Seizing the AI Opportunity
Located in Europe and US Markets, Corrux, looks to drive efficiency in the down and dirty civil engineering space. This is done by taking readily available data from construction vehicles to drive uptime and productivity through digital transformation.
How do we leverage the massive knowledge base of the people with so much industry experience and transfer that knowledge into an environment where the technology native generation can better utilise this data?
Corrux starts with a simple client request: let us review your machine list? There are many examples where customers do not maintain comprehensive lists of the machines they own, rent and use. It can take a week to gain this information!
The prototype thinking behind most construction sites means that any insights are not used to enlighten the next project, so the same inefficiencies are inherent in the process.
Today the capability exists to consolidate diverse data types. This means that, for example, data from machines, ERP systems and accounting packages can be seamlessly integrated using a single platform to deliver actionable insights for increased process efficiency and other project benefits.
An early predictive project with a large construction company generated a result of 89% accuracy in the predicted outcomes, based on the data available. Amazing! However, that was based on 25-years of detailed machine data, when combined with data from other areas of the business, where minimal data was available the accuracy was greatly reduced.
To provide predictive and prescriptive results data must be available, sufficient quality and quantity. Collaborative data is therefore not only important, but necessary. Large amounts of heterogeneous data used to generate collaborative models can be scaled to the requirements of large and small construction firms.
With multiple data points from each machine the models can be developed to represent different requirements. E.g. queuing tippers should stop engines rather than idle excessively and waste fuel and add engine time, whereas a crane may need to idle to retain control of a load, but the data simply indicates idling. These idiosyncrasies need industry rule engines, in order to ensure pragmatic solutions are developed.
Users on site, on a machine, or in a service environment, need clear visual prompts and speed of access to insights. The users in these environments need ‘three-click’ rule for the equipment, like mobile or Fitbit devices.
Reduced complexity of data access greatly benefits systems uptake, which increases user time savings and cost savings for the organisation. Increased uptime of equipment offers valuable insights that allow new and different ways to monetise the customer agreements; providing machine performance guarantees, increased warrantees and new opportunities not yet seen.