Performance Evaluation and Prediction of IT-Outsourcing Service Supply Chain based on Improved SCOR Model

Performance Evaluation and Prediction of IT-Outsourcing Service Supply Chain based on Improved SCOR Model 1, 2 1 International School of Software, Wuhan University, Wuhan, China *2 School of Information Management, Hubei University of economics, Wuhan, China, shily0118@163.com Abstract IT outsourcing service supply chain performance evaluation and prediction is an important part of IT outsourcing services supply chain management, it s a typical multi-objective decision making problems. Considering the nonlinear and complex characteristics of performance evaluation, the improved SCOR model is designed, and then a RBF neural -Markov chain's model of IT outsourcing service supply chain performance assessment and prediction is proposed. Use the radial basis function neural self-learning, adaptive and best approximation performance to train evaluation data, get the evaluation results, and then applies Markov forecast method to predict the future of IT outsourcing services supply chain, in order to achieve better control performance purpose. The example shows that the model of performance evaluation and prediction can provide the guide for effective analysis and rational decision-making of IT outsourcing service supply chain management. Keywords: IT-Outsourcing, SCOR, Radial Basis Function Neural Network, Performance, Markov Forecast 1. Introduction IT outsourcing service supply chain is the application of service supply chain in IT outsourcing industry; it is an important research area of service supply chain [1]. IT outsourcing service supply chain is a functional structure, which is formed by the IT outsourcing service integrators, IT outsourcing service providers and IT outsourcing customer. Among them, IT outsourcing service integrators and IT outsourcing service providers as supply main body, achieve IT outsourcing customer s requirements through ability cooperation. IT outsourcing service integrators in IT outsourcing services plays the leading position of supply chain core, be responsible for the operation of the whole supply chain. Because of the complexity of the outsourcing itself and rapidly development of IT technology, the rapid growth of the IT outsourcing is always accompanied by high failure rate, the domestic IT outsourcing service is still in the initial stage, management experience is not very short deficiency, many projects are still in accordance with the general IT projects management approach to management [2]. Gartner research report shows that 80% of the large IT outsourcing project did not meet the target contract in China. Therefore, how to choose the scientific and reasonable performance evaluation method has become an important problem to be solved in IT outsourcing service supply chain research. IT outsourcing performance evaluation is the indicator to measure IT outsourcing is the successful or not. And from what respect, using what of IT outsourcing index to evaluate the performance, which is the measure key to ensure the effectiveness and reliability of results. At present, due to business outsourcing performance evaluation research has not been formed a standard system or model, there are some simple methods for measuring the discussion. For example, performance is evaluated by accumulating the positive result [3], also in a single standard as a performance evaluation index, and from three aspects (the strategic, technology and economy) ; outsourcing performance is evaluated [4]. XuShu, HuMingMing (2008) [5] construct the index system from the strategic benefits and technical efficiency and economic benefit, and combining IT outsourcing operation and management of the process, LiYing [6] believe that contract management, cost management, knowledge management, risk management are important factors to influence enterprise IT service outsourcing. Index of IT service outsourcing performance is not much. In research methods, mainly concentrated in the ABC cost method and linear programming method [7] and analytic hierarchy process (AHP), and fuzzy comprehensive evaluation method [8], principal component analysis, grey comprehensive evaluation International Journal of Advancements in Computing Technology(IJACT) Volume5,Number5,March 2013 doi:10.4156/ijact.vol5.issue5.43 354

