4 Logistical decisions to be made by Artificial Intelligence

Whether in the digitization of processes or digital transformation, artificial intelligence is increasingly present among companies in any sector. But logistics stands out, as companies today seek to streamline their digital operations and/or change their strategy to meet current industry demands, looking for the best logistics decisions that Artificial Intelligence will make.

There are currently many options and tools with AI on the market that, rather than offering a magic solution to all problems, help in the management of the most complicated processes and assist in making decisions in the face of difficult challenges to solve. Some of them have already been heard a lot about due to their recent use in large companies, but others are not so well known.

The following are the types of logistics decisions that Artificial Intelligence will make in both operations and strategy.

Demand forecasting

It is no mystery to know the most popular peak demand times such as Christmas or Black Friday, when most industries are affected. However, each sector and, in particular, each company knows its own order increases in more or less detail depending on the times and data from previous years, among other aspects.

Despite this, the optimal number of stock is not always right in order not to suffer losses due to excess or broken stock due to its incredible complexity determined by both external and internal factors. That is where AI comes in to plan demand based on algorithms that calculate previous years, marketing campaigns and even taking into account some external circumstances such as changes in laws.

Reverse logistics costs

Reverse logistics is known to be one of the biggest challenges for a company running a supply chain. This is because the cost of a return is much higher than that of a shipment because it is not only a matter of bringing the product back to the warehouse. It is not only a matter of bringing the product back to the warehouse, but also a rigorous administrative management to reintroduce it to the active stock, repair it if necessary, or destroy it if the previous steps cannot be applied.

At this point is when Artificial Intelligence comes in to make decisions based on several criteria, but whose main objective is to assess whether to process the return of the order or not. To do this, the AI takes into account such obvious factors as the cost of the refund, but also other not so obvious ones such as the condition of the product to consider its resale and even the buyer's history to analyze his behavior with the brand.

Once these facts have been analyzed, the AI can make two decisions: either not to process the return, but to pay the customer if the costs are not profitable, or to process it when the losses are not so serious for the company's economy. Not forgetting that the dissatisfied consumer is also analyzed in terms of his brand loyalty and whether he will be penalized for processing too many returns.

Traceability

Given the complexity and relative length of a supply chain, it is often very difficult to keep track of stock status, slowing down the identification and verification process due to tedious tasks. In addition to the fact that there are multiple factors that obviate some errors and then it is more costly to remedy how to manage the returns of a defective product line.

A traceability system with artificial vision assists in the streamlining and operation of identification and verification. This is thanks to cameras with image processing and analysis software that verify the identity of the merchandise and its current status. They can even monitor the moments in which the supply suffers any damage in order to speed up the processes to remove the product.

Route optimizer

Among the logistics decisions that Artificial Intelligence will make, there is no doubt that they are those that optimize more or less complex operations. These programs aim to save time and resources, especially when increasing demands and rising prices are not in their favor. In particular, the route optimizer stands out.

Apart from automating the cumbersome task of planning the routes your fleet of vehicles will take on a daily basis, it uses its algorithms to search for the best routes to take based on a series of parameters and restrictions applied by the same staff. These limits can range from the size and number of the fleet to the time slot of the carriers, adapting to any situation.

Conclusion

AI is increasingly present in the logistics sector, both in process automation and decision making. These are necessary technological advances in important processes such as in the streamlining of tasks and in the strategy directly focused on customer satisfaction, among others. In fact, there are companies that already rely on the logistics decisions that Artificial Intelligence will make.

At Hedyla we understand the current need in logistics to optimize operations such as picking or transport management.