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Digital Twins in Supply Chain Management

In the movement towards Industry 4.0, the major focus is on digitalization and developing analytic real-time capabilities. Digital twins play a huge role here. They are essentially a virtual representation of a product or process across its lifecycle and utilize real-time and other sources of data to learn, reason and dynamically recalibrate for improved decision making. 

The global digital twin market is expected to grow at a rate of 58% CAGR from 3.1 billion USD in 2020 to 48.2 billion USD by 2026. The key technologies enabling digital twins include: internet of things (IoT), cloud computing, APIs and open standards, artificial intelligence, and AR/VR technologies (mixed reality).

In supply chain context, this virtual representation would include all aspects from the supplier to customer locations. These include the manufacturing factories, the warehouse and distribution centers, transportation routes and retailers. Businesses using digital twins can evaluate the complex interlinked trade-offs in capacity, service, inventory and total landed cost.

Due to measurable improvements it is gaining industry prominence. Businesses are able to understand the implications of their decisions by running 'what - if' scenarios and making recommendations based on simulations throughout the supply chain vertically and horizontally. The disruption caused by the pandemic has created challenges related to safety, supply chain resilience, labor etc. Real time access or visibility across company boundaries and down to the supply-demand chain has become crucial.

Supply chains are extremely complex especially in process industries like chemical, steel and industries where the production and distribution networks are interconnected. Despite forecasting there is a residual uncertainty/variability of demand and volatility of supply.

There are three key horizons where digital twins help the supply chain: long term planning, sales and operations planning and short-term planning.

In long term planning, these virtual representations help improve efficiency in capital expenditure and optimize the setup of the complete supply chain system by understanding structural bottlenecks and additional capacity requirements.

Digital twins also help in short-term planning and execution by identifying execution risks at an early stage and mitigating risks rather than managing crisis. Also helps idle time reduction of bottleneck assets and improves inventory positions.

Sales and operations plan optimization is carried out by feeding insights that are generated by running a plan back into the system for improvements. This will help identify misaligned plans, system constraints and latent bottlenecks. The insights generated also assist in maintenance plan alignment and inventory matching to market demand.

A supply chain digital twin is useful for:

·         Understanding supply chain dynamics and behavior

·         Optimizing warehouse design and operational performance

·         Creating a logistics network

·         Discovering bottlenecks in processes

·         Supply chain design change and development testing

·         Risk Monitoring & contingence testing

·         Transportation planning

·         Inventory optimization

·         Enhancing shipment protection

·         Predicting the performance of packaging materials

·         Cash and cost to serve analysis

·         Forecasting operations

There are some interesting startups in the space like Logivations based out of Germany which offer AI based software to create digital twins of a logistics network which is constantly optimized. Another startup, Cognition Factory offers AI based solutions to simulate warehouse planning, configuration and continuous optimization of material handling systems. US based Datumix provides 3D virtual simulations of equipment and then deploy algorithms to monitor in real-time for maintenance.

A few organizations who have utilized digital twins successfully include:

DHL: The global logistics giant created a virtual representation of their Tetra Pak product warehouse. It is used to identify optimal storage conditions and receives real time inputs from the equivalent physical warehouse which is located in Singapore to tracks performance in real time.

Unilever: The consumer-goods giant created virtual copies of its physical factories using AI/ML capabilities. The aim was to track physical logistic conditions and test operational changes. They are working in collaboration with Microsoft Corporation to create virtual versions of over 300 global plants.



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