UII Industrial Engineering Monthly National Webinar: Predicting Spare Part Requirements with Machine Learning

On Friday (25/04), the Master’s Program in Industrial Engineering, Faculty of Industrial Technology (FTI), Universitas Islam Indonesia (UII), once again hosted a national webinar titled “Informed Decision Making on the Prediction of Spare Part Requirements Based on a Machine Learning Approach” in April 2025. The UII Industrial Engineering Undergraduate Program regularly organizes this event as part of its monthly agenda. The National Monthly Webinar series is open to participants from various regions across Indonesia.

Muhammad Imam Baihaqi, the MC for this event, opened the webinar at 9:00 AM WIB. Following the opening remarks and the reading of the agenda, the national anthem of Indonesia and the UII hymn were sung, followed by a group photo session with all participants. Zahwa Putri Aghniya, the moderator, will accompany the participants for the next two hours.

Zahwa introduced the main speaker, Ir. Winda Nurcahyo, ST., M.Eng., Ph.D., IPM, ASEAN Eng., who currently serves as the Head of the Master’s Program in Industrial Engineering at UII. She is renowned as an expert in asset management and actively participates in various training and research activities at both national and international levels.

Presentation of Materials

Winda then explained the connection between industrial engineering and asset management within an integrated system comprising humans, materials, information, equipment, and energy. She emphasized that predicting spare part needs is crucial for maintaining a company’s asset performance, particularly in the oil and gas and energy sectors.

“We must view assets not merely as inanimate objects but as part of an integrated system comprising humans, materials, information, equipment, and energy,” Winda stated.

She also presented her latest research conducted with UII alumnus Rizky Wijaya on utilizing spare part purchase data in the oil and gas industry to predict future needs using a machine learning approach.

Following that, Winda presented the results of her research conducted with UII alumnus Rizky Wijaya on the use of machine learning methods to predict spare part needs based on purchase data in the oil and gas industry. This research won the Best Paper Award at the IEOM conference in Australia in 2023.

Through clustering analysis and association rules, they were able to identify purchasing patterns and relationships between spare parts that are often purchased together. These results are expected to help companies plan spare part procurement more efficiently and avoid losses due to machine downtime.

Q&A Session

After the presentation, the moderator opened the Q&A session for participants. One participant, Theofilus Bayu from PGRI University Yogyakarta, asked about the relationship between predictive maintenance and asset management. In response, Winda explained that the choice of maintenance strategy (preventive, corrective, or predictive) directly impacts costs, risks, and asset performance.

“The appropriate strategy must be chosen based on the criticality classification of each machine and supported by lifecycle cost analysis,” she explained.

Finally, Aghni concluded the webinar by encouraging participants to continue following the upcoming monthly webinar series by the Industrial Engineering Department at UII. Students can utilize this opportunity as a space for cross-campus and cross-industry discussion and learning. This event not only enriches academic knowledge but also provides technology-based practical solutions for the industrial sector in Indonesia.

Rani Novalentina