Digital Twin (Manufacturing) – Virtual Models that Mirror Regulated Production

This topic is part of the SG Systems Global regulatory & operations glossary.

Updated November 2025 • MES, ISA‑95, PAT, CPV, QbD • Pharma, Biologics, Devices, Food, CPG

A digital twin in manufacturing is a dynamic, virtual representation of a physical process, line, asset or plant that is continuously updated with real operational data. In practical terms, it is a data‑driven model that mirrors how a process actually behaves, so engineers, quality and operations can test changes, optimise performance and assess risk in a safe, simulated environment before touching the real line. In regulated industries, a digital twin is most useful when it is tightly connected to MES, historians and quality systems, and when it is governed with the same discipline as any other GxP‑relevant tool.

“A digital twin is only valuable when it behaves like the real plant on a bad day, not just like the slides in the project kick‑off.”

1) What a Digital Twin Is – and Is Not

Vendors use “digital twin” to describe everything from simple dashboards to full physics‑based simulations, which makes it hard to pin down. In a manufacturing context, a useful working definition is: a continuously updated virtual model of a process or asset, connected to real‑world data streams and used to predict, diagnose or optimise performance. The key elements are that it is based on actual plant data, that it reflects the current configuration and operating conditions, and that it supports closed‑loop decisions about the physical process.

A digital twin is not simply a static 3D model, a one‑off offline simulation, or an ungoverned machine‑learning model running on someone’s laptop. Those may be ingredients, but without ongoing data connection, configuration control and clear linkage to the physical process, they do not meet the bar. In regulated settings, the safest mental model is to treat a digital twin as a specialised analytical component living next to MES, PAT and advanced control—not as a free‑floating experiment disconnected from the quality system.

2) Where Digital Twins Sit in the ISA‑95 / Systems Stack

Standards such as ISA‑95 describe layers from enterprise planning down to shop‑floor control. Digital twins typically straddle several of these layers. They draw context and configuration from ERP and PLM (product definitions, routings, campaigns), real‑time events and states from MES, time‑series signals from historians and SCADA (SCADA), and constraints and limits from quality and regulatory documents. At the same time, they feed outputs back into planning, scheduling, process‑control and continuous‑improvement cycles.

Conceptually, you can think of the twin as sitting in a “model layer” that spans across existing systems. It is not trying to replace MES, LIMS or QMS; it is trying to make better use of the data those systems already generate, and to test “what if?” questions before your operators discover the answer the hard way in production. In ISA‑88 batch environments (ISA‑88), a digital twin may mirror recipes, phases and equipment modules, helping teams understand how changes in equipment, raw material properties or timing will ripple through the batch profile and quality attributes.

3) Types of Manufacturing Digital Twins

Not all digital twins are created equal; understanding the flavour you are building keeps expectations realistic. An asset‑level twin focuses on a single piece of equipment—such as a reactor, filler or lyophiliser—and attempts to mirror its dynamic behaviour, often for maintenance, capacity or control‑tuning purposes. A process‑level twin goes further, representing a full unit operation or chain of operations, including feeds, recycles, hold tanks and clean‑in‑place sequences. A line or plant twin models the flow of material and orders end‑to‑end, including constraints such as labour, changeovers and utilities.

In regulated settings, there is also growing interest in product and lifecycle twins, where the focus is on how a product’s critical quality attributes, process parameters and control strategy behave over its lifecycle—from tech‑transfer to routine Continued Process Verification (CPV). All of these belong to the same conceptual family; the difference is mainly in scope, fidelity and which questions they are designed to answer. Being explicit about the type of twin avoids fat design documents that promise everything from OEE optimisation to clinical‑outcome prediction in one leap.

4) Data Sources, Models and the “Living” Aspect

A digital twin only earns that label when it moves beyond a static model to something that is continuously refreshed, challenged and improved with real data. Historically, process models were built once during development and then left to rot as the plant evolved. A modern twin consumes ongoing feeds from historians, MES events, PAT sensors, OEE dashboards, maintenance systems and sometimes even supply‑chain and demand signals. Parameters are re‑estimated, prediction error is tracked, and the model is periodically re‑qualified, just like any other analytical method.

