Every manufacturer has a digital thread...at least, in theory.
Across the manufacturing industry, the digital thread describes the connected flow of product data across the full lifecycle of a product. It starts early with engineering and design data before extending to CAD, PLM, BoMs, suppliers, production, quality, service, maintenance, parts, warranty, and customer support.
Done properly, the digital thread gives everyone involved in the product lifecycle access to a shared, trusted understanding of the product.
The engineer knows what was designed.
The production team knows how it was built.
The service team knows how it should be repaired.
The parts team knows what needs replacing.
The dealer knows what to order.
The customer gets the machine back up and running faster.
That is the promise - but the reality for many OEMs is very different.
Manufacturers have invested heavily in systems of record: PLM, ERP, CAD, PDM, service management tools, ecommerce platforms, dealer portals, technical publications systems, document repositories, spreadsheets, PDFs, and knowledge bases.
The problem is therefore not the absence of data, but instead that the data is fragmented, duplicated, locked inside specialist systems, or only written for people who already know what they are looking at.
And that becomes a real problem at the moment of product failure when the clock starts ticking:
A customer is losing productivity.
A technician is trying to identify the issue.
A dealer is trying to order the right part.
An aftermarket team is trying to protect revenue, margin, and customer trust.
But too often, the product engineering data that could help solve the problem does not reach the people doing the work, sitting in CAD files they cannot access, BoMs they can't interpret, PDFs or manuals that are hard to search, or in the head of the experienced technician who's unavailable at the time.
This is where the digital thread stops being a technical concept and starts to impact commercial numbers, because when teams cannot understand the product quickly, they cannot act quickly either, which increases downtime, support teams get overloaded, wrong parts get ordered, warranty claims become harder to manage, and aftermarket revenue leaks out of the business.
As Sam Burgess, Founder and CEO of DRVEN, explains:
“I’ve seen this first-hand in environments where getting the right information quickly is the difference between progress and paralysis. The data usually exists somewhere, but when a product has failed, ‘somewhere’ is not good enough. The technician, dealer, or support team needs to understand the machine, identify the right part, and follow the right process in that moment. That is where the digital thread has to become operational, not theoretical.”
For OEMs, digital thread maturity is sadly not a switch that gets turned on, but instead is a progression over time, where specific layers need to be in place before the next layer can deliver value. However, OEMs often try to jump too far ahead, usually because the most advanced stages sound the most attractive or because they believe a parts catalog or new ERP will solve the issues.
AI automation, predictive maintenance, intelligent service recommendations, autonomous support, and next best action strategies are powerful ideas, but they are simply not achievable if the underlying product data is inconsistent, incomplete, or disconnected - ultimately because you cannot automate what you don't understand, and you can't recommend the right fix or trust a parts catalog if the parts data is always 6 months out of date, the data structure is wrong, or source data can't trusted.
Similarly, you cannot create a self-service support experience if technicians, dealers, and customers themselves are still dependent on PDFs, tribal knowledge, and manual interpretation, and that is why digital thread maturity has to be built in layers.
The DRVEN Digital Thread Maturity Ladder is a 7-stage framework, which recommends how OEMs can move from fragmented product data to connected product intelligence, and ultimately to AI-enabled product action...
The first stage is where most OEMs start: Product data exists, but is scattered across the business with poor data pipelines.
CAD files live in the engineering team, BoMs live in PLM or ERP systems, service manuals live in PDFs, parts information lives in spreadsheets, dealer knowledge lives in email threads, and historical fixes live in the head of an experienced Master Technician.
Each source is super valuable in isolation, but the experience for the person trying to use it is fragmented and slow to find - but aftermarket and service teams rarely have the luxury of time! When a machine is down, nobody wants to search across six systems, three PDFs, and a folder of outdated drawings to identify a component.
Disconnected product data creates predictable and un-necessary problems:
Wrong parts are ordered.
Support teams are interrupted.
Technicians lose time.
Dealers become dependent on OEM experts.
Customers wait longer than they should.
At this first stage, the digital thread exists only as a set of disconnected fragments. The first job therefore is not to chase automation or invest in a new 'AI-first' parts catalog. Instead, OEMs need to understand where product data lives, which sources matter, who needs access to them, and where the biggest operational gaps are.
This is the foundation and without it, everything built on top becomes fragile.
