Accuracy is essential in the logistics industry—errors can seriously harm supply chain efficiency.
This is one key reason why artificial intelligence’s (AI) role in the logistics industry has been growing more and more prominently. To better understand how AI is improving logistics, it is important consider the following examples as they illustrate some of the more noteworthy ways in which AI is boosting supply chain efficiency and preventing errors.
Improving Warehouse Management and Predicting Inventory Needs
Warehouse and inventory management requires a substantial degree of accurate predictive analysis. Companies must analyse data and historical trends to more accurately predict future inventory needs. On one hand, businesses don’t want to waste money by purchasing more inventory than they will need at any given time. Along with wasting money, this can also waste substantial warehouse space that could be put to profitable use. On the other hand, if the company fails to purchase enough inventory, it may not be able to serve its customers efficiently.
This is why many companies have begun to leverage AI. AI, unlike human employees, can analyse large streams of data 24/7 and is not prone to human error. This helps companies more accurately and efficiently predict their inventory needs. As a result, companies that have used AI in this capacity are better equipped than others to optimise their warehouse space and budgets.
Improving Supplier Selection
It is crucial that businesses choose the right suppliers if they regularly ship goods to customers. Even if they accurately predict their inventory needs with the help of AI, they won’t be able to optimise their supply chain if they work with suppliers who fail to provide reliable service.
This is yet another reason why companies have begun taking advantage of AI logistics solutions to a substantial degree. Again, AI analyses data efficiently and objectively. This is key when choosing a supplier. Businesses considering their supplier options have many options from which to choose. They need to review various factors when considering each supplier, including past performance, pricing, credit scores, assessments from former customers, and much more. For understandable reasons, it can be difficult to narrow down the best options when the process of choosing a supplier is this complex.
AI can help by analysing the numerous data points quickly and accurately. This is essential to a business’ success. Failure to choose the right supplier could easily result in failure to serve customers.
Identifying Inefficiencies
In the logistics industry, it’s always crucial for all points along the supply chain to identify and address inefficiencies. As with the functions described above, analysing data and trends makes identifying these inefficiencies much easier.
For example, when shipping items throughout the world, companies must choose the most efficient routes. Doing so isn’t always as easy as it may seem. The most direct route isn’t always the most efficient route. Factors that may affect shipping efficiency include traffic patterns, the manner in which items are processed and handled when they reach destinations along the route, the amount of fuel used to ship goods along a specific route, the actual means of shipping (plane, ship, truck, etc.), and much more.
Once again, AI can analyse all these factors with a greater degree of accuracy and efficiency than any human could. Companies are therefore beginning to use AI to review their shipping route options.
It is worth noting that AI doesn’t simply help companies make stronger decisions when choosing shipping routes, hiring suppliers, and predicting inventory needs, it also helps companies optimise one of the most valuable resources any business has: time. Companies have always analysed logistics data to improve efficiency. Without AI, human employees would need to perform this task on their own, taking up a considerable amount of time. By relying on AI to perform these types of analysis, companies can make much smarter use of employee time.
Autonomous Vehicles Offer New Shipping Options
The examples covered above demonstrate how AI’s analytical power is being harnessed to optimise supply chain management. However, AI is also leading to huge changes in transportation itself.
Consider the example of autonomous vehicles. Although they are currently still in the development phase, many industry experts predict the use of autonomous vehicles will one day be commonplace in supply chains. This is due to some of the drawbacks involved in using human drivers—for example, they are only allowed to drive non-stop for a certain amount of time, which can create logistical and scheduling difficulties when switching from one driver to another.
Human drivers, of course, can also make mistakes and unfortunately be involved in accidents, which can damage the goods being shipped, leading to loss of property, and even cause injuries and fatalities.
In contrast, autonomous vehicles may help companies ship more inventory more efficiently, save money on hiring and labor costs, and potentially avoid losses due to traffic accidents and related consequences of human error.
It is clear AI’s role in logistics is already immensely valuable. As more and more companies identify ways to leverage the technology, its role will only grow in value.