We see manufacturers and service Providers of Enterprise complex equipment being reactive to service. We use ML and IOT (where applicable) to not just be proactive but to drive efficiency around device, people and parts in Service
Our Applied Machine Learning solution takes device data exhaust and service records to predict failure. Using device prediction as a baseline, we again use ML to get right person with the right part to the right place at the right time. As data input increases, we go beyond service efficiency to service revenue and then to device as a service. Using eKryp, Service costs can be reduced by over 30%, and parts inventory by 10% by accurately predicting failure, and parts demand.
eKryp, an Intelligent Service AI solution delivered as SaaS provides predictive insights on device, parts and people by continuously learning from your service, field assets, and customer data.