Predictive analytics applied to retail demand is a form of advanced analytics that uses analytical and statistical techniques, data science, and machine learning to create a forecasting model that is as accurate as possible. It has been proven that the use of these demand forecasting models not only increases regular sales, but also reduces costs (due to excess inventory and warehousing). But what forecasting problems arise when dealing with perishable food products that have a limited shelf life and need to be replenished very quickly? In this article, we will show you how 7Puentes applies successful solutions to this particular problem.
Currently in a supermarket or mass food store, there are different types of products. Perishable foods are products that should be consumed shortly after purchase, even if refrigerated, due to their decomposition under environmental factors such as temperature and humidity. Some examples include fresh vegetables, fruits, and meats. Others include dairy products, cheese, deli meats, fish, processed foods, and juices.
As the days go by, the proteins and vitamins contained in foods begin to be lost as factors such as light, moisture, temperature, and dryness work their negative effects. This is because perishable foods are attacked by microorganisms such as fungi and bacteria, which seek to feed on the nutrients contained in these products, since they do not contain preservatives.
It is important to note that a typical consumer consumes this type of food on a daily or weekly basis. There is virtually no other type of food that can nutritionally replace, for example, fruits or vegetables, milk or meat.
However, the usual duration for each food is different. It is normal for most fruits and vegetables to be purchased weekly, since these products last a few days, while dairy products are in the middle of the duration, estimated at 3 to 4 weeks, and meats – which in this case can be refrigerated at low temperatures – have a longer shelf life, which can exceed a month, even a frozen chicken can last even longer.
This phenomenon is very different from what happens with non-perishable foods (canned, hermetically sealed or packaged) or hygiene products such as toilet paper, which can be stored for years. These are not as much of a concern for inventory problems, at least in terms of lost sales.
AI-powered demand forecasting for perishable products
What is the importance of demand forecasting for this type of perishable food? Clearly, with this type of product, I need to know exactly how much to produce so that I do not run out of stock, so that I can replenish quickly every day, and so that I can sell according to demand. But at the same time, I have to know that I should not have any leftover product because it will go bad very quickly, forcing me to either try to sell it cheaper or throw it away. So this model has to combine very precisely both supply and demand factors for our retail.
Let’s take bananas as an example. As a type of fruit, they have the characteristic that they are virtually impossible to replace with another fruit (unlike the tangerine, which is replaced by the orange, or the red apple by the green apple). So I have to have bananas available every day because the consumer always consumes them. So, from a supply point of view, I need to know how much stock I should have in my store, and from a demand point of view, I need to be able to predict how many bananas will be consumed weekly or monthly (it is very different from a product like toilet paper, which has a different forecasting model, since storage or overstocking is not an a priori problem).
A very important aspect to consider is the lead time: how long it takes for the product to be replaced. This aspect is important because it also affects the banana product. When I order bananas as a seller, it is convenient for me to have a local supplier for bananas that are half ripe and will last a week. If I order bananas from a distant supplier and it takes a week for them to arrive, they will arrive half ripe and I will have to sell them quickly.
So, if I have a product that has a long lead time and a very short spoilage time in terms of days, I need to have a very accurate and optimal forecasting method for that product to help me with my supply. On the other hand, if I have a low lead time, I go to a supplier day by day, I have a sales forecast that is not so precise, but I can buy them quickly and make them available for a certain amount of time until they go bad.
Let’s say the central market for perishables (in this case, fruits and vegetables), which supplies an entire city, is oversupplied with bananas, then the banana forecast can be seriously affected. The seller will have to buy the cheapest bananas from the central market and sell those bananas, but he will have too much inventory and will not make as much money. In fact, there are times when everyone comes with melons, watermelons, or strawberries because they are being given away. You even have to take into account the seasonality of each product in the forecasting model. Then your forecast has to work to know if the demand calculation for those products is working well or not.
The other case is the problem of lack of supply of fruits and vegetables. It has to be included in the general model, but the central market is the one that determines if there is a supply or not, if there is a lot or little. Then you can usually access this data online. You can know what the market for cattle and meat in Liniers is right now. Then you can know the curve of how the price is moving in the market to know if the meat is going up or down. Also, if you see that 150 thousand animals have entered the slaughterhouse, that is meat that is going to go to the butcher shops, so the price is not going to go down. Or if they are forced to sell it because there was a flood.
What we have to do as retail managers is try to minimize the error and optimize the model. We are not forecasting gurus, but we have to minimize, try not to keep too much stock (because it forces us to lower the price to sell), but also not to run out of stock, which means that we cannot satisfy the demand for the perishable product that the consumer guy buys every day. All of this will undoubtedly contribute to informed and data-driven decision making.
What are the main benefits of having a very accurate demand forecasting model for this type of food?
- Demand errors can be reduced by up to 50% thanks to AI-based methods.
- Loss of sales due to inventory is reduced by up to 60%.
- Reduce storage costs by up to 40%.
Machine learning’s own automatic learning algorithms perfect the predictive model as it processes new data. This increasingly refined model indicates the likelihood of a particular event occurring in the near future. How much product should I make to meet demand? Which stores should I stock more of a particular item to avoid running out?
Does this problem sound familiar to you as typical of your retail business? Then it is important that you know the value proposition of 7Puentes.
7Puentes: Experts in retail demand forecasting
We have more than 15 years of experience in developing solutions for this type of demand forecasting problem. We offer your company the ability to accurately forecast demand for your product, optimize inventory and maximize profits by using our AI demand forecasting solution. Leverage AI to make data-driven decisions that keep your business ahead in today’s dynamic marketplace.
The power of AI meets the science of product demand forecasting. Streamline your production processes and meet customer demand with unparalleled efficiency. Use AI demand forecasting to align your resources and strategies, minimize waste, and maximize profitability in today’s dynamic markets.
Some of the benefits you will experience include:
- Revenue maximization.
- Reduce costs.
- Improved inventory management.
- Increased customer satisfaction.
Our key features for accurate forecasting are:
- Real-time data analysis.
- Advanced predictive analytics.
- Customizable forecasting models.
- Friendly interface.
- Robust reporting & insights tailored to each client.
Understand the challenges of today’s markets: Revolutionize your business with AI-based demand forecasting
Our solution is designed to overcome these obstacles, enabling companies to increase operational efficiency and thrive in dynamic markets.
With your own Demand Forecasting solution, you will experience a seamless integration of advanced algorithms for accurate demand forecasting that can be customized to meet the unique needs of your business, even if you are dealing with products or product niches that are uniquely complex in the marketplace.
We guarantee a smooth transition that scales with the size of your business, ensures confidentiality and integrity, and provides real-time information for informed decision-making.
Are you ready to make the leap in quality and improve your decision making with accurate demand forecasting? Contact our business specialists today.
