Footwear Industry Case Study

Business Needs

Improving process capabilities is critical for plant productivity and quality, specifically in asset-intensive industries such as the Footwear Industry

Problem Statement

Currently, the consumables and indirect material costs account for a significant part of the plant expenses and are rarely monitored in real time or even close to real time. Because of disparate devices, systems, machines are not able to understand each other leads to inefficient, incompetent system which further leads down to revenue losses and product quality issues.

Phase 1 Problem

  • Iso and poly mixture is the base of their footwear sole. Both material , Temperature & pressure has to be fixed to make an ideal sole.

  • Mixture temperature is high, because of that their machine metal has to be replaced but there was no such monitoring when to replace it. So in a day they waste around 2 hours to change the metal.

  • Their machine runs 24*7 so their motor monitoring has to be done to reduce the downtime.


Multiple sensors including Vibration, temperature and pressure were installed at multiple nodes in a single system. Different sensors started sending real time data to CharIoT platform.


  • Using CharIoT’s AI engine, the machine productivity increased by 10%

  • We were able to track the slightest of downtimes and ascribing reason to reduce maintenance cost and increase OEE and overall productivity
  • Using machine learning we were able to set control and washing limits for temperature, pressure and viscosity

  • We enabled real time measurements of consumable and control specific consumption.