The next logical stage is about creating a reliable product data foundation (otherwise known as a Product Knowledge Hub), by thinking about the data pipeline and ingesting, structuring, normalizing, and mapping product data so it can be used beyond the teams and systems where it originated.
A Product Knowledge Hub for OEMs connects the essential building blocks: CAD, BoMs, PLM, PDM, ERP, manuals, PDFs, service documents, part metadata, product hierarchies, and assembly relationships.
While this might sound like a job for IT, it is far more necessary to have people running the project who understand how products are structured, how parts are identified, how versions are managed, how engineering and service views align, where the gaps are, and how teams trust (or don't trust) the data they are using.
For many OEMs, this is where the hard work really starts! Engineering data is often created for engineering purposes, and therefore is not always structured in a way that makes sense for service, repair, production, or aftermarket commerce.
An eBOM tells one story.
An sBOM tells another.
A service manual tells another.
The technician looking at the machine needs a usable version of the truth, and this is therefore why the unified knowledge hub is so important because it's essentially turning raw product data into actual product knowledge.
Once that foundation is in place, OEMs can start to make complex products easier to navigate, understand, and act on.
Once product data is structured, it can become visual, and this is where the digital thread starts to become useful to the people outside engineering.
A 3D parts catalog, exploded view, or clickable product model gives technicians, dealers, support teams, and customers a faster way to understand complex products.
Instead of searching for a part number in a PDF, they can navigate the machine visually.
Instead of guessing between similar components, they can see exactly where a part sits in context.
Instead of relying on an expert to interpret a drawing, they can identify the right assembly, subassembly, and component themselves.
This stage is important because visual understanding reduces ambiguity...and ambiguity turns out to be very expensive with needless non-active repair times creating inefficiencies and costs.
A small misinterpretation can create a wrong parts order, which increases non-active repair times, which extends downtime, which ultimately can lead to damaged trust.
3D product visualization is not just a nicer interface for technical data therefore, but in fact a better way to make complex machinery understandable.
But it only works when the data foundations are strong because an electronic spare parts catalog built on poor data just makes bad information look more convincing.
The next stage is where product understanding and reliable parts data connects to commercial and operational action, because it's not enough for someone to just identify the right part - they also need to take action.
They may need to order it.
Check availability.
Understand price.
Confirm compatibility.
Raise a warranty claim.
Trigger a service workflow.
Share the information with a dealer.
Connect the repair to a customer record.
These are all the factors that elongate repair times, and so this is ultimately where the digital thread starts to support the aftermarket business directly.
To connect parts and service together, it means linking product knowledge to the systems and workflows that help people act in the flow of work. That could include ERP integration, ecommerce ordering, dealer portals, service platforms, warranty systems, telematics or even QR-based access from the machine itself.
This stage is important because a large amount of OEM value sits in the deployed fleet. Effectively, every machine in the field is a potential source of service revenue, parts revenue, customer retention, or lifecycle insight, but that value can only be captured if the customer, dealer, or technician is enabled to move from problem-to-part-to-action - but without the friction.
When parts identification is disconnected from ordering, OEMs lose revenue.
When support teams have to manually guide every request, OEMs lose productivity.
When customers cannot get what they need quickly, OEMs lose trust.
Identifying the right part is only one side of the problem, because the people repairing, inspecting or even assembling the machine also need to follow the right process to get it back up and running. This is where guided work execution becomes critical.
Instead of static manuals, teams can use step-by-step visual work instructions that show what needs to happen, in the right order, using the right product context.
For OEMs, this helps reduce uncertainty for junior technicians and speed up repairs without leaning on others to get the work done. Similarly, dealers can improve their own consistency and reduce their dependency on OEM support, and for production teams it can standardize assembly and training to reduce scrappage or wastage costs related to inexperience.
For aftermarket leaders therefore who are held to account for first-time-fix rates, they can reduce avoidable escalations, and protect service quality at scale.
This stage is especially important because many OEMs still face a knowledge-transfer problem, and even smaller OEMs that make it to this stage are proven to have saved as much as 1,700 hours across 4 technicians.
Experienced technicians (AKA 'Master Builders' or 'Master Technicians') know how products behave in the real world, were there when the refurbishment happened, know which page of a 30k page manual is wrong, and understand all the shortcuts, symptoms, quirks, and common failure points. But that tribal knowledge is difficult to scale.
