Supply Chain AI: This Time It’s for Real

Data analytics and artificial intelligence (AI) are hot topics these days, especially in supply chain circles.

Data analytics is the science of examining raw data to help draw conclusions about information. Predictive analytics uses data to foresee trends and patterns.

Artificial intelligence is a continuation of the concepts around predictive analytics, with one major difference: An AI system is able to make assumptions, and test and learn autonomously. Then it applies machine learning, deep learning, and other techniques to solve actual problems.

Artificial intelligence technology has been with us for a long time. What has changed recently is the power of computing, cloud-based service options, and the applicability of AI to supply chain and logistics.


In general, AI can be used in two ways. First, it can assist people in their day-to-day tasks, personally or commercially, without having complete control of the output. It can also reduce errors, for example, using a virtual assistant and in data analysis. Second, AI can help automate processes by functioning without the need for any human intervention—for example, robots performing process steps in a fulfillment center.

Using AI to assist people and automate processes helps the top and bottom line because companies waste a lot of time and money on having humans perform basic supply chain tasks.

Companies can significantly improve network, capacity, and demand-planning decisions with AI predictive capabilities using big data. Big data insights, along with AI, can improve supply chain transparency and optimization, and can potentially revolutionize the agility and efficiency of supply chain decision-making.

On a more tactical and operational level, companies are using AI in robotics and automated vehicles to track, locate, and move inventory within warehouses. While totally autonomous vehicles might not happen for a while, we already see technology such as assisted braking, lane-assist, and highway autopilot.

Streamlining procurement-related tasks can happen through the automation and augmentation of chatbot capabilities, which require access to robust and intelligent data sets. This can allow for automating actions such as placing purchasing requests, researching and answering internal questions regarding procurement functionalities, or receiving, filing, and documenting invoices and payment/order requests.

Improved Customer Service

AI can also personalize relationships between logistics providers and customers. A logistics provider can now enable a customer to query Amazon’s Alexa to track a shipment. If there is a problem with the shipment, Echo users can ask for assistance and be directed to the logistics company’s customer assistance department.

Using predictive analytics for supplier selection and supplier relationship management with data generated from supplier assessments, audits, and credit scoring could provide a basis for decisions regarding supplier selection and risk management. The supplier relationship would be more predictive and intelligent.

There is no doubt that the potential of AI and machine learning will finally be achieved in the supply chain, enabling companies to eliminate waste, in many cases before it even occurs. Now that’s real artificial intelligence.

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