IPA를 활용한 물류 자동화 설비 예지보전 연구 = A Study on Predictive Maintenance for Logistics Automation Facilities Using IPA

다국어 초록 (Multilingual Abstract)

This study aimed to assess the current status of smart logistics automation in Korea during the digital transformation era, analyze the importance and urgency of automation equipment, and derive the direction for applying predictive maintenance. To this end, a survey was conducted with 21 experts possessing 5 to 30 years of experience in the logistics field. IPA items were established and analysis was performed based on actual annual automation equipment failure data from domestic logistics automation operators and the survey results.
The research results confirmed the maintenance importance and urgency levels according to automation equipment type and provided insights into future application strategies for predictive maintenance in logistics automation equipment.
According to the IPA (Importance-Performance Analysis) results, automation equipment positioned in the first quadrant—indicating both high importance and high urgency—included Stacker Cranes (storage), Sorters (sorting), RGVs (transport), and Lifters (transport). Among these, the Stacker Crane accounted for the highest proportion of actual equipment failures, reaching 42%. Expert opinions on appropriate predictive maintenance methods for each equipment type indicated a strong preference for IoT-based predictive maintenance. It was also confirmed that predictive maintenance solutions tailored to each process may vary between PdM-based and PLC-based approaches, depending on the configuration of the automation system.
The significance of this study lies in the fact that it is the first to compare and analyze key automation equipment using on-site failure data from domestic logistics automation facilities together with IPA results, while also examining applicable forms and strategies for predictive maintenance. The findings provide practical reference material for future smart logistics automation initiatives—particularly regarding the scale of automation facilities, maintenance personnel and costs, and inspection intervals—and can serve as a useful guideline for applying predictive maintenance systems to both existing and newly planned logistics automation operations.