نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دانشگاه قم.

2 دانش‌آموخته کارشناسی ارشد، دانشگاه قم.

چکیده

مدیریت جریان وجوه نقد از اهمیت روزافزونی در زنجیره‌های تأمین برخوردار است. در پژوهش حاضر این جریان در زنجیره تأمین دارو و از منظر یک شرکت پخش بهینه می­شود. در این زنجیره، شرکت پخش داروهای دریافتی از تأمین‌کنندگان بالادست را به خرده‌فروشی‌های پایین‌دست می‌فروشد و با پول دریافتی از آن‌ها صورت‌حساب‌های تأمین‌کنندگان را تسویه می‌کند. ناهماهنگی بین جریان‌های ورودی و خروجی وجوه نقد، علاوه بر تحمیل جریمه دیرکرد، به سوء­شهرت در بازار و در نتیجه عدم­تأمین مطلوب اقلام دارویی توسط تأمین‌کنندگان منجر می‌شود. در این شرایط، این پرسش مطرح می‌شود که با چه توالی پرداختی می‌توان ضمن یافتن ارزان‌ترین روش تسویه صورت‌حساب‌ها، ریسک تأمین ناشی از سوء‌شهرت را به حداقل رساند. برای پاسخ به این پرسش، مدل برنامه‌‌ریزی خطی صفر‌­و‌­‌‌یک دو­‌هدفه‌ای صورت‌بندی شد. حل مدل توسط الگوریتم ژنتیک ضمن تعیین بهترین توالی پرداخت صورت‌حساب‌ها، جریان مالی خروجی و ریسک ناشی از نقض موعد پرداخت صورت‌حساب‌ها را کمینه کرد.

کلیدواژه‌ها

عنوان مقاله [English]

Cash flow Optimization in Medicine Supply Chain: A Supply Risk Approa

نویسندگان [English]

  • Rahim Foukerdi 1
  • Zenab Talavari 2

1 Assistant Professor, University of Qom.

2 Master, University of Qom.

چکیده [English]

Despite the increasing importance of cash flow management in the financial supply chain, limited works have been conducted in this field. This research optimizes the flow of money in the medical supply chain from the viewpoint of a distribution company. In this context, the focal company receives the medical supplies from the upstream suppliers and sells them to the downstream retailers and makes payments to suppliers with earned money from retailers. The imbalance between the cash inflow and outflow causes the imposition of a penalty for late-payments and supply risk as a result of the poor reputation in the market. In this context, the question is which payment sequence will minimize the total monetary outflows and the risk of supply. To answer this question, a bi-objective 0-1 linear programming model was developed. Solving the model by genetic algorithm determined the best sequence of payments and minimized the cash outflow as well as the risk of violation of the due date for the invoices.

کلیدواژه‌ها [English]

