How to Improve Turnover with Effective Fresh Food Inventory Management

The fresh food category – fruits and vegetables, as well as meat, seafood, dairy, and baked items – generally accounts for up to 40% of grocery store earnings. They are also important drivers of client loyalty and store traffic. Most grocery retailers today face a lot of challenges when it comes to fresh food inventory management. This is typically due to the high level of food waste involved, which can be due to overstocking, caused by lack of fresh inventory visibility, as well as out of stocks, leading to missed sales.

Key Takeaways

The article discusses the need for effective management of fresh product categories to maintain product quality and minimize waste, while leveraging forecasting tools.

• Fresh product categories require high turnover. 

• Inventory management tools help maintain freshness. 

• Proper categorization ensures product visibility. 

• Demand forecasting helps reduce waste. 

• Accurate stock tracking minimizes lost sales.

In this article, we explore the specifics of fresh food inventory management, sharing strategies to minimize waste, maintain product freshness, and ensure optimal stock levels. As one of the most sensitive categories, it requires careful oversight to balance availability, prevent losses, and support operational efficiency.

Our company's team LEAFIO AI Retail Solutions has extensive experience in automating inventory management in retail. We help retailers manage all product categories at all levels of the supply chain: in stores, regional warehouses, and distribution centers. 

With our experience in implementing inventory management automation projects in this category, we have identified several rules and patterns and added them to the algorithms of our system so that our customers achieve economic success in the fresh category. 

The fresh produce category is a rather broad one and consists of different subcategories. To ensure the effective inventory management of fresh products and minimize waste, you need to understand not only the general features but also what the differences between these subcategories are. 

This subcategory is one of the most competitive. Dairy products are everyday goods, so they are traffic-generating products. There is a huge number of large vendors who produce milk, yogurt, and cheese, but local vendors do that as well. This diversity leads to increasing competition between brands. Vendors keep their prices at relatively the same level, and this reduces margins. Besides that, such an effect as cannibalization takes place a lot in this subcategory during promotional activities. Also, dairy products usually have rather a short shelf life - from 3 to 10 days.

The most complex subcategory in terms of inventory management among all products in the fresh category. Seasonality plays a huge role here, directly impacting demand planning and procurement decisions. As a result, vendors have different pricing policies. Purchasing managers compare prices for the same SKU and create the order based on the best pricing conditions. And two batches of goods might be ordered from two different suppliers within the same week.

Related:  Multi-echelon Supply Chain: Optimizing Inventory Management

Another negative phenomenon that takes place directly in stores is an inconsistency that happens during the inventory count. Sometimes while weighing the product, customers mistakenly choose a different SKU code, and as a result - wrong stock levels in the ERP happen. But the biggest pain point in managing this subcategory is the appearance of the product on the shelf and its expiration date. Two products from the same batch may differ, one item may look like the one to be written off, and the other one still looks good.

In some ways, this subcategory is similar to dairy products in terms of management. Bread is also a traffic-generating product, but the difference is in pricing. Bakery products can be divided into two types: own production and social products. The maximum margin for the social group is 5-7%, while the own production bakery margin can reach 50% or more.

One of the main features of this category is the frequency of orders, making automated replenishment crucial for maintaining optimal stock levels while avoiding overstocks that lead to food waste. Bread should be fresh and crispy. Deliveries are done every day or even twice a day. Shelf life is up to 3 days on average.

Two subcategories with relatively common characteristics might have cannibalization during promotions, requiring accurate forecasting to optimize inventory levels, reduce spoilage, and prevent unnecessary markdowns that impact profit margins. If regular tomato sauce meatballs are in the promotion, no one will buy the ones with cheese. The same logic works for meat or fish. If dorado is sold at a discount, demand for sea bass or trout will decrease. Also, sales depend a lot on how accurately and nicely the layout is made.

