Fresh Food Demand Forecasting: How to Produce Accurate Demand Forecasts for Perishable Products

As any grocer or retailer knows, fresh food is a high-stakes game. With limited shelf life and a host of variables that can impact demand, accurately forecasting how much inventory to stock can be the difference between success and failure. But fresh food demand forecasting is no easy feat - it requires a specific approach that takes into account the unique characteristics of perishable products, as well as a variety of internal and external factors that can influence consumer behavior.

In this article, we’ll explore the challenges associated with fresh food demand forecasting and offer practical tips and techniques for producing accurate demand forecasts. Whether you’re a grocer, retailer, or food industry professional, this guide will provide valuable insights into how to maximize profits and minimize waste by accurately predicting fresh food demand. So buckle up and get ready to dive into the fresh food forecasting challenge!

TL;DR

• Accurate demand forecasting is crucial for success in the perishable products and fresh food industry due to the short shelf life and various external factors that can impact demand.

• Fresh food demand forecasting requires a specific approach that takes into account unique characteristics of perishable products, such as product appearance, hidden out-of-stocks, and fluctuating delivery times.

• Key variables to consider when building a reliable fresh food demand forecasting model include seasonality, calendar of holidays and events, weather, pricing, product availability, consumer preferences and trends, marketing and advertising, competition, and transportation and storage requirements.

• Machine learning algorithms can help fresh food retailers and grocers optimize their operations, maximize profits, and reduce waste through accurate and timely demand forecasting and inventory management automation.

• Companies that implement AI solutions for demand forecasting and inventory management can benefit from reduced expenses, improved customer loyalty and engagement, and a competitive edge in the industry.

• nexocode AI experts have extensive experience in supply chain management and can help businesses in the retail industry implement AI solutions for accurate demand forecasting and inventory management. Contact us today to discuss your needs and find out how we can help.

The High-Stakes Game for Fresh Food Retailers and Groceries

demand prediction allows retailers to optimize their warehouse management and prevent both under and overstocking, but with fresh food, it is a totally new-level game. When the clock is ticking, inaccurate predictions can generate a significant financial loss. 

While other retailers would store the stock and try to gradually sell it out, for instance, using dynamic pricing techniques, food retailers cannot do so. They have to comply with various regulations which determine when the food can be sold and what to do with it afterward. Even if it seems perfectly fine, there is no chance of selling it when its shelf life comes to an end. 

If not sold on time, the food has to be handled in a specific and often strictly regulated manner, which, of course, implies additional costs. Thus, it’s far beyond a warehouse problem, like in the other retail niches. That’s why accurate predictions can be a game changer for businesses working with fresh food.

Why Fresh Food Needs a Specific Method for Forecasting Demand?

The forecasting methods used in retail predict demand based on historical and real-time data, taking various variables into account - from market and social media trends to even weather. Having learned what triggers changes in demand from the provided datasets, the models can predict them in the future. 

In the case of forecasting current and future demand for fresh food, the mechanism is similar. However, the pressure on accuracy is much higher since forecasting errors will have much more implications for the fresh food retailer than for any other business, forcing them to remove products from stock without a chance of selling and then dispose of them in the right way.

At the same time, there are various variables that impact the demand for fresh foods and their predictability, which are often not taken into account in other retail niches.

Product Appearance

Fresh food products are highly perishable, and their appearance can rapidly deteriorate. That, of course, may affect sales, as the customers will avoid choosing them, even if they know they are perfectly fine in terms of taste and quality. Once the product’s appearance deteriorates, the chances of selling are in a freefall. That’s why it’s crucial for fresh food retailers to forecast demand accurately to avoid overstocking and spoilage. They can actively prevent the deterioration of their products’ appearance by controlling the storage and shelf conditions and reducing food’s exposition to touch.

Hidden Out-of-Stocks

Another factor that enforces more accurate predictions is the possibility of having hidden out-of-stocks. Out-of-stock situations can occur even when inventory levels appear sufficient, as fresh food products may be damaged or unsellable due to factors like bruising, discoloration, or other issues. Forecasting enables retailers to prevent such situations, including a predicted percentage of out-of-stock fresh products in the estimations. 

