iPredicta RIOT
Lead Participant:
FITFACTORY TECHNOLOGY LTD
Abstract
This project addresses 2 Government priorities...improving productivity of manufacturing SMEs and reducing carbon footprint to achieve net zero by 2050\.
A major capex cost for manufacturing SMEs is new plant & equipment such as CNC or AM machines. However due to a lack of real-time machine data, business intelligence applications and planning systems, many SMEs operate under utilised plant which not only impacts productivity but also wastes energy.
Machine monitoring solutions have been on the market for more than 20 years however, despite the potential impact on productivity, their adoption has been limited especially within the SME sector.
We surveyed a representative sample of our 400+ UK manufacturing clients to understand why they had not adopted machine monitoring technologies previously and their reservations were as follows:
* cost of hardware and software
* complexity and cost of plant integration and implementation services
* lack of systems flexibility
Fitfactory have started to address these challenges by developing a super low cost vibration detection sensor that can be attached to the outside of any machine using magnets.
Our Fitfactory Business Intelligence application -- Insights - connects seamlessly with disparate ERP systems and our Rapid IOT vibration sensor to not only present utilisation data and automate alerts but also generates actionable insights and risks for production teams to monitor and add their lessons learned.
However, to create a game-changing platform, we must deploy machine learning AI that can be easily applied for multiple use cases, by taking the sensor vibration data, and analysing the lessons learned, to create an intelligent prediction tool that empowers operators so that they can make more informed shop floor planning decisions to minimise downtime and energy usage.
By collaborating with a leading UK RTO, we are confident that we can overcome the complex analytical challenges to create an intuitive platform that can be rapidly configured and deployed at scale to accelerate the optimisation of SME manufacturing plant utilisation by at least 10% and reduce energy usage by 5%.
STFC will provide specialist support to create a flexible machine learning solution that be applied to all manufacturing plant and equipment that can be quickly configured, at minimum cost, using a simple user interface for production operators to capture various qualitative root cause analysis reasons for downtime, combined with quantitative plant utilisation analysis to predict machine tool wear and to mitigate downtime through improved planning decision support.
A major capex cost for manufacturing SMEs is new plant & equipment such as CNC or AM machines. However due to a lack of real-time machine data, business intelligence applications and planning systems, many SMEs operate under utilised plant which not only impacts productivity but also wastes energy.
Machine monitoring solutions have been on the market for more than 20 years however, despite the potential impact on productivity, their adoption has been limited especially within the SME sector.
We surveyed a representative sample of our 400+ UK manufacturing clients to understand why they had not adopted machine monitoring technologies previously and their reservations were as follows:
* cost of hardware and software
* complexity and cost of plant integration and implementation services
* lack of systems flexibility
Fitfactory have started to address these challenges by developing a super low cost vibration detection sensor that can be attached to the outside of any machine using magnets.
Our Fitfactory Business Intelligence application -- Insights - connects seamlessly with disparate ERP systems and our Rapid IOT vibration sensor to not only present utilisation data and automate alerts but also generates actionable insights and risks for production teams to monitor and add their lessons learned.
However, to create a game-changing platform, we must deploy machine learning AI that can be easily applied for multiple use cases, by taking the sensor vibration data, and analysing the lessons learned, to create an intelligent prediction tool that empowers operators so that they can make more informed shop floor planning decisions to minimise downtime and energy usage.
By collaborating with a leading UK RTO, we are confident that we can overcome the complex analytical challenges to create an intuitive platform that can be rapidly configured and deployed at scale to accelerate the optimisation of SME manufacturing plant utilisation by at least 10% and reduce energy usage by 5%.
STFC will provide specialist support to create a flexible machine learning solution that be applied to all manufacturing plant and equipment that can be quickly configured, at minimum cost, using a simple user interface for production operators to capture various qualitative root cause analysis reasons for downtime, combined with quantitative plant utilisation analysis to predict machine tool wear and to mitigate downtime through improved planning decision support.
Lead Participant | Project Cost | Grant Offer |
---|---|---|
FITFACTORY TECHNOLOGY LTD | £21,216 | £ 21,216 |
  | ||
Participant |
||
STFC - LABORATORIES | £28,774 |
People |
ORCID iD |
Tom Dawes (Project Manager) |