生物技術產業的三大範疇為製藥、醫療器材及應用生技產業，2017年我國製藥產業在生技企業營業額中佔比高達41.8%，原料藥(Active Pharmaceutical Ingredients, API)於2018前半年產值年增也成長14.8%，在製藥業中佔有舉足輕重的地位。本文選擇位於台灣台南科學園區的原料藥上市公司進行研究，案例公司正面臨生產作業流動不流暢，製造系統缺乏彈性等問題，製藥業製程流程相對固定，因此人力安排將是關鍵。本文第一部份為應用精實工具改善，首先進行現地現物觀察掌握現況，嘗試採用精實方法解決問題，包括減少製程欄位紀錄、引進自動化模組、樓層工作任務模組化等，初步解決工廠內時間、人力及資源的浪費，人力使用從每班5人減少至每班4人；而每批次的投料間隔時間則從3.4天減少為2.2天；第二部份則以初步改善為基礎，探討製藥產業特性、人力派工與多層樓的特殊性結合發展的彈性派工系統，進行方案設計，共有樓層安排、人力數量及人員指派三個因子，並運用模擬軟體搭配全因子實驗設計分析現況與各情境找出最佳化的方案，雖最後選擇的方案並非前置時間最短，但由成本的角度來看實屬最優，達到縮短頭料間隔時間幅度約21%，減少人力1人，並提供有效降低員工樓層轉換次數的方法。此方法具有潛力提供連續性製程人力安排的重要參考。

The three major areas of the biotechnology industry are pharmaceutical, medical equipment and applied biotechnology industries. The export value of pharmaceutical industry has grown by 5.5% compared with last year, and API(Active Pharmaceutical Ingredients Manufacturing) plays a pivotal role in the pharmaceutical industry. Researcher selects a pharmaceutical company located in the Tainan Science Park in Taiwan for research. The case company are facing problems such as unsmooth flow of production operations and the lack of flexibility in the manufacturing system. The process of pharmaceutical industry is relatively fixed, so the operator arrangement will be the key element. The first part of this paper is to eliminate the waste with lean tools. First, we clarify the current problems by a site tour and solve the problem by using the lean thinking method, including reducing the columns of BPR, implementing the automation module, and modularizing the tasks of each floor. The waste in the factory was improved including cycle time, manpower and resources, the use of operator decreased from 5 people per shift to 4 people per shift; and the process cycle time of each batch decreased from 3.4 days to 2.2 days; The second part is builded with the foundation of previous improvement. We discuss the characteristics of the pharmaceutical industry, the way of operator dispatching and the feature of multi-floor building, shows the particularity of this flexible system. There are three factors are set including floor arrangement, the number of manpower and staff assignment,. We use a simulation software with full factorial experiment design to analyze the current situation and find out the optimal solution for each experiment. Although the lead time of final choice is not the shortest, it is optimal from the perspective of cost, and the production lead time is shorten to about 21%, and the use of operator is reduced by 1. We provide an efficient way to reduce the frequency of floor transitions. This method has the potential to provide an important reference for continuous process operator arrangements.

台灣神隆，2018，Availiable：http://www.scinopharm.com.tw/index.asp (2018.10.30取得)。
吳忠勳等，2018，生技產業白皮書，臺北市：行政院經濟部工業局。
李輝煌，2004，田口方法-品質設計的原理與實務，臺北市：高立圖書有限公司。
Abdulmalek, F. A., & Rajgopal, J. (2007). Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study. International Journal of Production Economics, 107(1), 223-236.
Abdulmalek, F. A., Rajgopal, J., & Needy, K. L. (2006). A classification scheme for the process industry to guide the implementation of lean. Engineering Management Journal, 18(2), 15-25.
Aitken, M., Kleinrock, M., & Nass, D. (2016). Outlook for Global Medicines through 2021, Balancing Cost and Value. New Jersey: QuintilesIMS Institute.
Antony, J. (2014). Design of Experiments for Engineers and Scientists. New York: Elsevier.
Banks, J., Carson, I., Nelson, B. L., & Nicol, D. M. (2005). Discrete-Event System Simulation. New York: Pearson.
Glover, F., & Laguna, M. (1998). Handbook of Combinatorial Optimization. New York: Springer.
Glover, F., Laguna, M., & Martí, R. (2000). Fundamentals of scatter search and path relinking. Control and Cybernetics, 29(3), 653-684.
Hopp, W. J., & Spearman, M. L. (2011). Factory Physics. Long Grove, IL: Waveland Press.
Kelton, W. D. (2002). Simulation with Arena. New York: McGraw-hill.
Kirk, R. E. (1974). Experimental Design: Procedures for the Behavioral Sciences. Pacific Grove, CA: Brooks/Cole.
Laguna, M. (1997). Optimization of complex systems with optquest. A White Paper from OptTek Systems, 1-13.
Ohno, T. (1988). Toyota Production System: Beyond Large-Scale Production. Boca Raton: CRC Press.
Rother, M., & Shook, J. (2003). Learning to See: Value Stream Mapping to Add Value and Eliminate Muda. Boston: Lean Enterprise Institute.
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston: Cengage Learning, Inc.
Womack, J. P., Womack, J. P., Jones, D. T., & Roos, D. (1990). Machine that Changed the World. New York: Simon and Schuster.
Yang, T., Kuo, Y., & Chang, I. (2004). Tabu-search simulation optimization approach for flow-shop scheduling with multiple processors—a case study. International Journal of Production Research, 42(19), 4015-4030.
Yu, C. H. (1977). Exploratory data analysis. Methods, 2, 131-160.