Through their collaboration, ROOTCLOUD and Putzmeister have developed an intelligent construction management service solution based on MachineLink+. By incorporating telecommunication networks, GPS, and hardware including embedded smart devices, ROOTCLOUD designed a closed loop for data collection and analysis that supports core services such as intelligent scheduling, service order management, performance visualization reports, and key parts tracing. Built upon the PaaS big data functions of the ROOTCLOUD platform, the intelligent solution enables Putzmeister to store, analyze and apply equipment status data, and effectively monitor and optimize the parameters and indicators for construction machinery and its operation paths. This allows the company to predict and prevent equipment issues, and perform intelligent scheduling on internal and external resources, to ultimately deliver smart services to end customers.
The solution includes the following features:
- Real-time data collection and transmission. It involves gathering various equipment operation parameters, such as information on geographical locations, fuel consumption and equipment operation, and storing and analyzing the data in real time;
- Remote asset management, geofencing. With real-time data collection and upload, customers can monitor and manage their equipment anywhere at any time. This feature enables them to manage the operation of their equipment and compute the amount of work done (in terms of the total work duration and volume, fuel consumption, engine speed, etc.), which facilitates their work arrangements and serves as useful data for service engineers. Geofences can also be deployed prevent the theft of equipment in high-risk locations;
- Intelligent prediction and diagnostics. This function helps extend the service life and reduce the failure rate of equipment, by providing technical support to maintenance services, and making predictions of equipment failures as well as service and parts requirements. Such diagnoses are carried out based on big-data analyses involving equipment and component data, usage parameters, component wear and tear, and other technical parameters, as well as parts replacement data and records of equipment failures;