Guided work instructions therefore help capture and distribute that expertise in the flow of work when it's needed. So rather than training technicians on how to assemble, fix or maintain products in a workshop 6 months ago, they have what they need while undertaking the work.
As with other stages in the ladder, work instructions depend on accurate product structures, reliable parts data, and clear visual context. So, if the underlying product information is weak, the guidance will be weak too.
At this stage, the digital thread becomes a feedback loop rather than a one-way flow from engineering to the aftermarket and support teams.
It starts to move the OEM from a position of guesswork to a position of intelligence to start making the right commercial decisions about the product lifecycle.
It helps to start answering:
Which parts are searched for most often?
Which assemblies cause the most support requests?
Which procedures are viewed repeatedly?
Which parts are ordered after specific failure events?
Where do technicians pause, abandon, or escalate?
Which product lines generate disproportionate service burden?
Which dealer networks are self-serving effectively?
Which machines are creating the most aftermarket opportunity?
This is where the digital thread becomes a strategic asset. Not just a way to publish product information, but a way to learn from every machine in service, making the deployed fleet a source of intelligence, margin, and competitive advantage.
Aftermarket teams can plan parts demand more intelligently.
Service leaders can identify training gaps.
Engineering teams can understand recurring failure patterns.
Product teams can improve future designs.
Commercial teams can see where lifecycle revenue is being captured or missed.
However, if product data, parts activity, service workflows, and user behavior are not linked, the business sees isolated metrics rather than lifecycle intelligence.
The final stage is the utopia for many OEMs, with many investing continuously to make their digital thread become increasingly intelligent and autonomous - and for good reason too:
Imagine a technician searching in plain language and being guided to the right part, procedure, and action.
Imagine service teams receiving recommendations based on product configuration, failure history, and known repair patterns.
Imagine work instructions being generated or updated from structured product data.
Imagine parts demand being predicted before the customer even knows they need support.
Imagine a dealer or end user being told the next best action based on the machine, the customer, the issue, and the available inventory.
This is clearly the direction of travel that Artificial Intelligence has the potential to unlock, but unfortunately an AI investment is not a shortcut around digital thread maturity.
Complex product AI depends on context, structure, trustworthy data and clear workflows - but most importantly AI models need feedback to continuously retrain and improve accuracy. Just like a digital parts catalog is useless if the latest product update isn't reflected in the catalog; your AI will make wrong decisions too.
Without that foundation, AI becomes just another disconnected layer - impressive in a product demo but unreliable in the field.
For OEMs, the real opportunity is therefore not to bolt AI onto fragmented product data. Instead, the opportunity is to build a digital thread that makes AI useful, grounded, and operational.
The biggest mistake OEMs can make is treating the digital thread as a software implementation. While parts catalogs, work instructions, dealer portals, ERPs, CRMs, returns solutions and other applications are important, the digital thread is a collection of tools, knowledge, capabilities and workflows that need to come together at the same pace.
It requires people to agree on what product truth looks like.
It requires processes that connect engineering, production, service, aftermarket, and support.
It requires data that is structured for operational use, not just technical storage.
It requires technologies that can connect to existing systems rather than demanding a full replacement.
And, it requires a culture that sees the product lifecycle as a connected revenue and customer experience opportunity.
That is what makes the digital thread real.
In conclusion, OEMs already have much of the data they need, but the challenge is making it useful to the people that need it, and ensuring that feedback loops are in place to enable the business to move from reactive maintenance to predictive maintenance and all the efficiency and profitability gains that come from product lifecycle insights.
Making it useful to the technician standing next to the machine.
Making it useful to the dealer trying to support the customer.
Making it useful to the aftermarket team trying to capture genuine parts revenue.
Making it useful to the support team trying to reduce avoidable escalations.
Making it useful to the production team trying to build with consistency.
And, Making it useful to the product team trying to learn from the machines already in service.
As you can observe from the 7-stage Digital Thread Maturity Ladder, step 1 has the potential to unlock everything, and as our CEO said in a recent LinkedIn Post:
Over the years, I've watched OEMs invest in parts catalogues, work instruction platforms, dealer portals, AI search and knowledge management systems. Every one of those investments solved a genuine problem, but they often addressed the symptom rather than the cause. Manufacturers should stop asking: "Which parts catalogue should we buy?" ...and start asking: "How do we create a connected product data foundation that every downstream capability can rely on?"