What can a television, refrigerator or washing machine tell us about product design? A lot actually – usage data provides valuable design input for new features and functions that customers value (or don’t).
As connected products (such as appliances, wearables, cars, and other devices), become ubiquitous, more and more data around actual product usage is created. Patterns buried in this data can be used to accelerate product innovation and development. IoT sensors embedded within these devices and machines provide hard evidence around actual product operations, responses, and user interaction patterns.
By understanding how customers are using their products, companies can quickly update features or adjust future models to align with customer desires and behaviors. They can also better anticipate where consumer preferences may be trending toward in the future. And, they can design more profitable products by eliminating unnecessary costs from the bill of materials.
For example, with connected appliances, a washing machine OEM can continue to monitor and improve their products after they’ve been sold. They can create custom wash cycles based on the types of clothes, minerals in the water, time of day, and other parameters. All of this can lead to reduced energy consumption, longer garment life, and wash cycles that truly fit the needs of the customer.
Sensor data can also record how many times a refrigerator door is opened, or how often certain buttons are pressed. In the manufacturing process, companies can redesign components or sub-systems based on actual product usage and malfunction data. They may also eliminate some features entirely that customer do not value, or do a better job at promoting them. This will help reduce warranty and maintenance costs, with higher quality products and more satisfied customers.
We shouldn't kid ourselves however - most product development teams are not structured to take advantage of this new source of insights. They may have raw data rather than insights; they may operate a process that can't react to what they are learning in line with with consumer expectations. There will be some "digital transformation" required.
Companies can take advantage of machine learning algorithms to optimize the performance and longevity of their products. They can also leverage the benefits of AI-enabled factory technology and processes. The ensuing insights are powerful – rather than learning through costly and time-consuming trial and error, their manufacturing systems will begin to understand, reason, and learn in real-time automatically. All of this will help them to leverage relevant data, optimizing insights from the data through analytics, leading to revamped operations and product development business processes, which will ultimately help drive and enhance customer experiences.
CES 2018
This year at CES this year, IBM rolled out interesting new ways to use consumer data to rapidly iterate their products. We showed how Watson and cognitive tools can listen to audio generated by the products to analyze product failures. We showed how our Visual insights tools use images of product components to identify defects. And of course, all of these tools are available to experiment with our market leading Bluemix platform on the IBM Cloud.
To help manufacturers of all types understand the benefits of an AI-enabled factory, we created a Model Factory simulator. Check out this this two-minute experience to see AI and data solutions in action.
For more insights about cognitive manufacturing, go here.
