Digital Twins for Manufacturers: A Guide for Mid-Size Teams
What Is a Digital Twin?
A digital twin is a virtual model of a physical asset, process, or system that is connected to operational data. It may use information from sensors, machine controllers, maintenance records, and production systems to reflect current operating conditions. A digital twin can be as simple as a visual model for monitoring equipment data or as detailed as a digital twin simulation of an entire production line.
Benefits of Digital Twins
Digital twins can help:
- review equipment performance and operating status
- identify recurring constraints in a process
- compare maintenance, staffing, or scheduling scenarios
- examine possible effects of a layout or product-mix change
- test production assumptions before applying changes on the floor
You might use a digital twin to examine data collected in real time. You might also use it to help predict failures before they occur. The National Institute of Standards and Technology estimates that widespread adoption of digital twins could produce approximately $37.9 billion in annual benefits across the U.S. manufacturing industry. This is an industry-wide estimate, not a forecast for a single manufacturer, but it illustrates why many businesses are assessing this technology.
What a Digital Twin Needs to Work
Digital twins vary in complexity, but most include three core elements: operational data, a model or simulation, and a process for using the insights.
Operational data
A digital twin needs information from your physical operation. This may include machine status, cycle times, downtime records, inventory levels, quality data, maintenance history, or production schedules.
You might already collect some of this information through programmable logic controllers, manufacturing execution systems, enterprise resource planning systems, or connected sensors. A project may begin by identifying which sources are available, reliable, and relevant to the problem being studied.
A model or simulation
A digital twin organizes data and represents how an asset or process operates. For a single machine, it may track condition and performance data. For a production line, it may simulate the flow of materials, work in progress, and labor across multiple steps.
This can allow teams to test “what-if” scenarios, such as how a schedule change, new product mix, or different batch size could affect a line.
A process for using the insights
A digital twin becomes valuable when the people closest to the work can learn and adapt from its insights. That may mean involving operations leaders, maintenance personnel, engineering, and IT early on in the project. Teams should decide who reviews the information, how often they act on it, and which decisions the model will support.
Overcoming Common Challenges
Digital twins can provide useful operational insights, but it’s not always easy getting started. Understanding potential barriers early can help you plan a more manageable project and determine what resources you need.
Limited awareness of capabilities
If your team is new to digital twins, it may be difficult to know where the technology can provide the most value. Start with a specific operational question, such as identifying the cause of recurring downtime or evaluating changes to a production schedule. A focused use case can help your team understand what the technology does before you expand its scope.
Fragmented data
The information needed for a digital twin may be spread across machines, spreadsheets, maintenance systems, and other software. Data may also be collected in different formats or from outdated software. First, identify which data sources are necessary for your specific use case and determine whether that information is accessible and/or reliable. You might not need to connect every system for your first project.
Lack of in-house expertise
Building and maintaining a digital twin requires extensive operational knowledge, and you might not have that expertise in-house. Depending on the project, you can consider supplementing your team with technology vendors, consultants, systems integrators, or managed platforms that provide some of the required capabilities.
How to Build a Digital Twin
| Stage | What Happens | Typical Timeframe |
| 1. Proof of concept | Rapidly build a basic model to demonstrate feasibility and refine impact estimates. | Weeks |
| 2. Minimum viable product | Higher-fidelity simulation; data feeds identified; future-state architecture designed. Early value capture (staffing, product mix, bottleneck insights) is often possible here. | 1–2 months |
| 3. Production integration | Simulation connects to live data feeds and moves from sandbox into daily operations. | Varies by complexity |
| 4. Optimization | Machine-learning-driven optimizer platforms identify optimal scheduling or production patterns in real time. | Ongoing |
Keep in mind, this type of development project can span two to three months, or more than a year—depending on your team’s expertise and the complexity of your operation. When scaled across your entire facility, digital twins can be transformative.
Are Enterprise Teams Required?
Not necessarily. The people and resources required depend on the scope and complexity of the digital twin you want to build. Large-scale digital twin projects may involve cross-functional teams of engineers, data scientists, IT architects, operations specialists, and other technical experts. But that level of internal expertise isn’t required for every project. Managed and off-the-shelf digital twin platforms can reduce the technical work involved in building models, connecting data, and maintaining the underlying technology.
Additional Technologies
Digital twins don’t operate in isolation. They work with various other cutting-edge technologies to enhance their capabilities.
| Technology | Role | Example in Practice |
| Internet of Things (IoT) | Sensors collect real-time data from the physical environment, mirrored by the digital twin. | Vibration sensors on a machine feeding a condition-monitoring dashboard. |
| AI & Machine Learning | Analyzes the data generated by a digital twin, offers predictive insights, and automates decisions. | Flagging a bearing likely to fail before it causes a line stoppage. |
| Virtual Reality | Lets engineers and operators immerse themselves in and interact with the digital twin. | Walking through a simulated line change before it happens on the floor. |
FAQ Section
What is a digital twin in manufacturing?
A digital twin is a virtual, data-connected replica of a physical asset, process, or production line. It uses live data—from sensors, machine controllers, or production systems—to mirror what’s happening on the shop floor in real time, so manufacturers can spot problems, test changes, and predict outcomes without touching the physical equipment.
How long does it take to implement a digital twin?
Timelines range from about two to three months for a focused, single-process pilot to more than a year for a full production-line implementation, depending on factory complexity and team experience. Most successful projects start with a narrow proof of concept, then expand in stages: MVP simulation, live production integration, and finally optimization with machine learning.
Do I need an in-house data science team to build one?
Not necessarily. A large-scale, fully custom build typically involves a cross-functional team of engineers, data scientists, and IT architects. Mid-size manufacturers increasingly have the option to work with managed or off-the-shelf digital twin platforms instead, which handle much of the underlying infrastructure and can significantly lower the technical lift required in-house.
What’s the ROI of a digital twin for a mid-size manufacturer?
Nationally, NIST estimates that widespread adoption of digital twins could deliver about $37.9 billion annually in savings across the U.S. manufacturing industry. At the plant level, the return typically comes from reduced unplanned downtime, better staffing and scheduling decisions, and catching production bottlenecks before they cause bigger losses.
What other technologies work alongside digital twins?
Digital twins rarely work alone. They’re commonly paired with Internet of Things (IoT) sensors that feed real-time data, artificial intelligence and machine learning that turn that data into predictions, and, in some cases, virtual reality that lets engineers interact with the twin more intuitively.
Start with a Focused Goal
Digital twins can range from a model of a single machine to a connected simulation of an entire facility. The right starting point depends on your operation, your available data, and the problem you’re trying to solve. Rather than trying to model everything at once, start with a focused question or process where better information could make a measurable difference. From there, you can evaluate the results, refine your approach, and decide whether expanding the technology makes sense for your operation.
Product Compliance and Suitability
The statements contained in this guide are intended for general informational purposes only. Such statements do not constitute a product recommendation or representation as to the appropriateness, accuracy, completeness, correctness, or currentness of the information provided. Information provided in this guide does not replace the use by you of any manufacturer instructions, technical product manual, or other professional resource or adviser available to you. Always read, understand, and follow all manufacturer instructions. Portions of this article were generated in part by ChatGPT, and edited by a member of the Zoro team.
Sources
- McKinsey — Digital twins: the next frontier of factory optimization: mckinsey.com/…/digital-twins-the-next-frontier-of-factory-optimization
- DataParc — Digital twin manufacturing: dataparc.com/blog/digital-twin-manufacturing
- Azilen — Digital Twin Cost Guide 2026 (cost-tier estimates added in this draft): azilen.com/blog/digital-twin-cost
