1. Business Intelligence & Reporting
Operating data is analyzed without real-time reference and presented in reports and dashboards. The focus is retrospective and answers the question, “What happened?”.
Digital Twin for Production and Factory: Virtually Plan, Test, and Control Processes and Systems
What if you could test every modification, every new production line, and every capacity bottleneck before a single machine is moved?
That is exactly what a digital twin enables: As a virtual, data-based representation of your production environment, it is continuously fed with real-time data from your factory, allowing it to reflect the actual condition of your equipment and process chains at any time.
This enables you to make fact-based decisions based on real data. It also protects your company from costly poor investments: Future measures can be safely tested in the model in advance, allowing you to determine the benefits of an investment before any actual costs are incurred.
What exactly is a digital twin?
A digital twin is a virtual, data-based representation of a real-world object, such as a machine, system, factory, or process. Through a closed-loop control system, it continuously exchanges information with its physical counterpart and maps its behavior throughout its entire life cycle. This makes it the highest maturity level of a digital representation: It not only shows what is happening in the factory, but also makes it possible to test potential scenarios in advance using simulations. The insights gained are then fed back into the system, for example in the form of adjusted parameters or control commands, allowing processes to be actively adjusted during ongoing operations.
Digital Twin: Characteristics, Maturity Levels, and How It Differs from a Digital Shadow
The term digital twin is used in nearly every digitalization project today, often with different meanings. That is why it is worth taking a closer look at what defines it: A digital twin remains permanently connected to its real-world counterpart and maps its behavior over time.
It is characterized by four key features:
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The digital and virtual representation of an object
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Bidirectional information exchange between the physical and virtual worlds
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Consideration throughout the entire life cycle
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A combination of different models, some of which are simulation-capable
Above all, bidirectional information exchange clearly distinguishes the digital twin from a mere visualization: The model actively feeds information back into the real-world system.
Digital Twin or Digital Shadow: What’s the Difference?
The two terms are often used interchangeably, but they do not mean the same thing. A digital shadow is the real-time-capable data foundation of a digital representation: It provides information on demand, but without automated feedback to the real world. A digital twin is created only when this data flow becomes a closed-loop system and the model itself feeds information back into the physical system. The digital shadow is therefore a logical preliminary stage of the digital twin: With comparatively little integration effort, it quickly creates transparency and can later be expanded into a fully developed digital twin.
From Reporting to a Closed-Loop System:
The Maturity Levels of a Digital Twin
The path to a digital twin consists of successive stages that build on one another. A company’s progress along this path can be assessed based on five maturity levels, ranging from basic data analysis to a self-regulating closed-loop system.
The Four Types of Digital Twins:
Product, Factory, Process, and Service Twins
A digital twin can be used in a variety of areas. From an individual component to a connected value creation process, different representations with different benefits are created depending on the specific application. Four types have become established:
Each of these types can be used as either a planning or operational twin: during the early planning phase to optimize based on models, and during ongoing operations enriched with real-time production data. This creates a connected system landscape that can deliver value across the entire value chain.
Applications in Production: From Virtual Commissioning to Predictive Maintenance
A digital representation of your production environment can give you crucial flexibility at virtually every stage of manufacturing, from early product planning and commissioning to potential optimizations during ongoing operations:
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Virtual Commissioning: Safeguard Systems Before Ramp-Up
With a digital twin, you can virtually commission an entire factory or production line. Control systems and processes are validated in the model, allowing errors to be identified and resolved at an early stage. This shortens commissioning time and reduces ramp-up risks.
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Production and Process Simulation: Validate Processes Before Implementation
Whether production simulation, process simulation, or factory simulation, changes to production lines, layouts, and processes can be tested in the model without risk. This makes it possible to verify in advance whether a system can achieve the planned output cost-effectively.
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Bottleneck Analysis and Capacity: Identify Bottlenecks Systematically
The digital twin makes bottlenecks and capacity limits visible before they affect your actual operations. A bottleneck analysis in the model reveals where throughput is being lost and how capacity utilization can be specifically improved.
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Predictive Maintenance and Condition Monitoring: Ensure System Availability
Condition monitoring continuously tracks machine and sensor data. Based on this data, predictive maintenance identifies maintenance needs at an early stage, reducing unplanned downtime and increasing system availability.
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Energy and Logistics Optimization During Ongoing Operations
During ongoing operations, the digital twin also provides a reliable basis for decision-making, for example, regarding how energy demand develops as throughput increases or how internal logistics should be designed.
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What-If Analyses: Avoid Poor Investments
By combining the digital twin with simulation models, what-if analyses can be performed in a virtual environment. Business decisions can be tested in advance, helping to avoid misallocation of resources and safeguard your investments.
For the specialized planning of these applications, we draw on our expertise in factory planning and material flow design. The digital twin brings both together: It transforms planning and material flow models into a data-driven representation in which results can be simulated, validated, and optimized together.
The Digital Twin in Production:
All Benefits at a Glance
The use cases clearly demonstrate the economic benefits: A digital twin reduces investment risks, accelerates decision-making, and creates transparency throughout the entire life cycle of the system it represents.
- Risk Reduction
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Decisions and modifications are tested in the model before capital is committed.
- Cost Reduction
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Fewer physical prototypes and early error detection measurably reduce costs.
- Time Savings
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By preparing processes in the model, development and commissioning can be completed faster and with fewer iterations.
- Quality Assurance
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Early identification of potential sources of error reduces rework and scrap.
- Transparency
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An end-to-end representation of the value chain enables rapid decision-making in the event of disruptions.
