This company is one of the largest suppliers of systems and components to domestic and international commercial aircrafts in service today. They design, manufacture and services systems and components for commercial aviation, including environment temperature control, electrical generation, cooling systems, brakes, starting motors, nitrogen generation and fire protection.
This company was struggling to keep track of what components were in use on an asset in a digital twin initiative. The initiative is a virtual representation of a real-world physical system that serves as the indistinguishable digital counterpart for the purposes of improving predictive maintenance and supply chain decisions.
The challenge to implementing solutions to improve predictive maintenance and supply chain decisions is having an accurate record of what systems and components are in use and keeping track when service technicians do a replacement operation. Keeping track of the cycles and hours of utilizations is a prerequisite to any predictive maintenance program as there is often a gap between the “as used” configuration and the applications that track asset configurations. This company needed assistance with keeping up with the current “on board” configurations on assets in a challenge when components were maintained, replaced or overhauled.
Baker Tilly worked with the company to deploy a computer vision application to identify model information and version number during maintenance operations to improve the accuracy of the digital twin by reducing friction from the tracking process. Maintenance technicians used a mobile device to take pictures of the asset tag number and then take pictures of identification numbers of parts before taking them out of the packing material. This enabled them to identify the model number and serial number which then updated the “on board” current configuration of the digital twin.
This solution improved the accuracy and coverage by 70% and helped reconciled the gap of between the configurations in the digital twin as compared to the parts and components in use on the asset.
