Senseye’s founding team originated from roots in Defence and Aerospace, so it shouldn’t come as a surprise that we work with companies on an international level. Below you’ll find the main industries in which we work.
Senseye works throughout the automotive supply chain, with major automotive Original Equipment Manufacturers (OEMs) as well as Tier 1 suppliers. With the costs of downtime in the automotive industry exceeding $3.5m per hour, there is tremendous pressure to ensure that production equipment is reliable and efficient.
Senseye helps automotive companies monitor all their machinery, not just the ‘critical points of failure’. Our solution has helped clients to:
- Reduce unplanned downtime by 50%
- Avoid 2 serious downtime events per week per site, helping each site produce an extra 5,000 cars per year
- Scale from 20 critical assets to thousands without increasing the work load of the on-site PdM team
- Experience an ROI of less than three months
Senseye is providing PdM capability across three European Nissan global production sites where models such as the Qashqai, X-Trail, Leaf and Infiniti are produced. Over 2,500 assets, including robots, conveyors, drop lifters, pumps, motors and press/stamping machines are remotely monitored using Senseye’s proprietary algorithms. More than 200 maintenance users actively use Senseye to optimize maintenance activities and make repairs months before predicted machine failure.
“Senseye is supporting our Predictive Maintenance programme across three production facilities and has helped us lower overall downtime and increase OEE.”
Damian Wheeler, Nissan UK Engineering Director
Senseye works with Nissan North America to automatically monitor a hybrid of machinery. Senseye was selected to provide a scalable Predictive Maintenance capability for providing advanced warning of possible machine failures. This helps to reduce unplanned downtime and increase OEE.
Senseye works with a number of Heavy Industry customers, in areas as diverse as Pulp & Paper, and Cement. We help reduce the cost of downtime across all their factory equipment by analysing asset health data, which is collected from different production and condition monitoring systems. Our solution has helped leading industrial companies to:
- Cover thousands of assets worldwide, just by adding more data
- Achieve useful results within 14 days
- Achieve an ROI of between 3-6 months
Senseye are supporting SKF with its cement and paper & pulp customers.
“After rigorously vetting over 10 different predictive maintenance offerings, our team’s decision was unanimous that Senseye was the best choice. Senseye offered a rare combination of a highly advanced back end data engine married to a front end user interface that was intuitive and user-friendly. We wanted a solution that our end users on the shop floor would see as a helpful diagnostic and prognostic tool, not just another system to acknowledge alarms on. Senseye delivered!”
Sourcing Manager, large heavy industrial organization
FMCG & CPG
Senseye works in the Fast Moving Consumer Goods (FMCG) / Consumer Packaged Goods (CPG) industries helping to reduce the risk of unplanned downtime which can affect product quality, safety and customer satisfaction. Our solution has helped major food and beverage brands to:
- Gain maintenance efficiency improvements and extend asset life
- Increase planned maintenance intervals and reduce inventory held
- Achieve an ROI of less than 6 months
Senseye are supporting a number of digital transformation projects with some of the world’s largest manufacturers.
Senseye works with any machinery that is collecting suitable data and as such can be applied to a number of different industries, to name but a few:
- Oil & Gas
- Renewable Energy
- Facilities equipment
- Fleet monitoring
Please join one of our upcoming webinars to find out more.
Senseye works with organisations like Siemens on global Industry 4.0 / Smart Industry projects. For example, enabling customers of Siemens MindSphere™ to rapidly experience the benefits of scalable PdM, making full use of their existing investments.
This dramatically reduces the implementation time of Predictive Maintenance projects, reducing it to a matter of weeks or even hours rather than months or years.