由主生產排程(Master Production Schedule; MPS)所計算出之可承諾量(Available-to-Promise; ATP)，是指尚未被用於承諾顧客訂單的存貨量以及主生產排程的計劃生產量，據以回覆顧客訂單可允諾之數量與交期。此訂單允諾機制採用先到先服務(First-Come-First-Served; FCFS)之原則，將所有訂單之重要度視為一致，且只考量成品之可承諾量，因此僅適用於存貨式生產模式(Make-to-Stock; MTS)。然而，由於目前大量客製化成為顧客訂單需求之趨勢，製造商逐漸將其生產模式轉變為接單後組裝的生產模式(Assemble-to-Order; ATO)或接單後製造的生產模式(Make-to-Order; MTO)，以滿足來自客戶需求端的要求限制，例如指定產品用料供應商、指定生產廠區等限制。而且，由於大量客製化也將造成不同的產品價位與訂單契約合作關係，導致差別化與區隔化顧客需求。
許多製造商導入先進規劃與排程系統(Advanced Planning and Scheduling; APS)來協助供應鏈環境下之生產相關規劃，其中的訂單允諾與可承諾量模組，即是在將可承諾之製造資源配置給各個需求訂單，進而回覆顧客可承諾之交期與數量。在ATO或MTO模式下，顧客必須等候產品的前置時間要比MTS模式長，不過顧客至少希望可以獲知可靠的允諾時間與數量，因此訂單允諾機制的可靠性就顯得非常重要。
首先，因應生產模式由MTS轉變為ATO與MTO，在進行訂單允諾時必須考量來自客戶需求端的要求限制如指定產品用料供應商、指定生產廠區等限制等，本研究以TFT-LCD產業為例，探討在ATO或MTO生產模式下之訂單允諾規劃，考量接單後之物料與產能等製造資源之特性與限制如：顧客指定用料、顧客指定廠區、物料相容性等，以及訂單產品之利潤與顧客之重要程度等，利用混整數規劃模式(Mixed Integer Linear Programming; MILP)，將產能與物料之生產製造資源進行最佳化之配置規劃。
其次，為了保留物料與產能等製造資源給重要的顧客或高利潤產品，本研究提出兩階段式的訂單允諾程序。在第一階段，依據預測之各顧客對各產品需求量，將可承諾量ATP根據優先配置法則，依序配置保留給重要的顧客或高利潤產品；在第二階段，則依據實際顧客之訂單需求量，考量可承諾之製造資源、來自顧客之要求限制(如：指定用料供應商等)以及階段一的保留量限制，將可承諾之製造資源配置給各個需求訂單，進而回覆顧客可允諾之交期與數量。其中利用混合整數規劃模式，將產能與物料之生產製造資源進行最佳化之配置規劃，並以TFT-LCD產業為例，探討所提出之兩階段式的訂單允諾程序的有效性。

Available-to-promise (ATP) calculating from master production schedule (MPS) exhibit availability of manufacturing resources that can be used to support customer order promising. This traditional order promising mechanism is adapted in MTS (make-to-stock) production model and all orders are treated the same on first-come-first-served (FCFS) policy. However, increasingly mass customization results in production model gradually transfers from MTS to ATO (assembly-to-order) or MTO (make-to-order) in order to fulfill the requests from customers such as customer’s preference materials/plants or specifications for the ordered products. Moreover, mass customization also drives the trend of customer demand to segmentation and prioritization according to differential product profit, sales growth potential, contracts or the relationships with customers.
Many manufacturers employ advanced planning and scheduling (APS) solutions with new planning and scheduling techniques to support supply chain planning. In which, the solution module of order promising & ATP is to match customer orders against available manufacturing resources and then to reply promised quantities and due dates. In ATO or MTO model, the manufacturing resource such as materials and capacity after order penetration point should be checked and allocated for customer orders considering customer’s preference constraints. An upstream CODP (customer order decoupling point) such as ATO or MTO involves rather long order lead-time but customers at least want to get a reliable promise that they can receive the products in the promised quantities at the promised dates. Therefore, order promising process is very important within such competitive supply chain environment to build core-competence through reliable order promises in order to retain customers and increase market share.
First, this research proposes one order promising mechanism that applies mixed integer linear programming (MILP) model to prioritize allocating manufacturing resource for high profit products or important customers and to consider material and capacity constraints after order penetration point. Furthermore, this order promising mechanism takes thin film transistor liquid crystal display (TFT-LCD) manufacturing as illustration for these material and capacity constraints after order penetration point.
Second, to reserve manufacturing resources for high-margin products or high-priority customers, this dissertation proposes two-phase order promising process. In phase 1, ATP are reserved (called Allocated ATP; AATP) first for the demand with higher profit or higher priority. And then in phase 2, customer orders are promised according to time-phase supply calendar of manufacturing resource and restricted by the AATP in phase 1 and requests from customers such as customer’s preference material/plant constraint. In which, mixed integer linear programming (MILP) is applied to prioritize allocating manufacturing resource for high profit products or important customers and to consider customer’s preference material/plant constraints after CODP.
One TFT-LCD manufacturing is taken as illustration for demonstrating the validity of the proposed two-phase order promising process. The results of numerical comparison show that the proposed two-phase order promising process can assist company to reserve manufacturing resources beneficially in advance phase 1 and then to provide more reliable customer order promises in phase 2 with considering available resources and requests from customers such as customer’s preference material/plant constraint.

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