This document provides an introduction and methodology for analyzing the technical efficiency of manufacturing SMEs in Malaysia. It discusses how SMEs contribute significantly to the Malaysian economy but often face challenges. The study aims to determine the average technical efficiency of SME manufacturers, factors affecting inefficiency, and how to improve efficiency. It will use a stochastic frontier analysis with a Cobb-Douglas production function and 2013 census data to estimate technical efficiency and inefficiency effects among SMEs. Hypotheses about inefficiency will also be tested.

1. TECHNICAL
EFFICIENCY OF
MANUFACTURING
SME’S IN MALAYSIA
Master Research
Proposal
Semester 1(2014/2015)
PREPARED BY:
NORAINI BINTI ISMAIL

2. INTRODUCTIO
N
• In the Malaysia economy, the role of SMEs is
considered as the backbone of the economy.
• Represented over 99.2% of all business
establishment in the country.
• Provide employment for about 56% of the
total workforce.

3. INTRODUCTIO
N
Table 1.1: New SME Definition Based on Size
Sources: SME Corp

4. INTRODUCTIO
N
INTRODUCTIO
N
• Contributed 33.1% of total GDP by 2013
• They were particularly dense in the;
− Services
− Manufacturing
− Agriculture
− Mining and quarrying
− Construction

5. 2005e
2006e
2007e
2008e
2009e
2010e
2011e
2012e
2013p
Agriculture 3.4 3.5 3.4 3.3 3.4 3.4 3.4 3.3 3.2
Miningandquarrying 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
Manufacturing 8.1 8.1 8.2 7.8 7.4 7.7 7.8 7.9 7.9
Construction 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.9 1.0
Services 17.0 17.2 18.2 19.1 19.9 19.8 20.1 20.2 20.6
plus Importduties 0.1 0.1 0.1 0.2 0.2 0.2 0.3 0.4 0.4
Year
Kindofeconomic
activitye
Total 32.7 33.129.4 29.6 30.7 31.2 31.7 32.0 32.5
Table 1.2: Contribution og SMEs to GDP by kind of economic
Sources: Department of Statistic Malaysia

6. • SMEs face common problems in respect of their
survival or take competitive advantages.
• According to the available resources more than
50% of SMEs are collapse within first five years
of operation. (Reiss, 2006; Ahmad and Seet,
2009)
• In general, problems encountered by SMEs are
caused by internal and external environment,
such as lack of capabilities and resources, poor
management, low technology, competition,
economics, technology, social, cultural and
international factors (Hashim, 2000)
PROBLEM
STATEMENT

7. Main objective -> to analyse in detail the
competitiveness performance of Malaysia
manufacturing SMEs as measure by their technical
efficiency. The specific objective as follow:
i. Determine the weighted average of technical
efficiency in SMEs manufacturing.
ii. Determine the factors that significantly affect
the technical inefficiency of production.
iii.Measure the key to improve the technical
efficiency of Malaysia manufacturing SMEs.
RESEARCH
OBJECTIVE

8. 1) Contributes to the literature by examining the
technical efficiency.
2) The study takes Cobb-Douglas to predict
technical efficiency and to estimate inefficiency
effects using cross-sectional firm level data.
Knowing the factor affect in efficiency help policy
makers in formulating appropriate policy to help
SME sectors.
CONTRIBUTION

9. LITERATURE
REVIEW
“Farrell (1957) was the first to use frontier
production functions to measure technical
efficiency. The method involves estimating a
frontier production function in order to calculate
the maximum output that can be obtained by each
production unit with a given combination of inputs.
Waheeduzzaman et. al. (1996).

10. LITERATURE REVIEW
Researcher Brief
(Saleh and Ndubisi,
2006)
A low level of productivity and input
quality may attribute to low levels of
value added in SMEs, which further
affects the level of optimum efficiency
in the production process
Geeta Batra and
Hong Tan,(2003)
Most of SMEs were less efficient on
average than their larger
counterparts in five countries
(Malaysia, Indonesia, Mexico,
Colombia, Taiwan), but a significant
number of highly efficient SMEs were
found, and they were relatively more
productive than some large firms

11. EMPIRICAL
REVIEW
Author Method Finding
Khai &
Yabe (2011)
SFA
Analysis
Technical efficiency analysis of rice
production in Vietnam
Shazali et
al. (2004)
Stochastic
Frontier
Production
Model
Examined the technical efficiency
of the Malaysian Furniture
Industry.
Radam et.
al (2008)
Stochastic
Frontier
Model
Examine the technical efficiency of
SMEs in Malaysia.
Teerawar
et. Al (2013)
SFA
Analysis
Thai manufacturing small and
medium sized enterprise technical
efficiency

12. METHODOLOG
Y
INTRODUCTION
• Using Stochastic Frontier Analysis (SFA) to
measure the technical efficiency and technical
inefficiency of manufacturing Malaysia SME.
• SFA is a parametric approach where the form of
the production function is assumed to be known
or is estimated statistically.
• Its also allows other parameters of the production
technology to be explored.

13. METHODOLOG
Y
• The formula as below :
Where;
• yi is the observed scalar output of the producer
while i, i=1,..I, xi is a vector of N inputs used by
the producer
• f(xi, β) is the production frontier
• β is a vector of technology parameters to be
estimated
• TEi denotes the technical efficiency defined as the
ratio of observed output to maximum feasible
output.

14. METHODOLOG
Y
SOURCE OF DATA
• Using 2013 industrial census data collected by
Department of Statistic of Malaysia (DOS)
• The census is based on a Stratified Systematic
Sampling methodology
• Data for Malaysia manufacturing SMEs are
categorized in three way: by size of SME (small or
medium), by aggregate manufacturing SMEs, and
by sub-manufacturing sectors classified by MSIC
Revision 4.

15. METHODOLOG
YVARIABLES:
• Data extracted from the census are those
pertaining to the estimation of a Cobb-Douglas
production function, and include value added (Y),
labor input (L), and capital input (K).
MODEL SPECIFICATION:
• A Cobb-Douglas production function can be used
to predict technical efficiency and to estimate
inefficiency effects model, such for sample of
Malaysia manufacturing SMEs, using cross-
sectional firm level data.

16. METHODOLOG
YHYPOTHESIS TESTS
• The estimation of a stochastic frontier production
function can be used to test the validation of three
null hypothesis:
Ho: absence of technical inefficiency effects
H1: absence of stochastic inefficiency effects
H2: insignificance of joint inefficiency variables
• The three hypothesis will be tested using the
generalized likelihood-ratio test (LR test) .