微生物發酵(Microbial Fermentation)產業具有產品種類多、批次式生產與長週期作業等特性，製程工時(Process Time)容易受到產品配方、菌種條件、設備規模、批量差異與現場紀錄行為影響，使得標準工時的建置不易。本研究以微生物發酵產業為對象，探討如何應用低程式碼系統(Low-Code Development Platform, LCDP)於標準工時系統建構與管理。自働生產管理環(Jidoka-JIT Cycle, JJC)強調透過現場資訊即時化、問題可視化與持續改善，使企業能在生產過程中及早發現異常並回饋至管理決策。對製造現場而言，若工時資訊無法被完整蒐集、即時傳遞與有效追溯，將影響後續排程規劃、標準工時檢討與改善活動之推動。本研究探討之案例公司雖已具備既有排程系統(Scheduling System)，但仍存在標準工時基準不足、現場執行紀錄不完整、工時資料難以追溯，以及異常判定缺乏系統化之依據等問題。因此，本研究從資訊流圖(Information Stream Mapping, iSM)改善的觀點出發，運用低程式碼系統建構標準工時系統與員工工作日誌系統，將原本分散之工時資訊與現場紀錄整合至既有排程流程中，使標準工時能成為排程規劃、現場執行、後續監控及未來員工績效評估之基礎。最後，本研究結合統計製程管制(Statistical Process Control, SPC)與Arena離散事件模擬，驗證當工時出現平均數偏移與變異擴大時，管制圖對異常工時之偵測能力。研究結果顯示，低程式碼系統不僅能協助標準工時落實於現場流程，也能提升工時資料完整性與可追溯性。此外，公司同仁可參與資料維護與系統改善，達成低程式碼平台公民開發者概念與JJC持續改善理念。

The microbial fermentation industry is characterized by product variety, batch production, and long-cycle operations. In this context, process time is easily affected by product formulation, strain conditions, equipment scale, batch differences, and shop-floor recording behavior, making the establishment of standard work time difficult. This study focuses on the microbial fermentation industry and investigates the application of a low-code development platform (LCDP) to the development and management of a standard work time system.
The Jidoka-JIT Cycle (JJC) emphasizes real-time shop-floor information, problem visualization, and continuous improvement, enabling enterprises to identify abnormalities during production and feed them back into management decisions. For manufacturing sites, if work time information cannot be completely collected, transmitted in real time, and effectively traced, subsequent scheduling, standard work time review, and improvement activities will be affected.
Although the case company examined in this study has already implemented an existing scheduling system, it still faces several problems, including insufficient standard work time baselines, incomplete shop-floor execution records, limited traceability of work time data, and a lack of systematic criteria for abnormality detection. Therefore, from the perspective of Information Stream Mapping (iSM) improvement, this study applies a low-code system to develop a standard work time system and an employee work diary system. These systems integrate previously dispersed work time information and shop-floor records into the existing scheduling process, allowing standard work time to serve as a basis for scheduling planning, shop-floor execution, subsequent monitoring, and future employee performance evaluation.
Finally, this study combines Statistical Process Control (SPC) with Arena discrete-event simulation to verify the ability of control charts to detect abnormal work time when mean shifts and variance expansions occur. The results indicate that the low-code system not only supports the implementation of standard work time in shop-floor processes but also improves the completeness and traceability of work time data. In addition, company employees can participate in data maintenance and system improvement, reflecting the concept of citizen developers in low-code platforms and the continuous improvement philosophy of JJC.

湯為敦 (2025)，結合低程式碼系統與混合整數線性規劃發展雲端排程系統，國立成功大學製造資訊與系統研究所碩士論文，台南。
楊大和 (2025)，「JJC自働生產管理環—從現場改善到AI導入」，機械資訊，No. 821，第27-36頁。
經濟部統計處 (2026)，工業產銷存動態調查，經濟部，台北，取自: https://service.moea.gov.tw/EE520/investigate/InvestigateDB.aspx，(2026.03.26取得)。
鄭春生 (Ed.). (2019). 品質管理: 現代化觀念與實務應用. 全華圖書.
