By combining operational data with maintenance history, customer information, and a digital representation of a machine, manufacturers from consumer appliances to industrial equipment can implement predictive service, fast diagnostics, and minimal downtime.
Once the domain science fiction, the concept of digital twins has accelerated into reality in recent years thanks to the proliferation of connected devices, abundant cloud computing resources, and three key technology drivers: digitization, virtualization, and artificial intelligence (AI). Whereas digital twin technology has typically been associated with diagnostics of large and complex assemblies such as turbines, it can now be applied in far more mainstream applications — and one of the biggest beneficiaries is the customer support group of today’s consumer and industrial manufacturers.
To understand how, first let’s define some terms. Gartner defines a digital twin as a software design pattern that represents a physical object with the objectives of understanding the asset’s state, responding to changes, improving business operations, and adding value.
Connectivity is a crucial ingredient of these emerging digital twin applications. The ubiquity of IoT, in which essentially any device can now be connected to the internet, means the range of connected equipment has expanded. The enormous array of connected devices, many of which are generating real-time data, represents a huge opportunity for manufacturers. By logging that data and performing analytics, digital twin technology can provide manufacturers with real-time insights into the state of their connected equipment in the field. These insights can transform the level of in-field service, identify product defects, and even improve the design of future products.
Today, manufacturers that support and service in-field equipment are generally working in a reactive mode: when a machine develops a problem, the customer logs a support request via a call center or web self-serve. That critical interaction may be sufficient to properly understand the nature of the technical problem, identify the cause, and schedule an on-site technician whose truck has been provisioned with the right part and tools to rectify the problem and get the machine back into service. Or… it may not. So much can go wrong along the course of that chain events, leading to an unhappy customer and a ding in the manufacturer’s customer experience (CX) reputation.
When you add connectivity to the machine and introduce the concept of a digital twin into this situation, several things happen — and they all benefit the customer:
- A connected machine can report malfunctions and errors as they occur. These may or may not be critical; we don’t know much more than something has been reported.
- When we combine this machine data with other relevant information (serial number, build date, parts supplier, location of manufacturing center, date installed, service history, and even factors such as usage statistics and climate), we can get a much clearer picture of the state of this machine.
- This information may be sufficient to proactively schedule a maintenance call to prevent downtime, and it certainly enable much more accurate diagnostics during that initial customer call.
- The technician is better prepared to fix the problem — likely during the first visit.
- Machine downtime is minimized, the lifetime value of the machine is increased, and the customer has an overall better experience.
How does this work in practice? This diagram shows the data flow both into and out of the digital twin.
Through our extensive work with manufacturers around the world, we’ve developed a deep understanding of the challenges they face — especially as they strive to deliver high uptime and efficient customer service. Bruviti digital twin technology can be applied to solve many of the challenges faced by service groups. By focusing specifically on improving the customer experience (CX), our team of data scientists has developed a digital twin solution for manufacturers of both connected and non-connected equipment.
Bruviti Digital Twin can add value to connected equipment
Perhaps the biggest advantage of adopting the Bruviti Digital Service Twin solution for connected equipment manufacturers is that it offers real-time monitoring and configuration abilities. This provides a current analysis of the health of a connected product, an understanding of behavioural and operational usage patterns, the effects of extreme weather conditions, how inconsistencies in set-up procedures have affected machine performance, and identification of design flaws. The data from connected equipment helps manufacturers to look for anomalies or abnormal patterns and identify problems that may be hard to find through traditional methods. The highly visual nature of a digital twin means manufacturers can run a real-time simulation on an asset and predict the nature and time of failure — long before the asset actually runs into that failure.
Non-connected equipment can benefit as well
One of the key opportunities that digital twin technology unlocks is the possibility of transforming service groups from a reactive model to a proactive model. But this doesn’t necessarily apply only to connected equipment. By analyzing historical service data records and the data gathered from sensors, and then collecting data and assigning it to a specific performance status, we can enable predictive service capabilities. For example, we can analyze the service history and performance status and use this information to predict when a specific component or machine will fail. This helps support and service teams be proactive—even for unconnected equipment—and reach out to customers to schedule service before their equipment fails, thereby reducing equipment downtime.
Bruviti Digital Twin technology marries the virtual and physical worlds so that field service and support teams can understand not only how products are performing, but how they will likely perform in the future. Armed with this important data, organizations can enhance their decision-making abilities with new insights into proactive service requirements, product innovation, lifecycle expectations, and even the development of new products and services.
In future posts we’ll take a closer look at how Bruviti’s digital twin technology has been applied in specific use cases.