Implementing an efficient data envelopment analysis method for assessing suppliers of complex product systems

Document Type : Research Paper


1 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

2 School of Industrial Engineering, Malek Ashtar University Technology, Tehran, Iran


Reduction of complex product systems (CoPS) manufacturing costs are the main factors of sustainability and survival of the manufacturers. Choosing proper CoPS suppliers can dramatically reduce these costs and increases competitive capability for manufacturers. This is due to the fact that in the complex industries, the costs of raw materials for the production processes or the purchase of components includes a substantial part of the product costs. In this regard, in this paper, a tailored data envelopment analysis (DEA) model is deployed to assess and select the supplier of CoPS, helping to deduct these costs as well as eventuate in productivity of the products. In the proposed model, various suppliers of CoPS are evaluated based on a set of economic, technical, and geographic criteria. The suppliers are ranked in accordance with the obtained scores and then the best ones are chosen. Eventually, to examine the applicability and usefulness of the proposed method, a case study is conducted via which important managerial outcomes are extracted.


Main Subjects

Abdollahi, M., Arvan, M., and Razmi, J. (2015). An integrated approach for supplier portfolio selection: Lean or agile? Expert Systems with Applications 42, 679-690.
Acha, V., Davies, A., Hobday, M., and Salter, A. (2004). Exploring the capital goods economy: complex product systems in the UK. Industrial and Corporate Change 13, 505-529.
Amorim, P., Curcio, E., Almada-Lobo, B., Barbosa-Póvoa, A. P., and Grossmann, I. E. (2016). Supplier selection in the processed food industry under uncertainty. European Journal of Operational Research 252, 801-814.
Ayhan, M. B., and Kilic, H. S. (2015). A two stage approach for supplier selection problem in multi-item/multi-supplier environment with quantity discounts. Computers & Industrial Engineering 85, 1-12.
Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I. E., and Omid, M. (2018). Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry. Computers & Operations Research 89, 337-347.
Bandyopadhyay, S., and Bhattacharya, R. (2013). Applying modified NSGA-II for bi-objective supply chain problem. Journal of Intelligent Manufacturing 24, 707-716.
Banker, R. D. (1984). Estimating most productive scale size using data envelopment analysis. European Journal of Operational Research 17, 35-44.
Çebi, F., and Otay, ─░. (2016). A two-stage fuzzy approach for supplier evaluation and order allocation problem with quantity discounts and lead time. Information Sciences 339, 143-157.
Chai, J., and Ngai, E. W. (2015). Multi-perspective strategic supplier selection in uncertain environments. International Journal of Production Economics 166, 215-225.
Charnes, A., Cooper, W. W., and Rhodes, E. (1979). Measuring the efficiency of decision-making units. European journal of operational research 3, 339.
Cheng, Y., Peng, J., Zhou, Z., Gu, X., and Liu, W. (2017). A Hybrid DEA-Adaboost Model in Supplier Selection for Fuzzy Variable and Multiple Objectives. IFAC-PapersOnLine 50, 12255-12260.
Cook, W. D., and Johnston, D. A. (1992). Evaluating suppliers of complex systems: a multiple criteria approach. Journal of the operational research society 43, 1055-1061.
Davies, A. (1996). Innovation in large technical systems: the case of telecommunications. Industrial and Corporate Change 5, 1143-1180.
Davies, A., and Brady, T. (1998). Policies for a complex product system. Futures 30, 293-304.
Davies, A., and Hobday, M. (2005). "The business of projects: managing innovation in complex products and systems," Cambridge University Press.
Dedehayir, O., Nokelainen, T., and Mäkinen, S. J. (2014). Disruptive innovations in complex product systems industries: A case study. Journal of Engineering and Technology Management 33, 174-192.
