Are you listening to your products?
As more connected objects or “things” are embedded in industrial, automotive and aerospace products, the management and use of machine diagnostic data will be critical to improving the reliability of existing and future products, while reducing the cost of nonconformance.
The sensors that monitor systems inside digital products are generating vast amounts of diagnostic trouble codes and operational machine data. This data, which is communicated to manufacturers using telematics, also provides information to its operators on machine health and status. Now, data scientists can use reliability-based advanced analytics to improve the detection, diagnoses and prediction of in-service product failures — that could reduce detection to correction (D2C) cycle times by months, or even years.
Most manufacturers are still using warranty-based submissions to detect and analyze failure events, which can result in the following challenges:
- Submissions can take 90 to 120 days to process for analysis.
- Specific in-service cycles, when the problem occurred, or operating conditions at the time of the issue are not represented.
- In-service failures that have the greatest negative impact to a product’s reliability are not represented. Some failures that cause downtime may not generate a warranty or service call.
- Incident or failure rate analysis may not detect an emerging issue until it is reported on a Pareto chart.
Increasing amounts of products are evolving from having sensors that simply inform operators, for example, the engine is overheating to communicating the health and status of an entire fleet of machines. In response, manufacturers need to be ready to manage and use this data to drive continuous improvements to existing and new products. Armed with this intelligence, engineering, manufacturing and aftersales service stakeholders can improve product quality and reliability, reduce costs, strengthen customer relationships and loyalty, and increase equipment and parts sales.
The speed that manufacturers develop or improve their ability to listen to their intelligent products, generate insights and integrate this information into action will provide tangible benefits that, ultimately, increase shareholder value.
The digital product and its digital twin
Machines with onboard controllers connected to remote data repositories via telematics are collectively called “digital products” and comprise what is referred to as the internet of things (IoT). Digital products have enabled comprehensive monitoring of a machine’s health and operations, allowing companies and customers to track a product’s operating characteristics, history and usage.
In the last decade, the concept of the “digital twin” has also emerged. As defined by Dr. Michael Grieves, “The concept of a virtual, digital equivalent to a physical product is the digital twin.” The model has three main elements:
- Physical products in real space
- Virtual products in virtual space
- The connections of data and information that tie the virtual and real products together
The unified repository (UR) provides the link between the physical in-service world of the product and its digital twin, operating in a virtual, data-driven environment. The diagram below shows all the potential interconnections between the UR, the product and the digital twin via data from the various internal enterprise systems, external sources and that generated by the digital product itself.
But the sheer volume of data generated makes analysis a major challenge. According to one North American vehicle manufacturer, a vehicle operating 6 to 10 hours can generate up to a petabyte of data per day.
There are several ways to address this issue. Some organizations store telematics data in inadequate conventional repositories — resulting in data being lost because it will not fit in the available space. Others try to reduce the data volume by recording it in units of hours instead of minutes, or even seconds. Often, despite these efforts, databases still grow at unmanageable rates and struggle to provide the responsiveness required to analyze fleet data. Many manufacturers have, simply, yet to determine what to do with their machine data.
Detection to correction
Because of the data warehousing challenge, as well as inadequate analytical tools and technical expertise, many digital product manufacturers still use traditional means, such as warranty submission analysis, to detect and diagnose product issues. This leads to significant D2C cycle times. One North American automotive manufacturer indicated that just one day on a major product issue can equate to US$1m in warranty and recall related costs.
By leveraging the data generated by digital products, a manufacturer can reduce the overall D2C cycle time by 50%–90%. This means that a problem with a machine is addressed within the warranty submission process, effectively bypassing the associated delays. Instead of a warranty claim signaling that a problem has occurred, reliability-based predictive analytics provide the signal by which a failure can be detected and prioritized.