The Digital Twin is the virtual representation of physical processes, assets or systems. Organizations build and maintain digital supply chain twins to manage their complex and global supply chain networks. Digital twins are utilized to provide horizontal visibility, identify opportunities by discovering patterns, eliminate inefficiencies by determining root causes, create responsive plans for potential disruptions, optimize current processes and available assets and so much more.
Building and maintaining a digital twin is not a simple network modeling effort. It requires cross-functional collaboration by operations, finance, IT and BI teams at the minimum.
The following steps provide you with a proven methodology for building a digital twin for your supply chain.
Step 1: Map out processes and/or assets
Organizations might prefer to start with creating an end-to-end digital twin of their supply chain or start with a section as a pilot and then scale it after testing. In either case, the teams in charge of each of the component of the supply chain operations and assets, e.g. transportation (fleets), warehousing, inventory, sourcing etc. should get together to map out the processes and assets to be virtually cloned end-to-end.
Step 2: Determine data sources
Traditional network modeling usually calls for past data and in some cases forecasts and plans. Digital twins on the other hand require real-time data. Therefore, they are generally linked hand in hand to Internet of Things (IoT). As all relevant operational and financial data is identified during the mapping process, the sources and owners of the data should also be identified.
Step 3: Choose hosting technology
There are many critical technological factors when creating and maintaining an IT architecture digital twin. The architecture should allow for connecting to multiple data sources including internal, external and real-time data, and support a wide variety of data formats. It should be scalable to run multiple simulation and optimization scenarios. Security and monitoring should be configured to provide easy access to users with diverse purposes and tasks.
Step 4: Model Supply Chain Twin
The Digital Twin should be built keeping the long-term objectives in mind in a similar fashion to when building supply chain models for optimization and simulation. The structure should allow for simulating and analyzing alternative processes, optimizing asset performances and predict occurrences.
Step 5: Connect to real time data
The two main distinctions of digital twin compared to optimization of simulation modeling is using real time data and minimal data aggregation to allow for detailed visibility. Once the digital twin is built, it is connected to internal as well as external real time data sources. This might require investment in new technologies such as sensors or RFIDs and collaboration with third parties.
Step 6: Simulate, Optimize & Analyze
Opportunities for prescriptive, predictive and advanced analytics using digital supply chain twin to drive decisions range from strategic to operational. Combined with machine learning when applicable, operations and assets can be simulated or optimized to gain insights, test alternative scenarios or become responsive to disruptions. The outputs should be shared across the organization to drive organization-wide action plans.
Step 7: Scale & Enhance
As the digital twin of the supply chain is built piece by piece and tested, it is only bound to evolve and expand. It can be scaled across organization to clone end-to-end supply chains, but when we think about the supply chain holistically, it can go even beyond the borders of the organization connecting with suppliers and customers. The twin can be enhanced with additional real-time data points from internal sources as well as third parties and industry organizations.
And as always, the whole process needs to be well documented in order to maintain, modify and scale.