The Influence of Business Analytics and Big Data on Predictive Maintenance and Asset Management
Article Sidebar
Main Article Content
Abstract
This study investigates the impact of business analytics and big data on predictive maintenance and asset management practices within the energy industry in Indonesia. A quantitative research approach, utilizing a survey methodology, was employed to gather data from stakeholders representing various sectors of the energy industry. The study analyzed the relationships between business analytics, big data, predictive maintenance, and asset management using structural equation modeling (SEM) with Partial Least Squares (PLS) regression. The results indicate significant positive relationships between the utilization of business analytics and big data and various performance metrics, including asset reliability, operational efficiency, and cost savings. Furthermore, organizational factors such as leadership support and data quality were found to play a crucial role in facilitating the adoption and implementation of predictive maintenance strategies. The findings underscore the transformative potential of data-driven maintenance strategies in enhancing operational efficiency, reducing downtime, and improving asset reliability within the Indonesian energy industry.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
F. Latif, N. Tambunan, and R. Dwika Heryani, “Kenaikan Harga Minyak Dunia dan Implikasinya Terhadap Perekonomian Indonesia di Masa Pandemi Covid-19,” SINOMIKA J. Publ. Ilm. Bid. Ekon. dan Akunt., vol. 1, no. 5 SE-Articles, pp. 1121–1126, Jan. 2023, doi: 10.54443/sinomika.v1i5.585.
A. J. Adellea, “Rangka Ketahanan Energi Nasional,” Indones. State Law Rev., vol. 05, no. 1, pp. 43–51, 2022.
S. R. Haq, R. M. Dewi, L. Erfiandri, P. H. Kasih, and A. Ardian, “Covid-19 and Coal Industry in Indonesia: A Preliminary Analysis,” J. Miner. Energi, dan Lingkung., vol. 5, no. 2, p. 60, 2022, doi: 10.31315/jmel.v5i2.6787.
P. Hariwan, F. Sunaryo, and M. Kholil, “Determining Factors of Energy Intensity in the Manufacturing Industry of Provinces in Indonesia,” J. Earth Energy Eng., vol. 11, pp. 136–145, Jan. 2023, doi: 10.25299/jeee.2022.10649.
K. Fikri, D. B. Darmadi, and D. Nugraha, “Implementation of Maintenance and Reliability Management System (Mrms) in Pertamina Hulu Energy Subholding Upstream (Phe Shu) Through Field Assessment of Iso 55001,” J. Rekayasa Mesin, vol. 14, no. 1, pp. 363–370, 2023, doi: 10.21776/jrm.v14i1.1589.
Agun Suryani and Achmad Budiman, “Analysis of Maintenance Optimization on Medium Voltage Overhead Lines (SUTM) in Reducing Energy Not Supplied (ENS) at PT. PLN (Persero) ULP Tarakan ,” J. Emerg. Supply Chain. Clean Energy, Process Eng. , vol. 2, no. 1 SE-ARTICLES, pp. 59–64, Apr. 2023, doi: 10.57102/jescee.v2i1.58.
D. Setyawati, “A Centralised Energy System of Indonesia BT - State-of-the-Art Indonesia Energy Transition: Empirical Analysis of Energy Programs Acceptance,” D. Setyawati, Ed. Singapore: Springer Nature Singapore, 2023, pp. 29–45. doi: 10.1007/978-981-99-2683-1_3.
I. Masudin, N. Tsamarah, D. P. Restuputri, T. Trireksani, and H. G. Djajadikerta, “The impact of safety climate on human-technology interaction and sustainable development: Evidence from Indonesian oil and gas industry,” J. Clean. Prod., vol. 434, p. 140211, 2024, doi: https://doi.org/10.1016/j.jclepro.2023.140211.
