grandeduc - Fotolia
The integration of IoT and PLM holds promise to extend those systems beyond their traditional engineering base. Organizations will need to come up to speed on some emerging technologies to make these work, but the real heavy lifting involves organizational and cultural changes to help drive new closed-loop processes that engage a broader audience of users.
The ability to capture real-time (or near-real-time) data from products in the field opens up myriad new use cases for product lifecyle management (PLM) systems beyond the traditional engineering-centered workflows of orchestrating change orders and managing a bill of materials. For example, the PLM system can be extended to enable field workers to perform predictive maintenance or funnel in-field performance feedback to engineers to guide future development. But in order to make this work, organizations will need to both create new processes and also foster cultural change to get everyone on board.
"It's really a cultural effort," according to Alan Griffiths, senior industry analyst specializing in IoT digital transformation at Cambashi, an IT market research firm. "It requires a major cross-departmental, cross-company initiative to make sure data is getting back to the right authority so they can use it to make improvements."
IoT and PLM can benefit from closed-loop workflows
Steve ChalgrenArena Solutions
One of the first steps is to identify a specific use case that can capitalize on the data streams that IoT enables and then isolate the specific data points that are essential for driving that particular application.
PLM systems that embrace an open architecture with state-of-the-art APIs are critical for ensuring the flow of data between IoT and PLM with other core enterprise systems, such as field service or quality management. With such a focus on open standards and integration, organizations can create a foundation on which to build closed-loop workflows. For example, IoT sensors can feed critical product-related data, like 3D models or supplier history, into a service tool so an on-site technician is armed with the proper materials and specific set of tools to fix the problem in short order.
"You can't lock in to a vertical, top-to-bottom, closed PLM system," according to Steve Chalgren, executive vice president of product management and chief strategy officer for Arena Solutions. "One database isn't going to have all the relevant IoT data. It's coming from multiple places, and you need a way to integrate data from all sources. You need an open system in order to do that."
Significant technical hurdles, but cultural change may be harder
In order to begin breaking down silos and integrating data from key systems, organizations should create digital twins -- virtual models of products that include the physical characteristics across mechanical, electrical, software and other domains, but also the behavior of the product. Using data gathered from IoT-sensored products in the field, companies can use the digital twin to gain a deeper understanding of how the product would operate under real-world conditions. This paves the way to new applications in areas like predictive maintenance, as well as for optimizing new designs and testing potential product improvements.
There are significant technical hurdles to building a digital twin, but there are also cultural barriers to getting different functional stakeholders to break down their existing silo mentality and be willing to collaborate and value each other's data.
"It's a tough thing for an engineer to recognize how a poorly designed product actually performs," said Chuck Cimalore, president of Omnify, a PLM systems provider. "Now, imagine getting this alignment internally. You have to make quality become part of the design process, and to do that, [it] requires buy-in on getting more direct feedback from customers and products in the field."
While such lifecycle collaboration was an original promise of PLM systems, there's going to have to be a lot of change management to encourage multidisciplinary decision-making.
"It takes a lot of work and lot of cultural shift," according to Joe Barkai, independent consultant and author specializing in IoT and PLM. "It's going to take some time."