Performance Evaluation in Green Supply Chain Using BSC, DEA and Data Mining

Document Type : Research Paper

Authors

Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran.

Abstract

Efficiency is regarded as an important factor for both managers in different companies and organizations and customers who are interested in using the services related to these companies and organizations. However, the biggest challenges managers are coping with include an increase in the competition between companies and manufacturing centers, an increase in the efficiency of production, and finding suitable suppliers. The present study aimed to investigate the efficiency of green supply chain (GSC) by using Malmquist productivity index (MPI) based on the input and output indicators of the BSC model and accordingly providing some rules using the decision tree. To this aim, the efficiency of 15 automotive parts manufacturer firms in Iran was evaluated in the state of constant returns to scale during 2013-2016. Then, the obtained results were used as the class label of Decision Making Units (DMUs) which are regarded as the inputs of decision tree method. Finally, the implicit rules in the data were extracted by using the decision tree. The results indicated that the proposed model had a high degree of accuracy and interpretation in evaluating performance compared to previous models and helps managers to make better decisions to increase the efficiency.

Keywords

Main Subjects


Alinezhad, A. (2016). An Integrated DEA and Data Mining Approach for Performance Assessment. Iranian Journal of Optimization, Vol. 8(2), pp. 59-69.
Amado, C.A., Santos, S.P. and Marques, P. M. (2012). Integrating the Data Envelopment Analysis and the Balanced Scorecard approaches for enhanced performance assessment. Omega, Vol. 40(3), pp. 390-403.
Amini, A. and Alinezhad, A. (2017). Integrating DEA and Group AHP for Efficiency Evaluation and Identification of Most Efficient DMU. International Journal of Supply and Operations Management, Vol. 4(4), pp. 318-327.
Amini, A., Alinehad, A. and Salmanian, S. (2016). Development of Data Envelopment Analysis for the Performace Evaluation of Green Supply Chain with Undesirable Outputs. International journal of supply and operations management, Vol. 3(2), pp. 1267-1283.
Arabzad, S.M., Kamali, A., Naji, B. and Tavakoli, M.M. (2013). Performance evaluation of HESA laboratory units: an integrated DEA-BSC approach. International Journal of Services and Operations Management, Vol. 16(2), pp. 225-239.
Asosheh, A., Nalchigar, S. and Jamporazmey, M. (2010). Information technology project evaluation: An integrated data envelopment analysis and balanced scorecard approach. Expert Systems with Applications, Vol. 37(8), pp. 5931-5938.
Beamon, B.M. (1999). Measuring supply chain performance. International Journal of Operations and Production Management, Vol. 19(3), pp. 275–292.
Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K. and Nudurupati, S.S. (2014). Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a collaborative decision-making approach. Production Planning & Control, Vol. 25(8), pp. 698-714.
Carboni, O.A. and Russu, P. (2015). Assessing regional wellbeing in Italy: An application of Malmquist–DEA and self-organizing map neural clustering. Social indicators research, Vol. 122(3), pp. 677-700.
Caves, D., Christensen, L. and Diewert, E. (1982). The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica, Vol.  50(6), pp. 1393-1414.
Chan, F.T. and Qi, H.J. (2003). An innovative performance measurement method for supply chain management. Supply chain management: An international Journal, Vol.  8(3), pp. 209-223.
Charnes, A., Cooper, W.W. and Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, Vol. 2, pp. 429–444.
Chen, C. and Yan, H. (2011). Network DEA model for supply chain performance evaluation. European Journal of Operational Research, Vol. 213, pp. 147-155.
Chiang, T.C., Cheng, P.Y. and Leu, F.Y. (2017). Prediction of technical efficiency and financial crisis of Taiwan’s information and communication technology industry with decision tree and DEA. Soft Computing, Vol. 21(18), pp. 5341-5353.