Dehghani, E., Behfar, n., and Jabalameli, M. S. (2016). Optimizing location, routing and inventory decisions in an integrated supply chain network under uncertainty. Journal of Industrial and Systems Engineering 9, 0-0.
Dehghani, E., Jabalameli, M. S., and Jabbarzadeh, A. (2018a). Robust design and optimization of solar photovoltaic supply chain in an uncertain environment. Energy 142, 139-156.
Dehghani, E., Jabalameli, M. S., Jabbarzadeh, A., and Pishvaee, M. S. (2018b). Resilient solar photovoltaic supply chain network design under business-as-usual and hazard uncertainties. Computers & Chemical Engineering 111, 288-310.
Dehghani, E., Jabalameli, M. S., SamanPishvaee, M., and Jabbarzadeh, A. (2018c). Integrating information of the efficient and anti-efficient frontiers in DEA analysis to assess location of solar plants: A case study in Iran. Journal of Industrial and Systems Engineering 11, 163-179.
Dehghani, E., Pishvaee, M. S., and Jabalameli, M. S. (2018d). A hybrid Markov process-mathematical programming approach for joint location-inventory problem under supply disruptions.
Diabat, A., Dehghani, E., and Jabbarzadeh, A. (2017). Incorporating location and inventory decisions into a supply chain design problem with uncertain demands and lead times. Journal of Manufacturing Systems 43, 139-149.
Dobos, I., and Vörösmarty, G. (2018). Inventory-related costs in green supplier selection problems with Data Envelopment Analysis (DEA). International Journal of Production Economics.
Du, B., and Guo, S. (2016). Production planning conflict resolution of complex product system in group manufacturing: a novel hybrid approach using ant colony optimization and Shapley value. Computers & Industrial Engineering 94, 158-169.
Du, B., Guo, S., Huang, X., Li, Y., and Guo, J. (2015). A Pareto supplier selection algorithm for minimum the life cycle cost of complex product system. Expert Systems with Applications 42, 4253-4264.
Entani, T., Maeda, Y., and Tanaka, H. (2002). Dual models of interval DEA and its extension to interval data. European Journal of Operational Research 136, 32-45.
Fallah-Tafti, A. l., Sahraeian, R., Tavakkoli-Moghaddam, R., and Moeinipour, M. (2014). An interactive possibilistic programming approach for a multi-objective closed-loop supply chain network under uncertainty. International journal of systems science 45, 283-299.
Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General) 120, 253-290.
Galankashi, M. R., Chegeni, A., Soleimanynanadegany, A., Memari, A., Anjomshoae, A., Helmi, S. A., and Dargi, A. (2015). Prioritizing green supplier selection criteria using fuzzy analytical network process. Procedia CIRP 26, 689-694.
Hansen, K. L., and Rush, H. (1998). Hotspots in complex product systems: emerging issues in innovation management. Technovation 18, 555-590.
Hobday, M. (2000). The project-based organisation: an ideal form for managing complex products and systems? Research policy 29, 871-893.
Hobday, M. (2007). Editor's introduction: The scope of Martin Bell's Contribution.
Hosseininasab, A., and Ahmadi, A. (2015). Selecting a supplier portfolio with value, development, and risk consideration. European Journal of Operational Research 245, 146-156.
Huang, E., and Goetschalckx, M. (2014). Strategic robust supply chain design based on the Pareto-optimal tradeoff between efficiency and risk. European Journal of Operational Research 237, 508-518.
Johnson, A. L., and McGinnis, L. F. (2008). Outlier detection in two-stage semiparametric DEA models. European Journal of Operational Research 187, 629-635.
Kar, A. K. (2014). Revisiting the supplier selection problem: An integrated approach for group decision support. Expert systems with applications 41, 2762-2771.
Lee, J., Cho, H., and Kim, Y. S. (2015). Assessing business impacts of agility criterion and order allocation strategy in multi-criteria supplier selection. Expert Systems with Applications 42, 1136-1148.
