Mention ‘digital twin’ to supply chain executives and you are likely to get a reaction that is somewhere between amazement and frustration. Amazement because of what digital twin can do to optimize their supply chains. Frustration because the pace of execution and adoption is much slower than they anticipated, with digital twin still remaining a concept. But this is set to change. Research firm Markets and Markets found that the digital twin market will grow at a compounded annual growth rate of 45.4%, from $3.8 billion in 2019 to $35.8 billion in 2025. This growth will be led by the increased adoption of internet of things and cloud, which are used to implement digital twin.
A digital twin for supply chain is a virtual replica of the physical supply chain world. Made possible by the significant evolution of IoT and digitalization, it combines models with sensor data to efficiently plan for the future and meet customer demands. A big question almost everyone has but few ask - what is the difference between digital simulation and digital twin?
While digital simulation is a replica of the physical asset, it is based on ‘what could happen in the physical world’. Digital twin represents actual events from the physical world. For instance, if supply chain planners wish to simulate the impact of logistics disruption on production stability, they can simulate multiple scenarios to get an idea of the potential impact, but the scenarios may not be actionable. However, digital twin allows them to visualize and understand real-life scenarios to make better decisions.
In essence, digital twin enables a responsive business planning process to rapidly meet evolving demand. So, should you create digital twin for your supply chain?
Digital twin in supply chain – Look before you leap
While almost every aspect of the supply chain—from scheduling and capacity management to operational demand planning, logistics, warehousing, and risk management—are worthy candidates for digital twin, it may not be a good idea to jump right in.
Here are four critical questions that organizations must consider before adopting digital twin for their supply chain:
#1 When is a good time to start?
These questions will help organizations determine their digital twin readiness:
· Are our processes digitized and standardized?
· Can our core supply chain assets and equipment sensors support digital twin?
· Can we share data across the supply chain ecosystem?
#2 Is it expensive?
The business value of digital twin far outweighs the cost to implement it. Advancements in IoT and sensor technologies are continuously driving down the overall cost of such initiatives. The cost to deploy a digital twin that replicates a simple production line, with the basic capability to transmit machine condition data, can range from a few hundred thousand to a million dollars. The actual cost varies depending on real-world engagement conditions such as the price of sensors, complexity of the processes, digital infrastructure, and more.
#3 How long would it take?
Typically, implementing digital twin takes a few months once the basic pre-requisites are in place. The time taken to deploy digital twin depends on the complexity of the process being addressed and the availability of information and infrastructure.
#4 Is there a real business case for digital twin in supply chain?
Absolutely! Think about the explicit and implicit cost (premium freight, inventory, schedule change, penalties for non-conformance, etc.) that organizations incur because of their inability to completely visualize the impact of changes in certain aspects of the supply chain on others. Each of these suboptimal decisions, which are mostly gut-based, frequently cost millions of dollars to companies. Now imagine having the ability to play the entire scenario with the capability to assess the actual impact and then select the right action.
Take the case of GE and Baker Hughes. They used digital twin to build and ship a fully assembled, 3,500-ton turbogenerator from Tuscany, Italy to Tenzig, Kazakhstan. The logistical supply chain for this mammoth project spanned thousands of miles and several months. In a traditional supply chain scenario, managing the supply of complex bespoke components required in such a project could have taken years, with even a small delay holding up production for months. Digital twin enabled the engineers from GE and Baker Hughes to view and manage the supply chain from beginning to end on a single analytical system that can be updated in real time.
Real-time intelligence in supply chain
Digital twin in supply chain enables always-on agility, end-to-end transparency, and holistic decision making, driving customer engagement and competitive advantage. Whether it is replicating customer demand, manufacturing, or logistics, digital twin enables businesses to transform their supply chains into an intelligent function that can not only optimize but also course-correct whenever required.
Stay tuned for our next blog on specific scenarios, where digital twin is transforming supply chains.