Proceedings of 7th Asia-Pacific Business Research Conference

Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
A New Network Data Envelopment Analysis Model by
Balanced Scorecard Approach for Projects Efficiency
Morteza Shafiee
Purpose of this paper is to provide a framework for evaluating the overall
performance of decision-making units by means of a data envelopment
analysis (DEA) model with balanced scorecard approach. DEA is a linear
programming problem approach for evaluating efficiency of decisionmaking units (DMU) that have multiple inputs and outputs .In this paper we
use a four stages DEA model for efficiency evaluation of DMUs. Many
papers have regressed nonparametric estimate of efficiency in the four
stages DEA procedure. All of these studies have several problems in the
benchmark or projection unit and present the DMU with relative efficiency.
Most of these papers do not present an efficient benchmark unit and
cannot evaluate the relative efficiency unit. In this paper, we review these
studies and then create a four-stage model (based on BSC) that does not
have these problems. Our model presents the efficient benchmark unit
and also, presents DMU with relative efficiency. In the next step we apply
our four stage model in Civil projects promoted by the Omran institute in
Keywords: Performance Evaluation; Data Envelopment Analysis, Balanced
Scorecard; Efficiency Evaluation.
Nowadays, due to the continuous change in economic conditions, evaluating
the performance of industrial and economical units has become one of the
most important factors in their improvement. The projects sector has
experienced profound changes over the past two decades or so.
Globalization, deregulation, financial innovation, automation and speed of
exchanges have been significant, leaving their impact on the performance of
the project sector (Akhtar 2010). So to maintain viability and continuous
improvement, organizations must evaluate their performance, efficiency and
productivity. The project organizations are forced to evaluate their projects
networks efficiency and effectiveness in order to improve the overall
performance of their branches. Industrial and economical units should be
evaluated by utilizing scientific methods in order to evaluate and improve the
performance and structuring a good position in comparison to other units.
Performance and measurement are two familiar words in the literature of
evaluation. Performance is predetermined parameters and measurement
sounds like the ability to monitor events and activities in a meaningful way, so
performance measurement can be defined as the process of quantifying the
effectiveness and efficiency of action (Neely et al, 1995). Several approaches
are used in order to measure the performance, some of them include:
balanced scorecard (Kaplan, Norton, 1992), the performance measurement
matrix (Keegan et al, 1989), performance measurement questionnaire (Dixon
et al, 1990), criteria for measurement system design (Gloderson, 1985) and
computer aided manufacturing approaches. Although effective, we can point
Morteza Shafiee, Department of industrial Management, Shiraz Branch, Islamic Azad
University, Shiraz, Iran.;;
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
out some shortages such as: lack of strategic focus, forcing managers to
encourage local optimization rather than seeking continuous improvement,
and they are also unable to provide adequate information about competitors.
The main aim of this paper is to study project profitability efficiency,
effectiveness and overall performance by utilizing four stages DEA model
based on balanced scorecard approach.
They argued that BSC provides managers with the means they need to
navigate future competitive success. It included more non-financial measures
derived specifically from the organization’s strategy. BSC is one of the most
comprehensive and simple performance measurement means that
emphasizes both aspects of financial and non-financial, long-term and shortterm strategies as well as internal and external business measures. The
strongest point of BSC is its ability to illustrate the cause and effect relations
between strategies and processes through the four BSC’s perspectives of
financial perspective; customer perspective; internal business process
perspective, as well as learning and growth perspective.
Since it is not possible to determine the efficiency evaluation score by
applying BSC, we used the data envelopment analysis model (DEA) to
calculate the efficiency score of supply chain performance.
Literature Review
Data Envelopment Analysis
DEA is a linear programming based methodology which can calculate multiple
inputs and outputs and can also evaluate DMUs both qualitatively and
quantitatively. DMU, which can be related to different firms or the condition of
the same firm over time, stands for decision-making unit. DEA was first
proposed by Charnes, Cooper and Rhodes (CCR) in 1978. The evolutionary
form of CCR model was suggested in1984 by Banker et al. In subsequent
years, several models were developed by a large number of researchers.
Orientation, disposability, diversification, and return to scale are different
aspects that can be seen in these models. DEA utilizes frontier function in
measuring efficiency. It then introduces a set of efficient and inefficient units.
The analysis of inefficient units has two aspects. First, it can show the
maximum input level in order to attain a given amount of outputs. Secondly, it
can also show the highest output level attained for a given amount of inputs.
These approaches are called "minimal principle of efficiency" and "maximum
principle of efficiency" respectively.
Balanced Scorecard
The Balanced Scorecard, developed by Kaplan and Norton at Harvard
Business School in the early 1990s [4], is undoubtedly one of the best-known
and most widely used frameworks for performance measurement proposed in
recent years. The BSC is a conceptual framework for translating an
organization’ strategic objectives into a set of performance measures
distributed among four perspectives: financial, customer, internal business
processes, and learning and growth. The BSC is developed from the
organization’s vision and strategy and its main strength is in the way it seeks
to integrate different measures and make explicit the links between different
dimensions of performance in a single system. By forcing senior managers to
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
consider all the important operational measures (some of which conflict) at the
same time, it is claimed that the BSC prevents sub-optimization of
performance [10,11].Despite its strengths and widespread use, numerous
authors have identified shortcomings in the BSC. One of the criticisms that
has been made is the fact that it does not specify how trade-offs are to be
made between different scorecard criteria [12], nor does it specify an objective
weighting scheme for the performance measures. It has also been argued
that an analysis based on the BSC may fail to identify inefficiency in the use of
resources [13].Furthermore, without a benchmarking exercise, the
identification of appropriate targets for each of the performance indicators is
difficult in practice. It is our conviction that the integration of DEA with the BSC
can overcome some of the limitations of the BSC, providing the basis for
enhanced performance assessment.