  • Genetic Algorithm
  • Cash Flow
  • Medicine Supply Chain
  • Financial Supply Chain
  • Mathematical Model Building
  1. Aljazzar, S. M., Jaber, M. Y., & Moussawi-Haidar, L. (2016). Coordination of a three-level supply chain (supplier–manufacturer–retailer) with permissible delay in payments. Applied Mathematical Modelling, 40(21-22), 9594-9614.
  2. Badell, M., Romero, J., & Puigjaner, L. (2005). Optimal budget and cash flows during retrofitting periods in batch chemical process industries. International Journal of Production Economics, 95(3), 359-372.
  3. Baumol, W. (1952).The transactions demand for cash: An inventory theoretic approach. The Quarterly Journal of Economics, 66(4), 545–556.
  4. Bertel, S., Fenies, P., & Roux, O. (2008). Optimal cash flow and operational planning in a company supply chain. International Journal of Computer Integrated Manufacturing, 21(4), 440-454.
  5. Blackman, I. D., Holland, C. P., & Westcott, T. (2013). Motorola's global financial supply chain strategy. Supply Chain Management: An International Journal, 18(2), 132-147.
  6. Brigham, E. F., & Houston, J. F. (2012). Fundamentals of financial management. Cengage Learning.
  7. Collette, Y., & Siarry, P. (2013). Multiobjective optimization: principles and case studies. Springer Science & Business Media.
  8. Comelli, M., Féniès, P., & Tchernev, N. (2008). A combined financial and physical flows evaluation for logistic process and tactical production planning: Application in a company supply chain. International Journal of Production Economics, 112(1), 77-95.
  9. Cooper, R. & Kaplan, R. (1998). How cost accounting distorts product cost. Management Accounting, 69(10), 20-27.
  10. 10. Deb, K., & Srivastava, S. (2012). A genetic algorithm based augmented Lagrangian method for constrained optimization. Computational optimization and Applications, 53(3), 869-902.
  11. 11. El-kholy, A. M. (2014). A multi-objective fuzzy linear programming model for cash flow management. Engineering Research and Applications, 4(8), 152-163.
  12. 12. Faraji, M. A., & Behnamian, J. (2020). A simulation-based Genetic algorithm to solve the workshop flow scheduling problem by considering the energy cost under uncertainty conditions. Journal of industrial management perspective, 10(2), 9-32. (in Persian)
  13. 13. Farris, T. M. & Hutchison, D. P. (2002). Cash-to-cash: the new supply chain management metric.  International Journal of Physical Distribution and Logistics Management, 32(4), 288-298.
  14. 14. Gardner, D. L. (2004). Supply chain vector: Methods for linking the execution of global business models with financial performance. J. Ross Publishing.
  15. 15. Garey, M. R., & Johnson, D. S. (1979). A Guide to the Theory of NP-Completeness. WH Freemann, New York, 70.
  16. 16. Gitman, L. J., & Zutter, C. J. (2015). Principles of Managerial Finance. Prentice Hall. Pearson.
  17. 17. Gormley, F. M., & Meade, N. (2007). The utility of cash flow forecasts in the management of corporate cash balances. European Journal of Operational Research, 182(2), 923-935.
  18. 18. Grubert, E. J. & Edwin. A. M. J. (1970). On the Cash Balance Problem. Operational Research Quarterly, 25(4), 553-572.
  19. 19. Gupta, S., & Dutta, K. (2011). Modeling of financial supply chain. European Journal of Operational Research, 211(1), 47-56.
  20. 20. Jahangiri, M. H., & Cecelja, F. (2014, December). Modelling financial flow of the supply chain. In 2014 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 1071-1075). IEEE.
  21. 21. John, A. O. (2014). Effect of cash management on profitability of Nigerian manufacturing firms. International Journal of Marketing and Technology, 4(1), 129-140.
  22. 22. Kardan B., Vadiei, M. H., & Rostami, A. (2016). Using fuzzy regression to explain the relationship between supply chain management and financial performance. Journal of industrial management perspective. 5(4), 119-141. (in Persian)
  23. 23. Khakbiz, M., Rezaei Pandari, A., & Dehghan Niri, M. (2017). Designing a mathematical model for stock portfolio diversification and solving it using genetic algorithms. Journal of industrial management perspective, 7(1), 173-196. (in Persian)
  24. 24. Kim, I. Y., & De Weck, O. L. (2006). Adaptive weighted sum method for ultiobjective optimization: a new method for Pareto front generation. Structural and multidisciplinary optimization, 31(2), 105-116.
  25. 25. Kraljic, P. (1983). Purchasing must become supply management, Harvard Business Review, 61(5), 109-117.
  26. 26. Kroes, J. R., & Manikas, A. S. (2014). Cash flow management and manufacturing firm financial performance: A longitudinal perspective. International Journal of Production Economics, 148, 37-50.
  27. 27. Krumrey, L., Moeini, M., & Wendt, O. (2017, June). A Cash-Flow-Based Optimization Model for Corporate Cash Management: A Monte-Carlo Simulation Approach. In International Conference on Computer Science, Applied Mathematics and Applications (pp. 34-46). Springer, Cham.
  28. 28. Longinidis, P., & Georgiadis, M.C. (2011). Manging the trade-offs between fincncial performance and credit solvency in the optimal design of supply chain networks under economic uncertainty. Computers & chemical Engineering, 48(10), 264-279.
  29. 29. Mashayekhi, E. N. & Alam Tabriz, A. (2017). The effect of upstream and downstream supply chain integration on performance and quality program, Journal of industrial management perspective. 6(4), 37-57. (In Persian)
  30. 30. Miller, M.H., Orr, R. (1966). A model of the demand for money by firms. The Quarterly Journal of Economics, 80(3), 413–435.
  31. 31. Moussawi-Haidar, L., & Jaber, M. Y. (2013). A joint model for cash and inventory management for a retailer under delay in payments. Computers & Industrial Engineering, 66(4), 758-767.
  32. 32. Peng, J., & Zhou, Z. (2019). Working capital optimization in a supply chain perspective. European Journal of Operational Research, 277(3), 846-856.
  33. 33. Pfohl, H. C., & Gomm, M. (2009). Supply chain finance: optimizing financial flows in supply chains. Logistics research, 1(3-4), 149-161.
  34. 34. Radfar, A. & Mohammaditabar, D. (2019). A vendor-based two-objective inventory optimization in a green multilevel supply chain. Journal of industrial management perspective, 9(3), 109-134. (In Persian)
  35. 35. Reider, R., & Heyler, P. B. (2003). Managing cash flow: An operational focus. John Wiley & Sons.
  36. 36. Righetto, G. M., Morabito, R., & Alem, D. (2016). A robust optimization approach for cash flow management in stationery companies. Computers & Industrial Engineering, 99, 137-152.
  37. 37. Righetto, G. M., Morabito, R., & Alem, D. (2019). Cash flow management by risk-neutral and risk-averse stochastic approaches. Journal of the Operational Research Society, 71(1), 55-68.
  38. 38. Ross, G.T., Soland, R.M., 1975. A branch and bound algorithm for the generalized assignment problem. Mathematical Programming, 8(1), 91–103.
  39. 39. Rostami, M. (2020). An optimal model for the closed-loop supply chain scheduling problem. Journal of industrial management perspective, 10(3), 29-52. (In Persian)
  40. 40. Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2018). A multi-objective approach to the cash management problem. Annals of Operations Research, 267(1-2), 515-529.
  41. 41. Salas-Molina, F., Rodriguez-Aguilar, J. A., & Pla-Santamaria, D. (2019). A stochastic goal programming model to derive stable cash management policies. Journal of Global Optimization, 1-14.
  42. 42. Tangsucheeva, R., & Prabhu, V. (2013). Modeling and analysis of cash-flow bullwhip in supply chain. International Journal of Production Economics, 145(1), 431-447.
  43. 43. Wu, H., Liu, Y., & Wu, M. (2013). Supply Chain Model of Finance Payment, Which Made up of Providers, Central Enterprises and Distributors. Information Technology Journal, 12(18), 4699-4704.
  44. 44. Yang, X. S. (2014). Nature-inspired metaheuristic algorithms. Luniver press.