Fluctuating Delivery Times from Farmers and Producers

Fresh food products are often sourced from local farmers or suppliers whose supply is subjected to internal and external parameters conditions. Weather and other unexpected events can lead to unpredictable delivery times and varying availability of particular products throughout the year. Whereas in other niches, they impact mainly the demand, in the case of fresh food, it’s the supply that gets affected. That’s why a forecasting model built for fresh food has to take the delivery times into account, drawing insights from historical data.

Lack of Data on How Products Look and Perish on Shelves

Fresh food can change its appearance in a relatively short time - every hour counts. Stocked in the morning, in the evening, it may already be in an entirely different shape. It doesn’t help that customers are often in direct contact with the products, packing and lifting them to compare the quality. 

That may speed up the deterioration of the appearance of the food while making it difficult to control how it looks on the shelves. Contrary to products with a longer shelf life, which are often tightly packed, fresh ones can lose their appeal easily if they are not organized in the right way. With computer vision paired with other AI solutions, retailers can solve this problem, but meanwhile, they need to include this factor in the estimations. 

Factors Affecting Fresh Food Demand Forecasting

We have already proved that fresh food needs a slightly different approach to forecasting than the majority of products, but which factors specifically affect its demand? Here are some key variables to include when building a reliable demand forecasting model.

Seasonality

Seasonality plays a key role in the supply predictions of fresh food. Just as the supply, the demand for fresh food varies depending on the season. Before globalization gained such a scale, the supply and demand would be more or less aligned. Fresh foods always taste the best when it’s their season, and due to their high supply, the prices can go down, so naturally, customers would opt for them. 

However, with globalization, they grew accustomed to having access to various types of fresh food all year. That affected the relevance of seasonality as a variable. Regardless, the habits still impact the preferences of the customers, and so does the quality of the seasonal food, so it should be taken into account when forecasting demand.

Calendar of Holidays and Events

This variable impacts sales in general, usually forming a classic pattern – just before the holidays and events, customers buy more than usual, and during them, they buy less. Thus, any demand forecasting model should include it. However in highly competitive retail industry, fresh food retailers get more impacted by it than any others due to the nature of their products. Besides adjusting their inventory and supply chain management to the sales peaks, they also have to think about the limited shelf life of the food. Precise historical sales data in this field is thus crucial for preventing financial loss.

Weather

Even though the farmers and suppliers can use preventive measures (like greenhouses, etc.) and get partially independent of the weather factors, droughts, frost, and plagues can leave retailers understocked. Some vegetables and fruits require specific conditions to grow, leaving them entirely dependent on weather circumstances. 

Relying on the weather records, the forecasting model can identify the fluctuations in weather conditions and the way they impacted the supply in the past. Natural disasters are difficult to predict - however, the predictive models can use recent data to detect more problematic events in the supplying regions, allowing retailers to make quick decisions to avoid supply shortages.

Pricing

Pricing affects the demand, regardless of which product we’re referring to. In order to use this variable correctly, fresh food retailers should understand their target groups very well, since they may react to fluctuations in price in different ways. For instance, pricey imported avocados may sell out faster in a shop placed in a rich neighborhood that targets customers aiming for a healthy lifestyle than in a supermarket or convenience shop. 

The share of particular target groups should be included in the estimations to reflect the demand in the most accurate way. On the other hand, prices established by the suppliers may rise due to particular weather conditions, so the model should combine these variables to predict demand accurately.

Product Availability

Product availability is another variable dependent on weather conditions and supply chains, but there’s more complexity to it, particularly in the case of fresh food. The availability of particular foods is often tied together, and the predictive model should reflect it. It’s simple – certain fresh food are considered to go well together, so they are often purchased in the package. Take the yogurt, banana, and a fresh bun for breakfast or the salad and a sandwich for lunch. These duos or trios, of course, depend on the local customs – for instance, Polish customers will likely pair young potatoes with kefir or cottage cheese with spring onion and radish. Including this aspect in the estimations may radically improve their forecast accuracy too.

Consumer Preferences and Trends

Trends have an enormous impact on demand, and the forecasting model should take that into consideration. They often boost the demand for certain foods to impressive levels, whether it’s avocado toast, shakshuka, or a basque cheesecake, to then suddenly drop, leading to overstocking. Fresh food retailers can multiply their margins if they observe these trends and adjust to them with their inventory management and marketing efforts. In order to stay competitive, they should use forecasting systems that include these. They can do that by integrating external data sources, most of which would be social media. 

