The value of digital twins in manufacturing
Digital twins in manufacturing give teams a safer way to test decisions, understand trade-offs, and improve performance before changes reach the shop floor.
Digital twins in manufacturing give teams a safer way to test decisions, understand trade-offs, and improve performance before changes reach the shop floor.
The modern value of digital twins in manufacturing has evolved from a long history of innovation and creative thinking. Anyone who’s familiar with the Apollo 13 mission, knows they solved several problems by “twinning” conditions and technologies in the module, to test and experiment with multiple scenarios in a no-risk setting back on Earth. And although today’s AI-powered and cloud-connected digital twin technologies are light years ahead of the manual tools they had back then, the core concept remains: creating an exact digital replica of a physical asset, process, or system to allow for a level of rigorous testing and deep understanding that could not be achieved or sustained in the real world.
A digital twin is a working digital representation of an actual operation. This could be anything from a machine to a process workflow, or even an entire facility. Twins use both live and historical production data to stay aligned with what’s actually happening on the floor. And rather than acting as static models, they evolve in real time as conditions change. Today’s best manufacturing teams regularly use digital twins to monitor equipment health, explore operating limits, and simulate wear or stress without fear of damaging physical systems or costly machines.
In order to reflect reality closely enough to be useful, the digital twin must continuously sync with what’s really happening in production. It must first be anchored to a specific scope such as a machine, a process step, a cell, or a flow. It can then pull in live or near-real-time signals such as run states, cycle times, quality results, material movements, or downtime events. These signals don’t have to reflect every possible eventuality – they just need to accurately represent the factors that influence the decision being explored on that twin.
Once grounded in real behaviour, the model can be used for controlled testing. Best practise is to only adjust one variable at a time – such as a sequence, a setting, a routing choice, or a buffer. This means that when you observe any impact upon the twin, you can be certain which variable caused it. And because the testing happens in the model rather than on the line, you’re free to explore trade-offs, spot side effects, and rule out poor options without worrying about breaking anything.
Digital twin technology has evolved to where there are specialized types of twins, built to specifically tackle the unique business challenges or scenarios you’re trying to better understand and improve.
| Type of digital twin | What it models | Common use case | Why teams choose it |
|---|---|---|---|
| Asset twin | A single machine or piece of equipment | Reducing downtime, diagnosing recurring issues | Clear ROI, fast learning, minimal disruption |
| Process twin | A process step and its operating conditions | Improving yield, quality, or cycle time | Helps teams understand cause and effect |
| Line / cell twin | Flow through a production line or work cell | Balancing throughput and sequencing | Reveals bottlenecks and handoff issues |
| Factory twin | Interactions across the facility | Capacity planning, layout, major changes | Supports higher-impact, cross-team decisions |
A digital twin isn’t a single application or feature. It’s the result of several established technologies working together to mirror how manufacturing actually runs – continuously, variably, and under real operating constraints.
Digital twins rely on industrial IoT connections to capture what’s happening on the shop floor. This includes run states, cycle times, alarms, sensor readings, and other operating signals from machines and control systems. These inputs keep the twin aligned with real behaviour rather than assumptions, even when only a subset of assets or sensors are connected at first.
At the heart of every twin are simulation engines that describe how work moves through an operation. They mathematically encode process steps, sequencing logic, capacity limits, buffers, and dependencies between stations. This is what allows teams to test changes and see likely downstream effects before they actually touch the line.
Analytics and machine learning take operational data and extract meaning from it at scale. They can spot recurring bottlenecks, correlations between conditions and defects, or patterns that aren’t obvious in raw signals. Over time, learning models can refine sensitivity ranges, improve forecasts, and strengthen the twin’s ability to address potential issues (or opportunities).
Computer vision uses cameras and image analysis to turn what is captured into structured data a digital twin can use. It can spot defects, confirm assembly steps, track material movement, or verify conditions that traditional sensors may miss. These visual signals feed directly into the model as consistent inputs rather than subjective human cheques.
This layer turns model outputs into readable formats like timelines, flow diagrams, dashboards, or 3D views – making the twin usable for day-to-day decisions. Instead of interpreting raw data or equations, teams get an understandable interface where they can clearly see how work moves, where constraints form, and how different scenarios compare.
