Determination of Inventory Management Policies in Process Manufacturing: Using Discrete Event Simulation

Document Type : Original Article


1 Ph.D student, Shahid Beheshti University.

2 Associate Professor, Shahid Beheshti University.


The increasing attention to customer demands in the product process, and the inevitable features and costs of production processes has led researchers to manage orders and choose the right policy for inventory management. This article identifies the structure for determining the optimal location of the Customer Order Decoupling Point and the optimal inventory management policy as one of the most important strategic decisions in the production process. So, we developed a discrete-event simulation model for realistic calculation of cost and Flow time, under different scenarios, and we used the production and sales information of a chemical plant for validation and implementation of the model. The results suggest that the use of a hybrid inventory management policy reduces the cost and delivery time.


  1. Azar, A. & Momeni, M. (2002). Statistics and Its Application in Management. Tehran. Samt press. (in persian)  
  2. Azimi, P., Farajpoor Nazari, M., Esmati, A. & Farzin, A. (2013). Optimization via simulation & Enterprise Dynamics tutorial. Islamic Azad University of Qazvin press. Qazvin. (in persian)
  3. Chung, Ch. A. (2003). Simulation modeling handbook: a practical approach, CRC press, Inc. Boca Raton, FL, USA, ISBN 0-8493-1241-8.
  4. Davoodi, S.M.R, Jolai, F., Mohaghar, A. & Mehregan, M.R. (2015). Designing a multi-Level Multi-Product inventory simulation model and comparing it with the selected models; Case: Iran steel industries, Journal of Industrial Management Perspective, 5:19, 9-38. (in persian)
  5. Dellaert, N. P. & Melo, M.T. (1996). Production strategies for a stochastic lot sizing problem with constant capacity, European Journal of Operational Research, 92, 281-301.
  6. Garn, W. & Aitken, J (2015). Splitting hybrid Make to Order and Make to Stock demand profiles, International Journal of Operations and Production Management, 15, 48-61.
  7. Ghazanfari, M.& Saghiri, S. (2015). Production management systems (the integrated approach). Tehran. Iran University of Science and Technology. (in persian)
  8. Gupta, D. & Benjaafar, S (2004). Make to order, make to stock, or delay product differentiation? A common framework for modeling and analysis, IIE Transactions, 36:6, 529–546.
  9. Kober, J. & Heinecke, G (2012). Hybrid Production Strategy between Make to Order and Make to Stock - A Case Study at a Manufacturer of Agricultural Machinery with Volatile and Seasonal Demand. 45th CIRP Conference on Manufacturing Systems, 453-458.
  10. Nahavandi, B., Moghbel, A., Azar, A. (2014). Provide a step-by-step approach to simulate a strategy map using fuzzy cognitive maps, Journal of Industrial Management Perspective, 4:14, 93-115. (in persian)
  11. Olhager, J (2003). Strategic positioning of order penetration point. International Journal of Production Economics, 85, 319-329.
  12. Rabbani, M & Yousefnezhad, H & Rafiei, H. (2013). A new approach to find optimal location order decoupling point of supply chain. Tenth International Conference of Industrial Engineering. Tehran. (in persian)
  13. Rogers, P. & Nandi, A. (2007). Judicious order acceptance and order release in make-to-order manufacturing systems. Production Planning & Control, 18: 7, 610-625.
  14. Shafiei Nikabadi, M., Hemmati, M., Khaleqi, I. (2014). Evaluation and selection of suppliers in terms of competitiveness indicators, Journal of Industrial Management Perspective, 4:13, 143-161. (in persian)
  15. Sharda, B. & Akiya, N. (2012). Selecting make-to-stock and postponement policies for different products in a chemical plant: A case study using discrete event simulation, Int. J. Production Economics, 136, 161-171.
  16. Slotnick, S. A. & Morton, E. (2007). Thomas order acceptance with weighted tardiness, Computers & Operations Research, 34:10, 3029-3042.
  17. Soman, C. A., Van Donk, D. P. & Gaalman, G. (2004). Combined make-to-order and make-to-stock in a food production system. International Journal of Production Economics, 90:2, 223-235.
  18. Soman, C. A., Van Donk, D.P. & Gaalman, G. J. C. (2006). Comparison of dynamic scheduling policies for hybrid make to order and make-to-stock production systems with stochastic demand, International Journal of Production Economics, 104, 441-453.
  19. Su, J. C. P., Chang, Y. L. & Ferguson, M. (2005). Evaluation of postponement structures to accommodate mass customization. Journal of Operations Management, 23, 305-318.
  20. Van Donk, D. P. (2001). Make to stock or make to order: the decoupling point in the food processing industries. International Journal of Production Economics, 69:2, 297-306.
  21. Wikner, J. & Rudberg, M. (2005). Integrating production and engineering perspectives on the customer order decoupling point. Production and engineering perspectives, 25:7, 623-641.