Danesh Asgari, S., Haeri, A. and Jafari, M. (2018). Right indicators of urban railway system: combination of BSC and DEA model. International Journal of Transportation Engineering, Vol. 5(3), pp. 275-299.
Eilat, H., Golany, B. and Shtub, A. (2008). R&D project evaluation: An integrated DEA and balanced scorecard approach. Omega, Vol. 36(5), pp. 895-912.
Epstein, M.J. and Wisner, P. (2001). Good neighbours: implementing social and environmental strategies with the BSC. Balanced Scorecard Report, Vol. 3(3), pp. 8-11.
Fare, R., Grosskopf, S., Lindgren, B. and Roos, P. (1994). Productivity developments in Swedish hospitals: A Malmquist output index approach. In Data envelopment analysis: Theory, methodology, and applications, Springer: Netherlands.
Ferguson, B.R. (2000). Implementing supply chain management. Production and Inventory Management Journal, Vol.41(2), pp. 64-67.
Garcia-Valderrama, T., Mulero-Mendigorri, E. and Revuelta-Bordoy, D. (2009). Relating the perspectives of the balanced scorecard for R&D by means of DEA. European Journal of Operational Research, Vol. 196(3), pp. 1177-1189.
Haghighi, S.M., Torabi, S.A. and Ghasemi, R. (2016). An integrated approach for performance evaluation in sustainable supply chain networks (with a case study). Journal of cleaner production, Vol. 137, pp. 579-597.‏
Hervani, A.A., Helms, M.M. and Sarkis, J. (2005). Performance measurement for green supply chain management. Benchmarking: An international journal, Vol. 12(4), pp. 330-353.
Kadarova, J., Durkacova, M., Teplicka, K. and  Kadar, G. (2015). The proposal of an innovative integrated BSC–DEA model. Procedia Economics and Finance, Vol. 23, pp. 1503-1508.
Kaminski, B., Jakubczyk, M., and Szufel, P. (2018). A framework for sensitivity analysis of decision trees. Central European journal of operations research, Vol.26(1), pp. 135-159.‏
Kaplan, R.S. and Norton, D.P. (1996). Using the balanced scorecard as a strategic management system. Harvard business review, Vol. 74(1), pp. 75-85.
Kianfar, K., Ahadzadeh Namin, M., Alam Tabriz, A. and Najafi, E. (2016). Presentation of a Novel Integrated DEA-BSC Model with Network Structure in Multi Objective Programmig. International Journal of Data Envelopment Analysis, Vol. 4(2), pp. 967-984.
Kim, J. and Rhee, J. (2012). An empirical study on the impact of critical success factors on the balanced scorecard performance in Korean green supply chain management enterprises. International Journal of Production Research, Vol. 50(9), pp. 2465-2483.
Parker, C. (2000). Performance measurement. Work Study, Vol.49, pp. 63-66.
Quinlan, J.R. (2014). C4. 5: programs for machine learning. Elsevier.
Rahimi, I. and Behmanesh, R. (2012). Improve Poultry Farm Efficiency in Iran: Using Combination Neural Networks, Decision Trees, and Data Envelopment Analysis (DEA). International Journal of Applied Operational Research, Vol.2(3), pp. 69-84.
Seol, H., Choi, J., Park, G. and Park, Y. (2007). A Framework for Benchmarking Service Process Using Data Envelopment Analysis and Decision Tree. Journal of expert systems with applications, Vol. 32, pp. 432-440.
Sohn, S. Y. and Moon, T. H. (2004). Decision tree based on data envelopment analysis for effective technology commercialization. Expert Systems with Applications, Vol.26(2), pp. 279-284.
Tan, Y., Zhang, Y. and Khodaverdi, R. (2017). Service performance evaluation using data envelopment analysis and balance scorecard approach: an application to automotive industry. Annals of Operations Research, Vol. 248(1-2), pp. 449-470.
Toloo, M., Sohrabi, B. and Nalchigar, S. (2009). A New Method for Ranking Discovered Rules from Data Mining by DEA. Journal of expert systems with applications, Vol.36(4), pp. 8503-8508.
Tsai, C. F., and Tsai, J. H. (2010, March). Performance Evaluation of the Judicial System in Taiwan Using Data Envelopment Analysis and Decision Trees. In Computer Engineering and Applications (ICCEA), 2010 Second International Conference on, 2, 290-294. IEEE.
Wu, D. (2009). Supplier selection: A hybrid model using DEA, decision tree and neural network. Expert Systems with Applications, Vol. 36(5), pp. 9105-9112.