Artificial intelligence has shaken up many industries, affecting workflows and decision-making processes. Many fleet managers and other logistics professionals are interested in how AI supply chain management applications could improve their outcomes. What innovations can leaders expect by using them?
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Increased Driver Productivity
Many logistics industry authorities begin researching AI solutions once they recognize numerous downsides in their existing processes. Such was the case with a less-than-truckload carrier that wanted to upgrade legacy technology and incorporate more advanced options. Executives selected a user-friendly, AI-powered platform that was implemented in nine months across a fleet of 2,500 drivers and 25 terminals.
The system operates on smartphones and tablets, allowing drivers to record information or do other important tasks without staying in their vehicles. They can also flag issues in real time, such as when customers refuse deliveries or rescheduling is needed because the trucks arrive after business hours. Additionally, this solution has improved driver-dispatcher communications, keeping the two groups informed about traffic conditions, inclement weather or other concerns.
This tech upgrade saved drivers 20 minutes daily on average and sparked decisions about other improvements. Leaders plan to eliminate paper from the company’s processes, allowing vehicle operators to take digital images of confirmation documents when they pick up loads. The chosen AI software can also improve route planning by accounting for factors such as stop lengths and break frequency.
Artificial intelligence algorithms can process vast amounts of data, allowing leaders to detect patterns they would otherwise miss. The increased insights can help them pinpoint inefficiencies, such as why drivers on specific routes usually do not meet their daily targets. Those insights can show them when and how to improve processes and which aspects to tackle first.
Some companies use AI algorithms for advanced demand forecasting. The availability of historical data allows parties to improve resource allocation to better serve customers. Such enhancements can also ensure drivers have appropriate daily workloads and strategically planned routes.
Enhanced Knowledge of Product Conditions in Supply Chains
If customers receive broken products, an important question to answer is when the damage occurred. Did poor quality control processes result in defective items leaving factories unnoticed, or were problems in other parts of the supply chain to blame? AI supply chain management efforts can determine those matters and others.
Decision-makers can use that information to find the best preventive measures. That might involve shipping the items in a more appropriate type of packaging that keeps the contents from moving and ensures they are well-protected. Alternatively, getting to the bottom of things might mean using connected sensors to learn more about what happens to parcels in transit. Suppose the data shows specific supply chain partners have a history of rough handling or other events that could break or spoil the products. Then, leaders may need to set new metrics that those parties must meet to abide by their contractual obligations.
At one of the world’s largest e-commerce companies, up to five employees perform six-point visual inspections on products before they leave fulfillment centers. However, this is a time-consuming process, and workers typically do not find faulty items. That is why internal researchers are working on a better system that would use AI algorithms to find abnormalities and flag things that do not meet minimum standards. That approach should be faster, and it would allow workers to stop those items from progressing through supply chains.
Those working on this project trained the AI by exposing it to millions of images of damaged and undamaged goods. This approach allows the technology to create a gallery of pictures to refer to when checking products before shipment. It is three times more effective than manual checks, and the company plans to use it to assess more than 40 million products monthly.
Improved Maintenance Planning
Many vehicle manufacturers publish suggested time frames for maintenance to keep trucks running well. However, particulars can vary depending on the driving environment, daily operating hours and driver behaviors. Fortunately, AI can help fleet technicians decide which steps to take and when to do them.
Some artificial intelligence tools work with telematics data, ensuring the algorithms get accurate details about individual vehicles. Others can recognize abnormalities such as excessive heat or abnormal vibration, prompting maintenance professionals to investigate further before the matter escalates, resulting in the truck breaking down.
Even if things get to that point, AI supply chain management tools could streamline roadside assistance routing so rescue efforts are more efficient. This means broken-down trucks get the repairs they need sooner.
Artificial intelligence can also aid fleet managers in determining if specific maintenance issues are due to driver habits that need corrective action. Although extensive options exist, some include driver-facing cameras to detect potential drowsiness. Others recognize hard braking, sharp turns or other aggressive behaviors that could shorten the periods between necessary servicing.
It’s beneficial for managers to have video footage and data of events. That makes it easier to see them in context and analyze whether drivers need coaching to keep them safer and make trucks last longer. Leaders can also refer to specific instances, giving operators specific feedback rather than vague generalizations.
Numerous factors shape the maintenance requirements of individual trucks, contributing to overall fleet performance. Although they can be challenging to track without help, AI streamlines the efforts.
Anticipated Gains From AI Supply Chain Management
These are some of the exciting benefits logistics professionals can expect after deploying artificial intelligence in their supply chains. Those involved in the early stages should choose particular goals and time frames to set expectations and track progress. Seeking worker feedback at all levels of the organization is also important for making them feel included and emphasizing that their willingness to adopt new technology is essential for its overall success.
Thinking of short- and long-term uses for AI will also encourage decision-makers to view the technology as vital to the future of their businesses. Although the logistics industry includes many factors outside the direct control of those affected, artificial intelligence can help them manage what they can still influence.
