Adapting the SCOR model for supply chain network assessment and improvement in oil industry

Janaki, D.

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International Journal of Data and Network Science

ISSN 2561-8156 (Online) - ISSN 2561-8148 (Print) Quarterly Publication Volume 3 Issue 4 pp. 331-338 , 2019

Adapting the SCOR model for supply chain network assessment and improvement in oil industry Pages 331-338 Download PDF

Authors: Daryosh Mohammadi Janaki

DOI: 10.5267/j.ijdns.2019.4.003

Keywords: Supply Chain Network, Uncertainty, Network DEA

Abstract: Supply chain management in oil and gas industry plays an important role for the success of these companies in most countries. A reliable supply chain helps on time delivery of goods and services and leads to better performance of the firms and yields higher profitability. This paper presents an empirical investigation to measure the relative efficiency of different oil distribution companies in Iran. The proposed study uses a five-stage Supply-Chain Operations Reference (SCOR) technique to measure the relative efficiencies of 40 distribution oil companies. The study designs a questionnaire based on four balanced scorecard perspectives and distributes it among various experts who were familiar with supply chain issues. The results indicate that the network performed relatively efficient since the study did not detect any unit with low performance and most of them maintained relatively high scores.

How to cite this paper

 Janaki, D. (2019). Adapting the SCOR model for supply chain network assessment and improvement in oil industry.International Journal of Data and Network Science, 3(4), 331-338.

Refrences

Aydın, S. D., Eryuruk, S. H., & Kalaoğlu, F. (2014). Evaluation of the performance attributes of retail-ers using the scor model and AHP: a case study in the Turkish clothing industry. Fibres & Textiles in Eastern Europe.
Battese, G. E., & Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20(2), 325-332.
Beamon, B. M. (1999). Measuring supply chain performance. International journal of operations & pro-duction management, 19(3), 275-292.
Charnes, A., Clark, C. T., Cooper, W. W., & Golany, B. (1984). A developmental study of data envel-opment analysis in measuring the efficiency of maintenance units in the US air forces. Annals of op-erations Research, 2(1), 95-112.
Charnes, A., Cooper, W. W., Lewin, A. Y., & Seiford, L. M. (Eds.). (2013). Data envelopment analy-sis: Theory, methodology, and applications. Springer Science & Business Media.
Estampe, D., Lamouri, S., Paris, J.L, & Djelloul, S.B. (2013). A framework for analyzing supply chain performance evaluation models. International Journal of Production Economics, 142, 247–258.
Georgise, F. B., Thoben, K. D., & Seifert, M. (2012). Adapting the SCOR model to suit the different scenarios: a literature review & research agenda. International Journal of Business and Manage-ment, 7(6), 2.
Gunasekaran, A., & Ngai, E. W. (2004). Information systems in supply chain integration and manage-ment. European Journal of Operational Research, 159(2), 269-295.
Jagadesh, R. (2015). Performance assessment of global supply chains and moving towards optimization of efforts and challenges. International Journal of Recent Advances in Organizational Behaviour and Decision Sciences, 1 400-416.
Kaplan, R. S., & Norton, D. P. (2001). Transforming the balanced scorecard from performance meas-urement to strategic management: Part I. Accounting horizons, 15(1), 87-104.
Kaplan, R. S., Robert, N. P. D. K. S., Davenport, T. H., Kaplan, R. S., & Norton, D. P. (2001). The strategy-focused organization: How balanced scorecard companies thrive in the new business envi-ronment. Harvard Business Press.
Talluri, S., Narasimhan, R., & Nair, A. (2006). Vendor performance with supply risk: A chance-constrained DEA approach. International Journal of Production Economics, 100(2), 212-222.
Mohamadi Janaki, D., Mirzazadeh, A., Mohamadi Janaki, M. (2019), Reducing barriers to the imple-mentation of strategic management by providing improved strategies: Using fuzzy QFD applied to Petropars Company. International Journal of Industrial and Systems Engineering, 31(4), 535-557.
Ntabe, E. N., LeBel, L., Munson, A. D., & Santa-Eulalia, L. A. (2015). A systematic literature review of the supply chain operations reference (SCOR) model application with special attention to envi-ronmental issues. International Journal of Production Economics, 169, 310-332.
Piotrowicz, W., & Cuthbertson, R. (2015). Performance measurement and metrics in supply chains: an exploratory study. International Journal of Productivity and Performance Management, 64(8), 1068-1091.
Ross, A., & Droge, C. (2002). An integrated benchmarking approach to distribution center performance using DEA modeling. Journal of Operations Management, 20(1), 19-32.