In general, there are the following special characteristics of the fresh food supply chain management: 

• First of all, fresh products differ from others because of their limited shelf life, requiring meticulous inventory management to maintain product freshness, ensure food safety, and avoid unnecessary food waste. Often, their shelf life is no more than 30 days. Because of this feature, fresh is considered the riskiest category in groceries. Short shelf life requires a very precise balance between product availability and write-offs because striving for high availability can significantly increase write-offs, which will immediately hurt the company's financial results. 
• The second peculiarity is a large share in the company turnover. For some retailers, this number can reach up to 60%. And at the same time, the number of SKUs can be smaller compared to other categories. Not having the highest margin, such products generate significant sales volume, bringing the consumer back to the store regularly.
• Third, the category is distinguished by the peculiarities of orders and delivery. For fresh goods, orders are sent at least 1-2 times per week. Mostly, it's direct deliveries from suppliers to the stores. And the most important thing is the peculiarities of logistics because, during the transportation, special temperature conditions must be kept. 

With the proper fresh inventory software, a retailer can improve the inventory turnover in general, earn more and attract customers to the store since the fresh produce category is a traffic-generating one. So let's talk about handling this sensitive fresh category and earning more without additional cost for demand stimulation.

Inventory data accuracy determines the final quality of the replenishment order because with incorrect inventory data at the input, high-quality demand forecasting can't be expected at the output. In terms of fresh inventory, there are often problems with the accuracy of balances due to late fixation of deliveries in the ERP, late write-offs, and returns to suppliers (if they are possible according to the agreements and company policy). Also, especially in the category of fresh fruits and vegetables, there is often an inconsistency during the inventory cycle count, so the final balance for the fresh inventory category might be correct, but not for each item in particular.

So the main recommendations in terms of improving fresh item management efficiency are:

• Establish time limitations for fixation of the fresh produce delivery in the ERP. In this case, it is possible to minimize the risk of “negative” balances. 
• Determine the schedule for the inventory count, according to which the count for fresh goods is done more frequently than for dry goods. 
• Establish a time limitation for backdating documents if your company has such a practice. Even the presence of strict rules can stimulate a more responsible attitude to change information from the past periods. This is important because when you change something in the past, it is possible that you will not be able to analyze why certain decisions were made. 

In the LEAFIO AI company, we have cases in our project implementations where we are investigating the reasons why the system calculated an order that was mismatched with the manager's expectations.

It's no secret that creating and maintaining batch accounting in the food retail industry is impossible since in one shipment may be the goods from different batches with different expiration dates. So it's very hard to keep track of from what batch the goods were sold and with what expiration date.

But it is important for grocery retailers to consider expiration dates as part of their food inventory management strategy, because ignoring this information can cause write-offs, especially for goods with bad inventory turnover, leading to increased waste management costs.

Shelf life should be taken into account at the time of calculating the forecasted balance on the date of the goods delivery. It is important to understand whether the balance will be valid (and appropriate for sale) at the time the next order arrives. 

So our recommendations will be the following: 

• To consider the residual shelf life - the number of days starting from when the product is delivered to the store till the end of its shelf life. This inventory data can be considered to calculate the forecasted stock balance for the product's delivery date. 
• Consider the sales by the LIFO inventory management method - last in first out - assuming that the customer will buy the freshest goods first. This approach considers the fact that the entire balance won't be available for sale even if the goods were not written off in time. Unfortunately, we often face untimely write-offs in implementations and it takes time to change the process. Therefore, we recommend doing a balance check using the LIFO method to minimize the impact of incorrect balances. 

Every product has its demand fluctuations across the week, making demand forecasting an essential tool for maintaining optimal stock levels while ensuring customer satisfaction. A good example here can be that home appliances are sold significantly better on Friday and weekends than during weekdays. But this pattern characterizes the fresh food category as well. And since goods in this category are frequently supplied - daily or several times a week - and the shelf life is limited, we recommend using this weekday-related demand variability coefficients to predict demand for the next fresh food delivery. These coefficients can be calculated by statistical methods or by more advanced methods using AI. 

