Digital Product Twins for Manufacturing and Field Intelligence

Despite growing investment in digital twins, many manufacturers continue to face fundamental barriers to operational impact. Cross-domain traceability between design, production, and diagnostics remains limited. Engineering and execution data are fragmented across systems and teams, hindering the ability to explain failures, localize faults, or understand product behavior at the variant level and across the entire lifecycle.

According to McKinsey, 86% of manufacturing executives see digital twins as relevant to their operations. Yet most cite fragmented data landscapes and system complexity as key obstacles to adoption (McKinsey, “Digital twins: The next frontier of factory optimization,” 2024).

Unlocking value requires more than visualization. Manufacturers must structure their proprietary product data, requirements, functions, ECUs, software, wiring, and signals—into functional Product Twins that reflect how each variant is built and behaves. When deployed at VIN level, these twins enable faster diagnostics, clearer root cause analysis, and more consistent repair execution—raising first pass yield (FPY), improving first time right (FTR), and reducing total resolution cost.

Digital twins that stop at visualization fall short

Many current digital twin implementations remain limited to surface-level visualization: mapping assets, layout, or sensor states without exposing underlying system logic. These models rarely support diagnostic traceability at the level required in complex, variant-rich environments.

If a twin cannot resolve why a specific issue occurred, on a given VIN, with a defined software version, routed through a particular signal path or connector, it cannot serve as a reliable tool for root cause analysis. In the absence of configuration-specific data, production teams frequently default to conservative workflows, diverting vehicles to offline rework to minimize risk.

Similarly, field technicians often work with generalized 150% wiring diagrams, static PDF documentation, and toolchains that lack VIN-specific configuration data, limiting their ability to isolate faults efficiently. The result is reactive troubleshooting workflows, extended resolution times, and persistent reliance on expert judgment rather than verifiable product logic.

Beyond immediate operational inefficiencies, the lack of granular, VIN-specific feedback limits the organization’s ability to identify systemic design issues, adjust BoM configurations, or refine platform-level engineering decisions based on field performance.

Build a digital twin around the product to capture cross-lifecycle value

Traditional digital twins often prioritize assets and processes, starting at the level of machines, sensors, and production tags. While useful for factory optimization, this approach lacks the product-centric context required for diagnostic precision or design feedback. A product-centered approach begins with modeling the product’s functional logic and dependencies: how each function is realized across ECUs, software modules, wiring, and signals, and how those mappings vary by configuration and version. This structure can be encoded into a functional Product Twin, implemented on a semantic knowledge graph, and linked to test data, diagnostics (DTCs), and field tickets.

When deployed, two operational benefits typically emerge. In production, engineering teams can trace an ambiguous fault code directly to the affected signal path, connector pin, component variant, and software version, reducing time-to-resolution and minimizing unnecessary rework. In service, technicians diagnose against the VIN-specific build, navigating between 3D vehicle representations and detailed wiring diagrams, supported by ranked fault candidates. These capabilities contribute to higher first pass yield (FPY), improved first time right (FTR) rates, and reduced warranty-related costs.

To understand how this architecture works in practice, consult SPREAD understand the tech document.

Longer-term value arises when structured field and production insights inform upstream engineering. Failure-prone signals and variant-specific issues can be systematically identified, feeding into requirements and platform design decisions. This enables data-backed prioritization of BoM changes, architecture adjustments, and potential redesigns, closing the loop between operational behavior and product development.

Realizing this level of traceability and feedback requires more than static models or rule-based mappings. It depends on a data architecture capable of representing complex system relationships, managing versioned configurations, and integrating evidence over time. This is where the combination of a semantic graph and machine learning becomes essential.

Graph and AI work together inside a functional Digital Product Twin

Unlike traditional data lakes or static models, a functional Product Twin is built on a semantically explicit, version-controlled engineering knowledge graph. This graph integrates structured data from across the lifecycle, including PLM artifacts, wiring harnesses, BoMs, software configurations, diagnostic trouble codes (DTCs), and test results, and encodes relationships between requirements, functions, ECUs, signals, and physical connectors. Runtime data such as MES or PLC events, sensor anomalies, and field tickets can be bound directly to these entities.

AI complements this foundation by identifying behavioral patterns and deviations at scale, across production lines, vehicle variants, or in-field conditions. The graph contextualizes these patterns by linking them back to functional architecture, enabling deterministic reasoning: teams can understand not just that something is anomalous, but why, where, and under what configuration it occurs.

Functional Product Twins are already being applied across production and aftermarket contexts. The following examples illustrate how OEMs are using them to reduce rework, accelerate diagnostics, and translate product data into operational efficiency, at scale.

Production: €500k per line per year saved, 75% faster troubleshooting, plus 5 vehicles per day per line kept inline

A European premium automotive OEM faced rising complexity with the launch of a new vehicle generation built on centralized E/E architecture. As system integration challenges increased, first pass yield declined, and offline rework volumes grew. Existing diagnostic methods lacked the traceability needed to resolve faults efficiently across software versions, harness variants, and configuration-specific behaviors.

To address this, the OEM implemented SPREAD’s Error Inspector solution focused on VIN-level traceability. The solution combined 3D vehicle representations and 2D wiring diagrams with a knowledge graph linking DTCs to pins, signals, components, and software variants. Root cause suggestions were prioritized based on system context and runtime anomalies. After a three-month integration phase, covering harness data, BoMs, diagnostics, and CAD models, the deployment scaled across five plants and more than thirty vehicle programs.

Results:

• €500,000 in annual savings per production line
• 75% faster troubleshooting in rework scenarios
• Five additional vehicles per day per line retained in inline production

As one plant manager noted: “In the future, workers will rotate more—even to other plants where processes might be different. With solutions like this, everyone will be able to work anywhere.”

Read the case study: Accelerating troubleshooting by 75% at an automotive OEM

Aftermarket: More than €10m per year saved, up to 50% faster troubleshooting, higher first-time-right rates

A European OEM operating a global service network across more than 50,000 locations faced persistent challenges in diagnosing complex E/E issues. Technicians relied on static, non-specific diagrams and fragmented systems. Root causes were difficult to isolate, leading to repeat repairs, extended downtime, and increased warranty exposure.

To address this, the OEM deployed SPREAD’s Error Inspector purpose-built for aftermarket diagnostics. The solution provided VIN-specific interactive vehicle and wiring views, linked to DTCs, signals, and configuration metadata. Following initial deployment and data integration, the system was scaled across the workshop network.

Results:

• More than €10 million saved annually through improved fault localization and fewer repeat repairs
• Up to 50% reduction in troubleshooting time
• Higher first-time-right rates enabled by build-specific diagnostic context

The Head of IT for Aftermarket commented: “I rarely in my career have met a company that in the first meeting convinced me so strongly with their expertise and solution. Because of this, we are discussing making it an essential element of our group’s digital twin architecture.”

Read the case study: Saving >€10M yearly through faster troubleshooting at an auto OEM

Get started by turning your product data into a Digital Product Twin

Organizations can begin by selecting a single high-impact scope—such as a vehicle project on a production line or a top warranty cost driver in the field. Most already have the necessary data: Excel or CSV BoMs, signal lists, wiring diagrams in PDF, and diagnostic trouble codes. From this, a functional Product Twin can be established within weeks, enabling VIN-specific traceability and driving measurable improvements in FPY, FTR, and resolution cost.

The same architecture can be extended across additional models, production lines, and service locations. Each new use case builds on the existing foundation—accelerating deployment and compounding value. A structured session with SPREAD helps map available data, define the initial scope, and quantify the expected return. Talk to an Expert at SPREAD.