Proposal Model
In this stage, we create two stage DEA model and then modify it into four
stages DEA model based on the four perspective of Balanced Scorecard.
Consider a two stage model that is shown in Figure 1. Suppose, we have n
DMUs. Each DMU has m inputs to the first stage,
, and D
outputs to the first stage,
. These outputs then become the
inputs to the second stage. DMU has S outputs to the second stage,
. We use the notation in Chen and Zhu (2004), Chen, Liang and
Zhu (2009) and Kao and Hwang (2008). Based upon the properties of PPS
and arranging the data sets in matrices
, we can
define the production possibility set of a two stage model that satisfies 1
through 4 expressed below.
Stage 1
Stage 2
Figure 1: A two stage model
Property 1) The observed activities
to this property, we have:
belong to P. According
Property 2) Any semi positive linear combination of activities in P belongs to
P. According to the convexity axiom, we have:
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
Property 3) For activities
in P, any semi positive activity
is included in P and for an activities
in P, any semi positive
is included in P. With this property we have:
Property 4) If activities
belong to P, then the activities
belong to P
for any positive scalar t. We call this property the constant return to scale
assumption. With accept this assumption the PPS to become changed into:
Now, to build the model with the use of PPS definition, we have:
With these properties and the definition of PPS, the two stage DEA model is:
The dual of this model is:
Definition: Projections of a network are defined as follows:
Now, we modify the two stage model onto four stages DEA model based on
BSC. Suppose, we can four stages based on BSC perspectives. These
stages are shown in Figure 2.
Learning & Growth
i= 1, 2 ,… , m
Internal Process
Figure 2. A four stage model
We can define the production possibility set of a four stage model that
satisfies 1 through 4 expressed below.
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
Now, to build the model with the use of PPS definition, we have:(9)
With these properties and the definition of PPS, the two stage DEA model is:
The dual of this model is:
: vx0 = 1
Results and Discussions
In the next step we apply the performance for a sample of the Iranian Project.
We used our four stage DEA model to evaluate the Iranian projects
performance. In this case study, we have several inputs and outputs for the
each stage.. The results show that the DMU 1, 2, 4, 7, 8, 10, 11, 12, 14 and
15 are efficient.
A general framework to evaluate the overall performance in terms of
profitability efficiency and effectiveness by means of four stage DEA model
and BSC approach is proposed. There are several studies about Network
stage DEA model in the literature of DEA. The most of these models have
several problems, such as, they cannot determine the relative efficiency of
DMUs, nor can they determine an efficient projection unit. In our model, these
problems have been solved. The proposed model can determine the relative
efficiency and an efficient projection unit.
We believe that our proposed model can be modified into a multi-stage model.
Future researchers can extend this improvement. Note that, in the proposed
model, the values of immediate measures are fixed so we propose to the
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
future researchers that modify this model into the changeable immediate
Akhtar MH (2010). Are Saudi banks productive and efficient? Int. J. of Islamic
and Middle Eastern Finance and Management. 3(2): 95-112.
Neely A, Gregory M, Platts K (1995). Performance measurement system
design: a literature review and research agenda. Int. J. of Operations and
Production Management. 15(4): 80-116
Kaplan RS, Norton DP (1992). The balanced scorecard: measures that drive
performance. Harvard Business Review. 70(1): 71-79
Keagan DP, Eiler RG, Jones CR (1989). Are your performance measures
obsolete? Management Accounting. June. 45-50
Dixon JR, Nanni AJ, Vollmann TE (1990). The new performance challenge:
measuring operations for world class competition. Dow Jones. Irwin.
Homewood. IL.
Gloderson S (1985). Issues in developing a performance criteria system for
an organization. Int. J. of Production Research. 23(4): 639-646.
Charnes A, Cooper WW, Rhodes E (1978). Measuring the efficiency of
decision making units. European J. of Operational Research. 2(6): 429-444.
Banker R, Charnes A, Cooper WW (1984). Some models for estimating
technical and scale inefficiencies in data envelopment analysis. European J.
of Operational Research. 9: 1078-1092.
Chen Y, Zhu J (2004). Measuring information technology’s indirect impact on
firm performance. J. of Information Technology and Management. 5(1/2): 922.
Chen Y, Liang L, Zhu J (2009). Equivalence in two-stage DEA approaches.
European J. of Operational Research. 193(2): 600-604.
Kao C, Hwang SN (2008). Efficiency decomposition in two-stage data
envelopment analysis: an application to non-life insurance companies in
Taiwan. European J. of Operational Research. 185(1): 418-429.