The modelling techniques themselves vary: first‑principles physics models, empirical regression, hybrid models, discrete‑event simulation for flows, or full machine‑learning architectures. From a GxP perspective, the technique matters less than the governance. You need to know what the model is intended to do, what data it was trained or calibrated on, how it is monitored for performance drift, and how its outputs are used in decision‑making. Unclear provenance and uncontrolled updates are exactly the sort of behaviour regulators flag as high‑risk, particularly when the twin influences release, process set‑points or safety‑critical decisions.

5) Use Cases: From Design and Scale‑Up to Scheduling and OEE

Practical manufacturing digital twins rarely start as grand “whole plant” visions; they start with sharp use‑cases. In development and tech transfer, a twin can help translate design‑space knowledge from lab to pilot to commercial scales, testing different combinations of equipment, batch sizes and operating ranges while staying aligned with the control strategy defined under Quality by Design (QbD). During validation, models can be used to stress‑test process‑performance qualification (PPQ) strategies and confirm that sampling plans and acceptance criteria are sensitive to the right kinds of variation.

Once in routine operation, twins become powerful tools for bottleneck analysis, OEE improvement and scheduling. They allow planners to test campaign plans, changeover strategies and maintenance windows before committing to them in the real plant. In some environments, twins also support energy‑optimisation, “what‑if” outage planning, or evaluation of alternate raw‑material grades and suppliers. The common thread is that decisions are made based on a model that has been anchored in actual line data, not just rules of thumb and heroic spreadsheets.

6) Digital Twins in GMP, QMS and Regulatory Context

Regulatory documents rarely use the phrase “digital twin”, but they do talk at length about process understanding, modelling and real‑time control. Concepts from ICH Q8–Q11, FDA’s PAT guidance and CPV expectations in modern GMP are natural homes for digital twin thinking. A well‑governed twin is essentially an advanced form of process‑understanding and prediction tool—one that can support identification of critical process parameters, evaluation of worst‑case operating conditions, and justification for real‑time release strategies, provided its limitations are clearly documented.

Within the Quality Management System (QMS), a digital twin should be treated much like any other model or algorithm that influences GxP decisions. Its development and maintenance belong under change control; its intended use and limitations should be documented; and any use in release or regulatory submissions should be clearly explained. The most sustainable posture is to view the twin as part of the process‑knowledge toolbox, not as an unregulated shortcut to bypass established process validation and SPC practices.

7) Validation, Model Risk and Use in Decision‑Making

One of the sharpest questions regulators now ask is not “do you have models?” but “what decisions do you make based on them, and how do you know they are fit for that purpose?”. Digital twins are no exception. If a twin is used purely as a sandbox for brainstorming and scenario exploration, then its validation burden is relatively light: you must show that data are correct and that the model is not mis‑represented as truth, but formal qualification can be modest. As soon as its outputs feed into GxP decisions—such as release, adjustment of set‑points, or justification of narrower sampling—then its behaviour must be characterised, challenged and documented just like any other analytical method.

For many sites, a practical approach is to borrow concepts from model lifecycle management in PAT and test‑method validation: define the intended use, specify performance criteria, challenge the model with independent data and stress cases, monitor it over time, and periodically re‑qualify or retire it. Where machine‑learning components are used, the rules for retraining, redeployment and version control must be crystal clear; “we tweak the model when it looks off” is not sufficient when patient safety, product quality or regulatory filings rely on its outputs.

8) Digital Twins, QRM and Scenario‑Based Risk Assessments

Risk‑management techniques such as QRM, FMEA and HAZOP have historically relied on expert judgement and sparse historical data. A digital twin allows teams to make those discussions more concrete by simulating specific failure modes, extreme conditions or combinations of deviations and seeing how the process and product respond. Instead of arguing abstractly about whether a certain valve failure is “medium” or “high” risk, the team can show how quickly critical parameters drift, how likely alarms are to fire, and how much material would be at risk before detection.

That said, a twin does not replace the human structured‑risk process; it complements it. It can highlight combinations of factors that might not have been obvious, or show that some theoretical failure modes are effectively mitigated by existing controls. Results can be fed back into risk registers, control plans and process control plans, strengthening the link between model‑based understanding and documented risk‑control strategies. When regulators ask “how did you determine this design space or these alarm limits?”, being able to reference both empirical data and twin‑based scenario work is a strong answer—provided the model’s pedigree is clear.

