Artificial Intelligence (AI) is a term applied when a machine performs human-like cognitive functions, such as making decisions or predictions. Artificial intelligence seems to be everywhere these days. It’s in cars that can self-parallel park or drive themselves. It’s in computers, like Watson, that beat the TV program Jeopardy. Artificial intelligence is poised to transform the supply chain.
Artificial intelligence got its evolutionary start decades ago when computers began to analyze information and make predictions based on pattern recognition. Carnegie Mellon University states that AI has "evolved to address problems of probabilistic and numeric nature, leading to the incorporation of approaches from mathematics, engineering, operations research and economics."
In the supply chain, AI can analyze large data sets and make recommendations that will improve customer service and operations while supporting better working capital management. The opportunity to leverage AI increases as corporate systems become more inter-connected giving access to a wider breadth of supply chain data.
Let’s examine the benefits of AI linking transportation data with order data. In this scenario, the carrier may say, “we can deliver this product in 2 days,” but using AI will reveal the carrier’s past performance for shipping that product on a specific day, using a specific lane, which indicates that there is a 30% chance the order will arrive in five days, not two. This information supplies customer service and supply chain professionals with proactive alerts of potential fulfillment challenges.
To take this a step further, AI could also compare historical shipping data to the customer’s requested delivery date to provide recommendations whether this low-cost carrier’s performance is good enough to meet the requirement, or if you need to consider carrier B who is 20% more expensive, but 30% more likely to deliver the product on-time.
A third level of artificial intelligence may further inform this decision based on the “required level of customer service.” Real-time data supports manufacturers or distributors making critical decision to avert potential issues before they occur and even delight customers with service excellence.
Other examples of the benefits of AI in Supply Chain:
Demand Visibility – AI technology can be used within supply chains to better sense and respond to complex operations, helping to better meet customer demands.
Spend Efficiency – AI drives down costs by creating real-time visibility of the spend data, effectively eliminating fraud and uncovering errors, while presenting opportunities for savings.
Manufacturing Flexibility – SIRI-like assistants can communicate to robots, telling them to make a certain number of pieces based on a surge in seasonal demand, effectively overriding previous instructions.
Operational Improvements- Autonomous vehicles such as forklifts support 24x7 operations in high-volume warehouses driving higher service levels and immediate signaling of inventory movements.
Critical to AI for supply chain systems is to avoid data silos. Every transaction associated with a customer order or supplier order needs to contribute to the collective knowledge used for machine learning. As each interaction occurs and data is collected, AI can use the data to make sense of what is going on – and what will happen in the future, effectively predicting actions and reactions that will take place.
Elemica’s supply chain operating network is the backbone for conducting commerce between supply chain trading partners connected to the network, effectively capturing event and transaction data across the end-to-end supply chain. Elemica eliminates the data silos that often are a barrier to AI. Elemica’s Artificial Intelligence can be applied to the collected information using machine-learning concepts to generate actionable insights based on predicting future supply chain behaviors.