Document Type : Original Article


1 Master Student, Iran University of Science and Technology.

2 Assistant Professor, Iran University of Science and Technology.


Integrated management and coordination of different parts of supply chain (e.g. procurement, production and distribution) result in significant financial benefits. Financial flow alongside with information and material flow are the three essential flows in supply chain which should be planned simultaneously to achieve the maximum possible efficiency. In this paper a master planning model which includes integrated procurement, production and distribution planning for a multi-product supply chain is taken into account. In order to escape from sub-optimality caused from ignoring the financial flow, the proposed model is able to integrate the material and financial flows all through the supply chain. Various financial measures are used to model the financial flow in the concerned problem and goal programing method is applied to effectively control the deviation of these measures from the planned target values. To solve the proposed bi-objective optimization model, an interactive fuzzy solution is used. This approach s is able to generate both balanced and unbalanced efficient solutions based on decision maker preferences. To show the usefulness and effectiveness of the proposed model numerical and comparative experiments are provided. The numerical results endorse the validity and practicability of the rendered model as well as presenting the efficiency and flexibility of the developed approach.


1. Badell, M., Romero, J., Huertas, R., & Puigjaner, L. (2004). Planning, schedulingand budgeting value-addedchains.Computers and Chemical Engineering, 28(1–2), 45–61.
2. Chandra, P., & Fisher, M. (1994). Coordination of Production and distribution planning. European Journal of Operation Research, 72, 503-517.
3. Cohen, A. M., & Sangwon, M. (1991). An integrated plant loading model with economice of scale and scope. European Journal of Operation Research, 50, 266-276.
5. Cowen, S.S., & Hoffer, J.A. (1982). Usefulness of financial ratios in a single industry. Journal of Business Research, 10(1), 103–118.
6. Davis, T. (1993). Effective supply chain management.Sloan Management Review, 34(4), 35–46.
7. Durugbo, C., Tiwari, A., & Alcock, J. (2013). Modelling information flow for organisations: A review of approaches and future challenges. International Journal of Information Management, 33, 597– 610.
8. Guille´ n, G.,Badell, M.,Espun˜ a, A., & Puigjaner,L.(2006).Simultaneous optimization of process operations and financial decisions to enhance the integrated planning/scheduling of chemical supply chains .Computers and Chemical Engineering, 30(3), 421–436.
9. Guille´ n, G., Badell, M., & Puigjaner, L. (2007). Aholisticframeworkforshort-term supplychain management integrating production and corporate financial planning. International Journal of Production Economics, 106(1), 288–306.
10. Hammami, R., Frein, Y., & Hadj-Alouane, A.B. (2009). Astrategic-tactical model for the supply chain design in the delocalization context: mathematical formulation and acasestudy.International Journal of Production Economics, 122(1), 351–365.
11. Hahn, G.J., & Kuhn, H. (2012). Designing decision support systems for value-based management: A survey and an architecture. Decision Support Systems, 53, 591–598.
12. Horrigan, J. (1966). The determinants of long-term credit standing with financial ratios. Journal of Accounting Research, 4 (Suppl.), 44–62.
13. Jayaraman, V., & Pirkul, H. (2001). planning and coordination of production and distribution facilities for multiple commodities. European Journal of Operation research, 133, 394-408.
14. Jahangiri, M.H., & Cecelja, F. (2014). Modelling Financial Flow of the Supply Chain. Proceedings of the IEEM, 1071-1076.
15. Laı, J.M., Guille, G., Badell, M., Espun, A., & Puigjaner, L.(2007). Enhancing corporate value in the optimal design of chemical supply chains .Industrial and Engineering Chemistry Research, 46(23), 7739–7757.
16. Lai, Y.J., & Hwang, C.L.(1993). Possibilistic linear programming for managing interest rate risk, Fuzzy Sets and Systems, 54, 135–146.
17. Longinidis, P., & Georgiadis, M.C. (2011).Integration of Financial Statement Analysis in the Optimal Design of Supply Chain Networks Under Demand Uncertainty. International Journal of Production Economics, 129, 262–276.
18. Melo, M.T., Nickel, S., & Saldanha-da-Gama, F.(2009). Facility location and supply chain management—a review. European Journal of Operational Research, 196(2), 401–412.
19. Papageorgiou, L.G. (2009).Supply chain optimization for the process industries: advances and opportunities. Computers and Chemical Engineering, 33(12), 1931–1938.
20. Park, Y.B. (2005).An integrated approach for production and distribution planning in supply chain management, Internat. J. Prod. Res, 43(6), 1205–1224.
21. Patterson, L.J., & Kim, M. (2000). Strategic sourcing: a systematic approach to supplier evaluation, selection and development. Caps Research, 4, 112-125.
22. Pirkul, H., & Jayaraman, V. (1998). A multi- commodity ,multi plant, capacitated facility location problem: formulation and efficieient heuristic solution. Journal of operation research, 25, 10, 869-878.
23. Rushinek, A., & Rushinek, S.F. (1987). Using financial ratios to predict in solvency. Journal of Business Research, 15(1), 93–100.
24. Romero, J., Badell, M., Bagajewicz, M., & Puigjaner,L.(2003). Integratingbudgeting models in to scheduling and planning models for the chemical batch industry. Industrial and Engineering Chemistry Research, 42(24), 6125–6134.
25. Sakawa, M., Yano H., & Yumine, T. (1987). An interactive fuzzy satisfying method for multi objective linear-programming problems and its application. IEEE Transactions on Systems, Manand Cybernetics SMC, 17, 654–661.
26. Sabri, H., & Benita, M. (2000).A moulti objective approach to simultaneous sterategic and operational planning in supply chain design. Omega, 28, 581-598.
27. Selim, H., Ozkarahan, I. (2008).A supply chain distribution network design model: an interactive fuzzy goal programming-based solution approach. International Journal of Advanced Manufacturing Technology, 36, 401–418.
28. Shapiro, J.F. (2004). Challenges of strategic supply chain planning and modeling. Computers and Chemical Engineering, 28(6–7), 855–861.
29. Sodhi, M.S., & Tang, C.S. (2009). Modeling supply-chain planning under demand uncertainty using stochastic programming: a survey motivated by asset-liability management. International Journal of Production Economics, 121(2), 728–738.
30. Stevenson, W. (2004). Operation management, New York, Mc Graw Hill.
31. Torabi, S.A., & Hassini, E. (2008).An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159, 193–214.
32. Thomas, D.J., & Griffin ,P.M.(1996).Coordinated supply chain management. Eur. J. Oper. Res, 94, 1–15.
33. Werners, B. (1988).Aggregation models in mathematical programming. Springer. Berlin Heidelberg. NewYork,. 295–305.
34. Wuttke, D., Blome, C., & Henke, M. (2013). Focusing the financial flow of supply chains: An empirical investigation of financial supply chain management. Int. J. Production Economics, 145, 773–789.
35. Yi, G., & Reklaitis, G.V. (2004). Optimal design of batch-storage network with financial transactions and cash flows. AIChEJournal, 50(11), 2849–2865.
36. Zhao, L., & Huchzermeier, A. (2015). Operations-finance interface models: A literature review and Framework. European Journal of Operational Research, 10, 1016-1044.
37. Zhongdai, W., Minghai, Y., & Jin, L. (2014). A Novel Collaboration Management Method Based Finance Logistics Management Platform. International Journal of Multimedia and Ubiquitous Engineering, 9(10), 409-418.
38. Zimmermann, H.J.(1978).Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1, 45–55.