Abstract

Mostly, the concept of smart manufacturing is addressed based upon how to effectively facilitate the production activities by using the automation equipment; however, causing the fluctuation of production may frequently root to the uncertain incoming sales orders. These uncertain factors may be influenced by various economic parameters, such as changes within trade regulations, competitor innovations, and changes within the market. In order to reduce the difference between the forecasted demand versus actual demand and to minimize risk, these factors need to be taken into account and be fully investigated. The current widely applied forecast methods are factory capacity-driven and based on the trend against the activity history. When the uncertainty comes from the external, then the forecasts derived from these models cannot provide convincing insights to let the firms make decisions confidently. Many previous prestigious studies focused on the problemsolving optimization mathematic methods and articulated the causality among latent factors; few have addressed to a holistic framework that the firms can practice on. This study presents a clear operable step-by-step framework to manage and cushion the impact from the external uncertain factors. It also introduces three novel and feasible production planning models with the consideration of the economic parameters. The empirical case was a multi-nation machinery-making firm who has adopted the proposed framework to optimize the material forecasts pursuing their smart manufacturing goals.

Keywords: material planning, supply chain management, smart manufacturing, advanced analytics, AI application
