Bruviti’s Predictive Suite and IoT Core are designed for manufacturers of home appliances, commercial equipment, and industrial equipment. The suite consists of an integrated set of cloud-based software applications that enables support and service organizations to reduce operational costs by predicting which parts will fail, thereby reducing truck rolls and boosting call-center effectiveness.
Among the factors that service and support executives must contend with are:
- It costs $300, on average, per truck roll for field service technicians to respond to a support ticket
- The need to carry a wide array of costly spare parts (on-vehicle and in-depot)
- The duration and effectiveness of customer-support calls at contact centers, and the need to satisfactorily deflect an increasing call burden
- Deriving customer-usage insights to improve the design of future machines
Deriving insights from the data generated by IoT-enabled equipment, business operations and call-center agent/customer interactions lies at the heart of addressing these costs. The problem: OEMs have typically amassed volumes of this data, but it is of disparate types (engineering, manufacturing, machine runtime, customer, call center, and field service) and located in data silos that defy integration.
The Bruviti Predictive Suite provides use-specific applications that leverage the power of machine learning and artificial intelligence powered by its IoT Core, which runs on the cloud to integrate and orchestrate disparate machine, business, and human data. Users of Bruviti’s applications can easily analyze data to derive valuable insights for a range of operational use cases that accelerate resolution of customer-support calls and unlock transformative operational models.
Bruviti delivers three key benefits for OEMS: increased productivity, improved customer loyalty, and a pathway to new business models. For support teams, Bruviti improves the quality of customer-service interactions by unifying vital machine and customer data on one platform, and then leveraging this information to reduce mean time to detect (MTTD). Similarly, for service operations, data-driven insights help teams ensure the right parts are taken on a service call, thereby optimizing costly customer visits and truck rolls, and reducing the MTTR for improved overall profitability.
Bruviti Predictive Suite applies machine learning and AI technologies to increase the productivity of all users. These applications are designed for different roles (such as field service technician, customer support representatives, technical experts, parts inventory managers) and assists users by providing dynamic recommendations. The Predictive Suite includes the following applications.
- Predictive Support is used to view status and fault codes derived from the equipment data and apply machine-learning algorithms to triage issues reported by the customer when calling the service center.
- Predictive Monitoring is used by service center and employs machine-learning with self-learning capabilities to detect if the machine is operating outside specifications.
- Predictive Service is used by field technicians for assistance in their job orders to service equipment to reduce the number of truck rolls needed to keep the machine operational.
- Live Help uses augmented reality technology to bring the technical expert closer to the problem in real time and help field technicians reduce the mean time to repair (MTTR).
- Parts Predictor allows OEMs to address the problem of over- or under-stocking of replacement parts and ensures the appropriate parts are carried during a field call.
- Chatbot supports the objective of call-deflection initiatives. This application employs artificial intelligence to assist customer support teams, and quickly identify problems by asking customers pertinent questions by applying a dynamic, algorithm-based decision tree.
Underpinning Bruviti Predictive Suite is Bruviti IoT Core, which runs on open IoT cloud platforms such as AWS IoT, Azure IoT Hub, and Google Cloud IoT. Bruviti IoT Core can support thousands of machines and devices, while its modular design allows it to be tailored for the specific UI, security, performance, and flexibility needs of any OEM.