Behind the scenes, cloud platforms provide the storage, processing, and elasticity that different twins need to maintain history, run simulations, and support multiple scenarios at once. They make it practical to scale twins across assets, lines, or sites while keeping insights accessible and secure, and making sure they’re always up to date.
A digital twin doesn’t need “perfect data” to be useful. But it does need the right signals for the particular decision you’re trying to understand, plus enough context to interpret them. A common approach is to start with partial inputs and expand as the twin learns and proves its value.
Digital twins are useful in many different ways, depending on the kind of manufacturing you do. The common thread isn’t so much the technology itself but, rather, the type of decisions you’re trying to make and the cost of getting them wrong. Potential use cases are practically infinite. The examples below reflect just few typical scenarios in different manufacturing environments.
Even a small process change can create an operation-wide ripple that doesn’t become visible until much later in the programme. A digital twin can model key process steps and operating conditions so teams can test adjustments, understand likely downstream impacts, and tighten control before issues surface at final inspection or certification.
Line balance can be disrupted by seemingly minor issues such as late parts, inconsistent cycle times, or a station that keeps falling behind. A line or cell twin allows you to try different responses such as adjusting the build sequence or redistributing work. This lets you then choose a more stable path to keep production rolling smoothly.
Yield can shift based on subtle changes in materials, machine settings, or inspection thresholds. By using a process twin, teams can explore which factors are the most likely to be contributing to defects and which adjustments are worth testing. Armed with these insights, you can avoid turning actual live production into a frustrating cycle of trial and error.
In engineering-to-order environments, every job has unique requirements and late changes are par for the course. A digital twin helps you assess the impact of each engineering change on capacity, routing, and delivery timelines. This leads to more informed and faster approvals, and greater confidence in your decisions – before you commit to anything you can’t undo.
Bottlenecks and rework can often emerge gradually and aren’t always obvious until schedules start slipping. A digital twin gives you the power to model how constraints behave and how delays propagate downstream. With the benefit of these accurate and robust insights your teams can better decide where to intervene first without creating new choke points elsewhere.
While building a new digital twin is an increasingly straightforward task, it still requires time and resources. For this reason, twins tend to present the most value when they can be aimed at tasks that are both expensive and repeatable. The goal isn’t to model everything. It’s to reduce the specific kinds of uncertainty that create the most disruption and cost in your operation.
The long-term benefits of digital twins and simulation technology show up not as a single operational win, but as a business-wide shift in how risk, investment, and growth can be managed. Over time, new operational ideas and optimisations become easier to test, justify, and scale – letting you focus more on growth and competitiveness and less on firefighting.
Digital twins tend to succeed or fail based less on technology and more on how they’re introduced into day-to-day decision-making. The items below represent some common manufacturing challenges with quick tips on how to overcome them.
Deciding on what “success” should look like can stall adoption. When outputs exist but no one agrees on what good performance looks like, trust and confidence drop. A small set of visibly shared, practical measures helps anchor the twin to outcomes people understand ahead of time.
Trying to model too much at once can blur focus and make results less reliable and useful. Teams make faster progress when they narrow the scope to a particular decision or problem where uncertainty is costly and improvement could be clearly felt and measured.
Models can drift from reality if they’re built on over-simplified assumptions. Delays, workarounds, and variability add up in ways the model may not capture. Compare the twin to a recent production run to help identify and correct assumptions before it’s used to inform decisions.
If signals are inconsistent or poorly understood, people stop relying on the twin, especially in the early days of adoption. Manage expectations by starting with fewer, higher-confidence inputs – to build credibility and give teams a solid base to expand from.
Not all changes are captured automatically. Updates to routings, work practises, or decision rules can leave a model out of step with reality. Clarifying which inputs update on their own and which need review helps keep the twin reliable and teams clear on what’s needed.
Lack of ownership risks turning twins into side projects with low stakes. Without a clear business owner, usage fades and insights go unused. Assigning responsibility to someone who relies on the outputs in daily decisions helps keep the work relevant and grounded.
Digital twins don’t eliminate uncertainty. They simply take learning and experimentation and move it to a safer place. For manufacturing leaders, that shift matters because it replaces “we’ll find out after we do it” with “we can see it before we commit.” Over time, the benefit isn’t just a better model – it’s a more confident way of running the business.
See how Infor’s industrial manufacturing software can help you take full advantage of the latest technologies – including digital twins.