Project Overview

The client’s warehouse operations relied on a fleet of forklifts and conveyors critical to daily logistics. Frequent unplanned breakdowns caused costly production delays and disrupted outbound schedules. The project aimed to implement a data-driven predictive maintenance solution that would proactively identify potential equipment failures before they occurred.

Problem Statement

Maintenance followed a reactive pattern — equipment was serviced only after failure or during fixed intervals, regardless of actual condition. This led to inefficiencies:

• Unexpected downtime interrupted order fulfillment.

• Spare parts were overstocked without clear consumption forecasts.

• Maintenance resources were allocated inefficiently, with preventable breakdowns occurring between service cycles.

Solution

We collected and analyzed operational and sensor data from forklifts and conveyor systems, including vibration frequency, motor temperature, running hours, and downtime logs.

Using these datasets, we developed a predictive model based on machine learning regression algorithms (Random Forest and Gradient Boosting) to estimate remaining useful life (RUL) for each component.

The project deliverables included:

• Automated data pipeline for continuous monitoring of equipment KPIs.

• Predictive dashboard displaying failure risk levels and recommended service intervals.

• Alert system integrated into maintenance scheduling tools to trigger proactive interventions.

Result

• Optimized maintenance scheduling, reducing over-servicing by 20%.

• Extended asset lifespan through condition-based servicing.