Designing an IoT-Based EMIS Using Linear Regression and CUSUM for Real-Time Anomaly Detection in Pharmaceutical Industry

Freska Lionia Darlion; Muhammad Isa Lufti

doi:10.22441/pasti.2025.v19i3.011

Authors

Freska Lionia Darlion Department of Digital Business, Institut Pariwisata Tedja Indonesia, Indonesia
Muhammad Isa Lufti Universitas Mercu Buana, Indonesia

DOI:

https://doi.org/10.22441/pasti.2025.v19i3.011

Keywords:

Energy Management Information System, Digital Transformation, IoT, CUSUM, SDGs

Abstract

The pharmaceutical industry is a high energy-intensity sector requiring strict operational stability. However, many companies still rely on manual energy monitoring methods, leading to information latency. A case study at a multinational pharmaceutical company in Indonesia revealed a baseload inefficiency of 14.3 kW during non-operational hours, which remained undetected due to an ad-hoc energy management system. This study aims to design an IoT-based Energy Management Information System (EMIS) architecture to transform the energy management business process from reactive to proactive-predictive. The study utilizes secondary data from the 2025 Energy Audit Report. The system design integrates a linear regression model (R²=0.80) for Energy Performance Indicators (EnPI) determination and the Cumulative Sum (CUSUM) algorithm for real-time anomaly detection. Investment feasibility is evaluated using techno-economic analysis. The implementation of EMIS requires an investment of IDR 225,000,000 with potential annual energy cost savings of IDR 44,172,687. Although the Simple Payback Period (SPP) is 5.1 years, the project is considered feasible due to its strategic value in data transparency, operational risk mitigation, and ISO 50001 compliance. Furthermore, this digital transformation supports the achievement of Sustainable Development Goals (SDG 7, 9, and 12) by promoting energy efficiency and responsible industrial consumption. Digitizing energy systems is not merely a tool replacement but a strategic transformation that turns energy data into critical business decision assets.

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References

Abidin, A. Z., Enriko, I. K. A., & Pramudita, A. A. (2025). Leveraging IoT, Digital Twin and Machine Learning for Smart Energy Audit in Office Building: A Systematic Literature Review and Recommendations. e-Prime—Advances in Electrical Engineering, Electronics and Energy, 14, Article 101124. https://doi.org/10.1016/j.prime.2025.101124

Abujiya, M. R., Lee, M. H., & Riaz, M. (2015). Increasing the Sensitivity of Cumulative Sum Charts for Location. Quality and Reliability Engineering International, 31(6), 1035–1051. https://doi.org/10.1002/qre.1661

Beck, A., Wiedergut, M., & Neubauer, N. (2025). Towards Fossil-Free Energy Supply: Cost-Effective Decarbonization Measures in Pharmaceutical Energy Systems. Applied Thermal Engineering, 280(2), Article 128213. https://doi.org/10.1016/j.applthermaleng.2025.128213

Cesarotti, V., Introna, V., Rotunno, R., & Scerrato, G. (2016). Investigating the Relationship Between Energy Consumption and Overall Equipment Effectiveness for Improving Manufacturing Systems' Productivity: An Application in the Thermoforming Process. International Journal of Productivity and Quality Management, 18(2–3), 279–300. https://doi.org/10.1504/IJPQM.2016.076711

Chen, Y., Kotamarthy, L., Dan, A., Sampat, C., Bhalode, P., Singh, R., Glasser, B. J., Ramachandran, R., & Ierapetritou, M. (2023). Optimization of Key Energy and Performance Metrics for Drug Product Manufacturing. International Journal of Pharmaceutics, 631, Article 122487. https://doi.org/10.1016/j.ijpharm.2022.122487

Gao, Z., Geng, Y., Wu, R., Chen, W., Wu, F., & Tian, X. (2019). Analysis of Energy-Related CO₂ Emissions in China's Pharmaceutical Industry and Its Driving Forces. Journal of Cleaner Production, 223, 94–108. https://doi.org/10.1016/j.jclepro.2019.03.092

