臺灣博碩士論文加值系統

存貨管理的基礎為準確的需求預測，醫療衛材的不確定性，造成間歇性的需求型態，使預測過多造成囤積，預測過少導致無法立即滿足病患之需求，影響病患之安全，皆直接或間接影響醫院營運成本及醫療服務品質。因此，為了達成較好的預測準確性，能即時滿足病患而不過度領用，而增加存貨成本或導致衛材過期，本研究考量衛材需求的變異性(Square coefficient of variation；CV2)與平均需求區間(Average inter-demand interval ; ADI)，將消耗型態分為波浪型、不穩定型、平滑型與滯銷型需求，並比較簡單移動平均法、指數平滑法、自我迴歸整合移動平均模式(Autoregressive integrated moving average model；ARIMA)及Croston預測法之準確性，結果驗證Croston之指數平滑法可使間歇性需求之預測誤差最小化，簡單移動平均法則可使平滑需求之預測誤差最小化，其可避免衛材囤積與缺貨，避免危及病患安全，並降低醫療院所之衛材管理之成本，本研究之方法可做為其他醫療院所衛材管理之參考。

The accuracy forecasting is the basic of inventory management. Intermittent demand is random demand with a lot of zero values. Materials have different types and frequency of usage in healthcare., Usage is affect by patients’ wound size and age. Due to the uncertainty of materials usage, it is difficult to predict materials. Therefore, this paper classify demand by square coefficient of variation (CV2) and the average inter-demand interval (ADI) and use Simple Moving Average(SMA), Single Exponential Smoothing(SES) , Autoregressive Integrated Moving Average model(ARIMA) and Croston’s method to forecasting. By a hospital’s datasets, it shows that there are optimal forecasting method in each classification. In lumpy, erratic and intermittent demand, Croston single exponential smoothing produces more accurate forecast. Simple moving average has better performance in smooth demand. Based on the classification and forecasting method, it can decrease inventory , shortage and the cost of inventory management . Finally, the proposed model could be applied on other hospital replenishment case.

目錄
摘 要 I
ABSTRACT II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VII
第一章 緒論 2
1.1 研究背景與動機 2
1.2 研究目的 3
1.3 論文架構 3
第二章 文獻探討 4
2.1 常見的預測方法 4
2.2 間歇性需求需測 6
2.3 需求的消耗型態 8
2.4 預測的準確性 11
第三章 研究方法與個案介紹 12
3.1 研究架構 12
3.2 個案介紹 13
第四章 實務驗證與分析 15
4.1 資料篩選 15
4.2 需求的消耗型態 17
4.3 ARIMA 模型 20
4.4 CROSTON預測法 20
4.5 預測能力之評估 22
4.6 小結與討論 24
第五章 結論與建議 28
參考文獻 29
附錄一 32
附錄二 37


表目錄
表 2. 1藥品的消耗型態 8
表 4. 1個案PICU衛材之料庫之ABC 存貨分析 15
表 4. 2個案PICU衛材資訊(共30期) 16
表 4. 3PICU衛材的ADI與CV2及消耗型態 17
表 4. 4PICU衛材資料庫之間歇性衛材需求資料統計表(共30期) 19
表 4. 5酒精棉片之適合模型之適配度比較 20
表 4. 6PICU範例衛材預測能力之評估─MAPE 23
表 4. 7PICU衛材依藥品消耗型態分類之結果 26
表 4. 8PICU衛材之CROSTON SES(0.9)與S&B(0.9)及PICU現行作法的預測結果比較 27



圖目錄
圖 1. 1間歇性需求之示意圖 2
圖 2. 1預測之分類 4
圖 2. 2需求的消耗型態 9
圖 3. 1研究架構圖 12
圖 4. 1PICU衛材消耗型態分布圖 18
圖 4. 2需求消耗型態與預測方法之交互作用圖 25
圖 4. 3本研究之預測結果 25

中文部分
1. 沈志陽(2007) 。以ARIMA 季節相乘模式預測汽車售後服務進廠台數之研究-以裕隆日產體系為例，碩士論文，國立交通大學，新竹市。
2. 吳良玉(2012)。限制理論用於醫療材料存貨管理之研究－ 以某區域級教學醫院為例，碩士論文，中原大學，桃園縣。
3. 袁立德(1993)。藥品消耗型態與庫存管理之實證研究─以二所群醫學中心為例，碩士論文，國立陽明大學，台北市。
4. 詹琇伃(2004) 。結合ARIMA模式與倒傳遞網路以降低預測誤差，碩士論文，國立成功大學，台南市。
5. 賴順益(2010)。智慧型藥品需求量預測專家系統之建置，碩士論文，亞洲大學，台中市。
6. 賴仕傑(2012)。醫檢實驗室試劑耗材管理及需求預測資訊系統，碩士論文，朝陽科技大學，台中市。

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