本研究針對資源有限、數位化不完全與永續壓力升高的中小製造企業之智慧轉型，提出以「人機協同」與「可解釋人工智慧（XAI）」為核心的「智働化」模式。該模式將「人」與「機」視為同一決策系統中的互補角色：人工智慧負責大量資料擷取、趨勢預測與異常偵測；現場人員基於情境知識進行判讀、採納與修正，並透過解釋機制形成「人調整 AI、AI 學習人」之持續優化迴路，以提升決策透明度、可採信性與落地性。本文首先界定智働化之概念與運作流程，於決策層納入XAI 強化人機互信；其次規劃適用於製造領域之異常分析與趨勢預測與重要影響因素分析之技術，模型以統計統計方法、XGBoost 與 LSTM 支援非線性關係與時間依賴性，解釋則以 SHAP 進行全域與在地之重要影響因素分析。

為回應製造資料特性與作業情境，本研究以能源管理案例驗證智働化模式在人機協同決策與可解釋 AI 中的應用價值，並展現其可移植性以支援其他製造情境。實證包含三項實驗：（一）人機協同提升預測準確率驗證：結合領域洞察進行週期性特徵建構後，回歸誤差顯著下降，證實人機協同對模型調適之增益；（二）趨勢預測技術比較：在同一資料與評估體系下，XGBoost 於 MAE、MAPE、MSE、RMSE等指標整體表現最佳，顯示其對能耗資料之非線性與交互效應具有較佳擬合能力；（三）重要影響因素分析驗證：以 SHAP 辨識之關鍵特徵施以高斯噪音擾動，模型性能明顯劣化，據以驗證特徵歸因結果之可信度與模型對關鍵因子的依賴性，並將解釋結果轉化為可執行之節能與維護策略。

綜合而言，研究結果顯示智働化可同時促進（1）預測與診斷精度、（2）決策可解釋性與採納度、以及（3）人機互信與協作品質。所提出之架構與技術管線具有可擴充與可遷移性，可自能源管理平移至品質控管、產能排程與設備健康管理等場景；對中小企業而言，提供一條成本可控、循序導入且具操作性的智慧製造轉型路徑，回應永續與競爭壓力下的實務需求。

Small and medium-sized enterprises (SMEs) in manufacturing face increasing challenges from limited resources, incomplete digitalization, and sustainability pressures. This study proposes a Human–Machine Collaboration framework that integrates domain expertise with explainable artificial intelligence (XAI) to support intelligent transformation. In this framework, AI manages data acquisition, forecasting, and anomaly detection, while humans provide contextual judgment and adaptation. Through XAI, a feedback loop of “human adjusts AI, AI learns from human” enhances transparency, reliability, and trust.

The framework combines anomaly analysis, trend forecasting, and feature attribution tailored to manufacturing data. Statistical methods, XGBoost, and LSTM are employed to capture non-linear and temporal patterns, while SHAP provides global and local interpretability. Validation is conducted through an energy management case study with three experiments: demonstrating the value of human–AI collaboration in improving prediction accuracy, comparing model performance, and confirming the reliability of feature attribution.

Results show that Human–Machine Collaboration improves forecasting accuracy, interpretability, and the quality of decision-making, while building trust between humans and AI. The framework is scalable, applicable not only to energy management but also to quality control, production scheduling, and equipment health management. For SMEs, it provides a cost-effective, stepwise pathway toward smart manufacturing transformation under sustainability and competitive pressures.

周大為。（2022）。我國中小企業數位化導入困境與政策協助。資策會產業情報研究所。

財團法人台灣經濟研究院（2023）。2023/2024產業技術白皮書。經濟部產業技術司。

經濟部中小及新創企業署。（2024年7月22日）。認識中小企業減碳服務站。

Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access, 6, 52138-52160.

Ancona, M., Ceolini, E., Öztireli, C., & Gross, M. (2018). Towards better understanding of gradient-based attribution methods for Deep Neural Networks. International Conference on Learning Representations (ICLR).

Armstrong, B., & Shah, J. A. (2023, March–April). A smarter strategy for using robots. Harvard Business Review.

Asutkar, S., & Tallur, S. (2023). An explainable unsupervised learning framework for scalable machine fault detection in Industry 4.0. Measurement Science and Technology, 34(10), 105123.

Bányai, T., Tamás, P., Illés, B., Stankevičiūtė, Ž., & Bányai, Á. (2019). Optimization of municipal waste collection routing: Impact of industry 4.0 technologies on environmental awareness and sustainability. International journal of environmental research and public health, 16(4), 634.

