How to avoid stock-outs with AI?
In companies, especially in the mass distribution sector, stock-outs are often a daily fear. Beyond the customer experience that can be inevitably impacted, since they may find their products at a competitor's and not come back, it also implies considerable financial losses for the involved business.
To avoid such a situation and to optimize inventory management, the company must anticipate its sales and foresee the buying behavior of its customers. If the methods of the past were not always accurate, we can now rely on AI, namely artificial intelligence, based on machine learning, a high-precision innovation already adopted by several giants of the mass distribution.
To help you understand how to avoid stock-outs with artificial intelligence, we take a look at this future solution.
What is a stock-out?
To understand how the use of artificial intelligence can be of great service to the supply chain, we must first understand what an out-of-stock situation implies and the reasons for it.
Indeed, we talk about an out-of-stock situation when a store or a company runs out of some of the items offered for sale. Note that this can not only concern a particular product, but also all products of the same brand, as was the case during a commercial dispute between Coca-Cola and Leclerc in 2018. In this case, the store no longer has the items that customers are interested in. It can therefore no longer meet their demands, unlike its competitors who continue to sell them most of the time.
An out-of-stock condition means a significant loss of turnover, not only for the company selling the product, but also for the producer of the sold-out product. Indeed, the customer can easily change his choice to a similar article of a competitor. For all these reasons, it is advisable to avoid any problem related to the supply and to take the necessary measures.
What are the reasons for a stock-out?
There can be many reasons behind an inventory shortage, even if they are mainly management issues. In any case, it is important to keep in mind that a company's inventory is largely dependent on the many players in the supply chain and the performance solutions of each. Therefore, the slightest concern in the supply chain can have a profound impact on the store that puts the products on sale, starting with a stock-out.
In general, outages can be caused by:
- a sudden change in customer behavior;
- a difference in data between the computerized inventory and the physical inventory;
- a problem in the processing of the supply;
- poor sales forecasting;
- a logistical concern related to suppliers;
- a just-in-time supply policy;
Regardless of the cause of the stock shortage, the company facing this problem must quickly implement solutions to improve its performance and avoid a repeat of the situation.
Why choose AI to prevent stock-outs?
Ensuring the availability of its products must be a priority for the company, which implies that it takes a minimum of measures. While the market can sometimes be very uncertain, as was the case with the retail sector during the health crisis, new solutions now make it possible to avoid supply-related worries. One of these solutions is machine learning, an innovation that uses artificial intelligence to analyze data and make highly accurate forecasts.
This solution is all the more effective because effective inventory management is based on a defined strategy, which includes both the characteristics of the business and those of the external environment. Market trends, seasonality, product popularity, carrier strikes, etc. - a multitude of parameters can come into play throughout the supply chain and negatively impact supply.
In order to put all the chances on its side, a company must therefore have control over its supply threshold, but also over the volume of orders and the quantities needed to avoid any risk of shortage or overstock. This requires an increased knowledge of market trends, as well as a mastery of customer behavior and supply chain specificities.
Indeed, AI allows to act against stock-outs as well as against overstocks, which also represent financial risks for a company. By optimizing sales forecasting, machine learning leads de facto:
- improved sales performance;
- Improved inventory management, for example by limiting the supply of less popular products;
- a better understanding of customer behavior and its causes;
- optimized delivery management, but also supply chain management;
- an increase in customer satisfaction;
It should be noted that artificial intelligence and machine learning are solutions that are suitable for many fields, whether it is the medical sector, mass distribution, retail, or other types of businesses.
How to prevent out-of-stock situations with AI?
To prevent a stock-out, it is necessary to set up a well-functioning process, not only by anticipating the needs and volumes of stock studied, but also by an effective communication, as much with the actors of the supply chain as with the final customer, and this, to minimize the consequences of a potential error. In all cases, stock-outs and overstocking are events that can be avoided thanks to artificial intelligence. This explains why online retail giants have already embraced this solution, such as Amazon, which has invested heavily and bases a large part of its business on these algorithms. However, even with machine learning, it is necessary to follow a few essential steps to make this innovation an effective tool.
Gathering data for AI
The basic principle of AI for preventing stock-outs is inevitably based on data analysis, whether the data is internal or external to the company. Before choosing such a solution to optimize its supply, it is therefore necessary to have sufficient information, or to collect it if necessary.
Internal data can include details on products on sale, prices, promotions, or points of sale. For external data, we are talking about everything related to seasonality (Christmas, Valentine's Day, etc.), the economic context, the health context, the weather, or even the competition.
Setting a clear business goal
Before going further in the implementation of a machine learning solution, the company that turns to this innovation must lay the foundations of its main goal. Is it about optimizing inventory to prevent any risk of stock-outs? Is it to gain a better understanding of the changing buying behavior of customers? Is it about avoiding overstock and financial losses at all costs?
In order for artificial intelligence to show its full potential, it is necessary to determine an analysis model based on the objective set by the company. The more this model is adapted to the questioning of the structure and its uncertainty, the more it leads to precise results.
Training the AI with the recovered data
Once the internal and external data is in hand, in addition to the company's desired objective, it is necessary to train the AI and the selected model. For example, in order for the AI to recognize a car, it needs to be able to analyze hundreds or thousands of photos of vehicles. In the case of a business objective, whether it's to prevent a stock-out or an overstock, the AI's operation is not very different and lends itself to the same principle.
Thus, it is necessary to enter the collected data into the appropriate algorithm to allow it to literally "train" itself before giving convincing results. It is only after this step that machine learning can generate predictions on the problem in question. In other words, inventory optimization using AI takes time and cannot be improvised in a few days.
Analyzing AI predictions
Once artificial intelligence has been able to provide its predictions, it still needs to be understood and analyzed in order to draw the appropriate conclusions. This implies using real experts and specialists, as well as tools specifically designed to visualize the results and help interpret them. In addition, it remains to adapt this data to the expectations of the company and to the issues that led it to implement this solution.
As you can see, this step cannot be improvised. On the contrary, it requires advanced skills and knowledge, both in terms of data and forecast analysis.