The future of manufacturing is inexorably linked to big data. In fact, Accenture suggests that the industrial Internet of Things (IoT) could add $14.2 trillion to the global economy by 2020. Manufacturers will be able to use a mix of production data from IoT sensors and analytics, along with consumer experience data from sensors and social sources, to create a proactive approach to manufacturing in the future.
In many ways, the hard work is just beginning. Companies are starting to get a grip on the importance of data lake technology and collecting all the information they can from a variety of sources. As such, they’re building the foundations for more nuanced uses of big data—such as analytics and machine learning—in the future of manufacturing. These use cases might include key business aims and performance benefits, such as process optimization and machine uptime.
However, there is an even stronger use case that involves drawing on disparate data sources to improve both the end-to-end production life cycle and, even more radically, the consumer experience life cycle. Rather than just increasing the quality of an individual product, total enterprise improvement emerges through the use of production and consumer data.
In some ways, the road toward this brighter future is already being traveled. While some firms are adopting data lake technologies, others are implementing sensors in their products, which helps provide feedback on manufactured goods that are used in the field. By correlating this usage information with data from the design and manufacturing stages, companies can create a view of the entire product life cycle.
Even more change is coming, particularly via the use of digital twins. According to analyst Gartner, a digital twin is a representation of a physical object that also includes data from the object and the ability to monitor it. This gives businesses the ability to respond faster to changing conditions, particularly for asset optimization and preventive maintenance, as these twins are fed IoT sensor data.
Gartner predicts half of all large industrial companies will use digital twins by 2021. Such twins will give manufacturing firms the opportunity to access rich new sources of data. They will help companies to move beyond product life cycle management and into consumer experiences, where they will be able to analyze how something is operating and how it can be modified to improve performance.
By adding data from external sources like social media and sentiment analysis, manufacturing companies can develop deeper insights. The result of all this knowledge is a digital thread across product and customer life cycles. Instead of information gaps between design, use, and experience, a confluence of data is created, and products can be monitored and analyzed throughout their entire life cycle.
This convergence of life cycle information means manufacturers can start to hone products according to customer demands. Too much of the current manufacturing process is reactive, and companies often only make changes to products once defects are noted. The focus is often on post-production refinement instead of the creation of high-quality goods that meet customer requirements.
Connecting the product and customer digital threads means businesses can use the data they collect to proactively monitor products and make modifications as necessary. This creates the potential for new service models, where individuals involved in the manufacturing process spend less time servicing and more time improving.
This circle of improvements will help create a broader definition of quality in manufacturing. Right now, once a product enters the field, it’s tough to get information on quality—and much of this information is coarse and reactive in nature, such as customer surveys and feedback scores.
The new age of connectivity allows manufacturing companies to benefit from real-time data collection. Information is fed from sensors to businesses automatically, allowing firms to monitor customer experiences and to respond proactively. Insights on these experiences can be fed back into the design and manufacturing processes to help create new products that please customers.
This increased configurability of the design process will see manufacturing change the dynamic with clients. Rather than building a single product for many customers and hoping it matches requirements, manufacturers will build tailored, and even personalized, products. They will be able to learn the design elements that made customers happy in the past and use that knowledge to create better products in the present.
Manufacturers, therefore, will be able to mine their data to continually improve the quality of the goods they produce. They will use the power of machine learning to hone the manufacturing production chain in real time. The result, as we have seen above, is a much broader definition of quality that draws on both production life cycle and customer experience life cycle information.
In short, the future of manufacturing and big data is improved insights and better quality products for customers. Companies that successfully pull together all their disparate data sources will be able to produce goods that are of a much higher quality—and that level of quality will be defined by the customer.
Read about how big data is helping to revolutionize the manufacturing industry.