Proposing an Ordered Clustering Based on the PROMETHEE Principles to Develop Purchasing Strategy in the Supply Chain

Document Type : Research Paper


1 Ph.D. Candidate, Department of Industrial Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran.

2 Associate Prof., Department of Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran.


Objective: Purchasing portfolio models have received a great deal of attention in both academic and practice fields as suitable purchasing strategies. Purchasing portfolio applies as a diagnostic and prescriptive purchasing tool. The core purpose of this study is to introduce a quantified portfolio for developing purchasing strategies that are aligned with competitive priorities. The quantitative method of this study relies on data mining (ordered clustering) and MADM (Best Worst method) to classify purchased items with the aim of creating a strategic fit in the supply chain and developing purchasing strategies in accordance with the competitive priorities of organizations.
Methods: In portfolio models, the determination of the dimensions and the manner in which they are measured is important. In this study, firstly, the proper dimensions for commodity classification were introduced. These dimensions were competitive priority (as introduced in the literature review, including the costs, quality, speed, flexibility, and innovation), supply market analysis (an important dimension that should be considered in commodity classification analysis), and product features (that describe the characteristics of the commodities). Next, the proper criteria for each dimension were determined using the Delphi method. After that, the selected criteria using the Delphi method were weighted using the Best Worst method. In the following, purchasing items were classified using ordered clustering based on the PROMETHEE method. In this study, the clusters determined by PSO-K-means were ranked using the total unicriterion net flow of clusters in each dimension introduced in this study. Then, the preference profile was used to measure the preferential quality of each cluster on the different criteria in each dimension. The profile helps the purchasing managers with selecting the best proposed working methods for purchasing in each class of commodity by the preference profile. Finally, the proper working method and purchasing strategy were proposed for highly strategic commodities.
Results: The approach and method presented in this study were implemented for 100 purchased items in a steel company. In this study, the company selected cost and quality as competitive priorities. The method construct bases on these competitive priorities and proper criteria for each dimension. The most appropriate purchasing methods for each class of high-strategic purchased items were presented taking into account the proposed methods and the opinion of experts. The strategies and working methods that were introduced in purchasing Chessboard were applied in this study.
Conclusion: The approach of this research helps purchasing and supply managers to have more and more accurate choices of purchasing methods for each category of purchasing items. By considering the profile of preferences in the ordered clustering method based on the PROMETHEE principles it helps them to improve supply management and supply chain performance. Also, the alignment of purchasing strategy with business strategy could improve competitiveness.