Considering such coefficients allows a more accurate forecast of the demand. But it should be included in the auto-ordering algorithm because otherwise it might be labor-intensive and can generate a lot of errors. Every product in every store will have specific weekly demand, which depends on its location and format. 

Also, it's worth mentioning that it is important to consider the time during the day when the delivery is made. This will allow the delivery schedule to be made more accurately and takes into account the required number of days in the forecast. We call this a delivery slot and it helps us make the order even more accurate. 

Ultra-fresh goods have some interesting inventory management specifics. In most cases there is no stock balance by the end of the day. In practice, it looks like this: goods are ordered, received, and sold on the same day. But it's hard to understand if there was enough quantity of the fresh produce for each particular day to satisfy the customer demand.

• First of all, for such perishable goods, we should consider the time of day when the last sales were made according to the receipts. This allows you to understand whether there was enough stock balance by the end of the day and whether the demand for the day was covered. If not, the order quantity should be increased to optimize inventory levels and cover the demand. But this is only relevant if the availability of goods has not been reduced on purpose to minimize write-offs. 
• OOS (out of stock) is an important indicator for the category, but if the stock balance is zero each day, it becomes more complicated to calculate it. Therefore, another assumption can be made for a correct calculation: if there was not enough balance to calculate lost sales at the end of the day, it is worth using the statistical ADU. 

We would like to emphasize again that for ultra-fresh it's critically important to have correct data on inventory and sales.

Forecasting and promo management is already a difficult process, and taking into account all the above specifics of fresh food inventory management, it turns into a very complex, risky, and expensive process with a lot of mistakes. This is why leveraging data analytics can provide valuable insights that improve planning accuracy and reduce food waste.

Therefore, we recommend:

• Use demand forecasting algorithms in promotions that take into account all of the above-mentioned specifics of the fresh category and the cannibalization of demand within the category for the most accurate demand forecasting.
• Take into account actual promotional sales in real-time as the forecast may differ from actual promotional sales. And the sooner you start considering reliable statistics for a particular product in a particular promo, the better. 
• Be very careful with additional promotional layouts at stores. Additional promotion planning tools are needed to automatically consider that it's additional equipment to make sure that there won't be overstock when the promo ends.

Let's focus on the indicators that are specific to the fresh food inventory management system. Taking into consideration the mentioned specifics, it's important to analyze: 

• Writes-off and its dynamics.
• Bottlenecks are caused by the situation when MOQ is higher than sales for the term of the product's shelf life. 
• Bottlenecks when the number of days for delivery is higher than the residual shelf life.

The LEAFIO Inventory Optimization solution has a separate block of reports for analyzing the fresh item management. 

For example, this particular report shows specific SKUs in specific locations for which the minimum order quantity is bigger than the average number of sales per shelf life of the item. The system also calculates the estimated percentage of sales of the supplier's packaging and predicts write-offs of that product based on that logic. 

In this report, we can see last week's write-off history, sorted in decreasing order by SKU at storage locations. 

Such an important category as fresh food requires precise analysis and control. Key outcomes of it:

• Fresh inventory is one of the most difficult categories in terms of inventory management. And each subcategory requires a separate fresh approach.
• Bad input - bad output. Multiply by 10 and you get the impact of data quality on the quality of fresh item management. That's why it's important to do paperwork and inventory count in time and limit document backdating. 
• A specific approach is needed for estimation balances at the projected order arrival date. We recommend doing it by their remaining shelf life using the LIFO model. 
• Considering that fresh inventory is a risky group of products, we recommend applying coefficients of variability across the week to factor in demand fluctuations. 
• For calculating statistics and out-of-stocks for the ultra-fresh category, it's important to understand the reason for zero balance at the end of the day. Was the balance not enough to cover the demand?
• Advanced tools should be used to forecast promotional demand and should be based on reliable, current statistics during the ongoing promotions. 
• Ongoing analysis and monitoring of KPIs are needed, especially such KPIs as availability, write-offs, and analysis of various bottlenecks that are specific to this category of products.