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 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.
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.
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 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.
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.
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.
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.
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.
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.