Closing the loop on product usage data

“With IoT data, you’re calling the machines and asking, ‘How are customers really using the products?’” PTC’s JP Provencher on the increasing use of field usage data to improve other business functions.

Much of industry today operates on silos of information—on products, on customers, on manufacturing processes. But what if all these silos could work together with field data from products themselves as they’re being used? It’s not too hard to imagine the improvements in logistics, service, billing, sales, manufacturing, quality, inventory management and product development that could be achieved.

Such is the revolution in decision-making enabled by Internet of Things (IoT) technology and by the integration of IoT data with other business systems described at Smart Industry 2015 by JP Provencher, senior director of IoT solution strategy at PTC/ThingWorx, together with Rachel Trombetta, software director for DevOps & Service Reliability at GE Intelligent Platforms Software.

"IoT is real, IoT is now," said Provencher, "and machine data is not enough. Companies are already driving their IoT initiatives today" to bring detailed product usage data back into the hands of designers and marketing in order to achieve outcomes such as optimizing the next set of product features.

CRM upside down

"Think of CRM upside down," Provencher said. "Currently you pick your 10 best customers and call them to understand how they are using your products. With IoT data, you’re calling the machines and asking, ‘How are the customers really using the products? What capabilities are they using? Are they using products in ways that are compatible with how they were designed?’—all to better understand and serve the customer."

Provencher sees great opportunity in the potential for the IoT to increase operational performance and reduce service time in manufacturing. "Customers are achieving a 20% reduction in service costs" by reducing the number of service calls through remote data analysis and troubleshooting, Provencher said. "And, when service technicians do get sent, the technician will already have had the opportunity to understand the problem, diagnose the problem, and show up with the right service parts for a better chance for a first-time fix." On an operational level, organizations that deploy smart connected operations can generate 5-10% process improvements in the form of higher yield, higher throughput, lower inventory, reduced lead time, and improved operator productivity, Provencher added.

Four digital pillars

GE's Trombetta visualizes the integration of machine and enterprise data sets as a "digital thread" that runs throughout the facility, connecting embedded machine sensors to execution and planning systems as well as to sales data. The end result is improved visibility into how equipment is performing and how to optimize production. The thread weaves together the four pillars of the digital factory:

1. Sensor enablement: The new generation of workers "can't go knock on the machines anymore and hear what's going on." They need the data itself and a visualization layer to drive productivity on the shop floor.

2. Factory optimization: How do you move data across all enterprise systems to make decisions faster? "Nobody should be doing whiteboards anymore, we should all have the data and the intelligence at our fingertips."

3. Supply chain optimization: How do you get "quality information from your suppliers and vendors before it ever leaves their manufacturing plant, and bring it into yours?" Deliverables include lower total cost of ownership (TCO) and reduced quality risk.

4. Virtual manufacturing: Using simulation to anticipate and solve process-related issues before the processes are even built.

She added that GE Transportation CIO Jamie Miller characterizes the "digital thread" approach as “Lean on steroids with lots of data,” incorporating both design feedback and production feedback loops to drive efficiencies.

When asked at the end of the panel what level of payback that GE was achieving at their IoT-enabled manufacturing facilities, Trombetta cited an example from GE's Transportation group, where the operational insights at one plant resulted in learning that an operator was doing things backwards, resulting in $200,000 savings in the first month.

Provencher added that ThingWorx had been applied by one global manufacturing organization across approximately 30-35 plants to understand reasons behind unplanned downtime, and that shared benchmarking resulted in a productivity improvement of 10% in productivity and are able to sustain that improvement.