CONTROL OF RAW MATERIALS INVENTORY PROBABILISTIC MODEL USING MONTE CARLO SIMULATION AND DYNAMIC SYSTEM

Increasing efficiency and effectiveness is a fundamental problem for every company. Optimization of raw material inventory is an effort for this problem. PT XYZ is a company engaged in manufacturing various flavors of beverages, one of which is sugar. The level of sugar usage in each month is probabilistic and dynamic. PT XYZ's raw material inventory is determined based on field assumptions, causing a shortage or excess of raw material inventory. This study aims to determine the policy of raw material inventory P backorder model with the integration of Monte Carlo simulation and Dynamic System. The raw material inventory policy is determined based on the order time interval, safety stock, and maximum capacity and compares the actual total inventory cost with the Monte Carlo simulation. Dynamic system simulation is carried out to ensure that the P backorder model inventory policy can be applied. The results showed that the total inventory without Monte Carlo was better than the total cost of the Monte Carlo simulation of Rp198.846.582,98 with an order time interval of 0,30929 years, a maximum inventory capacity of 11149,8 kg and a safety stock of 4335,83 kg. From the results of the comparison using the one sample T-test, it was found that there was no statistical difference between the results of the dynamic system simulation and the results from the calculation of the P backorder model so that the P backorder model policy could be applied to PT XYZ to determine the minimum inventory cost.