The future of PLM: creating the “Things” for the Internet of Things

Posted on 21 Jun 2017 by The Manufacturer

Consumers, analysts, and product development companies agree: the Internet of Things is not only the future of the products that will shape our world – in many ways, it’s already here.

Dave Grammer, ‎Vice President Sales Northern Europe at PTC, looks at the future of PLM.

IoT technology is already changing how companies develop products, get information back from products, and improve future products. But although they’re known as “smart” connected products, the things on the Internet of Things cannot do this by themselves. Product lifecycle management (PLM) technology will be instrumental in developing, managing, deploying and leveraging these products: although it must evolve to meet this challenge.

Fulfilling the promise of PLM

Dave Grammer, ‎Vice President Sales Northern Europe at PTC
Dave Grammer, ‎Vice President Sales Northern Europe at PTC

With the advent of popular new technologies like IoT, PLM has only grown in importance. This is because, to be equipped for IoT, products require intricate systems of software, electrical, and mechanical components. PLM will help companies design products for the IoT in the same ways it always has: by enabling the systematic processes, change control, and workflows required to effectively collaborate across increasingly diverse and cross-functional teams – including ECAD design, systems engineering, and software development teams to meet the specific needs of IoT-enabled products.

As the single source of truth for product data, configurations, and processes, PLM provides each team with close control over the development of highly complex and multi-disciplined product data, while apprising collaborators of changes from other teams that will impact their work. It houses a fully accurate and up-to-date record of the product as it was designed and planned – which will include all of its software, electrical, and mechanical systems.

But what happens today to information about the product after it launches? Although its name would imply that PLM manages data from the entire product lifecycle, it does not in fact manage much if any product data from the operational phase: the longest phase and arguably the most valuable to product development and service. Information about real-world usage, operating conditions, performance, and quality remains largely inaccessible to product development teams and processes – creating an open loop with very little systematic use of operational data to improve the work they do.

If PLM could close this loop by leveraging IoT data captured in real time during the operation of the physical products it creates, the entire product lifecycle would reap the benefits. Product planning and design could leverage value analysis to improve features that customers use most, configure offerings to usage patterns, and redesign parts or systems to save on costs while meeting design requirements. Engineers could be more quickly apprised of quality escapes through the automatic capture, communication, and analysis of sensor data as soon as a product failure occurs.

More complete root cause analysis data would enable faster and more accurate corrective and preventive actions, and a standard set of measurable data related to every product failure would drive an unprecedented ability to trend, understand, and avoid conditions that lead to quality issues. This would benefit service teams, as well, equipping them to monitor fielded products for these trends, initiate proactive measures, and prevent product failures before they occur: improving the user experience while streamlining service delivery.

Want to hear more about how leading companies have implemented and utilise Cloud PLM? Join us for a webinar titled: Flexible Deployment Options for PLM, which will take place on Wed 4 July at 2pm BST (GMT +1).

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Realising the Potential of IoT

So what is keeping more product development companies from tapping into this valuable IoT data? The problem isn’t sensors: they are growing cheaper and more ubiquitous every day. And it isn’t data storage: hard disk space increases at a stunning pace even as it too plummets in price. In fact, these two factors together mean that product development companies can literally collect more data than they can use. And, without the means to translate this data into actionable intelligence, they frequently do.

Industry 4.0 concept, smart factory with icon flow automation and data exchange in manufacturing technologies.Uncovering meaning from a sea of operational data is a process of identifying variability from expected values, targeting and analyzing these outliers, and communicating them to the stakeholders who can investigate why they occurred and take action. But knowing what constitutes an “expected” value is a complex problem because product development itself is so complex. And PLM is in the business of managing these complexities.

While PLM manages variability within a product, as a range of teams from across the enterprise introduce highly governed changes to see a product through to its completion, it also manages variability across products: governing the different configurations of a product that will be launched to market. PLM creates and manages the final, unique baseline values defining how each configuration was designed and built to perform: including expected operating conditions, user behavior, performance results, quality predictions, and service planning. And knowing a product’s expected values is the first step in identifying and analyzing outliers: making this information vital to IoT.

But PLM does more. Each final configuration managed in PLM houses a range of associated product development data – part information, documentation, software versions, and powerful CAD visualizations – that, if tapped, can be leveraged to inform and enhance IoT technologies and apps themselves. Leveraging IoT to form a lasting connection between a product’s complete digital definition in PLM and its physical counterpart or “twin” operating in the field would enable PLM to serve up this valuable product data to IoT technologies and apps.

This connection would communicate information about predicted product performance – including expected quality, reliability, and usage KPIs for each product configuration – to assist in the analysis of IoT data. And, it would drive valuable information from product development – including visualizations, service plans, predicted failure rates, software configurations, and more – into IoT technologies and apps, to enrich their functionality.

Connecting the physical and virtual instantiations of a product is just as vital to communicating IoT data back into PLM. PLM already controls the flow of product data throughout an organization. Extending its reach into the operational phase of the product’s lifecycle is a matter of associating operational data about a physical product with the digital product definition that drove its development. This will leverage the strengths of PLM to deliver the essential intelligence from IoT analyses, comparing actual and expected product behavior, to the development stakeholders who need it to improve the work they do: in context with the PLM technology they already use as the single source of truth for the products and services they develop.

Re-imagining PLM for IoT

Leveraging the strengths of PLM – its ability to govern configurable product logic, to house the single source of truth for predicted product KPIs (performance, quality, operating conditions, and usage) essential to IoT data analysis, to manage configuration-controlled product data including visualizations, and to drive collaboration across multiple enterprise teams who can also benefit from access to operational data – is key to realizing the potential of IoT.

But PLM as it exists today cannot meet this need alone. To fulfill its promise to manage the entire lifecycle of a product, PLM must be equipped with new capabilities and integrated with new technologies that will help it better develop, manage, and deliver the things for the Internet of Things. To better create these products, existing PLM technologies must be equipped to bring ECAD, systems engineering, and software development data and processes into its purview. And to better manage smart, connected products throughout their complete lifecycle, including operation, PLM technology must be closely integrated with the IoT platform technologies and big data analytics that connect to and process data returning from sensored products.

Finally, to leverage data coming back from IoT, PLM needs new capabilities, including both connectivity and scale, to consume sensor data and resulting analyses and interpretations in order to connect that data with the product development stakeholders who need it to drive improvements across the product lifecycle – completing a closed loop approach.

The importance of PLM to IoT cannot be understated. While the Internet of Things will fundamentally change the way companies develop products, its burgeoning new technologies will need to be well-integrated with PLM in order to succeed. This will bring new challenges – and new opportunities for growth – to the people, processes, and technologies instrumental in building the products that shape our world.

Want to hear more about how leading companies have implemented and utilise Cloud PLM? Join us for a webinar titled: Flexible Deployment Options for PLM, which will take place on Wed 4 July at 2pm BST (GMT +1).