How Digital Twin Helps You Control Your Supply Chains?

What a Digital Twin Really Is

A warehouse digital twin is a live 3D visualization, data-fed model that mirrors the current state of your operations. It is built from continuous data streams coming from the warehouse management system, ERP, IoT sensors, order feeds, and physical constraints such as racking layouts and material handling equipment capacities. This integrated data set allows the digital representation to stay synchronised with the physical site.

Unlike a one-time simulation study, a digital twin is not static. It updates continuously, reflects actual performance, learns from outcomes, and closes the loop by sending improved plans back into execution systems.

How the Data Twin Action Loop Works

The first stage of the loop is data ingestion and normalisation. Orders, inventory balances, manpower and equipment capacities, lead times, and SLAs are collected from different systems and harmonised into a consistent format. This step ensures that the twin is working with reliable, comparable information.

Next, the model is calibrated with actual layouts, and material handling characteristics. It then generates optimised operating plans, such as pick paths, dock schedules, or routing proposals, which are written back into operational tools. Once execution takes place, actual performance is captured and compared with the plan, and parameters are adjusted to improve the next cycle.

High-Impact Use Cases in Daily Operations

• Warehouse layout and slotting are among the best use cases. The twin can test different placement strategies for fast movers and simulate put-away and pick paths. This can be particularly useful when designing or re-designing warehousing solutions for large sites.
• In the case of manpower and material handling equipment, the digital twin can determine alternative shift structures,measure picker travel distances and create congestion heat maps. These insights allow managers to deploy people and machines where they add the most value, which is critical in high-throughput FMCG warehouse operations that run to strict timelines.
• Inventory posture is another high-impact area. The model can test safety stock and service-level trade-offs by SKU and region, supporting more robust integrated warehousing distribution strategies. As these decisions are linked with routing, rail share, and ePOD, they naturally strengthen end-to-end distribution solutions that balance cost, speed, and reliability across the network.

Outcomes to Track with a Digital Twin

To realise full value, organisations need structured governance around the twin. This starts with comparing a pre-twin baseline against post-twin performance each month across metrics such as order cycle time, pick productivity, space utilisation, and damage rates. Exception drill-downs then guide root-cause analysis, and a continuous improvement pipeline ensures that ideas are prioritised and implemented in a consistent way.

As more sites are onboarded, the twin becomes a central tool for coordinated warehousing and distribution planning. In parallel, insights from the warehouse twin can be used to inform transport network design.

Conclusion

Logi-View, Mahindra Logistics’ 3D Digital Twin of warehouses, brings-these advantages into operations. If you’d like to learn more about Logi-View and how it can be applied to help your own operations, please get in touch with us at: enquiries@mahindralogistics.com.