Artificial intelligence (AI) has revolutionized applications from detecting financial fraud to diagnosing cancer, but it is also tailor-made for improving modern supply chain operations. While supply chain leaders are already using AI to dramatically drive efficiency gains, the impact of AI in supply chain analytics and supply chain automation is just beginning to be fully understood and appreciated. As of December 2020, only 12% of supply chain managers reported utilizing AI as part of their supply chain management process. 

What is artificial intelligence?

AI systems evaluate incredibly large and unstructured data sets, finding patterns and deviations which allow them to make intelligent, data-based decisions to reach optimal outcomes. This unique ability to create meaning from unrelated, disparate information allows AI systems to match or sometimes exceed human problem-solving ability, and do so at scale.

How is AI affecting supply chain analytics?

Because of the vast amount of data involved in modern supply chain management, and because of the propensity for human error to create critical and pervasive issues, AI’s proficiency at streamlining and automating supply chain management is game-changing.

Here are five ways supply chain operations can use AI and supply-chain analytics to increase their efficiency, reliability and effectiveness.

1. Automating and improving visual inspection

Logistics organizations have used cameras for many years, to store pictures of packages and bar codes so that there was a record in case of a dispute over accountability for packages that were damaged or delivered to the wrong recipient. But machine vision infuses cameras with something approaching a human’s ability to make sense of what those cameras see. By leveraging an approach to artificial intelligence called machine learning, cameras can be trained to “look” for the existence of a readable bar code, to make sure a cardboard box isn’t coming apart or even to identify that that large round object with the irregular tread pattern is a tire and not a hula-hoop.

AI-based visual inspection can be used in combination with other technologies for a variety of valuable applications. It can enhance supply chain automation, by enabling robots to spot quality problems with packages that might be missed by less capable systems. Visual inspection is particularly useful for quality control processes, by making it possible to do root-cause analysis of problems, thereby freeing up logistics’ personnel to work on higher-level, less repetitive tasks. These findings and insights can also be shared with vendors to help them comply with policies and practices required to keep automated systems running smoothly, an increasingly critical requirement given the rising volume and customer satisfaction requirements facing all logistics organizations.

2. Facilitating more accurate inventory management

AI systems can help organizations spend only as much as is necessary on holding and carrying costs. Because AI systems can hold and process so much data, accurately and efficiently, their use can dramatically reduce inventory-related losses.

For example, an AI data model could be created to process images of address labels. Besides learning to spot labels with missing information or that are positioned on packages improperly, they can also calculate which warehouse to ship from, how to most efficiently store remaining inventory and how and when to place new “just in time” orders to replenish stocks.

3. Improving delivery time and reliability

Distribution organizations that use AI to optimize their delivery processes have reported up to 20% efficiency increases. For example, UPS’s AI-based ORION fleet management system cuts fuel costs by 9 million gallons a year, just by showing drivers how to avoid time-consuming left-hand turns. The company has said that every mile cut from its drivers’ daily rounds thanks to Orion saves $50 million.

AI is a natural match to use in concert with technology like GPS and telemetry. This combination reduces delivery time by increasing efficiencies across the supply chain, and especially from the time a product leaves a warehouse.

AI’s data processing capacity:

• Facilitates optimal organization of the warehouse for fastest distribution
• Identifies the most useful local warehousing facilities to reduce unnecessary shipping where possible
• Isolates areas where products can, or should, be produced more locally to reduce transportation cost
• Optimizes delivery routes with real-time GPS and weather data, order configuration and last-mile operations

All of these pieces combine to create a more streamlined delivery process that can get products into the hands of consumers more quickly than ever.

4. Increasing operational efficiency

An added benefit of supply chain AI is that the longer the software processes information, the smarter it gets — allowing it to identify areas of fraud, inconsistency, inefficiency, loss and more. The more it recognizes patterns and trends, the better it gets at creating added benefits for supply chain analytics like:

• Reducing reliance on manual reporting: AI reduces the potential for error and fraud by leveraging its real-time data analysis, facilitating near-instant recognition of fraudulent chargebacks, for example, or other suspicious activity on a particular account or within an inventory record.
• Reducing human error in data collection and reporting: Organizations lose an average of $150,000 each year as a result of invoicing and carrier mistakes. And that’s the result of human error from one very small portion of the supply chain. Automating this process with AI eliminates inconsistencies from absentmindedness or human “autopiloting.”
• Improving individual performance by supplementing known problem areas within supply chains: Many warehouses struggle with picking times, which is the time it takes a particular item to be retrieved from its place in a warehouse. AI can help a warehouse worker identify products that are stored nearest to each other, allowing workers to pick and package items in order of greatest efficiency, reducing both time spent and physical distance covered. In some cases, AI can also be used to do the picking for workers, reducing time while increasing overall order fulfillment accuracy and safety.

5. Enhancing end-to-end visibility

Especially when coupled with emerging technology like IoT (Internet of Things), AI can provide unprecedented transparency and confidence in the supply chain. This is especially invaluable for logistics organizations in an omni-channel world, in which ecosystem partners need to know as early as possible whether the product will be delivered into inventory at a retail location, or delivered directly to a home or business via an e-commerce or mobile app. As soon as a product is logged into such a system, AI can help give manufacturers, marketers, distributors and others full visibility over order fulfillment, inventory management, returns and other metrics.

This level of detail can also help prevent product fraud and tampering, as well as ensure that shipping specifications — such as temperature or handling requirements — are consistently met to ensure end products are safe to consume and meet any necessary regulations or specifications.

With AI’s added visibility into every step of the supply chain, managers have more actionable and accurate data to make crucial inventory decisions. In some cases AI can even make those decisions automatically so managers don’t have to think about it.

While it’s hard to know exactly what the future holds for supply chain analytics, it’s safe to assume AI will have an increasing role in all components of supply chain management, leading to increased efficiency and improved performance across all touchpoints.