Predictive Maintenance Optimization for Smart Vending Machines Using IoT and Machine Learning

Abstract

The increasing proliferation of vending machines in public and commercial environments has placed a growing emphasis on operational efficiency and customer satisfaction. Traditional maintenance approaches either reactive or time-based preventive are limited in their ability to preempt machine failures, leading to unplanned downtimes and elevated service costs. This research presents a novel predictive maintenance framework tailored for vending machines by leveraging Internet of Things (IoT) sensors and machine learning (ML) algorithms. The proposed system continuously monitors machine components and operating conditions in real time and applies predictive models to forecast failures before they occur. This enables timely maintenance scheduling, minimizing downtime and extending machine lifespan. The framework was validated through simulated fault data and performance evaluation using classification algorithms. Results show a significant improvement in early fault detection and a reduction in redundant service interventions. The findings indicate that predictive maintenance systems, when integrated into vending infrastructure, can transform operational efficiency and service reliability.