Model your production line, simulate its behavior and compare improvements before changing the real plant. People and agents work with the same Digital Twin inside Dedalo Studio.
Click a view to explore the model, results and experiments.
See the line as a system.
Machines, buffers and connections share one model. Explore the layout and review the results in the same workspace.
Dedalo Digital Twin
Capabilities in detail
Before promising a delivery, changing a shift or investing in a machine, test the decision on a model of your plant. Dedalo helps you compare time, cost and capacity without interrupting production.
Assess a new order before promising a delivery date
Run the new component’s operations through a model of your plant. Estimate time and cost against available resources before committing to the customer.
Check whether the plan can handle disruption
Include downtime, changeovers, scrap and rework. See how they affect deliveries and compare alternatives before the problem reaches the shop floor.
Share a clear view of the plant
Explore the line and watch parts move with production, maintenance and management. Discuss the same scenarios without rebuilding the context each time.
Find where time and capacity are lost
Identify machines that slow the flow, waiting time and accumulating work. Focus on changes that could improve production.
Try improvements before investing
Compare staffing levels, work sequences and buffer capacity. Assess the expected benefit before reorganizing work or buying another machine.
Bring evidence to decisions
Share readable reports with assumptions, comparisons and results. Help colleagues and customers understand your recommendation and its expected effects.
Ask an agent to run the tests for you
Describe the outcome you need: agents prepare the model, run simulations and explain the differences. Explore alternatives through a conversation.
Plan maintenance with fewer surprises
Assess how downtime and repairs affect production. Compare when to intervene and which resources can limit the impact on deliveries.
Separate improvement from a lucky result
Repeat tests under different conditions to see how stable the results are. Compare alternatives while accounting for variation in processing times and operations.
Plan around the resources you actually have
Consider shifts, breaks, skills and material movements. Check how to combine people and machines for the scheduled work.
Put it to work
Illustrative workflows to adapt to your team, data and processes.
From a new component to time and cost estimates
A customer sends a component to manufacture. Before preparing an offer, you need to understand how it will run through your plant.
A concrete basis for discussing feasibility, price and delivery.
New component
The agent analyzes the component and the customer’s requirements.
Four agents, one production plan
Scheduled work changes. Four agents collaborate to observe the model, assess priorities and find a better way to organize the line.
Coordinated decisions across the line, with the effects of alternatives visible.
Observe the plan
Four agents observe workloads, resources and scheduled operations.
From machine manuals to a production line model
You have the machine documentation and want to represent your plant without starting from scratch.
Less manual setup: start with the documents you already have.
Upload manuals
Upload machine documents to Dedalo’s company knowledge base.
One product for your team and your agents.
Ask an agent to build or modify a line, run a simulation, explain queues and bottlenecks, or test improvement levers on a copy. More than 100 MCP tools support this work. Agent runs appear in Studio with seed summaries, and proposals identify the relevant window, panel and field.
The MCP interface covers 100% of the functionality available in the traditional UI. People and agents work with the same product and data inside Dedalo Studio.
Part of the wider Studio workflow
Combine simulation with analysis skills, project knowledge and a gap-analysis pipeline to compare results with operating targets. REST APIs, WebSocket and command-line execution support integration and automated runs alongside the browser workspace.
