An improved grey-based approach for early manufacturing data forecasting

Global competition has shortened product life cycles and makes the trend of industrial demand not easily forecasted. Therefore, one of the key points that will enable enterprises to survive and succeed is the ability to adapt to this dynamic environment. However, the available data, such as demand and sales, are often limited in the early periods of product life cycles, making traditional forecasting techniques unreliable for decision making. Although various forecasting methods currently exist, their utility is often limited by insufficient data and indefinite data distribution. The grey prediction model is one of the potential approaches for small sample forecast, although it’s often hard to amend according to the sample characteristics in practice, owing to its fixed modeling method. This research tries to use the trend and potency tracking method (TPTM) to analyze sample behavior, extract the concealed information from data, and utilize the trend and potency value to construct an adaptive grey prediction model, AGM (1,1), based on grey theory. The experimental results show that the proposed model can improve the prediction accuracy for small samples.