A Digital Twin is a virtual replica of a physical object, process or system that reflects its actual state, behaviour and performance in real time. The Digital Twin uses IoT sensor data, artificial intelligence (AI) and data analytics to monitor, simulate and optimise the performance of the real object.

Application of Digital Twins:
Industry and manufacturing – machine monitoring, failure prediction, performance optimisation.
Supply chain and logistics – simulation of transport and warehouse processes.
Infrastructure and construction – management of intelligent buildings and cities.
Health sector – modelling of human organisms for precision diagnosis and therapy.
Automotive and aerospace – testing vehicles and systems prior to actual production.

Key features of Digital Twins:
Mirroring the actual facility – continuous updating based on sensor data.
Predictive analysis – predicting faults and optimising maintenance.
Simulation of different scenarios – testing changes before implementing them in the real world.
Integration with AI and IoT – using advanced technologies to automate processes.

Digital Twins help companies improve operational efficiency, reduce maintenance costs and minimise the risk of failure.

Digital Twins in the supply chain enable dynamic, virtual models of logistics, warehousing and transportation processes, allowing them to be optimised in real time. By integrating data from IoT sensors, ERP, AI and machine learning, Digital Twins help companies anticipate disruptions, improve efficiency and reduce operational costs.

How are Digital Twins optimising the supply chain?

Improved visibility and real-time monitoring
Digital Twins create an accurate supply chain model that takes into account inventory, shipment location and operational efficiency.
With IoT and AI, companies can track the flow of goods, storage temperatures (e.g. for pharmaceuticals) and identify potential delays.

Optimisation of transport routes and logistics costs
Digital Twins analyses weather conditions, traffic, vehicle availability and selects the most efficient transport routes.
Simulations minimise empty journeys, reduce CO₂ emissions and make better use of transport resources.

Inventory management and demand forecasting
Digital Twins model demand in a variety of scenarios to intelligently manage inventory and reduce warehousing costs.
AI analyses historical data, seasonality and market trends to help companies avoid stock shortages or overstocking.

Predictive Maintenance
In warehouses and logistics centres, Digital Twins can monitor the condition of machinery and transport systems, predicting breakdowns before they occur.
This reduces the risk of downtime and repair costs.

What-if analysis
Companies can test various scenarios, e.g. what happens in the event of a supplier strike, a sudden increase in orders or production delays.
This allows them to better prepare their response strategy to unforeseen disruptions.

Reducing CO₂ emissions and sustainability
Digital Twins help minimise energy consumption and carbon footprint by optimising transport and storage processes.
Simulations help companies comply with ESG regulations and reduce the environmental impact of their operations.

Benefits of using Digital Twins in the supply chain:
Reduced operating costs – better inventory management and more efficient transport.
Faster response to disruptions – identification of risks and automatic recommendations for action.
Greater efficiency – improved operational performance through process optimisation.
Improved customer service – shorter delivery times and greater predictability of order fulfilment.

Summary
The use of Digital Twins in the supply chain allows companies to more accurately manage every aspect of their logistics operations. Through simulations, predictive analytics and process optimisation, companies can increase their competitiveness, reduce costs and better respond to market changes.

DATURE ENTERPRISE software uses artificial intelligence and machine learning in the process of demand forecasting and inventory optimization. The Dature system allows information about the supply chain to be collected and analysed and used to calculate optimal stock control parameters dal various scenarios of supply chain constraints and costs. The system provides indicators that measure the effectiveness of stock management from various points of view, enabling a proper assessment of the current and future situation of the company.
The system provides methods for forecasting seasonal demand and demand influenced by calendar days. Inventory management methods allow for both pre-season inventory building approaches, dynamic safety stock control and JIT.

TheDATURE application ENTERPRISE can also use expertise in the demand forecasting process. Authorized users can enter expert forecasts and adjust statistical forecasts with them. The process is fully auditable in terms of who changed the forecast when and how. This makes it possible to track the accuracy of both statistical and expert forecasts. As a result, the organization learns how to forecast more accurately and improve process efficiency.