Development of an Adaptive Forecasting System: A Case Study of a PC Manufacturer in South Korea

• Main Authors: Chihyun Jung, Dae-Eun Lim
• Format: Article
• Language: English
• Published: MDPI AG 2016-03-01
• Series: Sustainability
• Subjects: demand forecasting; product life cycle; Bass diffusion model; Bayesian updating
• Online Access: http://www.mdpi.com/2071-1050/8/3/263

We present a case study of the development of an adaptive forecasting system for a leading personal computer (PC) manufacturer in South Korea. It is widely accepted that demand forecasting for products with short product life cycles (PLCs) is difficult, and the PLC of a PC is generally very short. The firm has various types of products, and the volatile demand patterns differ by product. Moreover, we found that different departments have different requirements when it comes to the accuracy, point-of-time and range of the forecasts. We divide the demand forecasting process into three stages depending on the requirements and purposes. The systematic forecasting process is then introduced to improve the accuracy of demand forecasting and to meet the department-specific requirements. Moreover, a newly devised short-term forecasting method is presented, which utilizes the long-term forecasting results of the preceding stages. We evaluate our systematic forecasting methods based on actual sales data from the PC manufacturer, where our forecasting methods have been implemented.