Digital Factory Twin
The Digital Factory Twin is a virtual mirror image of physical components such as plants, machines and transport vehicles from manufacturing and logistics, as well as the associated work processes within the plant boundaries.
ROI-EFESO has comprehensive content-related, technological and methodological expertise in the design and construction of Digital Factory Twins. We do not map the challenges of our customers with digital standard solutions, but create realistic, industry-specific simulation models. Our portfolio of offerings includes the following services in particular:
- Optimization of material flow
We mirror the entire material flow from the receipt of raw materials and supplied components to the manufacturing of the product and its intralogistics (e.g. packaging, storage, distribution) with a digital twin. Details such as the characteristics of conveyor belts or times for path distances are taken into account. This creates a precise picture of the material flow - and the data basis for simulating the impact of specific changes, such as volume increases. - Planning the physical infrastructure
The information on plant utilization and possible process improvements obtained with the digital twin is valuable in the context of digitization and automation projects. For example, they support investment decisions when purchasing new plant and machinery: not only with regard to criteria such as their performance and consumption data in relation to the application target, but also in space planning. 3D models can be used to visualize, for example, the size of buffer areas, the dimensions of conveyor belts, or the advantages and disadvantages of different warehouse designs. - Rebuilding or new construction of the factory
A digital twin of the entire factory enables realistic planning for new construction or rebuilding measures. The spatial representation quickly shows the dimensions in which machines, systems, areas, transport routes and warehouses should be "configured" in order to achieve an optimum material flow. The data basis can be, for example, historical values from the production of the plant or another location. - Minimizing risks
These historical values also help companies better prepare for future events. Process simulations of the digital twin can "replay" the site's historical orders with different disruptions and problems. This shows, for example, which capacities are affected by production fluctuations and how they should be dimensioned. In the actual situation, decisions on measures are then correspondingly faster and better. At the same time, these simulations sharpen the view for risks.
Beyond the plant boundaries, we use Digital Supply Chain Twins to create a virtual mirror image of a supply chain or supply network that integrates suppliers and customers in real time. Learn more about Digital Supply Chain Twins Digital Supply Chain Twins here.
Case Studies
Digital Process Twin: Process optimization through Predictive Quality and Predictive Production
An automotive supplier improved the transparency of work and organizational processes in a production plant for dashboards. With a "Digital Process Twin" from ROI-EFESO, the company reduced the reject rate and made improvement potentials in its value creation networks visible.
Project: Design of a sustainable production and distribution network taking into account cost and robustness/flexibility criteria.
Approach: Use of a digital twin/dynamic simulation model with several variables such as depth of value creation, structure and role of plants and suppliers, personnel requirements in direct and indirect areas, and KPIs at network and site level.
Project: Improve material flow in a site with complex conveyor lines and expensive buffer times; evaluate alternative equipment investments in terms of their impact on material flow.
Results: The use of a digital twin/dynamic simulation model resulted in a 30% reduction in buffer and inventory investments compared to the static approach. Production planning/control uses the model for decision making.