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9 AI-Powered PLM Software Solutions for Manufacturers in 2026

What is AI Powered PLM?

AI-powered PLM refers to Product Lifecycle Management systems enhanced with artificial intelligence to improve how manufacturers manage, analyze and act on product data across the lifecycle. Traditional PLM systems are systems of record. They store CAD files, manage engineering change orders, track part structures and maintain BOM integrity. AI-powered PLM systems go further. They transform structured product data into decision intelligence.

In practice, AI in PLM can mean:

  • Automatically classifying and cleansing part data
  • Predicting the impact of engineering changes
  • Optimizing simulation models
  • Mapping multi-tier suppliers
  • Filling gaps in material or process data
  • Enriching BOMs with cost, risk, carbon or compliance signals
  • Enabling real time trade off analysis across engineering and procurement

For enterprise manufacturers managing thousands of components across global supply chains, AI-powered PLM becomes less about automation and more about infrastructure. It connects engineering, procurement, compliance and sustainability inside the digital thread.

However, not all AI in PLM is equal.

Some vendors embed AI directly into engineering workflows. Some apply AI primarily to simulation and digital twins. Some use AI to harmonize enterprise data across ERP and PLM. Others focus on sustainability intelligence and supplier risk modeling. For global enterprise manufacturers above operating complex, configurable BOMs, the critical question is not whether AI exists inside the platform.

The critical question is: Does the AI operate at BOM level and influence real product decisions across engineering, sourcing and compliance?

Below are nine AI-powered PLM software solutions shaping enterprise manufacturing in 2026.

1. Makersite

Makersite is a granular, AI-powered Product Lifecycle Intelligence platform purpose built for complex manufacturing sectors, with a strong presence in electronics, automotive, industrial machinery, construction, chemicals and industrial goods.

Makersite tackles the core issue of enterprise PLM environments: structured product data exists, but cross functional intelligence does not. BOMs sit in PLM. Supplier data sits in ERP. Environmental data lives in separate tools. Critical decisions are made without a unified intelligence layer. Rather than replacing PLM systems, Makersite connects to them and enriches structured product data using deeply specialized AI.

How AI is used:

  • Context rich gap filling: Dedicated industry trained AI agents infer missing supplier, material and process data by analyzing BOM structure, manufacturing context and sourcing patterns across multi tier supply chains.
  • Automated background database matching: AI automatically maps BOM inputs to environmental datasets, risk databases and compliance indicators, reducing manual mapping effort dramatically.
  • What if scenario modeling: AI enables real time trade off analysis across carbon, cost, supplier risk and regulatory exposure at configuration level.
  • Multi tier supplier mapping: AI reconciles inconsistent supplier naming and identifies relationships across complex global networks.

Differentiator:

Makersite’s differentiator is its combination of a large structured manufacturing data foundation with highly specialized AI agents trained on industrial context. Its AI understands manufacturing logic, making it highly accurate for complex, configurable BOMs. Best for enterprise manufacturers managing complex BOMs who need accurate environmental, cost and compliance modeling integrated into engineering workflows.

2. Siemens Teamcenter with AI Capabilities

Siemens Teamcenter is a leading enterprise PLM system with embedded AI-focused on engineering optimization and digital twin enablement. Teamcenter addresses the need for structured product data governance at global scale. Its AI capabilities enhance internal engineering processes rather than external supplier intelligence.

How AI is used:

  • Intelligent part classification to reduce manual categorization
  • Change management automation through predictive impact analysis
  • Digital twin optimization using simulation driven AI
  • Knowledge reuse across engineering programs

Differentiator:

Teamcenter’s differentiator is the depth of AI embedded directly inside core engineering workflows and digital twin environments. The AI operates within the system of record rather than as an external layer. Best for large global manufacturers with mature PLM environments focused on engineering performance and simulation optimization.

3. PTC Windchill

PTC Windchill combines PLM with IoT data through its broader ecosystem, using AI to enhance lifecycle visibility and configuration management. Windchill addresses the need to connect product data with real world performance signals.

How AI is used:

  • Predictive analytics on product performance
  • Configuration optimization across variants
  • Closed loop lifecycle insights from connected product data
  • Automated impact analysis across engineering changes

Differentiator:

Windchill’s differentiator is its integration of PLM with IoT and service data, allowing AI to inform decisions using real world performance feedback. Best for industrial machinery and heavy equipment manufacturers managing connected assets and configurable products.

