本研究針對針劑製藥產業在生產管理上面臨的問題，特別是傳統紙本批次記錄效率低落、生產資訊分散導致管理困難，以及製程變異性影響準時達交率等問題。本研究旨在結合數位化工具與精實管理，導入自働生產管理環（JJC 2.0）概念，利用低程式碼開發平台（Low-Code Development Platform, LCDPs）建構資訊系統，並整合統計產出控制（Statistical Throughput Control, STC）模型，以期優化生產資訊流、提升製程監控能力與決策管理，最終達成改善生產力與服務水準（如準時達交率）的目標。
本研究以一家針劑製藥廠為案例。首先，運用資訊流圖（Information Stream Mapping, iSM）方法，評估此系統導入前後的資訊傳遞效率與潛在浪費。解決生產瓶頸，Microsoft Power Platform建構一套雲端生產過帳系統，藉此取代傳統紙本記錄與分散式表單管理，實現生產數據的即時收集與集中化。其次，系統收集的生產數據進行分析以目視化識別異常狀況與瓶頸製程（異檢作業），並針對該瓶頸導入統計產出控制（STC）模型，建立動態監控與預測機制，用以評估不同生產配額（Quota）、製程變異程度及人力配置組合下的目標達成績效。最後，利用 Python進行離散事件模擬，比較在 STC 預警機制下，採取「中途增加人力」與「傳統加班」兩種應對策略的有效性與成本效益。
實證結果顯示，導入雲端過帳系統顯著改善資訊流效率，多項 iSM 指標獲得提升。變異數分析顯示，生產配額、製程變異與人力配置對生產績效的影響並非獨立，而是存在顯著的因子間交互作用。 STC 模型能監控生產進度並預測潛在延遲，其模擬結果與歷史數據吻合（多數誤差在±0.3小時內）。改善策略模擬顯示，基於 STC 監控即時增加人力，相較於傳統加班，多數能提升準時達交率且降低平均人力成本（本研究案例中約 3.66%）。
本研究成功建立一套雲端過帳系統，並已實際導入作業現場，同時整合 STC 模型輔助管理者決策，利用模擬驗證其優化生產力之可行性與成本效益。

This study addresses critical issues in the production management of the Injectable pharmaceutical industry, specifically the inefficiencies of traditional paper-based batch records, managerial difficulties stemming from dispersed production information, and the adverse impact of process variability on on-time delivery rates. This research aims to integrate digital tools with lean management principles by introducing the Jidoka-JIT Cycle concept. It leverages Low-Code Development Platforms (LCDPs) to construct an information system and integrates a Statistical Throughput Control (STC) model. The objective is to optimize production information flow, enhance process monitoring capabilities and decision-making, ultimately improving productivity and service levels, such as on-time delivery rates.

Employing a case study approach at an Injectable pharmaceutical plant, Information Stream Mapping (iSM) was initially utilized to assess information transmission efficiency and identify potential waste before and after system implementation. To address production bottlenecks, a cloud-based production posting system was developed using Microsoft Power Platform, replacing traditional paper-based records and decentralized form management, thereby enabling real-time data collection and centralization. Subsequently, production data collected by the system were analyzed to identify anomalies and bottleneck processes (specifically, visual inspection operations). The STC model was then implemented for this bottleneck, establishing a dynamic monitoring and predictive mechanism to evaluate target achievement performance under various combinations of production quotas, levels of process variability, and staffing configurations. Finally, discrete-event simulation was conducted using Python to compare the effectiveness and cost-benefit of two response strategies under the STC early warning mechanism: "mid-process manpower augmentation" and "overtime."

Empirical results indicate that the implementation of the cloud-based posting system significantly improved information flow efficiency, with enhancements observed in multiple iSM metrics. Analysis of Variance (ANOVA) revealed that production quotas, process variability, and staffing configurations non-independently affect production performance, exhibiting significant interaction effects. The STC model effectively monitored production progress and predicted potential delays, with its simulation results aligning closely with historical data (most discrepancies within ±0.3 hours). Simulation of improvement strategies demonstrated that real-time manpower augmentation based on STC monitoring, compared to an overtime strategy, can generally improve on-time delivery rates while reducing average labor costs (by approximately 3.66% in this case study).

This study successfully developed a cloud-based posting system, which has been implemented in on-site operations. Concurrently, the STC model was integrated to support managerial decision-making, and simulation was used to validate its feasibility and cost-effectiveness in optimizing productivity.

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