Case study · 2025

Raising OEE 14 points by making the shop floor visible

A components manufacturer recorded production on paper and entered it days later. Nobody could see the current shift. We connected the machines and built the floor and office layers around what operators could realistically maintain.

Client: Precision Components Ltd (auto components, Coimbatore)
Sector: Manufacturing & Industrial
Duration: 5 months

Manufacturing & Industrial

Precision Components Ltd (auto components, Coimbatore)

Laravel Python TimescaleDB Vue.js OPC UA

+14pts

OEE within two quarters

29%

reduction in unplanned downtime

5 min

to trace a batch, previously 2 days

Live

production visibility, previously 3 days delayed

The challenge

Where things stood

Output, downtime and scrap were recorded by hand on shift sheets and entered into the ERP two to three days later. By the time a problem appeared in a report, the shift, the operator and the cause were all gone. Two previous digitisation attempts had failed because the data entry screens took too long for operators working to a target.

Our approach

What we did about it

The previous attempts failed on a fourteen-field entry form. We designed a terminal interface with three actions on the primary screen — start, stop, report scrap — and made everything else optional or automatic.

Where machines had PLCs we read them directly over OPC UA and Modbus, so run time and cycle counts required no operator input at all. Older machines were retrofitted with simple sensors, which still removed most of the manual capture.

The office layer got the analytical depth: live OEE, downtime Pareto analysis, and a maintenance model that flags rising cycle-time variance before a failure rather than after it.

What we delivered

The system, in parts

Built with

Laravel Python TimescaleDB Vue.js OPC UA MQTT Grafana

OPC UA and Modbus connectivity across 31 machines

Operator terminals with three-action primary screens

Live OEE calculation with availability, performance and quality breakdown

Downtime reason capture with Pareto analysis

Predictive maintenance alerts from cycle-time variance

ERP integration for production confirmation and material consumption

Two vendors had already failed here. The difference was that this team asked what the operator could do in ten seconds, and designed backwards from that answer.

Plant Head, Precision Components Ltd

Services used