Abdulnour, S., Baril, C., Abdulnour, G., & Gamache, S. (2022). Implementation of Industry 4.0 principles and tools: Simulation and case study in a manufacturing SME. Sustainability, 14(10), 6336.
Almaraz Luengo, E., & Gragera, C. (2023). Critical analysis of beta random variable generation methods. Mathematics, 11(24), 4893.
Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2005). Discrete-event system simulation (4th ed.). Prentice Hall.
Bock, A. C., & Frank, U. (2021). Low-code platform. Business & Information Systems Engineering, 63(6), 733–740.
Breznik, M., Buchmeister, B., & Vujica Herzog, N. (2023). Assembly line optimization using MTM time standard and simulation modeling—A case study. Applied Sciences, 13(10), 6265.
De Felice, F., De Luca, C., Petrillo, A., Forcina, A., Ortiz Barrios, M. A., & Baffo, I. (2025). The role of digital transformation in manufacturing: Discrete event simulation to reshape industrial landscapes. Applied Sciences, 15(11), 6140.
Demain, A. L. (2000). Small bugs, big business: The economic power of the microbe. Biotechnology Advances, 18(6), 499–514.
Di Ruscio, D., Kolovos, D., de Lara, J., Pierantonio, A., Tisi, M., & Wimmer, M. (2022). Low-code development and model-driven engineering: Two sides of the same coin? Software and Systems Modeling, 21(2), 437–446.
Dogan, O., & Areta Hiziroglu, O. (2024). Empowering manufacturing environments with process mining-based statistical process control. Machines, 12(6), 411.
Gartner. (2025, July 28). Magic Quadrant for Enterprise Low-Code Application Platforms. Gartner.
Gorobets, V., Holzwarth, V., Hirt, C., Jufer, N., & Kunz, A. (2021). A VR-based approach in conducting MTM for manual workplaces. The International Journal of Advanced Manufacturing Technology, 117(7–8), 2501–2510.
Hajej, Z., Nyoungue, A. C., Abubakar, A. S., & Mohamed Ali, K. (2021). An integrated model of production, maintenance, and quality control with statistical process control chart of a supply chain. Applied Sciences, 11(9), 4192.
Huang, W. H. (2021). Control charts for joint monitoring of the lognormal mean and standard deviation. Symmetry, 13(4), 549.
Huberts, L. C., Goedhart, R., & Does, R. J. M. M. (2022). Improved control chart performance using cautious parameter learning. Computers & Industrial Engineering, 169, 108185.
Jangam, S. K. (2024). Scalability and performance limitations of low-code and no-code platforms for large-scale enterprise applications and solutions. International Journal of Emerging Trends in Computer Science and Information Technology, 5(3), 68–78.
Kanawaty, G. (Ed.). (1992). Introduction to work study. International Labour Office.
Kelton, W. D., Sadowski, R.P., & Zupick , N.B. (2014). Simulation with Arena , 6th edition, McGraw-Hill Education, New York.
Ki, I., Song, H., Ryu, J., & Jeong, J. (2023). Production improvement rate with time series data on standard time at manufacturing sites. Applied Sciences, 13(19), 10937.
Kirin, S., Kralj, D., & Hursa Šajatović, A. (2024). Using the Methods-Time Measurement Calculator to determine the time norms for technological sewing operations in the clothing industry. Processes, 12(8), 1763.
Koulouris, A., Misailidis, N., & Petrides, D. (2021). Applications of process and digital twin models for production simulation and scheduling in the manufacturing of food ingredients and products. Food and Bioproducts Processing, 126, 317–333.
Microsoft. (2026). Create responsive layouts in canvas apps. 取自: Microsoft Learn. https://learn.microsoft.com/en-us/power-apps/maker/canvas-apps/create-responsive-layout (2026.03.26取得)。
Montgomery, D. C. (2020). Introduction to statistical quality control. John wiley & sons.
Nesterak, J., Szelągowski, M., & Radziszewski, P. (2025). Workplace performance measurement: Digitalization of work observation and analysis. Journal of Intelligent Manufacturing, 36(5), 3569–3585.
