成本預測模型之研究-以X公司線圈薄型電感為例;A Study on predicting the product cost-Take the Slim Coils Inductor of X Company as an example

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Title:	成本預測模型之研究-以X公司線圈薄型電感為例;A Study on predicting the product cost-Take the Slim Coils Inductor of X Company as an example
Authors:	周育玲;Chou,Yu-Ling
Contributors:	工業管理研究所在職專班
Keywords:	加權移動平均;學習曲線;成本習性;成本預測;最小平方法;移動平均;高次迴歸;Cost Behavior;Cost Prediction;Learning Curve;Least-Squares Method;Moving Average;Multiple-Regression;Sample Regression
Date:	2013-01-19
Issue Date:	2013-03-25 15:47:57 (UTC+8)
Publisher:	國立中央大學
Abstract:	企業經營以追求營收為目標，面臨微利時代，成本預測重要性日趨升高，成本預測影響報價多寡，執行準確成本預測有助於後端報價之適當決策，同時，亦影響企業未來經營盈虧。X公司線圈薄型電感之前採取直接材料成本加成預測，導致部份電感報價偏低，侵蝕到此產品群的獲利，因此重新建立更準確的成本預測模型，乃X公司當務之急。本研究採取「帳戶分析法」，以散佈圖與迴歸分析，決定出適合之作業水準以及成本習性，同時亦定義出四大銷貨成本項：直接材料、直接人工、製造費用與非製造成本；各方法論導入後，研究結果發現，X公司線圈薄型電感各成本項適合之預測模型為：直接材料適合移動平均法、直接人工成本則適合累計學習曲線下之工時配合移動平均法下之平均工時費、製造費用適合移動平均與費用率法、非製造成本適合簡單三次迴歸。本研究與其它產品成本預測作比較，發現銷貨成本項均以會計上之銷貨成本分類；成本分析基礎，其它產品多採作業基礎成本制，個案受限於無法收集全部作業成本資訊故無法採用；而在成本預測模型方面，其它產品使用的「作業基礎成本制」與「類神經網路」預測方法，個案因無法齊全收集作業成本資訊以及成本資訊不足無法再修正，因此也不採用。關鍵字：成本預測、成本習性、學習曲線、移動平均、加權移動平均、簡單迴歸、最小平方法、高次迴歸 Most businesses are revenue-oriented, but facing the time of micro-profit, the importance of cost prediction is increasing cost prediction impacts quotation , and accurate cost prediction hleps the decision-making of report recipients. At the same time, it also impacts the profits and losses for business in the future. X Compnay’s taking direct material cost plus forecast before for the Slim Coils Inductor caused the lower sales price of some inductors, and which eroded net profit of product group, so re-establishing a more accurate model for cost prediction is imperative for the company. This study takes ”Account Analysis Method” that uses “Scatter-Diagram Method” and “regression analysis” to define the cost behavior, and at the same time define those 4 costs: raw material, Direct Labor, overhaed, and non-manufacturing cost. After applying each method to the company , we find the suitable model for cost prediction of the Slim Coils Inductor is : “Raw Material” with “Moving Average”, “Direct Labor” with “cumulative learning curve” for working-hour and “moving average” for hourly salary , “Overhead” with “moving expenses rate” and “simply regression” for “Non-manufacturing cost”. This study also conducts comparison with other cost prediction methods to get the following results:(1)Sales Costs kind were almost use classified by accounting classification .(2)Cost analysis basis: Other products usually apply the method of Activity-Based Costing, but our product was use “Cost behavior analysis”.(3)Cost Prediction:The prediction methods of "Activity Based Costing" and "neural network" were used in other products, but our products being unable to fully collect operation cost information and cost information alone is insuffient to make modifications, don’t adopt this method.Keyword:Cost Prediction, Cost Behavior, Learning Curve, Moving Average, Weighted Moving Average, Sample Regression, Least-Squares Method, Multiple-Regression.
Appears in Collections:	[Executive Master of Industrial Management] Electronic Thesis & Dissertation

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