4. Dassault Systèmes 3DEXPERIENCE

Dassault’s 3DEXPERIENCE platform embeds AI primarily within simulation and advanced modeling workflows. The platform addresses the need for design optimization and performance simulation in highly engineered environments.

How AI is used:

  • Simulation driven optimization of materials and structures
  • Predictive modeling of performance scenarios
  • AI-assisted design exploration
  • Digital twin refinement

Differentiator:

Dassault’s differentiator lies in simulation depth. AI enhances computational modeling rather than multi tier supplier intelligence. Best for aerospace and automotive manufacturers with heavy reliance on simulation and advanced materials engineering.

5. SAP PLM with AI

SAP integrates PLM functionality into its ERP backbone, using AI for data harmonization and predictive enterprise analytics. SAP addresses enterprise wide data consistency and financial integration.

How AI is used:

  • Master data harmonization across systems
  • Predictive supply chain insights
  • Demand forecasting and risk identification
  • Intelligent workflow automation

Differentiator:

SAP’s differentiator is enterprise integration. AI connects lifecycle data with financial and procurement systems at scale. Best for global enterprises prioritizing unified ERP and lifecycle data governance.

6. Aras Innovator

Aras Innovator is a flexible PLM platform that supports AI extensions through configurable architecture. Aras addresses manufacturers that require adaptable lifecycle workflows across diverse product portfolios.

How AI is used:

  • Custom analytics and reporting extensions
  • AI powered document search and knowledge retrieval
  • Configurable workflow automation

Differentiator:

Aras differentiates through architectural flexibility. AI capabilities are shaped by implementation rather than delivered as fixed modules. Best for manufacturers seeking customizable PLM infrastructure with tailored AI workflows.

7. Oracle Agile PLM

Oracle Agile remains strong in compliance driven PLM environments, particularly in electronics and high tech sectors. Agile addresses structured documentation, regulatory management and controlled product record environments.

How AI is used:

  • Automated classification and search
  • Compliance analytics through Oracle Cloud services
  • Risk monitoring across supplier documentation

Differentiator:

Oracle Agile differentiates through compliance centric PLM strength, with AI augmenting documentation and regulatory tracking. Best for electronics manufacturers managing strict compliance and documentation requirements.

8. Propel PLM

Propel is a cloud native PLM built on Salesforce infrastructure, targeting modern manufacturing companies. Propel addresses collaboration and lifecycle visibility in cloud first environments.

How AI is used:

  • CRM integrated product insights
  • Workflow automation through Salesforce AI
  • Analytics across customer and product lifecycle data

Differentiator:

Propel differentiates through tight CRM and PLM integration, bringing AI insights across customer and product domains. Best for growth oriented manufacturers aligning product management with customer intelligence.

9. Sustainability Platforms Adjacent to PLM

Platforms such as Sphera focus on compliance databases and environmental risk monitoring that operate adjacent to PLM systems. These platforms address regulatory intelligence rather than engineering integrated intelligence.

How AI is used:

  • Automated regulatory tracking
  • Risk signal monitoring
  • Data normalization for reporting

Differentiator:

These platforms differentiate through regulatory database breadth and compliance depth rather than embedded product level intelligence. Best compliance focused sustainability programs that operate parallel to engineering workflows.

 

When evaluating AI-powered PLM Software Solutions, enterprise manufacturers should ask these questions

1. Is the platform a system of record or an intelligence layer?

Some platforms replace or serve as core PLM systems. Others operate as AI intelligence layers that integrate with existing PLM and ERP environments.

If your organization already runs Siemens Teamcenter, PTC Windchill or Dassault, replacing PLM may not be realistic. In that case, an AI enrichment layer may be more strategic.

Clarify whether you are modernizing infrastructure or augmenting it.

2. Does AI operate at BOM level depth?

High level dashboards are not enough for complex manufacturing.

Ask:

• Can the platform ingest multi level BOMs?
• Can it analyze configuration variants?
• Does AI enrich individual line items?
• Can it model trade offs at component level?

For manufacturers managing thousands of components per product, BOM level intelligence is critical.

This shifts sustainability from retrospective reporting to proactive design decision support.

3. How does the platform handle missing supplier or material data?

Incomplete data is the norm, not the exception.

Evaluate:

• Does the system rely solely on declared supplier data?
• Does it use context aware AI to infer missing attributes?
• Are modeling assumptions transparent and traceable?
• Can estimated values be replaced with primary data later?