Garrido-Zafra, J., Gil-De-Castro, A. R., Savariego-Fernandez, R., Linan-Reyes, M., Garcia-Torres, F., & Moreno-Munoz, A. (2022). IoT Cloud-Based Power Quality Extended Functionality for Grid-Interactive Appliance Controllers. IEEE Transactions on Industry Applications, 58(3), 3909–3921. https://doi.org/10.1109/TIA.2022.3160410

Jagtap, S., Rahimifard, S., & Duong, L. N. K. (2022). Real-Time Data Collection to Improve Energy Efficiency: A Case Study of Food Manufacturer. Journal of Food Processing and Preservation, 46(8), Article e14338. https://doi.org/10.1111/jfpp.14338

Jeevitha, D., Jose, D., Mohanty, S. N., & Latha, A. (2024). Energy Management in Industry 4.0 Using AI. In AI-Driven IoT Systems for Industry 4.0. CRC Press. https://doi.org/10.1201/9781003432319-20

Kementerian Energi dan Sumber Daya Mineral Republik Indonesia. (2025, September 18). Audit Energi Ungkap Potensi Penghematan Puluhan Juta Kilowatt-Hour di Industri. Direktorat Jenderal Energi Baru, Terbarukan, dan Konservasi Energi. https://ebtke.esdm.go.id/artikel/berita/audit-energi-ungkap-potensi-penghematan-puluhan-juta-kilo-watt-hour-di-industri

Mahandari, C. P., Purwanto, A. H. D., & Yamin, M. (2025). Energy Baseline for Measurement and Verification on Energy Audit for an Oil and Gas Industry. Smart Innovation in Green and Sustainable Energy, Springer, 505-513. https://doi.org/10.1007/978-981-97-8197-3_49

Maitra, S., Arora, K., & Kundu, S. (2026). Real-Time Anomaly Detection in Smart Energy Systems Using Statistical and Adaptive Learning Techniques. Lecture Notes in Networks and Systems, 1535, 325–335. Springer. https://doi.org/10.1007/978-981-96-9191-3_28

Naji, K. K., Gunduz, M., & Al-Qahtani, A. (2024). Unveiling Digital Transformation: Analyzing Building Facility Management’s Preparedness for Transformation Using Structural Equation Modeling. Buildings, 14(9), 2794. https://doi.org/10.3390/buildings14092794

Pelser, W. A., Vosloo, J. C., & Mathews, M. J. (2018). Results and Prospects of Applying an ISO 50001 Based Reporting System on a Cement Plant. Journal of Cleaner Production, 198, 642–653. https://doi.org/10.1016/j.jclepro.2018.07.071

Pokane, S. S., Shilenge, M. C., & Telukdarie, A. (2023). Architecture Approach to Manage Electricity Utility in a Smart City. IEEE European Technology and Engineering Management Summit (E-TEMS), 44–48. https://doi.org/10.1109/E-TEMS57541.2023.10424593

Prudenzi, A., Fioravanti, A., & Ciancetta, F. (2019). Smart Distributed Energy Monitoring for Industrial Applications. Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT), (274–278). https://doi.org/10.1109/METROI4.2019.8792861

Reichardt, A., Murawski, M., & Bick, M. (2025). The Use of the Internet of Things to Increase Energy Efficiency in Manufacturing Industries. International Journal of Energy Sector Management, 19(2), 369–388. https://doi.org/10.1108/IJESM-12-2023-0017

Tesch da Silva, F. S., da Costa, C. A., Paredes Crovato, C. D., & Righi, R. R. (2020). Looking at Energy Through the Lens of Industry 4.0: A Systematic Literature Review of Concerns and Challenges. Computers & Industrial Engineering, 143. https://doi.org/10.1016/j.cie.2020.106426

Vetrivel, S. C., Saravanan, T. P., & Maheswari, R. (2026). Smart Factories and Energy Efficiency in Industry 4.0. In T. Choudhury, R. K. Singh, S. Balamurugan, & J. C. Patni (Eds.), Distributed Time-Sensitive Systems. Wiley. https://doi.org/10.1002/9781394197798.ch4

Zsebik, A., & Novák, D. (2018). ISO 50001—Energy Planning and Monitoring Tools and Examples. Energy Engineering, 115(6), 46–61. https://doi.org/10.1080/01998595.2018.12027901