When evaluating strategic changes, one of the basic decision processes companies make is answering the question:

“What is the capital investment required to expand or build new manufacturing capacity?”

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To frame an informed and accurate response to the question above requires a virtual twin model of production systems, so that manufacturers can accurately evaluate what new or changed facilities and manufacturing process should look like.

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To know how much money a new or expanded plant will really cost, and to gain a competitive advantage, you first need to picture the plant in full detail by pulling together every scrap of digital evidence manufacturers have — drawings, CAD models, bills of material — and turning them into one living, three dimensional replica of the line.

With the virtual twin in place, teams can “fly through” the factory before it exists, test different layouts, and watch the flow of parts, people and robots to see where space, time or energy will be wasted. This helps to reduce inefficiencies by finding them ahead of going live with the operation, allowing businesses to make adjustments ahead of time, reducing product waste and robot downtime. 

In short, the virtual twin turns a risky capital guess into a confident business plan — well before the first robot is ordered, taking all of the risk out of the investment made by businesses.

Step-by-step Planning Guide

Here’s how the virtual twin can come together:

1. Consolidate all data: Pull 2 D drawings, CAD models, bills of material and point cloud scans into one platform so equipment, layout and process data share a single source of truth.
2. Process engineering: Convert the engineering service BOM software into an MBOM, sequence operations, assign resources and generate work instructions. This locks the product and the process together before any physical spend.
3. Machining and addiitive: Simulate NC toolpaths or additive layers directly on the twin, validating surface quality and spindle loads while updates from design propagate automatically.
4. Virtual factory and flow: Lay out machines, conveyors and aisles in 3 D, then run flow simulations to check throughput, buffer sizes and shift patterns, exposing bottlenecks early.
5. Robotics and automation: Drop any robot models into the scene, generate collision free paths and optimize cycle time and energy use, ensuring the chosen automation mix meets takt without overspend. 
6. Ergonomics and safety: Evaluate reach, posture and load handling for every manual task, and verify that robots, AGVs and people maintain safe clearances. 
7. Virtual commissioning and go-live: Plug real PLC or robot code into the model, debug logic and train operators in the twin, cutting on site commissioning time.
8. Continuous optimization: Once the plant is running, stream MES/IoT data back into the twin to monitor OEE and rerun “what if” scenarios as tariffs, demand or product mix changes into its cycle.

Turning Scattered Assets into a Clear Vision

Following this chronology turns scattered engineering assets into a clear vision with a living factory model that answers the capital expenditure cquestion. By the time the first robot is ordered — or cancelled — you have already:

• Flown through the future plant, spotting space, time or energy waste.
• Stress-tested dozens of tariff, wage and logistics scenarios in hours, not weeks.
• Locked product, process and people into a configuration that maximizes OEE on day one.