The ability to manage uncertainty intelligently often determines scalability.

4. How well does it integrate with existing enterprise systems?

AI-powered PLM should not create new silos.

Assess:

• API depth with PLM and ERP systems
• Compatibility with supplier portals
• Ability to export structured outputs for reporting
• Security and data governance controls

Enterprise adoption depends on seamless integration into current workflows.

5. Does it support cross functional decision making?

PLM historically served engineering.

Modern AI-powered PLM must also serve:

• Procurement teams evaluating supplier risk
• Sustainability teams modeling Scope 3 impact
• Compliance teams tracking regulatory exposure
• Finance teams analyzing cost exposure

Ask whether the platform enables concurrent evaluation of carbon, cost and compliance trade offs.

6. Can it scale across global, multi-tier supply chains?

Enterprise manufacturers operate across regions, currencies and regulatory regimes.

Evaluate:

• Multi tier supplier mapping capabilities
• Localization for regulatory frameworks
• Ability to support digital product passport requirements
• Performance at enterprise data volumes

Scalability is not just about user count. It is about data complexity.

7. Does it influence decisions before design freeze?

Many tools accelerate reporting. Fewer influence product design.

The most strategic AI-powered PLM solutions:

• Integrate directly into early design workflows
• Enable what if scenario modeling
• Provide insights during sourcing decisions
• Support engineering trade off analysis in real time

If intelligence only appears after the product is finalized, the strategic value is limited.

Final Thought: The Future of PLM Is Decision Intelligence

PLM modernization is no longer a technology upgrade.

It is a strategic shift in how manufacturers make product decisions.

As supply chains become more complex and regulatory expectations intensify, intelligence cannot remain siloed in reporting tools or disconnected systems. AI-powered PLM must operate inside the digital thread, linking engineering structure with supplier visibility, cost dynamics and sustainability impact in real time.

The competitive advantage will not come from managing more product data.

It will come from transforming product data into actionable intelligence at the exact moment decisions are made.

Still Have Questions? Let’s Dig Deeper

What makes PLM software “AI-powered” versus traditional PLM systems?

Traditional PLM systems act as systems of record. They manage CAD files, BOM structures, engineering change orders and product documentation. Intelligence typically comes from human analysis layered on top of structured data.

AI-powered PLM introduces machine learning, semantic mapping and predictive modeling directly into the lifecycle workflow. Instead of simply storing product data, AI enriched systems classify components automatically, infer missing attributes, predict the impact of engineering changes, map suppliers across inconsistent naming structures and generate scenario based insights in real time.

The key difference is that AI-powered PLM transforms product data into decision intelligence rather than static documentation.

How does AI in PLM handle incomplete or inconsistent BOM data?

Incomplete BOM data is one of the biggest constraints in enterprise manufacturing. Supplier declarations may be missing. Material compositions may be partially defined. Multi tier sourcing data is rarely transparent.

AI-powered PLM platforms address this through context aware modeling. Instead of relying solely on declared attributes, AI analyzes the component’s category, application, manufacturing context and known supplier patterns to infer likely material compositions or process assumptions.

More advanced platforms also reconcile duplicate supplier records, normalize inconsistent naming conventions and map parts to standardized datasets automatically. This reduces manual cleansing and accelerates time to insight without compromising engineering governance.

Can AI powered PLM replace sustainability or compliance tools?

In most enterprise architectures, AI-powered PLM does not replace sustainability or compliance platforms. It complements them.

PLM remains the system of record for structured product data. Sustainability tools manage regulatory reporting frameworks. Compliance systems track substance declarations and documentation.

AI-powered PLM acts as a connective layer. It enriches product data with environmental, cost and risk intelligence before reporting begins. Instead of exporting static BOMs to downstream tools, manufacturers can integrate intelligence upstream in the product development lifecycle.

This shifts sustainability from retrospective reporting to proactive design decision support.

How accurate are AI generated environmental or supplier estimates?

Accuracy depends heavily on the platform’s underlying data foundation and modeling methodology.

Some tools rely primarily on spend based emissions or generalized industry averages. Others use contextual AI trained on manufacturing datasets to infer missing attributes more precisely.

For exploratory portfolio level analysis, estimated modeling may be sufficient. For regulatory reporting, digital product passports or configuration level carbon footprints, manufacturers typically require platforms grounded in verified engineering logic and structured lifecycle datasets.

AI should enhance data quality, not obscure it.

