Optimizing Fresh Produce Quality Control: Strategies for Grocery Retailers

Grocers are the last stop on the journey that fresh fruits and vegetables make from the field to the supermarket. This is a challenging place to be, for a number of reasons. Firstly, grocers and food retailers tend to bear the brunt of costly recalls and fresh produce food waste. Even worse, they are, to some degree, at the mercy of a chain of decisions that happen upstream – and that they can do little to influence.

The good news is that this is changing. AI powered quality control software is empowering grocers and food retailers to optimize their inventory management, reduce shrinkage and improve efficiency throughout the supply chain.

Inventory Shrinkage: A Persistent Challenge

Inventory shrinkage, or fresh produce shrink, can rapidly eat away at food retailers’ profitability. It happens when grocery stores order more fresh produce than they actually sell. The difference usually arises from waste – around 12% of fruit becomes waste at the store level, by some estimates.

Other factors such as spoilage, theft, and inaccurate inventory counts contribute to this challenge. But AI-based solutions are now available that can help mitigate these losses.  These automated tools provide real-time insights (into current inventory), as well as predictive analytics (to more accurately forecast how much inventory is needed tomorrow). This finally gives grocers a way to exert more control over their inventories, and check the quality of the produce they receive from suppliers with greater accuracy and transparency.

Rationalizing Variability in Supply: the Promise of Machine Learning

Everyone calls their field “dynamic” – but the fresh produce industry actually warrants this title. Short assortment periods, along with the unpredictability of harvests, mean that supply is almost always variable, either above or below what retailers predict. Post harvest losses exacerbate the situation, because what’s actually available at the end of the season may be far less than what retailers need to service demand. 

This variability in supply can lead to either overstocking or stockouts, both of which are costly. Solving this calls for continual updating of data based on dynamic inputs: in other words, the ideal use case for machine learning (ML) technology. Machine learning plays a crucial role in adapting to these fluctuations by analyzing historical data and predicting future trends. This enables retailers to better manage inventory, ensuring that they can meet consumer demand without compromising on quality.

Many Product Variants in Each Cart: Managing Varied Customer Demands

Another factor that complicates demand forecasting is the fact that consumer preferences are constantly changing and evolving. Consumers want their fruits and vegetables to deliver nutrition and taste, as well as living up to their ethical principles: natural and organic products are seeing steep year-on-year growth in demand.

Managing these expectations adds another layer of complexity to fresh produce quality control. Advanced AI systems provide the necessary flexible control, allowing retailers to tailor their strategies based on specific product characteristics. These systems ensure that each variant is handled appropriately, maintaining consistency in quality and availability. This integration of AI and ML also reduces waste, because it enables quality inspectors further up the supply chain to more accurately weed out defective produce. 

The Role of Store-Level Expertise in Quality Control

While these technologies certainly reshape fresh produce management for the better, none of what we have said here implies that store-level employees are redundant. In fact, the exact opposite is the case.

These employees have a crucial role to play in implementing new software, and they’re uniquely placed to do so because of their specific industry expertise. Manual ordering and decision-making based on experience are crucial for maintaining product quality, and making the right call where finer details are concerned, like calibrating the distribution process for climacteric and non-climacteric fruits. A piece of software may accurately assess the amount of dry matter in a piece of produce with pinpoint accuracy – but this information is most valuable in the hands of someone who understands what it means for the broader quality control process.

Integrating mobile tools can augment this expertise, providing employees with real-time data to make informed decisions, ultimately leading to better outcomes for the retailer and the consumer.

Empowering Grocers: AI Quality Control Software for the Future 

The digitization of fresh produce is exciting for everyone, but especially for those who have traditionally held less control over the supply chain. Grocers and food retailers are seeking software like Clarifresh to automate their quality control processes and remove the guesswork and volatility from inventory management. Find out how Clarifresh’s platform could transform the way your organization manages fresh produce for greater transparency and profitability.