method [9] and the method of integrated application of law, these methods supply chain performance just static evaluation, cannot be used to forecast the development trend of supply chain expected [10-11]. In order to solve these problems, on the basis of SCOR model, combining the characteristics of IT outsourcing services, the improved SCOR model is constructed, and performance evaluation index system is established, and then use the radial basis function neural, according to three neurons layers of quantitative evaluation method to train data from IT outsourcing service supply chain performance evaluation, based on evaluation results, using Markov forecast method to predict IT outsourcing services supply chain state and development trends. For policy makers, it can provide the guide for effective analysis. 2. It outsourcing service supply chain evaluation and prediction model 2.1. Improved SCOR model framework and performance evaluation index selection and measurement SCOR (Supply-Chain Operations Reference-model) model is the common model in the Supply Chain performance evaluation; it is developed by the international Supply Chain association (Supply- Chain Council), and it is a Supply Chain operation Reference model suitable for various industries [12]. SCOR is based on four different processes, as shown in figure 1. It was emphasized that supply chain performance evaluation must be based on business process evaluation, for the supply chain performance evaluation information system standardization, it made beneficial attempt, but did not point to the supply chain performance evaluation causal relationship between dimensions, and not including the customer service evaluation, and so on. Because the production is intangible product in IT outsourcing service industry, therefore we can combine the characteristics of IT outsourcing services, simplify SCOR as shown in figure 2, it shows improvement SCOR model framework [12]. Figure 1. Supply-Chain Operations Reference-Model Figure 2. Improved SVOR of IT Outsourcing Service Supply Chain 355

At present there have been many scholars to do a lot of research in the supply chain performance evaluation index system, but the research Angle and theory are not the only about the supply chain performance evaluation in academic circles, any a paper cannot cover the entire content of the supply chain performance evaluation. Based on the analysis of the existing service performance evaluation system of supply chain [6-10], combined with the characteristics of IT outsourcing service industry and improve the structure of the SCOR model, an IT outsourcing service supply chain performance evaluation index system is established from three aspects of customer satisfaction, profitability, cost management, which is shown in figure 3. Figure 3. Index System of IT-Outsourcing Service Supply Chain Performance Evaluation In the evaluation system, some indicators are qualitative indexes, some are quantitative indexes. Formulas about some quantitative indexes are shown in Table 1. Table 1. Some quantitative indexes and formulas indicator Formula value-added productivity Total income/total number Fund turnover time account receivable time- account payable time delivery performance (Time to pay - failed orders) / total orders financial rewards revenue/total assets order to perform rate Time to pay/ total orders 2.2. Training evaluation data by using RBF neural Radial Basis Function [13] is proposed by Moody and Darken in the late 1980s, it is a kind of the neural learning method according to extend or pretreatment input vector to high dimension space, the structure is very similar to the multi-layer perceptron (MLP) Radial basis Function neural, it is an excellent, especially in convergence speed, BP has the incomparable advantage. There are three layers in Radial basis function (RBF) : input layer, hidden layer and output layer, the general structure of the is shown in figure2 [13]. In practical applications, we base on the specific circumstances of each layer to determine the number of nodes. Figure 4. General Structure of the Radial Basis Function Neural Network 356

Using RBF neural to train data, the steps of evaluation procedure are as follows: Step 1, the design. RBF neural is a Three-layer to the, including the input layer, hidden and output layer. The input layer nodes are determined by the number of index; Hidden layer node is composed of radial effect function like a Gaussian function. Output layer is implicit unit output of the linear weighted and learning speed. The self-organizing selection center algorithm is introduced in RBF neural learning methods [14]. The first step of this algorithm is the organization learning stage, including solving hidden base function and variance of the center; Gaussian function as the radial basis function: 1 2 R( X p ci ) exp( X ) 2 p ci (1) 2 Variance can solve by the formula: cmax i, i 1,2,..., h (2) 2h The second step of this algorithm is a supervised learning stage to solve the weights between the hidden layer and output layer. Connection weights of neuron between the hidden layer and output layer are calculated by using least squares method, the formula as follows: h 2 wexp( x ), 1, 2, ; 1, 2,, 2 p ci p P i h (3) c max The output of the is: h 1 2 yj wij exp( X ), (1,2,..., ) 2 p ci j n (4) i1 2 Below Figure 5 shows the different mean value and variance of the Gaussian function comparison chart. Figure 5. Comparison Chart of the Different Mean Value and Variance of the Gaussian Function Step 2: the training. To collect samples of the normalized later, using Matlab7, the RBF neural is trained, as well as to the data validation. Step 3: the evaluation. Select the test samples to evaluate the performance of the model validation. 2.3. Markov chain prediction of performance evaluation Markov process [15] is a special kind of random process, named for the Great Russian mathematician Markov. The characteristic of this process exist to determine the transition probability, and has nothing to do with the previous history of the system. There is a very image of the parable to describe this process: the frogs in the pond jumping around on the lotus leaf, if will it, at a time of lotus leaf is called state, the frog future in what state and it only the place where the state, and it regardless of the status of the place before. For Markov process, the current state of the known system, the status of 357