When should AI-powered PLM be used in the product development lifecycle?

Historically, lifecycle analysis and risk assessments were conducted after product design was largely finalized. This limited the ability to influence outcomes.

AI-powered PLM shifts intelligence earlier into R and D and sourcing workflows. Because AI can instantly evaluate alternative materials, suppliers or configurations, engineering and procurement teams can compare carbon, cost and compliance trade offs before tooling or production begins.

The greatest value of AI in PLM is realized when intelligence informs decisions before design freeze, not after product launch.

Is AI-powered PLM relevant for companies with mature PLM systems?

Yes. In fact, mature PLM environments benefit the most.

Enterprise manufacturers using systems such as Teamcenter, Windchill or 3DEXPERIENCE already have structured product data. What is often missing is cross functional intelligence layered across cost, supplier risk and sustainability dimensions.

AI-powered PLM does not replace core engineering systems. It supplements them by enriching structured data and connecting it to broader enterprise objectives.

For organizations with global supply chains, AI becomes an infrastructure enhancement rather than a system replacement.

How to secure a sustainable future: In conversation with PTC’s James Norman and Dave Duncan

“The trend of AI has been unavoidable, and seeing the proliferation of AI being used so effectively to help solve really hard problems for the good of society and the planet has been pretty eye opening.”

Recently, Makersite sat down for a wide-ranging conversation with James Norman and Dave Duncan from PTC. James is Director at their Global PLM Center of Excellence, and Dave is Vice President of Sustainability.

Makersite and PTC have a well-established relationship, but they’re also one of the most advanced and proactive organizations in the world when it comes to following through on sustainability best practices. Indeed, as their website states, “We don’t just imagine a more sustainable world—we help create it.”

Both Dave and James have fascinating backgrounds, and both took a slightly circuitous path into their sustainability careers. However, the learnings they were able to take from other positions in other industries – and in different cultures entirely – shaped the way they approach sustainability today.

Among many other topics, we discuss:

  • The proliferation of AI and its impact on how businesses can approach sustainable practices
  • Why accurate measurements – and accurate reporting – are so important
  • The through line between PLM and sutainability
  • Where the world should be by 2050

Makersite: What does sustainability mean to you?

James Norman: Most often I come back to what it means to me on a personal level – having a strong desire to do what I can to leave a world for my kids that is at least as good – if not better – ecologically, socially, and economically than the world I get to live.

Dave Duncan: Mine’s similar. It’s also pretty textbook, which is to make sure that we’re able to meet our needs without sacrificing the needs of future generations. That’s the aspiration. And it’s not just for humans, but it’s for anything that lives on the planet as well.

When it comes to ESG, the ‘S’ and the ‘G’ is just as important as the ‘E’ because even if we can have the best technology ever, if the world is angry and has strife, one, it won’t be implemented fast enough, and two, it’ll be continuously destroyed.

Makersite: When we’re talking about ESG, do you feel that from the ‘S’ and the ‘G’ perspective businesses and executives are catching up, or do you still think there’s a long way to go there before there’s kind of an equal playing all elements of ESG?

Dave: I think the ‘S’ and the ‘G’ can have more regional differences. It’s more complex from a regional perspective. And it needs to have regional flavors to deal with different cultures and priorities and politics, because, particularly ‘S’, if it’s taken too far or too fast in a given culture, then it can have a damaging backlash. It has to be balanced so that it has the effect of being seen as good progress and fair for the citizens, which can have different definitions in each area.

PTC Interview Dave Duncan

Makersite: In terms of the skills that you’ve acquired of your careers, what do you think helps you most in your roles?

James: For me, the one that comes to mind first is the ability to apply a systems thinking approach to problem solving and innovation. That is without a doubt one of the most useful things I got out of my academic training as an ecologist.

As everything is this space is constantly evolving, the ability to deal with ambiguity and be adaptable has been really beneficial as well.  And because “sustainability” can mean different things depending on who you ask, having some interpersonal and political savvy helps a lot when trying to align stakeholders with disparate points of view around a common set of goals and actions around sustainability.

Dave: For me, my role at PTC focuses on industrial sustainability. I think what’s helped me – and I never realized this would be such a big help – but it’s all the dirty jobs I had growing up. I drove a loader up in Alaska on an oil field. I was in the military where like many junior officers, they make you the battalion maintenance officer for one of your first platoon assignments. At the time, it was like the last job that you want. You’re stuck in motor pools fixing things.  But it’s an important job, and gave me a lot of hands-on intuition for my current role.

