Digital twin production planning
Make informed decisions by running different scenarios through a virtual model of your production system.
Imagine knowing exactly what to produce, when to produce and in what batch sizes. That is now possible thanks to our digital twin production planning solutions.
Digital Twin Production Planning is when your business uses a virtual model to simulate its physical production system. This helps you make informed decisions on your entire manufacturing process. By predicting potential problems and testing different scenarios, you can optimize production, reduce costs, and improve efficiency.
Digital twin production planning uses dynamic scenario analysis to assess production capacity and bottlenecks in real-time. It considers various factors that can impact production, such as different temperatures, offering a complete view of the production process and its environment. The system is designed to be easy to use, with visualization tools that help users quickly understand the impact of different production schemes.
Digital twin production planning supports sensitivity and bottleneck analysis, allowing your business to test and evaluate different production strategies. It helps you make informed decisions by answering questions like, "Should we prioritize producing a high-value product, even if it slows down other production lines?" or "Should we produce a lower-value product to keep the factory running at full speed?"
Digital twin production planning s a powerful tool that helps your business make better investment decisions. How? By accurately estimating ROI. This way, you can make informed decisions and optimize your production processes, which helps you succeed in a fast-paced production world.
Business Value
Ready to boost your manufacturing process? Thanks to digital twin production planning, you can make your business more data-driven and efficient. It’s the best way to gain insight into new ways to improve your production processes and to think beyond day-to-day operational decisions.
Gradually introducing digital twins will keep your business prepared for any upcoming situation in the future and allow the system to make its own adjustments, without needing human intervention.
Support data-driven decision-making
Make quick informed decisions using simulations to test multiple strategies and ideas. Digital twin production planning also suggests the best parameters for achieving a target. It can handle more parameters and values than a human can, making it useful for product and service design, resource management, portfolio decisions and pre-sales analysis. For example, it can help you determine whether meeting a client’s needs will be sustainable in the production process.
Proactive risk mitigation
Predictive risk management strategies can help you prevent problems in manufacturing. Real-time reporting and predictive maintenance through the use of digital twin technology can alert manufacturers to potential problems before they occur. This proactive approach helps you manage your manufacturing operations more effectively.
Process optimization and operational excellence
Improve processes by using a digital twin to get a complete view of the production chain and its environment. The digital twin can also help with production planning and make sure that plants are running efficiently. By digitizing processes and testing them under different conditions, you can identify areas where innovation can improve the performance of your business.
Cost reduction
Beyond predictive maintenance, digital twins can reduce costs in unexpected ways. They can, for example, optimize planning capacity and resource management, such as using trucks efficiently for production or increasing output while taking the entire fleet into account.
Challenges
When creating a content management platform, there are some functional and technical challenges to consider, such as:
Making implicit domain knowledge explicit
Digital twins generate models of business rules that are normally only available to experts and decision makers. This implicit domain knowledge can be extracted and used to manage complex product specifications by creating a plan of how things currently work.
Early capture and alignment on domain experts' expectations
To avoid losing stakeholders’ trust when outcomes don't meet expectations, clear and transparent communication is important. We use an iterative development approach, delivering small simulations or functionalities one at a time. We start simple and add complexity gradually.
Dependency on domain expert vision for prioritization
To make sure we're working on the most important simulation scenarios, we listen to what the stakeholders want. We focus on those scenarios in each development cycle and get feedback from them. Together, we set goals and work with ML6 coaches to help the organization be more agile.
A digital twin often implies a new way of working
A new way of working can be intimidating. However, we help your business in making this transition by aligning it with your organization's strategy and processes. That’s why we always analyze the current and desired states of the business before we begin the transition.
Value through user adoption
To make the tool easy to use, we need to know who will be using it and what they need. We work with key users to co-create the interface and identify their needs. We help guide users through this process.
Data is spread and data owners need to be involved
To implement a digital twin smoothly, data owners should understand how their data will be used and contribute to its development. The quality of the simulation depends on the quality of the data.
Required amount of business rules
Building a digital twin can be made easier by reducing the number of business rules that must be taken into account. Typically, the best solution is a digital twin that employs both machine learning and business rules.
Data quality and completeness
The digital twin often requires data from various sources, each with its own level of quality. Since the digital twin only works with high-quality data, extra technical work is necessary to check and maintain the data quality.
The solution
A content management platform is made up of several essential building blocks that work together to create a powerful and efficient system:
Digital twins rely on combining data from different sources, including sensors, ERP systems, and other manufacturing software. The data must be precise, consistent, and complete.
To create a digital twin, you need an accurate model of the physical system, which includes all relevant machines, materials, and processes. The model must predict performance and identify bottlenecks to be successful.
The digital twin should predict the production system's performance based on various factors, including demand, raw materials, equipment availability, and maintenance schedules, using multiple performance metrics.
The digital twin should be able to optimize the production process by identifying the most efficient use of resources, reducing waste, and improving the quality of output.
Developing an intuitive dashboard for users is important since it's often their only way of interacting with the solution.
Get in touch
We currently have four locations across Europe and are excited to make your business grow. Let us know how we can help.