the past and future state is independent of each other. This is the so-called first-order Markov property " or " no aftereffect". Definition 1: If any one state i i,..., in, i,, and n 0, and any random process 0, 1 1 j { xn, n 0} meets Markov properties: PX { j X i,, X i, X i} PX { j X i} (5) n1 0 0 n1 n1 n n1 n The stochastic process { xn, n 0} is namely for discrete time Markov chain. Definition 2: set { xn, n 0} to a discrete time Markov chain. For any i, j S, PX { n 1 j X n i} is known as Markov chain step transition probability, remember be n irrelevant, says the Markov chain has a smooth transition probability, and notes for transition probability has properties below: (a) p ij 0, i, j (b) pij 1, j i p n ij, n1. When the probability and p ij. Its stable Usually the transition probability in a (infinite dimensional) square, namely p00 p01 p0 j p10 p11 p1 j P (6) pi0 pi1 pij It is transition probability matrix of the Markov chain. Through several steps transfer, it will eventually make the system to reach a stable state, that is, followed a transfer, secondary,..., the result is no longer change, namely n p n p n p p n pp p p (7) ( 1) ( 2)... (0) According to the above model, the whole supply chain performance of IT outsourcing services can be predicted in a moment of the future. 3. Analysis of example Suppose selecting the performance evaluation data of 24 months between May 2010 and May 2012 about IT outsourcing services supply chain as 24 samples data. Among them, 16 sample evaluation data form the learning evaluation of samples; they will be used to be training data of RBF neural, the remaining eight samples of evaluation data will be used as validation data. The final evaluation results can only be "excellent", "good", "medium", "poor", so the output layer nodes of the RBF neural as 1. The three-layer radial basis function neural is established by Matlab7 [16], neurons are nine in the input layer, output layer neuron is one, node numbers of hidden layer are 13, 16 sample data are training on RBF neural. Selected the instance data from the rest of 8 group did not participate in the training samples as testing samples to test the accuracy of the model. To demonstrate the superiority of the hybrid model, the author made that the BP neural design, by contrast, through the experiment, the training results shown in Table 2. It can be seen from Table 2, compared with BP neural, error between the actual value and the output value in the radial basis function neural model is smaller, the result is more satisfied. Because of the random of the IT outsourcing service supply chain operation, its change trend is related to the now state. The Markov chain is used to predict performance of service supply chain in the future. 358

Sample ID Actual value Table 2. Comparison of Two training results Output Error of BP value of BP level neural neural Output value of RBF neural Error of RBF neural 17 87.3 good 87.952 0.0075 87.491 0.0022 18 91 excellent 90.847 0.0017 90.965 0.0004 19 75 medium 75.722 0.0096 74.928 0.0010 20 85 good 85.221 0.0026 84.942 0.0007 21 72.7 medium 73.531 0.0114 72.842 0.0020 22 85.9 good 86.032 0.0015 86.002 0.0012 23 62.7 poor 63.531 0.0136 62.842 0.0023 24 80 good 79.938 0.0008 79.968 0.0004 According to IT outsourcing services supply chain performance levels in Table 2, the state transition probability matrix of the service supply chain can be drawn: 0 0 1 0 1/3 0 1/3 1/3 p 0 1 0 0 0 1 0 0 Next, we use QM for Windows [17] to analyze Markov, the "final" stable performance evaluation results of IT outsourcing services supply chain is good. The result is shown in figure 6. 6. Conclusion Figure 6. Markov Analysis Results IT outsourcing service supply chain performance evaluation and prediction is a complex, nonlinear process. base on SCOR model, combining the characteristics of IT outsourcing services, constructing improved SCOR model, and combine RBF neural with Markov chain, using RBF neural to conduct the performance assessment, and using Markov chain prediction theory to carry on the forecast of IT outsourcing service supply chain performance. For managers, it provides to make decisions. 7. Acknowledgments The author wish to thank the scientific research project of Hubei Provincial Department of education (Grant B20121905), under which the present work was possible. 359

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