The digital thread is becoming manufacturing’s operating model
Manufacturers have spent years investing in PLM, ERP, MES, service platforms and an expanding range of digital tools, yet product information still breaks apart as it moves across the business. Engineering, manufacturing, quality, service and supply chain can continue to work from different versions of the same product, with different structures, identifiers and assumptions about who owns the data.
That weakness is becoming harder to tolerate as manufacturers look to AI to accelerate engineering and operational decisions. A digital thread that once promised greater efficiency is increasingly becoming the foundation on which automation, traceability and AI depend. Without it, companies risk applying faster decision-making to information that remains fragmented or simply wrong.
Paul Brooks, Principal, Solutions Consulting at PTC, argues that the persistence of the digital-thread problem says something important about the limits of technology-led transformation. “The digital thread remains unfinished because too many businesses have treated it as a systems integration exercise rather than an operational change,” he says. “Connecting software is the easier part; changing how engineering, manufacturing, service and supply chain teams share ownership of data is much harder. Most manufacturers already have plenty of digital tools, but the information often stops at departmental boundaries. From an engineer’s point of view, the real value appears when people can trust the same product information and make decisions from it without chasing spreadsheets or second opinions.”
PLM, ERP and MES were designed to solve valuable functional problems, so each became strong within its own boundary. Difficulties emerge when a change must cross those boundaries and one system’s definition of the product no longer matches another’s.
“When manufacturers talk about the digital thread, they are trying to connect the important decisions made across the full life of a product,” Brooks adds. “That means linking requirements, design, manufacturing, quality, service and supply chain information so cause and effect can be understood quickly. The biggest misconception is that there is one standard digital thread that every company simply installs. Every manufacturer has a different mix of systems, processes, products and legacy decisions, so the thread has to be built around the outcomes that matter most. The most useful question is not ‘how do we connect everything?’ but ‘which decision are we trying to improve first?’”
That focus on the decision rather than the architecture also changes how manufacturers can approach implementation. Instead of attempting to connect the enterprise in one move, they can begin where fragmented information is already creating a measurable operational problem and build outward from there.
Turning systems into shared decisions
The difficulty becomes visible when an engineering change moves downstream. Production may work from an old revision; procurement may order against the wrong bill of materials or service teams may use documentation that no longer reflects the installed product. Those decisions can be rational while still producing scrap, rework, delays or warranty exposure because the information reaching each function is incomplete or late.
Engineering thinks in terms of design intent, manufacturing in terms of build sequence and service in terms of the asset that exists in the field. Those views become difficult to reconcile when accountability ends at departmental boundaries.
“Technology is probably only a third of the challenge,” Brooks continues. “The bigger issue is organisational: who owns the data, who has the authority to change it, and who benefits when the connection works. Manufacturers often underestimate how many unwritten rules exist around product information and decision-making. A digital thread exposes those rules, which can be uncomfortable but also very useful. If the culture still rewards local optimisation, even the best platform will struggle to create an enterprise-wide thread.”
“The strongest progress is in industries where configuration control and traceability are not optional,” Brooks says. “Aerospace and defence, automotive, medical devices, industrial equipment and electronics all have a clear reason to connect design, build, quality and service information. The use cases that work best are usually bounded and practical, such as closed-loop engineering change, service parts accuracy, compliance evidence or connecting as-designed to as-built to as-maintained. That matters because the business can see the benefit in faster changes, fewer downstream errors and better confidence during audits or customer reviews.”
The pattern is significant because it shifts digital-thread implementation away from broad transformation programmes and towards specific workflows where fragmented information already creates cost, delay or risk. A successful first use case can then become the template for extending the thread into other parts of the lifecycle.
AI raises the cost of disconnected data
Roy Clarke, Fellow, Solutions Consulting at PTC, argues that AI increases the urgency of solving long-standing data problems because models need to understand not only the information they receive, but the product, configuration and lifecycle context surrounding it. “AI has made the digital thread conversation much more urgent because it depends on trustworthy, connected data,” he explains. “An AI tool can only reason properly if it understands which requirement, part, configuration, test result or service record it is looking at. Without that context, AI can sound very confident while giving advice based on incomplete or conflicting information. The digital thread gives AI the structure and traceability it needs to be useful in an industrial setting. In simple terms, AI raises the stakes because bad data stops being a back-office problem and starts becoming a decision-making risk.”
In manufacturing, that risk quickly becomes physical. A recommendation based on the wrong product version, supplier record or test result can lead to incorrect parts, missed compliance evidence, downtime or poor service decisions. The attraction of AI is speed, but speed amplifies the consequences when the underlying information cannot be trusted.
“A connected digital thread compresses the time between a signal and a sound decision. If a supplier fails, a regulation changes or a customer asks for a new requirement, the business can quickly see which parts, products, tests, plants and customers may be affected. That means people are not relying on long email chains or tribal knowledge to work out the impact. The value is being able to trace consequences before the problem becomes expensive. In a more volatile world, agility is really the ability to understand knock-on effects faster than competitors.”
The advantage is not simply better visibility. It is the ability to understand consequences early enough to change the response, whether that means finding an alternative supplier, updating production plans, revising compliance evidence or identifying which customers will be affected before disruption spreads through the business.
From efficiency improvement to operating requirement
Modern products increasingly combine mechanics, electronics, software, connectivity and ongoing updates, creating relationships that become difficult to manage manually. Supporting a configurable or software-defined product requires manufacturers to know which requirements, software versions and hardware configurations apply to each physical instance in the field.
“Sustainability is difficult because the data needed to understand impact is spread across the lifecycle. Materials, mass, supplier choices, substance compliance, manufacturing energy and service life all influence the footprint of a product. A digital thread brings that information closer to the design and engineering decisions where most of the environmental impact is actually committed. It also gives manufacturers a stronger evidence trail when customers or regulators ask for proof rather than broad sustainability claims. The thought-provoking shift is that sustainability stops being a report written at the end and becomes something engineered into the product from the beginning.”
Seen this way, the digital thread is not simply a mechanism for reporting environmental performance more accurately. It gives manufacturers a way to move sustainability decisions upstream, where choices about materials, design and product configuration can still influence the eventual impact.
Manufacturers that have struggled to make progress do not need to connect the enterprise in one move. Clarke recommends beginning with a workflow where fragmented information already creates measurable pain, defining the decision that needs to improve and connecting only the data required to support it.
“Over the next three to five years, the gap will come down to how well manufacturers can use connected data to make better decisions,” he concludes. “Companies with a governed digital thread will be able to apply AI, respond to disruption, manage compliance and support connected products with much more confidence. Those still operating from fragmented systems will lose time debating which data is correct before they can even act.
“That difference will show up in speed of new product introduction, service responsiveness, regulatory confidence and the ability to create new outcome-based business models. The uncomfortable truth is that the digital thread may stop being a differentiator and become the minimum requirement for competing in an AI-enabled industrial market.”
The digital thread has spent more than a decade being discussed as a transformation manufacturers ought to complete. AI is changing the urgency of that conversation. Connected product information is becoming less about creating a cleaner architecture and more about whether manufacturers can trust the decisions their people and machines are being asked to make.
