在這經濟全球化的體系當中，使得企業競爭越來越激烈。使得企業為了增加其市場競爭優勢，必須提高顧客滿意度及減少製造成本，使得決策者必須規劃適當生產計劃和安全存貨量，才能降低缺貨成本、延遲成本及存貨成本。因此預測的準確與否往往會影響製造成本及決策的品質。
但以目前造紙業計劃型生產模式而言，仍然以人工經驗值的判斷為主，有鑑於此，本研究修正二次預測、二次生產規劃之方法，並使用不同的類神經方式預測方式來取代人工經驗值之判斷來比較此新生產模式是否優於原先模式。
本研究運用類神經網路在預測上優越能力，學習現有資料之關連性進行需求預測模式之建構，以供決策者執行生產規劃之依據。研究中，使用二次預測、二次生產規劃之方法，除了修正原先生產模式外，並擴增資料年份，再擴增以不同類神經網路之預測方式(1.倒傳遞模式(BPN) 2.徑向基底模式(RBF))來探討此二次預測、二次生產規劃生產模式是否優於原先模式，以架構確認此新二次生產規劃模式之實用性。

In today globlized environment, enterprises must improve customer satisfaction and reduce the manufacturing cost in order to increase their market competition. So the accuracy of sale prediction will always influence the quality of decision and manufacturing cost.
However, in the paper industry, there was very few cooperation among the sales, inventory and production functions in an industry. Therefore, the production planning still heavily depends on the human beings experience. This research developed a two-stage forecasting and planning model, and use two kinds of neural network model to predict the sales and production.
The sales, production and inventory data from a leading paper industry in Taiwan were used to train the neural network. The experimental results showed that the two-stage forecasting and planning can achieve better results than the original industrial data.

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