Unplanned maintenance requests are sporadic and can be difficult to determine when they are likely to occur and to allocate resources. This often leads to underutilised resources. Using our machine learning algorithms on historical and real-time data sets we are able to predict when events are likely to occur and effectively schedule the most effective (based on qualifications/ locations etc) resources for these jobs.
InformedActions works with teams separated across multiple locations, servicing a large breath of areas. Predicting when jobs are likely to occur and scheduling resources to complete these jobs, bearing in mind various constraints including; engineer qualifications, location, vehicle type, available tools etc.
Companies include ambulance services, tool hire services, home maintenance, facilities management.
What makes us different?
We take the historic and real time insight of your organisation and arm your staff with the knowledge and tools so ensure they can become supremely efficient and productive.
How do we do it?
At the heart of our service is our Algorithmic Engine which analyses your real time and historic data to find the most efficient and productive means of carrying out individual tasks. Whether you’re in sales or service our approach ensures your people are supplied with the right knowledge & tools, they ask the right questions or advise to ensure your on-going success.
Harry’s focus is on using state of the art machine learning technology, such as spatio-temporal predictive modelling and dynamic optimisation algorithms for use in mobile asset optimisation. Harry graduated from the University of Westminster and York, where he studied computer science and electronic engineering