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FMCG company, UK - Senseye Predictive Maintenance enables monitoring assets automatically
One of the world's largest multinational consumer goods companies with headquarters worldwide, producing products and services such as home care, beauty and personal care and food and refreshment brands with world renowned brand.

With an increasingly competitive global market this FMCG company aimed to reduce costs whilst increasing capacity and efficiency. They set out to develop world class manufacturing with a major emphasis as part of their global Digital Factory program to improve maintenance practices. The vision was to make sustainable living common place.  

The solution has to maximize the value of the data as well as provide visualizations and insights for maintenance teams to act upon to help drive the improvement of maintenance practices.
Senseye Predictive Maintenance is a cloud-based solution for Predictive Maintenance 4.0:  
  • The service was deployed to maximize the value of data from existing sensors and has integrated with the FMCG existing systems and Microsoft Azure using Azure storage to connect Senseye Predictive Maintenance complete.  
  • Azure provides the critical bridge between data on the factory floor and cloud. Data is aggregated to provide condition indicators that are then sent to Siemens.  
  • Senseye Predictive Maintenance analyses the machine condition indicators against historical information by using proprietary and mechanical engineering focused machine learning algorithms that automatically provide maintenance engineers with alerts and diagnostics before any functional failures. 
We are pleased to have partnered with Siemens to enable Predictive Maintenance in support of our journey of efficiency improvement. We implemented the Senseye Predictive Maintenance via an easy integration with our existing systems, without installing further hardware or software on premise. 

Our focus has been on using the machine asset health algorithms to identify areas of concern before failure, which has automatically provided our engineers with alerts related to components before failure occurs, allowing time to plan interventions.
FMCG company
  • The solution is now used to monitor operational and critical machinery across several sites, covering homecare lines and ice cream manufacturing lines. The current project is undergoing a large scale up. This scale is enabled by use of Azure which provides the platform for Siemens to automatically monitor thousands of assets. 
  • By using the Siemens Senseye Predictive Maintenance with its proprietary machine learning algorithms to automatically forecast machine failure and remaining useful life maintenance, teams can reduce unplanned downtime, increase maintenance efficiency, and ultimately save money.
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