軟性印刷電路板（FPC）前段製程具有多站串列、平行設備、工單量差異及處理時間波動等特性。個案工廠之製造執行系統（MES）可取得工單產量與作業時間，卻缺乏完整的設備實際占用、停機、換線、重測及產品屬性紀錄，使傳統以個別損失機制為輸入的排程模型難以直接參數化。本研究因此以可得 MES 資料建立 FPC 前段製程之離線排程情境分析架構，比較複合訂單壓力、EF ／等效處理時間波動情境與派工法則對交期績效及系統流動特性的影響。本文所稱交期達成率（OTD）係依模型設定之情境交期計算，屬法則間的離線比較指標，不代表個案工廠實際客戶 OTD 的重建或預測。
原始資料包含 23,086 筆 MES 站別作業紀錄；排除 128 筆超過 10,080 分鐘之極端工時，並依各站四分位距準則篩選後，共 21,299 筆紀錄用於效率係數（Efficiency Factor, EF）分析。EF 定義為實際產出相對工程標準產出之比，作為多種未觀測損失共同作用後的綜合代理；因 MES 作業時間之事件組成尚未由正式資料字典完整確認，EF 所推導之時間應視為等效處理時間，而非已辨識之純設備加工時間。四站採 Gamma 邊際輸入，模擬將 EF 裁切於 [0.05, 2.50]，並校準裁切後平均與標準差。另由 5,288 筆工單建立正規化等效工作量分布，使 CR 與 BN 可反映工單加工需求差異。SimPy 模型比較先到先服務（FIFO）、最早交期（EDD）、臨界比率（CR）與壅塞觸發之低等效工作量優先派工（BN；沿用 bottleneck-aware dispatching 之縮寫）；BN 未達壅塞門檻時採 EDD，達門檻時依 𝑄𝑗 由小至大排序。主實驗採 4 × 3 × 2 全因子設計，在 180 個獨立壓力–波動–複製區塊內以共同亂數（Common Random Numbers, CRN）配對四種法則，共 720 次模擬；另分別以兩組各 480 次模擬，檢查 EF 下界之整體重新校準，以及交期指派與工單量–EF 相依假設。統計分析以第二類平方和 ANOVA 描述變異來源，以 Friedman 檢定比較六種情境內的整體法則差異，並聚焦 High/High 下 BN 相對三種基準法則之 CRN 配對平均差、信賴區間與效果量；ANOVA 僅作描述性變異分解，法則推論以 CRN 區塊內比較為主。
在本研究所選因子水準內，複合訂單壓力、EF 波動情境及其交互作用分別占 OTD 總平方和的 48.34%、10.36% 與 12.07%，法則邊際主效應僅占 0.04%。高壓力／高波動（High/High）主模型中，BN、FIFO、EDD 與 CR 的工單數 OTD 分別為 59.09%、55.06%、53.85% 與 52.35%；BN 相對三法則之 CRN 配對差介於 4.03–6.73 個百分點。惟 BN 的等效工作量加權 OTD 為 52.99%，與其他法則接近；High/High 之 CRN 配對檢核亦顯示，BN 相對三個基準法則的完整期間平均在製品數（work in process, WIP）降低 1.40–2.19 筆工單，但複製內逾期時間第 95 百分位數（P95）平均增加 11.45–20.71 小時，顯示工單數 OTD 優勢伴隨明確的多目標權衡，未形成跨績效指標的一致支配性。敏感度分析進一步顯示，改採經驗排序相依性後，BN 相對 EDD 的工單數 OTD 差距縮小或反轉，工作量加權 OTD 亦可能由 EDD 領先。
本研究結果顯示，FPC 前段製程之 OTD 表現主要受到訂單壓力與 EF 波動情境的共同影響，而派工法則的邊際平均差異相對有限。在本研究範圍內，複合訂單壓力與整體重新校準之 EF 波動情境所對應的 OTD 差異，大於派工法則的邊際平均差異；特定法則仍可能在個別情境產生達本研究統計設計下近似可偵測量級的條件式效果，但其方向與管理意義須結合交期指派、輸入相依、批量權重、逾期風險及 WIP 計量口徑判讀。由於 MES 作業時間語意與現場輸出層確效尚未完成，本模型定位為具可追溯假設、共同亂數比較及敏感度分析之離線研究原型，可供後續歷史資料回溯驗證、動態瓶頸驗證與現場試行使用。

This study develops an offline scheduling scenario-analysis framework for a four-station Flexible Printed Circuit (FPC) front-end process using Manufacturing Execution System (MES) data. Because the MES does not fully identify equipment active time, downtime, setup, retesting, or product attributes, Efficiency Factor (EF) is treated as a composite outcome proxy and the resulting duration as equivalent processing time rather than pure machine time. A SimPy discrete-event model compares first-in, first-out (FIFO), earliest due date (EDD), critical ratio (CR), and congestion-triggered lower-normalized-equivalent-workload priority (BN). The main 4 × 3 × 2 experiment contains 180 Common Random Numbers (CRN) blocks and 720 runs. Type II analysis of variance (ANOVA) provides descriptive variance decomposition, while CRN-paired comparisons evaluate dispatching-rule differences. Composite pressure, EF volatility, and their interaction account for 48.34%, 10.36%, and 12.07% of OTD sum of squares, whereas the marginal rule effect accounts for 0.04%. In the high-pressure/high-volatility baseline, BN has higher order-count OTD than FIFO, EDD, and CR, but its advantage changes under workload weighting and alternative due-date and input-dependence assumptions. The study therefore does not establish a universally best dispatching rule. The results indicate that scenario conditions generate larger overall OTD variation than the marginal average rule effect, while rule performance remains conditional on modeling assumptions and KPI definitions.

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