Excel Based Monte Carlo Simulation for the (Q,r) Inventory Control Model


  • Banharn Lila Department of Industrial Engineering, Faculty of Engineering, Burapha University
  • Adisak Nowneow Department of Industrial Engineering, Faculty of Engineering, Burapha University
  • Sanya Yimsiri Department of Industrial Engineering, Faculty of Engineering, Burapha University


Inventory Control Policy, Empirical Discrete Demand Distribution, Excel based Monte Carlo Simulation, Automotive Tires


In supply chain management, inventory plays a key role to deal with demand and supply uncertainty aiming to guarantee a smooth flow of materials and products along the chain. This paper focuses on determining the suitable values of Q and r in the (Q,r) inventory control policy model when it was applied to the situation of nonstationary but known to be empirically discretely distributed of product demands and lead times. The total cost (TC) and service level (SL) were used to measure the policy performance using an Excel-based Monte Carlo Simulation (MCS) approach with a set of actual historical demand data from an automotive tire service store. Results from the MCS indicated that the (Q,r) model could lead to 12.36% lower TC with 2.31% higher SL, on average, when the Q and r values were determined based on empirical discrete distribution compared to that of normal distribution. Therefore, the empirical discrete distribution of demand and lead time should be utilized in a situation where the assumption of normal and other traditional distributions is invalid.


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How to Cite

B. . Lila, A. . Nowneow, and S. . Yimsiri, “Excel Based Monte Carlo Simulation for the (Q,r) Inventory Control Model ”, Ladkrabang Engr J, vol. 39, no. 2, pp. 103–112, Jun. 2022.



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