9) Data Integrity, Governance and the Digital Thread

Because digital twins sit at the intersection of many data sources, they expose all the weaknesses of an organisation’s data‑integrity and governance practices. If sensors are poorly calibrated, if time stamps are inconsistent, if master data are duplicated across systems, the twin will either perform badly or quietly embed those defects in its logic. Treating the twin as part of the broader digital thread from design through planning, execution, release and post‑market surveillance is essential. That thread is governed by familiar tools: change control, configuration management, data‑ownership definitions and data‑integrity SOPs.

Practically, this means that the data sets feeding the twin must be traceable back to qualified instruments, validated systems and controlled transformations. The model artefacts themselves—code, parameters, training data sets—should be stored in version‑controlled repositories with appropriate access control and audit trails, just like any other GxP‑relevant configuration. When the twin is updated, there should be a clear, reviewable record of what changed, why, and what impact assessment and testing were performed. Without that discipline, the organisation ends up with a collection of clever but untrusted “shadow” models that cannot safely influence real decisions or survive inspection scrutiny.

10) Implementation Steps for a Manufacturing Digital Twin

Successful digital‑twin initiatives start from specific pain points, not from a desire to deploy a particular technology. A typical first step is to select a well‑bounded process or line with clear business value—such as a chronic bottleneck, a high‑variability step affecting yield, or a resource‑constrained packaging line—and assemble a cross‑functional team from operations, engineering, data, IT and quality. They map the process flow, identify critical data sources and decide what questions the twin must answer in order for the project to be considered successful.

The next steps include building a minimal viable model using historical data, connecting it to live feeds in a read‑only fashion, validating that it reproduces observed behaviour, and then gradually moving toward decision support (“what‑if” analysis, recommended set‑points, campaign plans). Throughout, the team documents assumptions, limitations and qualifications, and works with quality and validation to classify the model’s GxP impact. Only once the initial use‑case is demonstrably delivering value—shorter changeovers, higher throughput, reduced deviation rates—does it make sense to scale to additional lines or to integrate more tightly with MES and scheduling tools.

11) KPIs and Measuring Value

Digital twins are notorious for sounding impressive in PowerPoint yet struggling to prove their worth in the plant. To avoid that fate, teams should define concrete KPIs at the outset. For a throughput‑focused twin, those might include percentage increase in weekly output, reduction in average cycle time, or improved OEE. For a quality‑focused twin, metrics such as reduced batch‑failure rates, narrower variability in critical quality attributes, or faster resolution of deviations may be more relevant. In a tech‑transfer context, reduced number of PPQ batches, fewer surprises at scale and shorter time from development to commercial release are all valid measures of success.

There are also second‑order benefits that are harder to quantify but still real: better cross‑functional alignment around what the process actually does; clearer mental models of constraints and trade‑offs; and a stronger, evidence‑based narrative when presenting process‑understanding and control strategies to regulators and customers. Baking some of these softer outcomes into project charters and QbD documentation helps ensure that the twin’s contribution is recognised beyond pure throughput numbers, and anchors it firmly in the continuous‑improvement culture rather than in one team’s innovation budget.

12) Integration with MES, eBMR / eDHR and QMS

For SG‑style plants that already use electronic batch and device records, the natural question is how a digital twin interacts with eBMR, DHR or eMMR concepts. A pragmatic answer is that the twin usually sits “upstream” of those records as an analysis and design tool. It uses data drawn from eBMR/eDHR repositories to learn how the process behaves and, in turn, suggests better recipes, set‑points, sampling plans or campaign structures that are then implemented and enforced through MES and V5‑style execution logic.

To preserve data integrity and role clarity, it is often wise to ensure that the twin cannot directly change GxP‑critical parameters in production. Instead, it can propose changes, with those proposals routed through established change‑control or master‑data governance processes and, where appropriate, additional validation. This pattern aligns with regulators’ expectations that advanced modelling and AI tools support, but do not silently bypass, the existing controls around recipes, specifications, risk assessments and release criteria.

13) AI, Machine Learning and Digital Twins

Many modern digital‑twin implementations incorporate AI or machine‑learning components, especially for prediction of continuous variables, anomaly detection or optimisation. It is useful to keep the conceptual distinction clear: the digital twin is the overall construct—the virtual representation, its data connections and its role in the business process—while ML models are some of the algorithms living inside it. That clarity matters because regulatory and quality expectations around AI—transparency, explainability, bias control and lifecycle management—apply to the ML elements specifically, while broader system‑validation expectations apply to the twin as a whole.

