Reducing (food) waste with AI

Using data and AI to reduce (food) waste

Artificial intelligence brings great opportunities for companies, for example it can help to reduce (food) waste efficiently and effectively. Reducing waste is beneficial for both your company and the planet. In this article, we will share three crucial steps you should take when using data and AI to reduce waste within your company and how to do it ethically. 

1. Collecting data

First of all, you should collect relevant data. It is important to use data from the whole supply chain. This means data about suppliers, producers and customers. Examples of useful information are sales trends, customer preferences and supply chain inefficiencies.

When using this data, it is important to respect everyone's privacy. This means that you need permission or another basis to use the data, you need to be transparent about the use of the data and you need to have good security (protocols) in place to protect (sensitive) information about the data subjects. For example, medical dietary data of customers is sensitive information and you cannot use that data as easily as generic information such as the number of customers that visit your store or webpage daily.  

Furthermore, it is relevant to gather information on the different product types your company carries, expiration dates, purchase patterns, and historical sales data. Also, external factors like weather can play a part, especially in the food sector. 

An AI model is best trained using historical data that includes both instances of food waste and instances where waste was minimized. The algorithm then learns the relationships between various factors and the occurrence of waste.

2. Training and using AI 

By putting the relevant data from your supply chain into an AI model, you can attain valuable insights from the model about where possible waste is taking place and how you can prevent it in the future. It can give insights that enable you to make smarter decisions and apply more effective waste-reduction strategies, by recognizing trends. Such a model is especially valuable when trained with data of your company for a longer period of time. It is then possible to compare current data with historical patterns to identify potential instances of waste in the making.

It is important to use a responsible AI model. This means that a model that has no unintended negative bias, for example, crept into the decision-making process. You can read more about data ethics here. 

Once the model is trained, it can recognize patterns and correlations within the data. It learns to identify factors that contribute to food waste, such as excessive inventory, inefficient stocking practices, or mismatches between supply and demand. 

It is important to keep training the AI model because, over time, the AI model learns from its predictions and their outcomes. Feedback loops allow the model to fine-tune its predictions and recommendations, improving its accuracy and effectiveness in minimizing food waste. The AI model can also adapt and evolve as the retail environment changes. It can incorporate new data sources and adjust to shifts in consumer behaviour to stay aligned with waste reduction goals.

3. Sharing the output 

After getting the desired insights from the AI model, it could be worth sharing these insights with your suppliers or other participants of the supply chain. This way, they can respond to trends as well and the whole supply chain can get in sync to prevent possible (food) waste. Sharing predictive information allows everyone to make informed decisions that collectively reduce waste.

For instance, when the AI model detects signs of potential waste, it can generate alerts or recommendations for action to your store personnel, suppliers and producers. These could include adjusting inventory levels, modifying purchasing orders, or optimizing product placement.

Conclusion

In essence, an AI model for predicting (food) waste combines data-driven insights with advanced algorithms to forecast when and where waste might occur. This proactive approach empowers retailers to take preventive actions, minimize losses, and contribute to a more sustainable future. When training and using such an AI model, it is important to be aware of the rules that you must adhere to when processing and sharing data. Especially when it comes to sensitive data. In addition, you need to ensure that the model you train is fair and transparent and does not contain any unintentional negative biases.

If you have questions about how to use AI and data ethically in retail, feel free to contact us at info@legalair.nl.