A digital twin is a highly detailed digital replica of any system that uses comprehensive data to emulate the working of the system at all times. Therefore, a supply chain digital twin is a simulation model of a supply chain. You feed the model real-time data from all sources and systems of the organizations that can exactly work out the effects of macro and micro-changes on the system using advanced analytics and learning models.
As the paper “What Makes the Digital Twin an Ingenious Companion?” wonderfully explains, a digital twin, if properly implemented and integrated, can harness data in real-time from all working systems to represent the supply chain of any system perfectly, and grow along with it.
Digital Twin in the Supply Chain: History and Use Cases
A digital twin is not a new concept. As far back as the 1970s and 80s, engineers and designers would use 3D CAD models to assess the feasibility of a design and implement it without fatal issues.
The supply chain digital twin was being used in industries by the late 1980s. Dassault Systèmes had used it in the design and creation of the Boeing 777 to get information on the conditions and requirements of materials using real-time data.
More recently, DHL has been using a digital twin for its Singapore warehouse to keep track of its Tetra Pak packages in real-time. GE uses digital twins to monitor its wind turbines in the North Sea.
Unilever worked with Microsoft Corporation in 2018 to create a digital twin of one of its Brazil facilities that manufactured environment-sensitive products like soaps and ice cream.
Similarly, in 2019, CKE Holdings Inc. created a digital twin of all its facilities to introduce improvements in several areas like staff training, management, scheduling, customer demand fulfilment, work optimization, etc.
Why is it so popular now?
The onset of the pandemic resulted in two major changes in how industrial systems operated. The first is that there was a necessity to reduce the size of the workforce to prevent the further spread of COVID-19. As the personnel on workspace floors decreased, the need to have a system in place that could provide real-time data and predictions of operations without human interference using the existing data in the system arose.
Second, the pandemic proved that very few industries had viable contingency plans in the event of such a global challenge. There was no predictive or prescriptive model to tell executives how to streamline supplies and processes in an event such as this.
The digital twin solves both problems using available technology, making it a saviour in this situation for the supply chain.
The supply chain is riddled with challenges despite several counts of modernization and optimization. High volatility and variability despite systematic analytics, long delays for logistics and supplies, piling up of constraints and bottlenecks to affect all downstream systems, high complexity in the coordination of large systems are some issues afflicting the supply chain.
Applying a digital twin across the entire supply chain or some of its parts has various benefits. Like for instance:
- Application of variable “what-if” instances to make advanced predictions.
- Control over thousands of variables to find the best-fit solutions.
- Projection of stock-outs, surplus, and other such situations that require prior planning to cope.
- Designing and streamlining processes using real-time results.
- Short- and long-term planning for optimum use of resources and time.
- Increase in sales and operations efficiency and productivity through rigorous planning and virtual implementation.
- Dynamic and rapid optimization to avoid future issues.
- Better analysis of the supply chain performance.
- Timely process and resource mobilization for optimum performance.
- Better regulation of expenses and resources in real-time by identifying opportunities and hurdles.
What distinguishes a Digital Twin from regular Supply Chain Simulation Models?
Supply chains and logistics were some of the first processes fed into simulation models to get greater insight into their working and use analytics to predict results more accurately. But regular simulation models differ from a digital twin of a supply chain on many counts.
The most significant difference is that digital twins use live data fed into it constantly and from all possible sources to minimize the number of assumptions and emulate a real-world supply chain as far as possible. Simulation models work before the system, digital twins work in tandem. You can configure a digital twin using thousands of variables to predict and provide solutions for many future possibilities. You can set alerts, create action plans, and even set triggers for the automatic implementation of processes.
A digital twin behaves like the real deal, just in a virtual form. In brief, a digital twin emulates while a simulation model simulates.
With technologies like Big Data, Data Science, Machine Learning, Artificial Intelligence, Natural Language Processing, and more, models like the supply chain digital twin have become easier to implement and set to accuracy. Today, industries almost everywhere use this technology to automate and streamline their supply chains and remove past roadblocks for smooth operation.
Our platform uses Digital Object Twins (DOTs) to representing objects from the actual world. Using a digital twin of your supply chain and tracking improvement measures on an object level through our DOTs allows you to quantify benefits from these measures. Want to know more? Get in touch with us!