I worked on a factory assembly line for a few summers, ran a service outfit – both call center and field service – when I got out of the military. A lot of the hands on work is where the footprint’s emitted.

James and I, now we’re in industry-supporting positions where we’re suppliers of software to manufacturers who sell things to customers, and those customers have people that work for them and actually use these tools and operate them, service them and throw them away.

We might be several steps removed right now, but having some of that frontline experience in my prior roles helps with intuition about what can be effective, what sort of risks might arise and so on.

It’s important for us to still do ride alongs and do things and just have curiosity in our normal lives where we might say ‘let’s go try to repair something’ or whatever it might be. We have to continuously get down on that ground level if we’re going to design things that will make a difference.

Makersite: What motivates you to work for in your chosen field? I feel like we’ve covered most of that already, but is there anything else you’d like to add?

Dave: Just the amount of footprint that’s created from discrete manufacturing is probably low double digits contribution overall. And it’s a fairly consolidated market of vendors that drive the design of those products.

The effect that we can have as one of those consolidated providers on the vast amount of footprint causing machinery in the world is motivating.

James: We’ll talk about PLM a little bit later. But really a big focus for PTC is the concept of the Digital Thread, which at its core is really a systems thinking approach to product lifecycle management. A lot of product lifecycle management historically through today is silo by function starting in the factory and stopping at the gate.

The ability to evolve perspectives on product lifecycle management toward accounting for the entire lifecycle of a product to affect meaningful change across design, manufacturing, consumption/use, service, reuse/remanufacturing, and ultimately end-of-life is enabled by the tools PTC develops.  I don’t necessarily touch the topic of sustainability directly each day, but it’s always connected to the work we do in one form or another and that’s pretty motivating.

Dave: An example of that is from the Ellen MacArthur foundation where, for circularity, you want to have modules in your products where you can take off bigger components of an end-of-life part or product, and repair it, refurbish it, reuse it, remanufacture it, and only then, if you can’t do any of those four or five things, would you shred it or melt it down to recycle. The only way you can do that is with modular design.

And there’s other reasons to do modular design as well, because you want to have product variation for your customers needs. But with modularity, you make factory and service workers jobs much more complex. Rarely do they see the same configuration twice, even on an assembly line.

And with the digital thread and the systems thinking and capabilities that we have, when you design a modular product, you have that logic so that you don’t have to burden those frontline workers with the complexity of the module design. A factory worker can receive instructions that are specific to the product that they’re looking at. So can a service technician. And with that holistic systems approach that makes circularity possible, we have a unique span across engineering, manufacturing and service.

Makersite: Talk me through your career paths. How did you come to work in a sustainability role? Is it something you’ve always pursued?

James: I’m an ecosystem ecologist by training, and I spent my early career applying that training in various scientific advisory and policy making roles, both in academia and the nonprofit world, and then as a legislative fellow for a brief stint in the United States Congress.

I’d always envisioned staying on that applied science/policy path for my professional career. But a series of serendipitous events, in particular the Great Recession, pushed me off that path. I ended up landing in an early-stage start-up called Planet Metrics, the first product carbon accounting software founded in 2007 during Cleantech 1.0. I was the second employee there and we had similar aspirations to what Makersite is doing now.  It’s funny that Neil was across the pond working on a competing product that was quite similar around the same time, long before Makersite.

I knew nothing about software, but I had studied some industrial ecology as part of my academic training which, along with my ecosystem ecology background, formed the scientific underpinnings of Planet Metrics.

Helping build Planet Metrics put me on the path to working at PTC, as we were acquired by PTC in 2010 and I’ve been here ever since. While I couldn’t have predicted I have a career in software starting out as an ecologist, I’m happy I landed where I did.

Makersite: How about you, Dave?

Dave: Mine is from a while ago. I was a kid growing up between Boston and New York.  The 70’s cars were kinda cool, but I hated the pollution. There were very few emission standards and the cities I was in just smelled really bad. And there were some early electric cars and solar panels, and even when I was a youngster, I thought to myself: ‘wow, why aren’t we doing more of this?’

Then it really hit home when I was in the military, based in Germany and Bosnia. A lot of the units that I was based with in Germany had to cycle into the Middle East, largely for oil security. This was before some of the larger conflicts there. But our unit across the street, where I have a lot of friends, were across from the Khobar barracks that got blown up in Saudi Arabia in the mid-nineties. It just really hit home. I thought: ‘they really are putting our lives at risk for oil and we use a lot more oil than we need to.’

