a textbook or as a reference text. As a textbook, it probably contains too much material to cover in a single semester, so most instructors will want to design a course around a subset of chapters. For example, Chapter 2 is devoted to a review of production economics and could probably be skipped in a course for graduate economics majors. However, it should prove useful to undergraduate students and those doing a major in another field, such as business management or health studies.

Updated  20 Nov 2016,   12 June 2016,  19 June 2015
First published on 10 Feb 2012


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