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| Title: | 以成本效益為基礎的需求預測研究–個案分析
Cost-based Demand Forecasting Analysis–A Case Study |
| Authors: | 劉哲銘
Liu, Jhe-Ming |
| Contributors: | 唐揆
洪叔民
Tang, Kwei
Horng, Shwu-Min
劉哲銘
Liu, Jhe-Ming |
| Keywords: | 需求預測
成本效益
供應鏈
存貨 |
| Date: | 2020 |
| Issue Date: | 2020-08-03 18:44:26 (UTC+8) |
| Abstract: | 本研究探討主題為電子業品牌商於海外電子商務平台進行銷售時，肇因於前置時 間長而導致的高昂成本極需降低的管理議題。根據 B 公司負責供應鏈管理資深 主管表示當前多數電子產業的銷售預測方法，大多採取「銷售人員意見法」，由 負責該產品的銷售人員以競爭者概況、對通路端的了解加上專業知識，以主觀推 測客戶之預估需求量。而這種預估模式因為充滿人為因素，而產生高變動性以及 精準度不足的問題。
因此本研究首先預測平台銷售業務所需要的出貨量，並根據此預測建立庫存模型， 進而假設庫存持有成本進行分析。本研究採用 ARIMA、VAR 以及 ANN 三種不同模型，以驗證出貨量與其餘內生變數間的相關性以及探討不同模型的預測精準度。為了證明立論的代表性，本研究另模擬 270 組數據以分析產品的集群分佈特性，發現符合不同產品的變異係數關係。最後依照變異係數的高低，挑選出兩項產品進行成本效益分析。
本研究進一步進行獨立樣本的 T 檢定，嘗試比較各樣本平均數是否有顯著差異。 發現高變異係數的 ARIMA 與 VAR、ANN 有顯著差異，而 VAR 與 ANN 則無。 而在低變異係數方面，則是三者皆無顯著差異。推測若分析樣本數增加，可以在統計上有更好的顯著性差異。因此本研究建議未來當分析實務上需要尋找產品所對應的最適預測模型時，可透過不同變異係數進行分類，尋找最適的預測模型，以達到降低成本的目的。
The topic of this study is the management issue that the case company is facing a high cost caused by the long lead time and inaccuracy forecast when selling on overseas e- commerce platforms. According to the senior director in charge of supply chain management of case company, most of the current sales forecasting methods for the electronics industry mostly adopt the "salesperson opinion method". And this forecasting model is full of human factors, resulting in high variability and insufficient accuracy.
Therefore, this study first predicts the shipments required by the platform`s sales, and builds an inventory model based on this forecast, and then assumes inventory holding costs for analysis. This study uses three different models including ARIMA, VAR, and ANN to verify the correlation between shipments and other variables and discuss the prediction accuracy of different models. In order to prove the representativeness of the argument, this study also simulated 270 sets of data to analyze the cluster distribution characteristics of the products and found that they corresponded to the coefficient of variation relationship of different products. Finally, according to the coefficient of variation, two products were selected for cost-benefit analysis.
Therefore, this study suggests that in the future, when analyzing the practical need to find the optimal prediction model corresponding to the product, it can be classified by different coefficients of variation to find the optimal prediction model to reduce costs. |
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| Description: | 碩士
國立政治大學
企業管理研究所(MBA學位學程)
107363105 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107363105 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202000890 |
| Appears in Collections: | [企業管理研究所（MBA學位學程）] 學位論文
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