Abstract:
The objective of this research was to identify the optimal safety stock level so as to reduce the holding inventory cost in consumer products. The current safety stock level configurations were set by naïve forecasting from the planner. According to improper setting, the factory faced with the problem on the average inventory over stock to 423% comparing to the sales volume.
This research used ABC classification technique to categorize class A product, which identified as a group of maximum value of total inventory. Then, the safety stock level were calculated and evaluated based on historical data by three methods; based on statistical seasonal demand, based on median absolute deviation varies over time and based on the highest demand. The Monte Carlo simulation techniques by Microsoft Excel was also used to investigate the effects on three scenarios of sale behaviors; normal sale, 2-weeks/time sale promotion and 4-weeks/ time sale promotion.
The result suggested that the proper method to identify the optimal safety stock level with less impact on on-time delivery level for normal sale scenario was the method of using the statistical seasonal demand. While both methods based on median absolute deviation varies over time and based on the highest demand were properly used for 2-weeks/time and 4-weeks/ time sale promotion. The simulation result showed that the average inventory and holding cost could be saved 56% per month. After implementation on 6 items of class A product, the average inventory and holding cost also reduced 53% per month.