- Maintenance
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Real-time condition monitoring and predictive maintenance increase system availability and output.
Ingenics Consulting
Your Strong Partner for Your Digital Twin
For the implementation of your digital twin, we combine our methodological expertise with established technology partners. In particular, we rely on NavVis for 3D scanning of existing factories (reality capture, ideal for brownfield projects) and ASCon Systems for rapid implementation without extensive programming effort (no-code platform).
In addition, you benefit from three key advantages when working with Ingenics Consulting:
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Contributing to Industry Standards
As a member of VDI and a co-author on the guideline committee for the VDI 5000 “Digital Factory Twin” guideline project, we are actively contributing to the development of a standard that will shape how the industry operates in the future.
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Vendor-Neutral
We are not tied to any software provider and select the right technology solely based on your specific requirements and starting point.
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40 Years of Process Expertise
We develop your digital twin based on four decades of experience in process and organizational consulting.
Your Path to the Digital Twin:
Soundly Assessed, Vendor-Neutrally Implemented
As a key component of your overarching operations and digitalization strategy, the digital twin connects planning, operations, and optimization in a continuous cycle.
Ingenics Consulting supports you throughout implementation and rollout with a vendor-neutral approach tailored to your specific needs. As a co-author of the VDI 5000 “Digital Factory Twin” guideline project, which will define the recognized standard for digital twins in factories, we are also actively helping shape the binding rules in this field and know exactly what matters when it comes to implementation.
Implementation: Six Steps to a Digital Twin
We develop a digital twin in six structured phases:
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1. Digital Twin Strategy
We develop the target vision and define the purpose of the digital twin within your digitalization strategy.
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2. Analysis & Synthesis
We assess the current state and identify the potential of your IT and application landscape, including user-centric information requirements.
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3. Concept & Roadmap
We develop the roadmap, concept, and reference architecture, including piloting based on an agile mini-factory approach.
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4. Specification
All functional and technical requirements are defined in detail and documented in requirements specifications.
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5. Tendering
Vendor-neutral selection of providers and suppliers, including bid comparisons and clear cost transparency.
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6. Implementation
We support development, pilots, and rollout with IT project management through to a controlled go-live.
The Digital Twin as a Foundation for the Industrial Metaverse and AI
When implemented correctly, the digital twin becomes the central hub of a digital ecosystem comprising manufacturers, customers, and suppliers. For companies, this means end-to-end transparency beyond their own organizational boundaries: Disruptions, changes in demand, and optimization potential become visible earlier, and decisions can be coordinated across the entire value chain.
Two developments are reinforcing this forward-looking role: the Industrial Metaverse, in which digital representations interact across platforms, and artificial intelligence, which uses digital twins as a data foundation for predictive decision-making. Both share a common prerequisite: a clearly defined data strategy that determines how data is generated, maintained, and made accessible. Only then can the accuracy of the digital twin’s forecasts and the reliability of the resulting decisions be determined. After all, a digital twin is only as good as the data that feeds it.
The Data Foundation: No Digital Twin Without Reliable Data
A digital twin is the result of comprehensive system integration that extends across your entire organization and encompasses all relevant areas and processes required for your specific use case.
This requires a robust data foundation that is available in real time wherever possible. Only with this foundation can realistic and reliable simulations be performed.
Several components are required for this:
These individual components come together in the data backbone, which provides data from different systems in a user- and value-oriented manner.
Learn how we establish a robust data infrastructure for companies in the IT Architecture and Application Landscape section.
Additional Services
Strategy & Digital Transformation
Here, we define your company’s digital target vision. Within this vision, Business Intelligence is the capability to make and manage decisions based on data.
IT-Architecture & Application Landscape
Creates the technical foundation with end-to-end interfaces and data flows that enables Business Intelligence to access consistent data in the first place.
Software & MVP-Development
Learn how vendor-neutral software selection and proven MVP development can lay the foundation for successful digitalization projects.
Process and System Harmonization
Process and system harmonization creates uniform standards, reduces complexity, and makes your organization more resilient and scalable across locations.
Business Intelligence
It makes the harmonized data foundation and process KPIs measurable and manageable.
FAQ - Frequently Asked Questions About Digital Twins
How long does it take to implement a digital twin?
The implementation timeline depends on the scope, dimensions, and existing level of data maturity within your company. We recommend starting with individual components that deliver immediate value: An agile mini-factory pilot in a clearly defined production area can deliver initial results within a short period of time and lay the foundation for gradually expanding your digital twin.
Should we build a digital twin right away or start with a digital shadow?
In most cases, a digital shadow is the right place to start. It establishes the real-time data foundation and already provides transparency before the closed-loop system of a fully developed digital twin is in place.
Should we start in the planning phase or on the shop floor?
Both are possible. The key question is which use case offers the greatest potential for your company: In the planning phase, the digital twin helps validate investment and layout decisions, while on the shop floor, it optimizes ongoing operations. We work with you to determine the right starting point based on your individual target vision.
How can a digital twin be implemented at international locations?
A digital twin is location-independent because it is based on a centralized, cloud-based data foundation. Existing factories can be captured in 3D using reality capture and integrated as brownfield models. This allows international locations to be represented in a comparable way and managed using a shared data foundation.
Is a digital twin also worthwhile for small and medium-sized companies?
Yes, provided the approach is tailored to your company’s starting point. A step-by-step approach using individual components keeps the effort manageable and delivers measurable value for your production operations at an early stage. A low-code or no-code approach further reduces implementation effort, making it possible to implement a robust digital twin without a large development team.