I left the US in 1995. I came back in 1998. In that period of time, a few good things happened, like microbreweries, but a few bad things happened, like gas prices went down and SUVs got bigger. It upset me to the core, just watching people fill up monstrous SUVs who I don’t think really understood the blood that was shed to have oil security and just how much of a waste that was.

So that was how I got into it long before I knew about global warming. And that just added emphasis to it as far as how I got in the role at PTC. When they started the sustainability program, they were initially looking for a sustainability lead outside of PTC. And the hiring group came to the realization that it would be a lot easier to teach sustainability to someone that knows our PLM and SLM manufacturing digital markets.

Essentially, I just got lucky and I got to pursue a passion and get deeper into the field.

Makersite: How do you make your own lives more sustainable?

James: As a family, we really orient towards baking sustainability into every aspect of our lives as much as we can. I chose to pursue ecosystem ecology out of a personal desire to have a positive effect on the environment that I enjoyed in my youth, and that desire is something my whole family shares now. It informs all the choices we make about our daily actions – the cleaning products we buy or make (vinegar and lemon go a long way), how we conserve water and energy, the food we eat and grow in our garden, being scrupulous about the kinds of material goods we consume, minimizing household waste by repairing and reusing what we can and trying to recycle / donate / compost what we can’t, the car we drive or abstain from driving in favor of walking and biking.

With young kids, seeing how they are such sponges for life really keeps us motivated at every corner try to find other ways that we can be more conscious about making our lives and theirs more sustainable.

Dave: I would say as far as things that I’ve done – just coming from my initial passion of not liking oil and gas from my army experience – is electrifying everything I can. And renewing that electricity in my personal life. I Insulated the house as soon as we got our house.  Since then, anytime I have an opportunity to not throw something away and to give it to somebody else so that we can all  get the most out of it.

Being an early adopter of electronic tech is a big one for me. We got solar panels probably ten years ago when they were still a lot more expensive and less productive than they are now. I got one of the first Tesla Model 3’s off the line, which has still been a fantastic car, but it’s nowhere near as nice as the newer ones.

Even before that, I was the guy who would clear snow my driveway with a shovel. I raked my lawn with a rake I was the first with an electric lawn mower. I think the one internal combustion engine I had to buy in the last five years was snowblower. And that was just because I had some open chest surgery and I couldn’t shovel.

Always, top of mind for me is how do I get rid of a gas engine and how do I use renewable electrons as one of the priorities. I eat very little red meat now. I’m not full vegetarian. I still do chicken and fish, so I’d be a moderate in that respect. My children are into it as well. My son’s 21, my daughter is 14. And I’ve seen that generally with the youth, they’re very appreciative and proud of any green things that they do.

Download Makersite and Forrester’s new study for more detail on PLM and PLI.

Makersite: What about something new you’ve learned in the last year?

James: The trend of AI has been unavoidable, and seeing the proliferation of AI being used so effectively to help solve really hard problems for the good of society and the planet has been pretty eye opening.  It’s a topic in an area that I just hadn’t really paid a lot of attention to until about a year or two ago, particularly when Makersite first came across my radar. Seeing the ways that AI has been used for good – a lot of the research into protein folding, for example – has been inspiring and has me excited for what the future of AI holds for the field of sustainability.

Dave: I think the most promising thing that I’ve seen is the proliferation of Scope 3, Category 1 measurement.  At PTC we’re always talking with companies, asking them ‘what are your priorities?’

Starting about two years ago, it went from zero to 100 miles an hour where sustainability was top one, top two or top three for everybody at the same time. And I couldn’t figure it out when I learned greenhouse gas accounting and I learned more about the different levels of emissions with each category. Scope 3, Category 1, for PTC, is over 50% of our emissions.

We’re a software company. We don’t even have physical goods or a manufacturer. For most manufacturers, over 90% from what we’ve seen, they have the downstream too, but that’s number two in their reduction commitments. Now they’re all calling their suppliers and asking them about their emissions: ‘are they bringing them down? Because we’re going to be a lot less friendly of a customer if you don’t. Everybody is getting those calls from all of their customers now.

And I think that is a good thing because now  every manufacturer considers this a top line revenue priority, not just a nice thing to do. It’s because of the accounting and the disclosures and reduction commitments that need to be made on it.