Aghazadeh, H., & Maleki, H. (2020). Developing a Conceptual Framework of Buyer-Supplier Relationship Quality in the Supply Chain and Prioritizing its key Components: A Meta-Synthesis Method. Industrial Management Journal, 12(4), 578-608. (in Persian)
Amiri, M. & Jahani, S. (2010). Application of IDEA/AHP for Supplier evaluation and Selection. Industrial Management Journal, 2(5), 5-22. (in Persian)
Amiri, M., Hosseini Dehshiri, S.J. & Yousefi Hanoomarvar, A. (2018). Determining the Optimal Combination of LARG Supply Chain Strategies Using SWOT Analysis, Multi-criteria Decision-making Techniques and Game Theory. Industrial Management Journal, 10(2), 221-246. (in Persian)
Arabzad, S., Razmi, J., Tavakkoli-Moghaddam, R. & Ghorbani, M. (2012). Proposing a New Approach for Supplier Selection Based on Kraljic’s Model Using FMEA and Integer Linear Programming. Journal of Production and Operation Management, 3(1), 19-40.
(in Persian)
Bartezzaghi, E. & Ronchi, S. (2004). A Portfolio Approach in the e-Purchasing of Materials. Journal of Purchasing and Supply Management, 10 (3), 117–126.
Baykaso─člu, A., Subulan, K. & Karaslan, F. S. (2016). A new fuzzy linear assignment method for multi-attribute decision making with an application to spare parts inventory classification. Applied Soft Computing, 42(1), 1–17.
Bensaou, M. (1999). Portfolios of buyer-supplier relationships. Sloan management review, 40(4), 35-36.
Boujelben, M. A. (2017). A unicriterion analysis based on the PROMETHEE principles for multicriteria ordered clustering. Omega, 69, 126-140.
Caniëls, M. C. & Gelderman, C. J. (2007). Power and Interdependence in Buyer Supplier Relationships: A Purchasing Portfolio Approach. Industrial Marketing Management, 36 (2), 219–229.
Clerc, M. & Kenedy, J. (2002). The particale swarm-explosion, stability, and convergence in a multidimensional compelex space. IEEE Transactions on Evolutionary Computation, 6(1), 58-73.
Drake, P. R., Myung Lee, D. & Hussain, M. (2013). The Lean and Agile Purchasing Portfolio Model. Supply Chain Management: An International Journal, 18 (1), 3–20.
Dubois, A. & Pedersen, A.C. (2002). Why relationships do not fit into purchasing portfolio models-A comparison between the portfolio and industrial network approaches. European Journal of Purchasing and Supply Management, 8(1), 35–42.
Esmaelian, M., Khalili, S.A. & Tavakoli, M. (2020). Proposing a method for determining the appropriate purchasing strategy based on the purchasing portfolio approach. Industrial Management Perspective, 10(2), 55-82. (in Persian)
Ferreira, L. M. D., Arantes, A. & Kharlamov, A. A.(2015). Development of a Purchasing Portfolio Model for the Construction Industry: An Empirical Study. Production Planning & Control, 26 (5), 377–392.
Geist, M.R. (2010). Using the Delphi method to engage stakeholders: a comparison of two studies. Eval. Program Plan., 33 (2), 147–154.
Gelderman, C. J. & Van Weele, A. J. (2005). Purchasing portfolio models: a critique and update. The Journal of Supply Chain Management, 41(3), 19–28.
Hadi-Vencheh, A. & Mohamadghasemi, A. (2011). A fuzzy AHP-DEA approach for multiple criteria ABC inventory classification. Expert Systems with Applications, 38 (4), 3346–3352.
Hatefi, S. M., Torabi, S. A. & Bagheri, P. (2015). Multi-criteria ABC inventory classification with mixed quantitative and qualitative criteria. International Journal of Production Research, 52(3): 776–786.
Horner, K., Islam, M., Flygare, L., Tsiklakis, K., Whaites, E., (2009). Basic principles for use of dental cone beam computed tomography: consensus guidelines of the European Academy of Dental and Maxillofacial Radiology. Dentomaxillofac. Radiol., 38 (4), 187–195.
Kabir, G. & Sumi, R. S. (2013). Integrating Fuzzy Delphi with Fuzzy Analytic Hierarchy Process for Multiple Criteria Inventory Classification. Journal of Engineering, Project, and Production Management, 3(1), 22-34.
Kabir, G. (2012). Multiple criteria inventory classification under fuzzy environment. International Journal of Fuzzy System Applications, 2(4), 76–92.
Kalantari, R., Moeini, A., Safari, H. & Arabsorkhi, A. (2020). A Conceptual Framework for Measuring the Performance of the Information Security Service Supply Chain Based on Meta synthesize and Fuzzy Delphi Method. Industrial Management Journal, 12(1), 24-46. (in Persian)
Kraljic, P. (1983). Purchasing Must Become Supply Management. How Managers Can Guard Against Material Disruption by Formulating a Supply Strategy. Harvard Business Review, 107-117.
Lee, D. M. & Drake, P. R. (2010). A Portfolio Model for Component Purchasing Strategy and the Case Study of Two South Korean Elevator Manufacturers. International Journal of Production Research, 48 (22): 6651–6682.
Liang, F., Brunelli, M. & Rezaei, J. (2020). Consistency issues in the best worst method: Measurements and thresholds. Omega, 96, 102-175.
 Luzzini, D., Caniato, F., Ronchi, S., & Spina, G. (2012). A transaction costs approach to purchasing portfolio management. International Journal of Operations & Production Management, 32(9), 1015–1042.
Millstein, M. A., Yang, L., & Li, H. (2014). Optimizing ABC inventory grouping decisions. International Journal of Production Economics, 148, 71–80.
Montgomery, T. R., Ogden, A. J. & Boehmke, C.B. (2018). A quantified Kraljic Portfolio Matrix: Using decision analysis for strategic purchasing. Journal of Purchasing and Supply Management, 24(3), 192-203.
Olsen, R. F. & Ellram, L. M. (1997). A portfolio approach to supplier relationships. Industrial marketing management, 26(2), 101-113.
Padhi, S. S., Wagner, S. M. & Aggarwal, V. (2012). Positioning of commodities using the Kraljic Portfolio Matrix. Journal of Purchasing and Supply Management, 18(1), 1-8.
Rezaei, J. (2016). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
Yang, F., Sun, T., Zhang, C. (2009). An efficient hybrid data clustering method based on Kharmonic means and Particle Swarm Optimization, Expert Systems with Applications, 36(6), 847-852.