Oladipo Olugbenga Adekoya, Adedayo Adefemi, Olawe Alaba Tula, Nwabueze Kelvin Nwaobia, and Joachim Osheyor Gidiagba, “Technological innovations in the LNG sector: A review: Assessing recent advancements and their impact on LNG production, transportation and usage,” World J. Adv. Res. Rev., vol. 21, no. 1, pp. 040–057, 2024, doi: 10.30574/wjarr.2024.21.1.2685.
J. Gallegos, P. Arévalo, C. Montaleza, and F. Jurado, “Sustainable Electrification—Advances and Challenges in Electrical-Distribution Networks: A Review,” Sustainability, vol. 16, no. 2. 2024. doi: 10.3390/su16020698.
D. Khakhar, “Predictive Maintenance Strategies for Engineering Assets using Data Analytics,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 11, pp. 844–850, Jul. 2023, doi: 10.22214/ijraset.2023.54747.
Z. Znaidi, M. E. H. Ech-Chhibat, A. Khiat, and L. A. El Maalem, “Predictive maintenance project implementation based on data-driven & data mining,” in 2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), 2023, pp. 1–5. doi: 10.1109/IRASET57153.2023.10152915.
M. Molęda, B. Małysiak-Mrozek, W. Ding, V. Sunderam, and D. Mrozek, “From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry,” Sensors, vol. 23, no. 13. 2023. doi: 10.3390/s23135970.
K. Zadiran and M. Shcherbakov, “New Method of Degradation Process Identification for Reliability-Centered Maintenance of Energy Equipment,” Energies, vol. 16, no. 2. 2023. doi: 10.3390/en16020575.
N. Iftikhar, F. Nordbjerg, and Y.-C. Lin, Machine Learning based Predictive Maintenance in Manufacturing Industry. 2022. doi: 10.5220/0011537300003329.
O. Holmer, E. Frisk, and M. Krysander, Energy-Based Survival Models for Predictive Maintenance. 2023. doi: 10.48550/arXiv.2302.00629.
M. Hendi, F. Alawai, S. Eisawy, and A. Abdouli, Centralized Predictive Analytics & Diagnostics (CPAD) Program. 2023. doi: 10.4043/32150-MS.
J. Hou, C. Chen, C. Wang, W. He, J. Song, and Y. Li, “Framework of Cable Intelligent Maintenance Based on Big Data Analysis,” in 2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), 2023, pp. 1–8. doi: 10.1109/ICDCECE57866.2023.10151043.
S. T.R and S. Revathy, “Application of Big Data Analysis for Fault Diagnostics in Maintenance,” in 2023 International Conference on Inventive Computation Technologies (ICICT), 2023, pp. 681–685. doi: 10.1109/ICICT57646.2023.10134029.
H. Liao, E. Michalenko, and S. C. Vegunta, “Review of Big Data Analytics for Smart Electrical Energy Systems,” Energies, vol. 16, no. 8. 2023. doi: 10.3390/en16083581.
J. J. Montero Jimenez, S. Schwartz, R. Vingerhoeds, B. Grabot, and M. Salaün, “Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics,” J. Manuf. Syst., vol. 56, pp. 539–557, 2020, doi: https://doi.org/10.1016/j.jmsy.2020.07.008.
C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, “A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data,” Front. Energy Res., vol. 9, 2021, doi: 10.3389/fenrg.2021.652801.
S. Ayvaz and K. Alpay, “Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time,” Expert Syst. Appl., vol. 173, p. 114598, 2021.
P. Santoso and D. Al Mustaqim, “Analisis Hukum Kepemilikan Terhadap Big Data dan Essential Facility dalam Perspektif Hukum Persaingan Usaha di Indonesia,” JIHJ J. Ilmu Huk. JUSTITIA, vol. 1, no. 1, pp. 1–14, 2023.
O. Y. Yuliana, B. N. Yahya, and R. M. B. Kmurawak, “Contributions of Data Science Educational Paradigm in a Disadvantages Area of Indonesia: a case study,” Asian J. Community Serv., vol. 2, no. 6 SE-Articles, pp. 551–562, Jun. 2023, doi: 10.55927/ajcs.v2i6.4795.
