A DEMAND FORECAST METHOD FOR THE FINAL ORDERING PROBLEM OF SERVICE PARTS
Demand forecast for service parts for the final ordering problem at the end of life phase of durable goods is usually plagued with inadequate demand data, changing purchasing behavior, and lack of reliability information. The total number of data points for each service part sale is very limited. This paper presents an empirical study on developing a forecast method based on installed base information. The issue of data quantity and data recency is analyzed. The recency of demand data is shown to be more informative than the quantity of data. With a small quantity of data points, regular regression is shown to be inadequate in predicting future demand. A procedure based on two trend tests using different quantities of data is developed. The procedure is validated with two automobile series and notebook computer models and is shown to outperform a current method by large margin s in forecast errors.