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Despite a decades-long footing in industries like automotive and aerospace, product lifecycle management systems have struggled to break out of the engineering ranks and fulfill their promise as a repository for all product-related data and workflows.
The advent of IoT may turn out to be a lifeline for the stagnant PLM systems software category and business discipline, experts say. Because smart, IoT-connected products can collect and deliver key data points about performance, quality and usage in the field, PLM systems gain new relevance as hubs for orchestrating workflows and use cases for a wider swath of stakeholders across the entire product lifecycle -- not just in engineering.
"The promise of PLM was never fully realized -- you typically didn't have continuous feedback from the outside world," said Joe Barkai, an independent consultant and industry analyst focused on IoT and manufacturing.
This is a primary reason why PLM systems never gained significant traction outside of product development circles, despite their positioning as a clearinghouse for all product-related data throughout the extended lifecycle, according to Barkai.
"IoT puts the 'L' back into PLM," he said. "It gives PLM a second life to reach its vision of delivering product-related data to front-end all decision-making, not just in development."
PLM never really moved past product development because it wasn't able to capture data after products had been delivered to customers, which would be valuable for areas like field support, marketing or procurement. IoT data coming back into PLM systems enables all product-related data to be available for decision-making by all of these groups, not just product development or engineering.
Closing the loop
The prospect of true, real-time feedback on a product or industrial asset as it's used in the field is a game-changer for a variety of use cases and promises to upend the over-the-wall, linear mentality of traditional product design. Historically, engineers create product designs from a set of requirements then hand off the finished model to manufacturing, which manages production processes with its own systems and tools -- ERP or manufacturing resource planning. Field service and maintenance personnel typically have another set of data related to the product, housed in a separate system, and few of the disciplines are likely to share information, let alone work off a common data set, PLM experts say.
Alan Griffithssenior industry analyst, Cambashi
"PLM systems were never designed to track every instance of every product or track every part in every machine right into the field," according to Alan Griffiths, a senior industry analyst at Cambashi, who specializes in digital transformation and IoT. "It's technically possible now."
Now that every part in a machine can be tracked, organizations can begin to collect performance data on a product, which can then be used to fuel new kinds of predictive maintenance applications. Having a direct line into how a particular fuel pump operates in the field -- for example, tracking real-time vibration or pressure data -- can provide insights into whether a particular component is in danger of failing. This allows a proactive maintenance fix that protects against possible downtime.
"Leveraging PLM in this way can have a real effect on improving uptime and preventing failures, which have been expensive and complicated to do," Griffiths said.
IoT-enabled machines provide direct feedback into the system
In a similar vein, IoT data also helps PLM systems play a larger role in other downstream processes like quality. Historically, companies have relied on people who use mostly manual processes to track and facilitate quality issues, explained Chuck Cimalore, president of Omnify Software, a maker of PLM systems based in Tewksbury, Mass.
With an IoT-enabled PLM platform, the machines themselves can feed data directly back to the system, which enables automated workflows and alerts to address problems.
"Anything that directly delivers the voice of the customers can help with specifications and design practices and procedures focused on quality," Cimalore said. "PLM starts to become an analytical engine for helping identify problems that are out there."
Beyond advancing quality and maintenance applications, PLM also has new applications for design workflows in the age of IoT-connected products. In traditional PLM system workflows, once a product design leaves the engineering department -- whether for a fuel pump or a consumer appliance -- the design team has little information or insight into how that product actually gets used. With IoT, the design team gets direct feedback on how a product is used and can create a closed loop workflow that uses intelligence in future design iterations. For example, it can identify rarely used features so they can be removed or identify a particular part with excessive breakage for a supplier change.
"This extends the reach of PLM to be a governance mechanism for lessons learned by the product throughout the cycle," Barkai said. "It allows you bring a continuous understanding of the product into the conversation."