Liu, Y., and Hipel, K. W. (2012). A hierarchical decision model to select quality control strategies for a complex product. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 42, 814-826.
Mahapatra, S. K., Das, A., and Narasimhan, R. (2012). A contingent theory of supplier management initiatives: effects of competitive intensity and product life cycle. Journal of Operations Management 30, 406-422.
Mahdiloo, M., Saen, R. F., and Lee, K.-H. (2015). Technical, environmental and eco-efficiency measurement for supplier selection: An extension and application of data envelopment analysis. International Journal of Production Economics 168, 279-289.
Moncayo-Martínez, L. A., and Recio, G. (2014). Bi-criterion optimisation for configuring an assembly supply chain using Pareto ant colony meta-heuristic. Journal of Manufacturing Systems 33, 188-195.
Özdemir, E. D., Härdtlein, M., Jenssen, T., Zech, D., and Eltrop, L. (2011). A confusion of tongues or the art of aggregating indicators—Reflections on four projective methodologies on sustainability measurement. Renewable and Sustainable Energy Reviews 15, 2385-2396.
Paradi, J. C., Asmild, M., and Simak, P. C. (2004). Using DEA and worst practice DEA in credit risk evaluation. Journal of Productivity Analysis 21, 153-165.
Sadjadi, S. J., Makui, A., Dehghani, E., and Pourmohammad, M. (2016). Applying queuing approach for a stochastic location-inventory problem with two different mean inventory considerations. Applied Mathematical Modelling 40, 578-596.
Safdari Ranjbar, M., Park, T.-Y., and Kiamehr, M. (2018). What happened to complex product systems literature over the last two decades: progresses so far and path ahead. Technology Analysis & Strategic Management, 1-19.
Setak, M., Azizi, V., Karimi, H., and Jalili, S. (2017). Pickup and delivery supply chain network with semi soft time windows: metaheuristic approach. International Journal of Management Science and Engineering Management 12, 89-95.
Shankar, B. L., Basavarajappa, S., Kadadevaramath, R. S., and Chen, J. C. (2013). A bi-objective optimization of supply chain design and distribution operations using non-dominated sorting algorithm: A case study. Expert Systems with Applications 40, 5730-5739.
Shen, W.-f., Zhang, D.-q., Liu, W.-b., and Yang, G.-l. (2016). Increasing discrimination of DEA evaluation by utilizing distances to anti-efficient frontiers. Computers & Operations Research 75, 163-173.
Sheskin, D. J. (2003). "Handbook of parametric and nonparametric statistical procedures," crc Press.
Shi, P., Yan, B., Shi, S., and Ke, C. (2015). A decision support system to select suppliers for a sustainable supply chain based on a systematic DEA approach. Information Technology and Management 16, 39-49.
Soheilirad, S., Govindan, K., Mardani, A., Zavadskas, E. K., Nilashi, M., and Zakuan, N. (2017). Application of data envelopment analysis models in supply chain management: A systematic review and meta-analysis. Annals of Operations Research, 1-55.
Sueyoshi, T., and Goto, M. (2014). Photovoltaic power stations in Germany and the United States: A comparative study by data envelopment analysis. Energy Economics 42, 271-288.
Takamura, Y., and Tone, K. (2003). A comparative site evaluation study for relocating Japanese government agencies out of Tokyo. Socio-Economic Planning Sciences 37, 85-102.
Toloo, M., and Nalchigar, S. (2011). A new DEA method for supplier selection in presence of both cardinal and ordinal data. Expert Systems with Applications 38, 14726-14731.
Visani, F., Barbieri, P., Di Lascio, F. M. L., Raffoni, A., and Vigo, D. (2016). Supplier’s total cost of ownership evaluation: a data envelopment analysis approach. Omega 61, 141-154.
Wu, D., Wu, D. D., Zhang, Y., and Olson, D. L. (2013). Supply chain outsourcing risk using an integrated stochastic-fuzzy optimization approach. Information Sciences 235, 242-258.
Zeydan, M., Çolpan, C., and Çobano─člu, C. (2011). A combined methodology for supplier selection and performance evaluation. Expert Systems with Applications 38, 2741-2751.