Transforming Factory Safety and Efficiency with an AI-Powered Incident Management System

A bespoke internal application that revolutionized how a large-scale factory manages, resolves, and prevents workplace incidents, leveraging AI for predictive analysis.

Project For:
A large enterprise with 1200 employees
Transforming Factory Safety and Efficiency with an AI-Powered Incident Management System
Introduction

Project Overview

A leading enterprise in the manufacturing sector, operating a factory with over 1200 employees, was facing significant challenges with its outdated and manual incident reporting process. The system was slow, prone to human error, and lacked the data infrastructure to provide actionable insights, leading to recurring safety issues and operational bottlenecks.

PlusInfoLab was tasked with architecting and developing a centralized, real-time incident management application. The goal was to create a closed-loop system that not only streamlined reporting and resolution but also harnessed the power of AI to forecast and mitigate future risks, fostering a safer and more efficient work environment.

Our Strategic Goal

To develop a comprehensive incident management ecosystem that reduces Mean Time to Resolution (MTTR) by 50% and leverages predictive analytics to decrease recurring incidents by 30% within the first year.

Project Architecture

An enterprise-grade Laravel application with a Vue.js frontend, supported by a Python-based machine learning model for predictive analysis and integrated with the factory's existing IoT sensor network.

Pain Points

The Challenge

The client's manual, paper-based incident process was a critical liability, creating a reactive rather than a proactive safety culture.

01

Delayed Reporting and Resolution

Incidents were reported via paperwork, leading to significant delays in notifying the right personnel. The average time from incident occurrence to the start of resolution was over 48 hours.

02

Lack of Data Visibility

Data was siloed in filing cabinets, making it impossible to identify trends, root causes, or high-risk areas within the factory. This lack of insight meant that preventable incidents were recurring frequently.

03

Inefficient Resource Allocation

Without a clear, real-time overview of ongoing incidents, maintenance and safety teams were often misallocated, leading to wasted man-hours and extended operational downtime.

Our Approach

The Solution

We delivered a holistic, four-part digital solution designed to create a seamless flow of information from incident reporting to resolution and prevention.

Mobile-First Reporting

A cross-platform mobile app for all 1200 employees, allowing them to report incidents in under 60 seconds with photo/video evidence and precise location tagging from anywhere in the factory.

Real-Time Command Dashboard

A centralized web dashboard for supervisors and safety officers, providing a live map of all incidents, their severity, status, and assigned personnel, with automated escalation workflows.

AI-Powered Predictive Analysis

A machine learning model that analyzes historical incident data, maintenance logs, and IoT sensor readings (e.g., machine temperature, air quality) to forecast high-risk scenarios and recommend preventative maintenance.

Actionable Analytics & Reporting

An advanced analytics module that generates automated reports on key safety KPIs, helping management identify systemic issues and track the impact of safety initiatives over time.

Technology
Stack Used

Laravel
Inertia.js
Python (TensorFlow)
MySQL
Redis
WebSocket
AWS EC2
The Impact

Key Results

60%

Reduction in Incident Resolution Time

25%

Decrease in Recurring Incidents

95%

Employee Adoption Rate

The new system had an immediate and profound impact on factory operations. The digital reporting process and automated workflows slashed the Mean Time To Resolution (MTTR) from days to hours. The AI forecasting model successfully predicted and helped prevent several major equipment failures within the first six months, saving the company an estimated $250,000 in potential downtime and repairs.

"PlusInfoLab delivered more than just an app; they delivered a cultural shift. Our approach to safety is now proactive, not reactive. The predictive analytics feature is a game-changer, allowing us to solve problems before they even become incidents. The impact on both safety and our bottom line has been remarkable."

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