The following actions are the steps taken while undertaking evaluation of comparative efficiency of the set of construction organisations using DEA in line with Golany and Roll (1989) and Thanassoulis (2003):
(1) definition and selection of the organisations; (2) identification of the input-output variables;
(3) construction of the production possible sets (PPS); (4) establishment of the type of efficiency to be assessed; (5) determination of organisations’ sample size;
x m s
y
can produce x, y x y 0 j(6) determination of a DEA model;
(7) solving the linear program of the identified DEA model for all organisations;
(8) presentation and analysis of the outcome.
4.16.1 Strategic Grouping
There are always differences in the way organisations are managed that may lead to different decision making. Therefore, while the objectives of DEA analysis include identifying the differences in the performances of the organisations assessed, there is the requirement to have the organisations to be homogenous (Farrell, 1957).
The homogeneity of the operating enterprise to be assessed using DEA was ensured by conducting strategic group analysis of the identified engineering and construction organisations. A strategic group consists of those rival firms with similar competitive approaches and positions in the market. The detailed concept of strategic group in the construction industry was provided in section 2.6 of chapter two. Strategic groups provide an intermediate frame of reference between viewing an industry as a whole and considering each firm separately (Flavian and Polo, 1999; Dikmen et al., 2009).
4.16.2 Organisations’ Sample Size
The next step is to determine the size of the comparison group (N). A large population size of the organisations will tend to increase the probability of capturing high performance organisations which would determine the efficiency frontier. However, a rule of thumb is that the number of sampled firms should be at least twice the sum of the number of inputs and outputs variables (Ali et al., 1988; Bowlin, 1987).
4.16.3 Inputs and Outputs Variables
One of the fundamentals for the assessment of comparative efficiency by DEA is the construction of the production possible set (PPS) containing all
input-output level ‘correspondences’ which are capable of being observed. Correspondence of inputs and outputs in this context is based on a relationship of exclusivity and exhaustiveness between the two sets of variables (Thanassoulis, 2003).
The initial list of the variables to be considered for assessing organisational performance should be as wide as possible. Every dimension, the changes which may affect the organisations to be evaluated, should be included in the initial list. The input variables should capture all resources and the output variables all the outcomes having a bearing on the type of efficiency being assessed. In addition, contextual factors impacting the transformation of inputs to outputs should also be reflected which in our case include complimentary organisational resources. The initial set of potential input-output variables can be refined using a combination of statistical test and/or sensitivity analysis (Boussofiane, et al., 1991; Thanassoulis, 2003).
4.16.4 Solving a DEA Model
The linear program (LP) formulations are a function of a particular organisation about which we need to determine its efficiency classification. The procedure based on solving one LP for each of the organisations using the entire data set is standard. This is presented as follows (Ali, 1993; Dulá 2008):
1 For j = 1 to N 2 Initialize j* j
3 Define x0 xj, y0 yj
4 Solve equation (4.4) for * s* and * 5 Increase j j+1 for j<N
6 If j<1 go to 3 7 If j=N terminate
Figure 4.7 DEA Algorithm (Kassim et al., 2010c)
4.16.5 Interpreting DEA Model
DEA is used to measure the technical efficiency of enterprises; the transformation of inputs such as IT resources into outputs in a form of an organisation’s performances which is compared to a best practice organization. Thus DEA was applied to identify construction organisations that have efficiently utilized its IT resources, hence justify the investments with better performance. The inefficient organization (s) could be benchmarked and have role models that can guide them in
learning how they can improve the implementation of IT resources in their operations for competitive advantage (Jui-Chi, 2006).
Based on Pareto optimality the concept DEA defined an enterprise as
100% efficient when and only when (Wöber et al., 2004):
1. None of its outputs can be increased without either
a. increasing one or more of its inputs,
b. decreasing some of its other outputs; and
2. None of its inputs can be decreased without either
a. decreasing some of its outputs, or
b. increasing some of its other inputs.
Thus an organisation is Pareto efficient if and only if it is not possible to improve any input or output without worsening some other input or
output. (Cooper et al, 2006: 45).
DEA may be viewed from two perspectives: envelopment and multiplier (Seiford and Thrall, 1990).In the envelopment form of DEA, for each DMU taken in turn the linear combination of all DMU's is defined so that (Maital and Vaninsky, 1999):
(i) minimal inputs be achieved with outputs no less than existing ones, or
(ii) maximal outputs are obtained with inputs no more than actually used.
The first approach is called the input minimization DEA model, and the second, the output maximization.
DEA starts by building a relative ratio consisting of total weighted outputs to total weighted inputs for each organisation in a given data set. The best organisations in the data set form an “efficient frontier” and the degree of the inefficiencies of the other units relative to the efficient frontier are then
The capability and possible outcome of using DEA in evaluating the
performance of organisations include that DEA is (Chiang, 2006):
i. capable of analytically identifying the relatively more effective organizations from the less effective organizations;
ii. capable of deriving a single summary measure of the relative effectiveness of organizations, in terms of their utilization of resources and environmental factors, to produce desired outcomes; iii. able to handle non-commensurate, conflicting multiple outcome
measures, multiple resource factors and multiple environmental factors that lie outside the control of the organization being evaluated, and not be dependent on a set of a priori weights or prices for the resources utilized, the environmental factors, or the outcome measures;
iv. able to handle qualitative factors such as participant satisfaction, the extent of information processing available, the degree of competition, etc.;
v. able to provide insights into which factors contribute to the relative effectiveness ratings;
vi. able to maintain evaluation equity. (Lewin and Minton, 1986). Most importantly, DEA allows for identification of the best practices and benchmarks for the poor performing units. The ability of DEA to identify possible peers or role models as well as simple efficiency scores gives it an edge over other measures such as total factor productivity indices.