Makersite: Where do you think companies are lacking still in relation to that? In their approaches to sustainability?

Dave: It’s been a top discussion in our executive rooms with customers for 24 months. It has not proliferated down to the levels of our software users fast enough. Generally the attitude is: ‘we’ve heard of sustainability, but we don’t really have marching orders on it yet.’ I think CSRD going to do a lot to drive that faster from a global perspective.

James: Ultimately you can’t impact or manage what you don’t measure. And there is nowhere near enough measurement today. It’s great that we’re starting to see more of the greenhouse gas protocol measurements starting to happen. And while carbon is a relatively good proxy for other environmental impacts, and it’s not wholly sufficient to drive the sustainable change the present moment and our future requires.

To improve their approaches to sustainability, I think companies should orient around how they will provide value to their customers in an increasingly constrained world. Companies need to be asking themselves ‘if we want to be the same company or a better company than we are now in 20 years, what do we need to do now to get things in order to drive true sustainability?’ I think that’s where I feel like it’s still lacking, and it rings a little hollow in some of the conversations I’ve been having the last couple years that focus primarily on regulations and narrowly defined shifts in consumer preferences. Not taking that more holistic and strategic view misses the opportunity for business and society to realize what sustainability can and should mean.

Dave: I think a lot of the work that Makersite is doing – like automating some of the calculations that LCAs were never able to do at scale manually and making that data available to buyers in a way where they can compare suppliers – that’s going to move things forward a lot.

Because today when people make decisions based off global average data or qualitative data, then there isn’t as much of an urgent top-line incentive for corporations to do things. But when we get to a place where most buyers can get reasonably good footprint comparison data on their supply decisions, then I think things will move at a much faster pace.

All businesses know where they are in cost leadership today. They have the competitive intelligence to understand what all their competitors are doing on price and cost and when they get there with footprint, that’ll be a wonderful time. I don’t think it’s that far away with a lot of things that Makersite is doing.

PTC Interview-1

Makersite: In terms of legislation and regulation, is there anything we don’t have yet that you would wish to see?

James: Yes, I think I’d like to see more incentives. More carrots and perhaps less sticks. And I know that sometimes sticks are the path of least resistance and generally perceived to move the needle faster. But an interesting counter to that can be seen in the Inflation Reduction Act that passed in the US Congress almost two years ago.

When you look at what that’s done for creating a fully-fledged EV supply chain in places in the United States that had been resistant in less than 18 months, it’s incredible and it was all done through incentives, not penalties. If the legislation had been driven by penalties alone, this and many other notable projects that have broken ground since probably never would have happened.

There are still inefficiencies and it’s not a perfect piece of legislation (if such a thing exists), but it is a good example that shows if you design incentives in the right way, they can be very motivating and effective in driving change.

Dave: I think incentive based is a great way to get out of first gear into say, because then you get a critical mass of support and infrastructure moving. And once you have scaling at 1% to 2% or a superior approach, it’s going to move. But incentive-based does cost money. It’s less efficient and that won’t be lost on people and politicians will scream from the rooftops about it at some point.

Really the only efficient way to do it is with a carbon tax. But unfortunately, it’s called ‘carbon tax’.

Maybe it could be called carbon price, or somehow communicated in a way where it’s not about tax but about redistribution or properly priced pollution. That’s the only way to really generate the capital allocations that would be most efficient as well as the motivations, and would finally harmonize greenhouse gas accounting with financial accounting.

Makersite: Where do you think we’ll be in 2050, when it comes to sustainability and how we approach it? And where do you hope it will be?

Dave: My best guess and my hope is that we’ll be at Net Zero because we had some technology breakthroughs that helped us get there, in particular things like mechanical carbon capture or hydrogen.

I think the reality is we’ll be close, but unfortunately it will get close because there’ll be some really bad things that will happen and it will happen to rich people that finally move the ball. Like how much wealthy real estate is located in environmentally sensitive areas where insurers no longer provide coverage. Like Manhattan doesn’t have to have a very high sea level rise for there to be an impact.

I think the trajectory of greenhouse gas emissions and other pollutants will be largely solved. My concern is how much damage would have done before that and how quickly would we be able to unwind that damage when everything is more expensive to do?

James: I oscillate between optimism and pessimism on this topic. I do worry that a myriad of factors will prevent society from being as far as along as we need to be in 2050 to avoid the some of the worst outcomes, and that the resulting tumult will be the primary driver for the global cooperation and investment needed to advance the technology and policy breakthroughs required for a more sustainable future.

