Demand planning (DP) and sales forecasting (SF) are two critical issues to achieve successful supply chain analytics. Generally, DP refers to determining the aggregate demand for a common component or sub-assembly required by various finished products. In contrast, SF is conducted to estimate sales revenues of the firms. In the past, DP usually focused on optimizing resource allocation while SF is roughly based on historical data. In reality, DP and for the upstream motherboard and SF for the firm closely rely on the estimation of sales volumes of the downstream computer products. Meanwhile, the dynamic interactions between the main competitors significantly influences the performance of SF. To highlight the impacts of demand uncertainties and dynamic interactions, this research presents a novel framework to overcome difficulties: (1) demand uncertainties arising from seasonal variations and cyclic trends in computer products are captured, (2) DP and SF consider the change of product volatility, (3) the dynamic interactions between the MB and computer products are considered to elicit managerial insights. Experimental results show that the presented framework successfully achieves the above-mentioned goals and has potential to be generalized to other industrial components.
- Computer products
- Supply chain analytics