長交期物料之需求管理模式 -以油田生產人工舉升零件泵軸為例

NCUIR > School of Management at National Central University > Executive Master of Industrial Management > Electronic Thesis & Dissertation > Item 987654321/80268

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/80268

Title:	長交期物料之需求管理模式 -以油田生產人工舉升零件泵軸為例
Authors:	王曉茵;Wang, Hsiao-Yin
Contributors:	工業管理研究所在職專班
Keywords:	存貨管理;庫存匯集;相關分析;線性迴歸;模擬系統;Inventory management;Pooling inventory;Pearson correlation;Linear regression;Arena simulation
Date:	2019-07-10
Issue Date:	2019-09-03 12:27:26 (UTC+8)
Publisher:	國立中央大學
Abstract:	任何一個企業為了要滿足市場或顧客需求而生產產品，雖然各個企業生產運作模式有所差異，但是多多少少都有需要存貨。存貨過多可能造成資金的積壓，但是存貨不足可能造成生產的中斷、服務水準的降低，引起企業另一種危機。個案研究料號泵軸原料供應之前置時間大於客戶需求所給定時間的前置時間約四個月之久，所以存貨控制方面，長交期的物料相較於原料供應之前置時間小於客戶需求所給定時間之物料管理更具有挑戰性。本論文研究希冀由物料分類與編碼原則將客戶需求料號群組化後，找到三個高佔比料號始進行資料收集分析，並利用 Pearson 相關分析找出影響泵軸需求數量之相關因子，再利用線性迴歸模型建立可預測泵軸需求數量之迴歸方程式。同時，考量預測需求量可能面對許多無法掌控的因素（如運送的延誤、戰爭、天災等），造成未來市場的需求預測與原料市場的供給預測之偏誤，所以本研究以最低平均總體庫存成本為目標，搜尋最適存貨的決策變數 (s, S) ，以助於物料管理人員在需求時間和數量隨機的情境下，以及供應商有可能延遲交貨的環境，能快速且正確的決定再訂購點和訂購量。所以，本研究目的不僅對該物料需求的梳理，更希望追求最低存貨成本，提供個案公司在經營策略及存貨目標下，找到最適存貨水準。 ;In order to meet customers’ demand, every business needs to keep some inventory no matter what inventory management strategies operates. Companies constantly face the issue and potential risk of high inventory costs or high backlog cost including losing sales. In this study, we deal with the raw material of Pump Shaft with unexpected delivery, which is about four to five months longer than customers’ given lead-time for following three main purposes. First, to improve material management, material number conversion is based on the cutting points per customers’ required length. Utilizing pooling inventory method converts continuous material number into be discrete material number for differential customers’ service. Second, to improve forecasting method, evaluating the relationship between changes in Brent Oil Price, global rig counts and Pump Shaft demand in 2016 ~ 2018 and then modeling the forecasting liner regression. Third, at the same time, to mitigate the deviation of forecasting in case of occurring force majeure, building an inventory model in Arena seeks to give optimized order up to inventory position with targeting minimum costs using a heuristic optimization tool, OptQuest, take into account the discrete probability of demand arrives, and demand size and raw material delivery. Therefore, the study’s aim was to organize the material master, construct the forecasting of Pump demand and get the optimal inventory position in the target of minimize inventory cost.
Appears in Collections:	[Executive Master of Industrial Management] Electronic Thesis & Dissertation

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