Nielsen, J., Tillegreen, C. B., & Petranovic, D. (2022). Innovation trends in industrial biotechnology. Trends in Biotechnology, 40(10), 1160–1172.
Parratt, K., Newton, D., Dunkers, J., Dootz, J., Hunter, M., Logan-Jackson, A., ... & Lin, N. J. (2025). Measurement quality metrics to improve absolute microbial cell counting. Frontiers in Microbiology, 16, 1631377.
Paryanto, P., Faizin, M., & Franke, J. (2026). Improving manufacturing line design efficiency using digital value stream mapping. Journal of Manufacturing and Materials Processing, 10(3), 98.
Rathore, A. S., & Winkle, H. (2009). Quality by design for biopharmaceuticals. Nature Biotechnology, 27(1), 26–34.
Roh, P., Kunz, A., & Wegener, K. (2019). Information stream mapping: Mapping, analysing and improving the efficiency of information streams in manufacturing value streams. CIRP Journal of Manufacturing Science and Technology, 25, 1-13.
Rosa, C., Silva, F. J. G., Ferreira, L. P., Pereira, T., & Gouveia, R. (2018). Establishing standard methodologies to improve the production rate of assembly lines used for low added-value products. Procedia Manufacturing, 17, 555–562.
Sałaciński, T., Chrzanowski, J., & Chmielewski, T. (2023). Statistical process control using control charts with variable parameters. Processes, 11(9), 2744.
Satzer, P., Komuczki, D., Pappenreiter, M., Cataldo, A. L., Sissolak, B., & Jungbauer, A. (2022). Impact of failure rates, lot definitions and scheduling of upstream processes on the productivity of continuous integrated bioprocesses. Journal of Chemical Technology & Biotechnology, 97(9), 2393-2403.
Sawant, S. S., Park, H. Y., Sim, E. Y., Kim, H. S., & Choi, H. S. (2025). Microbial fermentation in food: impact on functional properties and nutritional enhancement—a review of recent developments. Fermentation, 11(1), 15.
Schutz, J., Sauvey, C., Nițu, E. L., & Gavriluță, A. C. (2025). A practical and sustainable approach to industrial engineering discrete-event simulation with free mathematical and programming software. Sustainability, 17(9), 3973.
Sufi, F. (2023). Algorithms in low-code-no-code for research applications: A practical review. Algorithms, 16(2), 108.
Umeda, T. (2024). Generation of normal distributions revisited. Computational Statistics, 39(7), 3291–3310.
Vázquez-Serrano, J. I., Peimbert-García, R. E., & Cárdenas-Barrón, L. E. (2021). Discrete-event simulation modeling in healthcare: A comprehensive review. International Journal of Environmental Research and Public Health, 18(22), 12262.
Verma, P. (Ed.). (2022). Industrial microbiology and biotechnology (pp. 667-690). Singapore:: Springer.
Wang, D., & Shao, X. (2024). Research on the impact of digital transformation on the production efficiency of manufacturing enterprises: Institution-based analysis of the threshold effect. International Review of Economics & Finance, 91, 883–897.
Waszkowski, R. (2019). Low-code platform for automating business processes in manufacturing. IFAC-PapersOnLine, 52(10), 376–381.
Wendler, A., Beruvides, M. G., & Tercero-Gómez, V. G. (2025). Cautious parameter learning for a joint mean and variance monitoring CUSUM scheme with guaranteed in-control performance. Quality Engineering, 37(4), 523–540.
Yang, T., & Fujimoto, T. (2026). Jidoka just-in-time cycle–from genba kaizen to the introduction of artificial intelligence. International Journal of Production Research, 1-19.
Yee, C. S., Zahia-Azizan, N. A., Abd Rahim, M. H., Mohd Zaini, N. A., Raja-Razali, R. B., Ushidee-Radzi, M. A., ... & Wan-Mohtar, W. A. A. Q. I. (2025). Smart fermentation technologies: Microbial process control in traditional fermented foods. Fermentation, 11(6), 323.
Zwetsloot, I. M., Jones-Farmer, L. A., & Woodall, W. H. (2024). Monitoring univariate processes using control charts: Some practical issues and advice. Quality Engineering, 36(3), 487–499.