Then there are countless regional and local examples of incredible sustainability innovations in the public and private spheres that give me great hope we’re building momentum towards a more bottoms-up, proactive, and collaborative approach to sustainability that will pave the path to much more sustainable society in 2050.

In either scenario, I’m more convinced than ever that technological innovations will be at the core of the most impactful approaches to sustainability we’ll see between now and 2050.  Technologies that drive sustainability are progressing so much faster than policy, and I think we’ll see that trend continue to accelerate.

Makersite: How do PLM and sustainability align, or how should they align?

James: Going back to the point about how you can’t manage what you don’t measure, you can’t measure what isn’t well defined.  As the backbone of the Digital Thread, PLM is intended to deliver the right product definition at the right time in the right context to the right person. At PTC, we think about PLM as more than a singular tool or platform, but rather a suite of enterprise cross-functional tools that enable true closed loop product lifecycle management.

Managing product sustainability requires a robust understanding of the inputs and outputs at each stage of a product’s lifecycle and in the context of how that product is designed, manufactured, used, serviced, and handled at end of life. PLM provides the digital infrastructure and framework to connect all that product data in context so that cross-functional teams can drive meaningful and impactful decision about a product’s sustainability impacts.  To put it simply, PLM enables sustainability to be integrated as core strand of the Digital Thread.

Dave: PLM is the ‘home system’ for design engineers. Accountants use ERP. Systems and finance folks use Bloomberg terminals. Our design engineers are in Windchill or Arena all day, every day, and they use it to aggregate data for multi criteria analysis on design decisions. And design decisions could be ‘what material should I use?’, ‘What supplier should I use? ‘How should I shape this part? ‘How should I manufacture it?’, ‘What sort of product service system would I put on this product?’, ‘Am I meeting my sustainability design requirements and how are they validating?’

It’s really the central decision making tool. And it can call out to a wealth of different data sources outside of PTC or supply chain data, material data, other data. And then it can also run subroutines of simulations, whether that’s for a streamlined LCA or for performance validation or other things.

But the promise that PLM has is it’s done multi criteria analysis on design decisions ever since it was incepted. More and more, it’s extending across the full product lifecycle, and the data that they’re able to gather in it and the simulations that they’re able to run for decision support are increasing.

Makersite: What are your frustrations with what PLM best practice is currently seen as, and how do we frame it to make it be more successful in the future? To be more adaptive to what we’re facing?

Dave: Some of the academic papers on design for sustainability say that you really don’t have to overhaul the PLM process, you just need to include sustainability as an additional criteria with performance, cost and time to market, and then everything else kind of takes care of itself.

So I don’t think that PLM needs to be radically overhauled. Rather, I think it’s a case that some of the foundations of PLM most of the market has not yet progressed to. A lot of customers just use PLM to vault their CAD designs and Word documents that inform designs.  They need to get towards bill of material management, modular design, derivative bills of materials for manufacturing and service, and then the information and instructions that link to that.

Those are all foundations that have value, that had value even before sustainability was a big thing. But that sets the plate nicely to add on another dimension of criteria for footprint.

Makersite: What about you, James?

James:  As Dave said, I’d like to see the expansion from engineering-centric PDM or product data management to a more comprehensive and cross-functional vision of PLM supporting the connected model-based enterprise.

Having an openness that allows collaboration and connectivity with PLM being the foundation for the Digital Thread and product digital twin is also crucial, as it allows you to go wild with microservices and APIs to different systems of engagement as well as niche tools that help you solve very targeted and specific problems.  You can then bring all that data and analysis back into a centralized view where you can manage it in the right context with the right product information delivered to the right person at the right time.

We need to get to more of a federated approach with PLM as the foundation for product definition and fanning out from there. It’s about more collaboration, more connected data, a faster exchange of information, and ultimately more precise and actionable data. This is critical to making enterprise PLM and sustainability initiatives efficient and effective.  It must involve more than just R&D and engineering. It necessitates more of that systems thinking and collaborative, multidisciplinary approach to developing a product referenced many times in our discussion, which in and of itself should drive us to a much better place.

All that said, technology alone will only get you so far. These evolved business and product lifecycle management strategies require disciplined and robust organizational change management to make them successful. This is something I think a lot of companies take for granted, and we’ll need a lot more focus there to drive alignment and best practices if we hope to realize the benefits at scale.