Next-generation AI platforms can accept billions of data points to build digital representations of real-world supply chains
If you are a fan of sci-fi movies and TV shows, you’ve probably seen movies where a person is cloned with the same body and memories as the original but is used to experiment to learn how they react to different scenarios in the real world.
While we are not quite at that stage yet, imagine having the ability to clone something that can think and learn. Imagine you have a robot version of yourself that copies everything you do and learns from your actions. That robot can help you predict what might happen next and suggest ways to avoid problems. You can then change environmental conditions or run scenarios on that robot to view how the robot would react, giving you an insight into possible scenarios you, the real person, may react in those hypothetical scenarios and environmental conditions, providing insight into your strengths and weaknesses. It’s like having a sidekick that knows everything about you and enables you to make better decisions. In the same way, an AI digital twin is a virtual copy of something that can analyze data and suggest ways to improve its performance or avoid issues.
An AI digital twin is a virtual replica of a physical object or system that can be used to analyze and optimize its performance. It can be fed static or real-time data from the physical object or system and then uses artificial intelligence (AI) algorithms to analyze and predict the behavior of the object or system.
AI digital twins can be used in various industries, including manufacturing, transportation, and healthcare, to optimize the performance of complex systems and improve decision-making. For example, an AI digital twin of a manufacturing plant could identify bottlenecks in the production process and suggest improvements. In transportation management, digital twins can optimize routes and schedules for delivery trucks and identify opportunities to reduce fuel consumption and emissions.
In supply chains, these twins are created using a combination of logistics, financial, and sensor data to create the exact replica of the environment. They can be used to simulate and analyze the performance of an object or system under different conditions. They can also be used to optimize the design of new techniques, identify potential problems before they occur, and improve the efficiency and effectiveness of existing systems.
Are AI digital twins an accurate representation of the real world?
Just how accurate are digital twins? While they are designed to be as precise as possible in representing the real world, the accuracy of an AI digital twin depends on the quality and completeness of the data used to create it.
Collecting a large and diverse dataset representing the object or system being modeled under different conditions and scenarios is necessary to create an accurate twin. This data is then used to train systems using machine learning or neural network algorithms that can accurately predict the behavior of the object or system.
However, suppose the data used to create the AI digital twin is incomplete or inaccurate. In that case, the digital twin may not be a reliable representation of the real-world object or system. Data scientists could use techniques to augment the missing data. There are various techniques to overcome these issues, which we won’t cover here, but will in a future blog post. In this case, the digital twin may not be 100% the image of the object or system.
Can AI digital twins help with supply chain improvement?
AI digital twins can help with supply chain improvement by analyzing real-time data from the supply chain network and using machine learning algorithms to forecast product supply and optimize production and distribution plans to meet demand.
By collecting data from various sources within the supply chain, such as sales, production, and transportation data, an AI digital twin can create a comprehensive supply chain model and use this model to make accurate predictions about future product supply and demand.
For example, an AI digital twin could analyze purchase order trends, production capacity, raw materials prices, geopolitical risk, and micro and macroeconomic data to predict a product’s future supply and optimize production and distribution plans to meet demand. This can help organizations better manage their inventory and reduce the risk of stock-outs or excess inventory.
Overall, using AI digital twins can help organizations in the supply chain make more accurate and data-driven decisions, leading to improved efficiency and reduced costs.
How far in advance can AI predict supply chain delays and disruptions?
It is difficult to predict with certainty how far in advance artificial intelligence (AI) can accurately predict supply chain delays and disruptions. This will depend on several factors, including the complexity of the supply chain, the availability and quality of data, and the sophistication of the AI algorithms being used.
AI can generally analyze data from various sources, such as market trends, commodities prices, publicly available data, and transportation data, to identify potential delays and disruptions to the supply chain. The more data available and the more advanced the AI algorithms, the more accurately delays and disruptions can be predicted.
However, new technologies that predict supply chain delays and disruptions are coming into play more accurately than ever. To proactively connect the dots and find trends, patterns, and associations to respond to future developments, the Nostradamus Al platform has incorporated over 17,000 data sets (as of this writing) from multiple sources. These sources come from geopolitical data, economic data, financial information, commodities prices, raw materials prices, and news events. These data sources are then coupled with the historical purchase order information for the system to find those trends and patterns to predict when a delay or disruption can occur. The system then learns which factors are most important to the client’s supply chain and their interactions to predict when a delay or disruption can occur for each product and supplier before the client places their purchase order. Building on this, we can create a digital twin on how those factors impact certain product and supplier delays. As in our robot example from before, the user can run a hypothetical scenario, such as increasing the geopolitical risk in China or increasing certain raw materials prices to view how that can impact their supply chain network. Since our system understands the different components of the client’s supply chain, the user can create what-if scenarios to understand their risks. If they see that certain price increases in raw materials or commodities prices affect their supply chain, they may want to hedge those prices or understand that when they do occur, which suppliers are at most and least risk of delays and disruptions under those circumstances?
Once you have predicted, then what?
Using the prediction data generated by the Nostradamus AI platform, one might ask, what can be done to avoid these delays? You can do many things with that prediction output, from reducing canceled orders to optimizing inventory. Another way to utilize the output is to create a digital twin to understand the strength and weaknesses of your supply chain. The digital twin would be able to simulate any changes based on variables selected to determine an optimized supply chain and allows the client to see the net effect of potential changes.
Overall, it is vital to continuously monitor and analyze the supply chain to identify potential delays and disruptions and take proactive measures to minimize their impact when they do occur.
For more information on how you can benefit from supply chain delay and disruption solutions from Ceres Technology, contact us today.