在目前全球運籌供應鏈體系運作的環境下,企業在全球不同地區擴建新廠或合併其他小廠,已為普遍的生產型態。且由於市場變化迅速,常造成企業銷貨預測與市場實際需求有所差異,為了滿足市場需求,下游客戶時常以緊急訂單方式下單給代工製造商,冀望自己的產品能以最快的速度在市場銷售。緊急訂單承接與否,必須將廠區產能限制及成本因子納入考量,若一昧地以個人主觀判斷接單,將可能導致因產能不足而造成訂單延遲,將嚴重地影響到企業的商譽。當多廠區企業面臨緊急訂單時,緊急訂單合理承接判斷及跨廠區的分配工作將更加困難,因此緊急訂單於多廠區的規劃顯得相當重要。 本研究針對多廠區緊急訂單承接評估及跨廠區產能規劃問題,建立兩階段規劃模式,第一階段將以企業獲利的立場,以緊急訂單利潤總和減去因承接緊急訂單造成的影響成本多寡判斷緊急訂單承接組合。第二階段考量各廠區產能限制及成本因素等,求解出緊急訂單製造成本總和最小之跨廠產能分配量。其中第一階段將可求得為企業帶來最高利潤的緊急訂單承接組合;第二階段將可求得各筆緊急訂單分配至各廠區的情形,並可得知各廠區所需完成的一般訂單與緊急訂單產品數量,如此,後續規劃人員將可依據本研究規劃結果安排後續生產細部規劃及排程規劃等工作。
In recent years, is has became a common situation for many enterprises built or merged new factories around the world. Due to the fast change in marketing, it always exist variations between sales forecast and actual market demand. In order to satisfy market demand, customers always purchase by rush orders. It should be take capacity limit and cost factor into account when justifying of accept rush order or not. It will be much difficult for a multi-site factory to decide accept the rush order or not. If enterprises justify rush order without objectivity, it may cause order delay because of capacity shortage. Consequently, a model for multi-site factories to justify the rush order acceptance and capacity planning is important in global logistics supply chain environment. This research is aimed at the rush orders acceptance and capacity planning in multi-site factories. The objective of this research is to develop two integrated models in two phases. In the first phase, we can justify the optimal rush order combination in maximal rush order rewards subtract extra cost of accepting the rush orders. In the second phase, we can obtain the minimal integrated cost including normal order production cost, rush order production cost, outsourcing cost and delay cost. We also can find out the optimal capacity disposition, volume of rush order producing, order lateness and outsourcing before manufacturing. Therefore, the follow-up staffs could arrange detail product scheduling and material requirement planning according to the optimal solution of this research.