In highly regulated environments, there is often a spectrum of acceptable use. Using ML inside a twin purely for soft‑sensing, monitoring or exploratory analytics may be relatively easy to justify, provided appropriate controls are in place. Using ML outputs to adjust set‑points in real time or to replace traditional release tests will attract far more scrutiny and may require explicit engagement with regulators. Positioning the digital twin as an “AI‑enabled extension” of established PAT and CPV frameworks can help manage expectations and avoid overselling black‑box magic where a well‑governed grey‑box model would be more appropriate.

14) How SG Systems / V5‑Style Platforms Support Digital Twins

SG Systems‑style architectures are natural foundations for digital twins because they already enforce structured data capture at the point of execution. V5‑class platforms manage recipes, routings, tolerances, material and equipment status, operator actions and quality events in a way that is inherently twin‑friendly: data are time‑stamped, tied to assets and batches, and protected by data‑integrity controls and audit trails. This means that a digital‑twin project can plug into a clean, well‑structured history rather than trying to stitch together CSV exports and ad‑hoc spreadsheets.

Operationally, V5‑style systems also form the enforcement layer for any twin‑derived insights. If the twin shows that a narrower operating window improves yield without compromising quality, those limits can be encoded as hard‑gated MES rules, enforced across all relevant lines and sites. If a twin suggests a new campaign structure or scheduling pattern, the execution platform ensures that it is followed and that the resulting performance is measured. In that sense, the relationship is symbiotic: the twin relies on execution systems for clean data and controlled implementation; the execution systems use the twin to continuously sharpen their recipes, plans and control strategies.

15) FAQ

Q1. Do regulators recognise or require digital twins?
Regulators do not typically mandate “digital twins” by name, but they strongly encourage robust process understanding, modelling and real‑time monitoring. Guidance on PAT, CPV and QbD is entirely compatible with digital‑twin approaches. What they care about is whether models are well understood, appropriately validated for their intended use, and governed under the quality system—not whether the marketing label used internally is “twin”, “simulator” or “advanced analytics”.

Q2. Does a digital twin need full CSV validation?
It depends on how you use it. If the twin is used only for exploratory analysis and does not directly drive GxP decisions, a lighter validation and governance regime may be acceptable, focusing on data integrity and clarity around limitations. Once its outputs influence set‑points, release decisions, sampling strategies or regulatory submissions, the twin becomes part of the validated control strategy and must be treated accordingly under CSV or CSA principles, with defined requirements, testing, change‑control and ongoing performance monitoring.

Q3. How is a digital twin different from basic simulation?
Traditional simulations are often static, offline models built once for a specific study and rarely updated. A digital twin, by contrast, is designed to be “alive”: it consumes ongoing production data, adjusts parameters, tracks its own accuracy and is embedded in operational decision‑making. A good litmus test is whether your model still feels relevant and trusted a year after installation or whether it has already drifted away from the reality of the line; if it is the latter, you probably built a one‑off simulation rather than a true twin.

Q4. Where should we start if we have no digital‑twin capability today?
The most effective starting point is rarely a technology platform; it is a specific, painful problem. Identify a process or line where variability, bottlenecks, yield loss or deviation rates are hurting performance, and where good historical data already exist. Build a focused model around that area, prove that it can explain and predict behaviour, and use it to support tangible decisions—such as revised set‑points, campaign plans or maintenance strategies. Once that first use‑case shows value and survives scrutiny from quality and operations, you can standardise the methods, governance and tooling into a broader digital‑twin programme.

Q5. Do we need AI to build a digital twin?
No. Many highly effective digital twins use relatively simple first‑principles models, regression and statistical process control, particularly when the goal is to capture well‑understood physics or chemistry. AI and machine learning can be powerful where relationships are complex or non‑linear, but they also introduce additional governance questions around transparency and lifecycle management. In regulated manufacturing, it is often better to start with the simplest model family that can answer the business question robustly and only add complexity when the case is clear.

Related Reading
• Systems & Architecture: MES | ISA‑95 | ISA‑88
• Process Understanding & Control: QbD | PAT | Process Validation | CPV
• Performance & Improvement: OEE | SPC | Production Scheduling
• Quality & Risk: QMS | QRM | Data Integrity | Change Control