Abstract:
Current inventory management of the company in this case study is facing with inventory management problem due to forecasting error. The previous forecast used in this factory case study is an estimate based on past experience. Thirty six monthly uses of lathe cutting tools- carbide insert types during January 2010 to December 2012 am used for forecasting in this factory case study. In this study, an ABC classification model that analyzes a range of items and groups them into three categories (A, B, and C) is use to indentify the highest cast of cutting tools use that indicates the most important items to minimize an inventory cost. Three lathe cutting tools carbide (AOMT123608PEER-H/VP15TF, AO MT184808PEER-H/VP15TF and DGJ80CFR1.0 /UTI20T SPECIAL) are grouped in an A category. A comparison of mean absolute deviation (MAD) values form Regression and Single Exponential Smoothing Method is made. It reveals that MAD value from Regression is less than MAD value from Single Exponential Smoothing Method. This means that a forecast with Regression is more accurate than a forecast with Single Exponential Smoothing Method for this factory case study. Economic Order Quantity (EOQ) and Reorder point (ROP) are then calculated. Results suggest that the company can save over 44,700 THB in buying those three lathe cutting tools over the past three years if they applied the new EOQ and ROP policy