To build up a manufacturing management model for a newly developed product is fundamentally a difficult problem, because the collected data in the early manufacturing stages is usually insufficient when data size is small. There are several researches on this topic, and most of them focus on the original data analysis such as building up virtual samples to increase the data number. As to other approaches, the usage of old or similar manufacturing experience may be an alternative approach to help in modelling a small data set, by taking advantage of the fact that the new product's manufacturing process could be based on the experience of the old one. This research proposes a combination of support vector regression (SVR) and the manufacturing experience to build up the manufacturing knowledge model for a new product. A real-problem of a new product yield forecast model in a polariser manufacturing company is demonstrated, where two approaches are proposed, and the results show that the presented approach is superior to the performance of a linear regression and back-propagation neural network. The case study shows that the input of the old or similar manufacturing experience into the forecast model can reduce the error rate and enhance the model forecasting ability.
All Science Journal Classification (ASJC) codes
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering