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研究生： 王逸鴻
WANG, YI HUNG
論文名稱： 結合預測誤差與需求波動之產能平準化決策—以M公司為例
Capacity Leveling Decisions Integrating Forecast Error and Demand Variability—A Case Study of M Company
指導教授： 謝中奇
Hsieh, Chung-Chi
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業與資訊管理學系
Department of Industrial and Information Management
論文出版年： 2026
畢業學年度： 114
語文別： 中文
論文頁數： 91
中文關鍵詞： 需求不確定性 、產能平準化 、負荷移轉 、先進排程系統 、預測誤差
外文關鍵詞： Demand uncertainty, Capacity leveling, Load shifting, APS, Forecast error
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    • 本研究探討汽車照明產業中，需求高度波動與交期要求嚴苛所造成的規劃挑戰。由於總產能規劃（Aggregate Production Planning, APP）與先進排程系統（Advanced Planning and Scheduling, APS）在決策層級與時間尺度上存在落差，實務上常導致排程不穩定與產能利用效率受限。針對此一問題，本研究以 M 公司後段組裝瓶頸製程為研究對象，提出一套結合預測誤差與需求波動特性的產能平準化決策架構。
    研究以平均絕對百分比誤差（Mean Absolute Percentage Error, MAPE）與預測誤差變異係數（Coefficient of Variation of Forecast Error, CV_err）量化需求不確定性，並透過四分位數矩陣對產品進行風險分群。為實作平準化邏輯，本研究開發一套 Excel VBA 模組，評估基準策略（Baseline）、前移負荷移轉（Forward Load Shifting, FLS）、後移負荷移轉（Backward Load Shifting, BLS）、雙向負荷移轉（Two-way Load Shifting, TLS），以及本研究提出的 MCAS 自適應策略（MAPE-CV_err Adaptive Strategy, MCAS）。各策略以 2024 年需求資料進行 N+8 週滾動式模擬，並匯入既有 APS 系統進行驗證。
    實證結果顯示，傳統平準化策略在生產穩定性與交期回應力之間存在明顯權衡關係。相較之下，MCAS 透過不確定性驅動的分層決策機制，有效改善此一限制。結果顯示，MCAS 在以預交日為基準之準時交貨率（OTD_Due）上達到 51.26%（p < 0.05），同時將重點工站之負荷變異係數（Load CV）維持於 0.5299。效率前緣分析進一步證實，該策略能在不增加產能資源的前提下，同時兼顧生產穩定性與交期績效。本研究所提出之架構，為 APP 與 APS 之間的整合提供一套具實務可行性的決策參考。

    This study addresses the decision gap between Aggregate Production Planning (APP) and Advanced Planning and Scheduling (APS) under demand volatility in the automotive lighting industry. A capacity leveling framework integrating forecast error is proposed for M Company’s bottleneck process. Using MAPE and CV_err to classify uncertainty, five strategies (Baseline, FLS, BLS, TLS, and MCAS) are simulated via Excel VBA. Empirical results reveal a stability-responsiveness trade-off in traditional strategies. However, the proposed MCAS strategy significantly improves On-Time Delivery to 51.26% (p<0.05) while maintaining load stability (CV 0.5299). Efficiency Frontier Analysis confirms MCAS optimizes performance, effectively bridging the structural gap between APP and APS.

    摘要 i Extended Abstract ii 誌謝 vii 目錄 viii 表目錄 xi 圖目錄 xii 符號與縮寫列表 xiii 第一章 緒論 16 1.1 研究背景與問題陳述 16 1.2 研究問題與待解議題 19 1.3 研究架構與研究流程 20 第二章 文獻探討 22 2.1 前言 22 2.2 總產能規劃與細部排程之銜接斷層 22 2.2.1 APP 在不確定環境下的限制 23 2.2.2 APS 對輸入品質的敏感性 23 2.3 產能平準化策略之演進與應用 24 2.3.1 從靜態節奏到動態負荷調整 24 2.3.2 平準化策略相關文獻比較 25 2.4 需求不確定性與預測誤差之衡量指標 25 2.4.1 單一構面指標的不足 26 2.4.2 不確定性雙重面向衡量架構之理論基礎與建構 27 2.5 模擬導向之決策支援系統 29 2.5.1 模擬技術在排程決策中的角色 29 2.5.2 整合型 DSS 的發展 31 2.6 研究缺口與定位 31 第三章 研究方法 33 3.1 研究定位與決策框架 33 3.2 研究資料基礎與需求不確定性量化 35 3.2.1 資料來源與預測誤差衡量指標 35 3.2.2 MAPE × CVerr 分群邏輯 36 3.3 平準化決策模組設計與策略運算邏輯 37 3.3.1 決策模組架構與資料輸入 38 3.3.2 策略控制條件與負荷平移運算邏輯 40 3.3.3 MCAS 驅動之動態策略指派 43 3.4 系統整合與排程模擬執行 45 3.4.1 平準化結果的標準化輸出與資料介接 45 3.4.2 模擬排程執行流程與控制條件 46 3.4.3 系統邏輯驗證與可行性檢核 47 3.5 績效驗證指標與統計分析方法 47 3.5.1 核心績效指標定義與計算 48 3.5.2 統計分析方法論 49 3.6 本章小結 50 第四章 個案實證分析 52 4.1 生產環境特徵與排程系統架構 52 4.1.1 垂直整合製程與瓶頸工站特性 52 4.1.2 現行 APS 排程模式與限制因素 53 4.1.3 平準化決策模組之介入與 APS 整合流程 55 4.2 需求特性分析與不確定性分群 57 4.2.1 歷史需求不確定性分析 57 4.2.2 MAPE × CVerr 二維不確定性分群架構與策略指派 58 4.3 平準化決策模組之實作與人機介面設計 58 4.3.1 使用者介面(UI)設計與操作邏輯 59 4.3.2 系統運作流程與演算法實作 61 4.4 模擬架構設計與績效指標 63 4.4.1 滾動視窗模擬架構 63 4.4.2 平準化策略設計與比較基準 64 4.4.3 績效指標與統計檢定工具 66 4.5 模擬結果與瓶頸績效分析 67 4.5.1 產能負荷變異(Load CV)分析 67 4.5.2 交期準時率差異分析(以預交日為基準) 70 4.5.3 交期準時率差異分析(以出貨日為基準) 72 4.5.4 平均延遲天數分析(以預交日為基準) 73 4.5.5 平均提前天數分析(以預交日為基準) 75 4.5.6 平均延遲天數分析(以出貨日為基準) 76 4.5.7 平均提前天數分析(以出貨日為基準) 78 4.6 整體比較與管理意涵 80 4.6.1 平準化策略之綜合比較 80 4.6.2 管理意涵與實務建議 83 第五章 結論與建議 84 5.1 研究結論 84 5.2 管理意涵 84 5.3 研究限制與未來建議 85 參考文獻 87

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