advertisement

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 Evaluation 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 IRAN. Keywords: Performance Evaluation; Data Envelopment Analysis, Balanced Scorecard; Efficiency Evaluation. Introduction 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. [email protected]; [email protected]; 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 , and , 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 (1) Property 2) Any semi positive linear combination of activities in P belongs to P. According to the convexity axiom, we have: (2) 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 and in P, any semi positive activity is included in P and for an activities activity with and with in P, any semi positive is included in P. With this property we have: (3) 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: (4) Now, to build the model with the use of PPS definition, we have: (5) S.t With these properties and the definition of PPS, the two stage DEA model is: (6) The dual of this model is: (7) Definition: Projections of a network are defined as follows: (8) 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. Zdj Xij Learning & Growth i= 1, 2 ,… , m d=1,2,…,d Tkj Internal Process Hej Customer k=1,2,…,k Yrj Financial e=1,2,…,e r=1,2,…,s 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) S.t (10) With these properties and the definition of PPS, the two stage DEA model is: (11) The dual of this model is: S.t : vx0 = 1 (12) 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. Conclusions 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 value. References 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.