N. Islam, K. Islam, and M. Islam, “Exploring the Potential of Big Data Analytics in Improving Library Management in Indonesia: Challenges, Opportunities, and Best Practice,” Internet Ref. Serv. Q., vol. 27, no. 2, pp. 111–120, Apr. 2023, doi: 10.1080/10875301.2023.2184900.
L. D. Rachmawati and F. N. Hasan, “Implementasi Business Intelligence untuk Analisa dan Visualisasi Data Penyebab Kematian Di Indonesia Menggunakan Platform Tableau,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 5, no. 1, p. 45, 2023, doi: 10.36499/jinrpl.v5i1.7584.
N. Kusbianto, E. G. Sukoharsono, and A. Darmawan, “Exploring the Impact of Big Data Analytics Capabilities on Indonesian Firm Performance - A Mediation Analysis of Business Process Agility and Process-oriented Dynamic Capability,” in 2023 6th International Conference on Information Systems and Computer Networks (ISCON), 2023, pp. 1–6. doi: 10.1109/ISCON57294.2023.10112001.
T. Suharto, M. D. Program, K. Suryadi, D. Systems, and B. P. Iskandar, “Predictive Maintenance in Bearing Production based on Machine Condition and Product Quality Data using Machine Learning Approach,” pp. 465–466, 2023, doi: 10.46254/ap03.20220072.
A. Kamariotis, K. Tatsis, E. Chatzi, K. Goebel, and D. Straub, “A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance,” Reliab. Eng. Syst. Saf., vol. 242, p. 109723, 2024, doi: https://doi.org/10.1016/j.ress.2023.109723.
M. Lubis, E. Raafi, and S. Prayogo, “Beyond Data Quality: The Assessment of Data Utilization in Indonesian Telecommunication Industry,” 2023, pp. 237–246. doi: 10.1007/978-981-19-7663-6_23.
T. Testasecca, M. Lazzaro, E. Sarmas, and S. Stamatopoulos, Recent advances on data-driven services for smart energy systems optimization and pro-active management. 2023. doi: 10.1109/MetroLivEnv56897.2023.10164056.
K. Psara, C. Papadimitriou, M. Efstratiadi, S. Tsakanikas, P. Papadopoulos, and P. Tobin, “European Energy Regulatory, Socioeconomic, and Organizational Aspects: An Analysis of Barriers Related to Data-Driven Services across Electricity Sectors,” Energies, vol. 15, no. 6. 2022. doi: 10.3390/en15062197.
W. Betz, I. Papaioannou, T. Zeh, D. Hesping, T. Krauss, and D. Straub, “Data-Driven Predictive Maintenance for Gas Distribution Networks,” ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A Civ. Eng., vol. 8, no. 2, 2022, doi: 10.1061/ajrua6.0001237.
R. Sala, F. Pirola, G. Pezzotta, and S. Cavalieri, “Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study,” Applied Sciences, vol. 12, no. 15. 2022. doi: 10.3390/app12157395.
M. Kans et al., “Data Driven Maintenance: A Promising Way of Action for Future Industrial Services Management BT - International Congress and Workshop on Industrial AI 2021,” 2022, pp. 212–223.
L. Hurbean, F. Miliaru, M. Muntean, and D. Danaiata, “The Impact of Business Intelligence and Analytics Adoption on Decision Making Effectiveness and Managerial Work Performance,” Sci. Ann. Econ. Bus., vol. 70, pp. 43–54, Feb. 2023, doi: 10.47743/saeb-2023-0012.
J. Lee, M. Mitici, H. A. P. Blom, P. Bieber, and F. Freeman, “Analyzing Emerging Challenges for Data-Driven Predictive Aircraft Maintenance Using Agent-Based Modeling and Hazard Identification,” Aerospace, vol. 10, no. 2. 2023. doi: 10.3390/aerospace10020186.
T. Benson, Illuminating the hidden challenges of data-driven CDNs. 2023. doi: 10.1145/3578356.3592574.