Marketing and Advertising

Trends shape the demand externally, but the retailers themselves can also drive it and control it through thoughtful use of marketing. If they decide to promote particular products, the model should reflect that - otherwise, they could be sabotaging their own efforts and, thus – multiplying expenses. If the advertised products sell out in an instant, customers who come to buy them after seeing the ad may migrate to another provider, with lost revenues, especially if it happens repeatedly.

Short Shelf Life

As we have already mentioned, the short shelf life makes demand prediction much more challenging, leaving less margin for forecasting errors. The model should be taught about the shelf life of the particular foods to make the estimations as accurate as possible. It is worth taking into consideration that different aspects may impact the length of a product’s shelf life, including the conditions of the transport, the complexity of the supply chain, the weather, or even the distribution on the shop shelves (products that are beyond reach may quickly deteriorate in quality).

Competition

The presence of the competing fresh food retailers in the area, their target groups, marketing efforts, pricing, and product availability of their suppliers – all these competition-related aspects may determine the product demand, so any accurate forecasting model should include it.

Transportation and Storage Requirements

Fresh food often has special requirements when it comes to transport and storage. It’s usually much more susceptible to damage and may quickly lose its vivid color and properties if not stored correctly. Some products, like eggs, may easily break, while others melt when not stored at the right temperature. The forecasting model should include this aspect in the estimations, predicting the likelihood of the food getting spoiled and suggesting a supply that the retailers can actually cope with given the required conditions.

Related case study: Optimizing drug distribution and inventory activities for a hospital pharmacies network

To improve current large-scale procurement processes, a pharma company approached us to use applied analytics to stock and distribute drugs among US hospitals.

Our challenge? Maximizing savings by streamlining the procurement of medication across the hospital network and their pharmacies. Read more about this case study.

The Machine Learning Advantage: Enhancing Fresh Food Demand Forecasting Accuracy

The demand gets impacted by numerous factors that often interfere with each other and depend on one another. In the case of rule-based programming, we would have to know and understand them all in order to establish the rules correctly and come up with accurate estimations. In machine learning, it is the algorithm itself that finds the correlations between the different variables and demand, improving itself with every feedback. 

You have seen how long is the list of variables that should be taken into account in fresh food demand forecasting.  Traditional methods make it harder to improve estimations over time and add tons of manual work, as you need to redefine the rules every once in a while.

Benefits of ML-Based Inventory Forecasting for Fresh and Ultra-Fresh Products

Using machine learning forecasting algorithms, fresh food retailers and grocers can keep up with customers’ expectations while optimizing their operations and maximizing profit. Having just the right amount of fresh food in stock, companies are capable of building customer loyalty and engagement. Accurate and timely forecast prevents them from generating excessive waste, which is very common for fresh food prone to quality deterioration, and facilitates optimal inventory allocation decisions. 

Machine learning forecasting models help enterprises tackle the challenges of short shelf life, supporting the daily work of inventory managers and purchasing directors. If they waste less supply, they save money on products that will never sell, their storage, and management. 

Limiting waste also works in favor of the food companies’ image. In recent years, the problem of food waste has become an object of public debate. New regulations appeared to encourage businesses to reduce the amount of produced waste. Today, a big percentage of customers put importance on that aspects when choosing between food suppliers. That means accurate forecasting could give food companies a competitive edge.

Machine learning forecasting algorithms also enable automation in replenishing and managing perishable inventory. With the ability to predict demand with greater accuracy, retailers can set up automated systems to restock their inventory, reducing the need for manual intervention and freeing up valuable time for their teams to focus on other tasks.

In addition, machine learning can be used to identify and track key performance indicators (KPIs) related to food sales, such as turnover rates and sell-through rates. This data can then be used to optimize promotions planning and determine the best times to launch new products, maximizing sales and revenue.

The Bottom Line: Accurate Demand Forecasting is Essential for Perishable Product Success

Predictive analytics is becoming increasingly popular among retailers as a way to optimize business processes while reducing expenses, and food retailers are starting to follow. If you would like to gain access to all the benefits listed above, we can help you create a model that takes all your needs and the specifics of your business into account. Reach out to us if you would like to discuss the details!