In this article, Megan R. Nichols, Schooled By Science, explains how artificial intelligence is disrupting logistics, plus she explores how experts believe it will impact the supply chain in the next few years.
Artificial intelligence has been one of the most significant disruptions of the past few years. Typically, AI is associated with the tech industry, but it’s also having significant impacts on just about any sector that produces large amounts of data, like logistics.
The ability of AI algorithms to rapidly analyse vast amounts of information in real time makes it an effective tool for optimising supply chain operations and automating systems. It’s also ideal for creating more accurate forecasts about everything from product demand to shipping timelines.
Here is how AI is disrupting logistics, plus how experts believe it will impact the supply chain in the next few years.
Predictive fleet maintenance
Conventional preventive maintenance can catch most operational issues before they cause machine failure. However, regular checkups can be expensive and sometimes require equipment downtime. They also won’t necessarily prevent every malfunction — some problems will arise and become serious issues in between regular checks.
Predictive maintenance is a new strategy that takes advantage of AI algorithms and data-collecting sensors to catch operational issues before they require maintenance. This is one of the most widely used applications of AI and smart tech in industries that rely on heavy equipment, like robots, vehicles and large machines. The supply chain has recently adopted this approach to monitor fleet health.
A sensor or set of sensors is installed on a piece of equipment to track data like vibration, fuel usage and temperature. This data is then transferred to a predictive maintenance platform. It uses an AI algorithm to detect subtle patterns in operational data that can show when a vehicle or machine is on the verge of failure. These systems can then alert drivers, managers or other staff to shut down equipment and order maintenance as necessary, preventing costly repairs and downtime.
This information is also often displayed on a predictive maintenance dashboard, giving managers a real-time snapshot of fleet health.
Automated warehouse robotics
The average warehouse worker spends only 20 per cent of their shift on revenue-generating activities. The rest is spent moving around the warehouse, collecting and transferring items as needed. In the past few years, big-name logistics companies have started deploying automated guided vehicles (AGVs) in their warehouses in a bid to cut down on these times. They are designed to work near people and follow preprogrammed routes to move items around the warehouse, sometimes with minimal need for human intervention. Some advanced models will also use a combination of sensors, cameras and AI machine vision tech to “see” their surroundings, allowing them to intelligently navigate the warehouse floor. In addition to these specialised vehicles, AI-powered smart warehouse tech has also been used to automate forklifts, pushcarts and small vehicle fleets. This equipment has typically been difficult or impossible to automate safely with conventional tech. These robots have become popular recently due to improvements to IoT tech and warehouse management systems. With these protocols in place, data can be used to optimise automated vehicle routes and functions.
Anticipatory logistics
The amount of data available to logistics companies has exploded over the past few years. This growth in available information has enabled new approaches to forecasting, like anticipatory logistics. This involves the use of predictive AI algorithms trained on logistics data, like sales and stock numbers, to predict supply chain operations. Forecasts generated by this AI algorithm can be used throughout the supply chain to determine, for example, how much of a given product a warehouse should hold on to. It can recommend how many trucks a logistics company will need to optimise the movement of cargo from one distribution centre to another. Because AI is excellent at picking up on subtle correlations in large data sets, it can help companies identify patterns that may have otherwise slipped past conventional analytics and forecasting methods. For example, it can find irregular seasonality shifts, nonlinear trends and other unusual demand patterns that are typically difficult to predict. These predictive algorithms can also help forecast when shipments of goods will arrive, allowing retailers and suppliers to provide more accurate information on shipping times to their customers.
The future of AI in the supply chain
While AI has been applied in a variety of ways across the supply chain, there is still room for further disruption. Much of the data produced by the supply chain is not machine-readable or typically collected into a single dataset that can be used by an AI analytics platform. Many logistics companies also do not have the necessary collection schemes to take full advantage of AI. There are several more AI innovations that experts believe may disrupt the logistics industry shortly. This includes advanced cloud-based AI management platforms that bring all company data together, allowing it to optimise and streamline supply chain operations. It is likely that as data-collection improves and more companies adopt tech, AI adoption in the supply chain will continue to grow.
