Smart, connected products offer manufacturers a wealth of opportunities to reduce operational costs while also driving new sources of revenue and higher levels of customer intimacy. But, in order to realize the promise of smart products, manufacturers will need to drive much more internal, cross-functional collaboration; cultivate new, closer ties with customers; and invest in upgrading legacy product and technology infrastructure.
These were some of the conclusions that were shared recently by Manufacturing Leadership Council members who have already rolled out smart products. The manufacturers imparted their experiences earlier this week as part of a ML Council Critical Issues Debate and Discussion entitled “Will Smart Products Revolutionize Your Manufacturing Strategy?”
One of the ML Council members sharing its smart product experience, Lexmark International, Inc., has embedded sensors and software into its high-tech office products in order to enable advanced predictive service applications. The sensors capture information about how the products are operating in the field, and algorithms developed by the manufacturer predict when a failure is about to occur and give it insights into its root cause.
According to Phil Carter, Service Operations Director, Predictive Service & Analytics, at Lexmark, this approach to predictive service has allowed the manufacturer to reduce customer-initiated service calls by 30% while also increasing the percentage of service requests that can be fulfilled remotely. And, just as importantly, it’s given manufacturing, engineering, and R&D functions better insights into how product designs and manufacturing processes can be strengthened to improve product performance and quality.
Another ML Council member built intelligence and connectivity into its rolling warehouse and distribution equipment, giving customers detailed data on how these products are used and allowing them to track and improve employee productivity and labor forecasting and even implement pay-for-performance strategies. The platform also improves field service on equipment by linking to ERP and warehouse management systems, making it easier and faster to stock replacement parts.
Both of these manufacturers said their smart product initiatives, besides reducing service costs and playing a role in improving quality and product design, have given them valuable insights into the requirements and use patterns of end user customers. In many cases, they said, this kind of visibility hasn’t been available to them because customers have engaged for sales and service with third-party resellers.
But, while these smart product initiatives have paid off, they have presented challenges and led to valuable lessons learned. Here are six of them:
1: Manufacturers deploying smart products will need to develop processes to navigate firewalls and other security infrastructure at customer sites. Smart products aren’t of much value if they can’t get data back to the OEM;
2: Smart product initiatives require tight, collaborative relationships between internal functions such as field service, engineering, manufacturing, and R&D because all of those groups will need to change their processes to make use of the data that is generated. One key to driving that collaboration is to designate a senior executive—preferably someone from the operations side—to be a clear product owner and to drive adoption;
3: Expect difficult culture changes. Armed with new customer use data, leaders will need to think differently about how their teams deliver value and how they measure performance. Rather than prioritizing feeds and speeds in new products, for example, the R&D organization will be able to use smart product-generated data to optimize total cost of ownership. Similarly, manufacturing will need to rethink how it performs end-of-the-line quality testing, replacing simple “pass/no pass” tests with predictive testing, based on real product performance data from the field, to make sure even marginal product doesn’t leave the factory;
4: Smart product initiatives must include strategies and cost justifications for updating legacy products already in the field.What will be the cost and payback on adding intelligence to legacy products?
5: Don’t forget to validate the quality of data already being collected from products in the field. In many cases, “semi-smart” products are already generating performance and diagnostic data, but often that data is flawed or incomplete;
6: While many smart product initiatives initially focus on a specific outcome such as predictive service, be aware that you are, in fact, creating a platform that can be used to deliver a wide range of product enhancements. In some industries, it’s estimated that, in the not-to-distant future, 80% of innovation will be delivered remotely via software after the product is shipped.