Bettoni, A., Montini, E., Righi, M., Villani, V., Tsvetanov, R., Borgia, S., ... & Carpanzano, E. (2020). Mutualistic and adaptive human-machine collaboration based on machine learning in an injection moulding manufacturing line. Procedia CIRP, 93, 395-400.

Candanedo, L. M., Feldheim, V., & Deramaix, D. (2017). Data driven prediction models of energy use of appliances in a low-energy house. Energy and buildings, 140, 81-97.

Chen, J., Song, L., Wainwright, M. J., & Jordan, M. I. (2020). Learning to explain: An information-theoretic perspective on model interpretability. Proceedings of the 37th International Conference on Machine Learning (ICML).

Dilda, V., Mori, L., Noterdaeme, O., & Schmitz, C. (2017). Manufacturing: Analytics unleashes productivity and profitability. Report, McKinsey & Company.

Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J., & Barbosa, J. (2020). Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Computers in industry, 123, 103298.

Fatima, S. S. W., & Rahimi, A. (2024). A review of time-series forecasting algorithms for industrial manufacturing systems. Machines, 12(6), 380.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.

Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586.

Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Final report of the Industrie, 4(0), 82.

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems (NeurIPS).

Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018). A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). Journal of manufacturing systems, 49, 194-214.

Montavon, G., Samek, W., & Müller, K. R. (2018). Methods for interpreting and understanding deep neural networks. Digital signal processing, 73, 1-15.

Matt, D. T., Orzes, G., Rauch, E., & Dallasega, P. (2020). Urban production–A socially sustainable factory concept to overcome shortcomings of qualified workers in smart SMEs. Computers & Industrial Engineering, 139, 105384.

Molnar, C. (2020). Interpretable machine learning.

Machlev, R., Heistrene, L., Perl, M., Levy, K. Y., Belikov, J., Mannor, S., & Levron, Y. (2022). Explainable Artificial Intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities. Energy and AI, 9, 100169.

Mirasçı, S., Uygur, S., & Aksoy, A. (2025). Advancing energy efficiency: Machine learning based forecasting models for integrated power systems in food processing company. International Journal of Electrical Power & Energy Systems, 165, 110445.

Nacar, E. N., & Daneshvar Rouyendegh, B. Optimizing Scrap Forecasting with Hybrid Machine Learning: A Sustainable Approach Using Catboost and Xgboost. Available at SSRN 5191430.

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson.

Rauch, E., Dallasega, P., & Unterhofer, M. (2019). Requirements and barriers for introducing smart manufacturing in small and medium-sized enterprises. IEEE Engineering Management Review, 47(3), 87-94.

Rheingold, H. (2000). Tools for thought: The history and future of mind-expanding technology. MIT press.

Shrikumar, A., Greenside, P., & Kundaje, A. (2017). Learning important features through propagating activation differences. Proceedings of the 34th International Conference on Machine Learning (ICML).

Shapley, L. S. (1953). A value for n-person games. Contributions to the Theory of Games.

Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020). Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. AAAI Conference on Artificial Intelligence.

Sofianidis, G., Rožanec, J. M., Mladenic, D., & Kyriazis, D. (2021). A review of explainable artificial intelligence in manufacturing. Trusted Artificial Intelligence in Manufacturing: A Review of the Emerging Wave of Ethical and Human Centric AI Technologies for Smart Production, 93-113.

Senoner, J., Netland, T., & Feuerriegel, S. (2022). Using explainable artificial intelligence to improve process quality: evidence from semiconductor manufacturing. Management Science, 68(8), 5704-5723.

Schmid, L., Roidl, M., Kirchheim, A., & Pauly, M. (2024). Comparing statistical and machine learning methods for time series forecasting in data-driven logistics—A simulation study. Entropy, 27(1), 25.

Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157-169.

Torres, J. F., Hadjout, D., Sebaa, A., Martínez-Álvarez, F., & Troncoso, A. (2021). Deep learning for time series forecasting: a survey. Big data, 9(1), 3-21.

Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of manufacturing systems, 48, 144-156.

West, D. M. (2018). The future of work: Robots, AI, and automation. Brookings Institution Press.

Walther, J., & Weigold, M. (2021). A systematic review on predicting and forecasting the electrical energy consumption in the manufacturing industry. Energies, 14(4), 968.

Zheng, P., Wang, H., Sang, Z., Zhong, R. Y., Liu, Y., Liu, C., ... & Xu, X. (2018). Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Frontiers of Mechanical Engineering, 13, 137-150.