Using AI for a Safer Factory
Written by Creme Global
Food safety and consumer health are of utmost importance for the food industry. But even with so many controls in place, foodborne illness can still occur. New approaches are required to enable companies to take a more preventative approach to food safety. So why are companies turning to AI to solve this challenge?
All foodstuffs suffer from the risk of microbial contamination. Why is this important? This contamination can lead to consumer illness and food spoilage. It’s particularly an issue for high throughput food processing plants, where many niches for microbial growth may exist. There are many control procedures in place to minimise food contamination, but contamination of food still occurs regularly. Many companies are approaching Creme Global to ask if AI can be used in their factory to reduce the chance of foodborne illness before it takes hold.
What’s the answer?
Yes, it can.
So what are the ways to limit these potentially harmful microorganisms in your factory? AI supports a predictive approach, rather than a reactive one – it will help you detect a risk before the presence of the microbe. Why is that useful? Because if a harmful microbe is or was present, it’s already too late.
Combining AI algorithms with a data-rich microbial dashboard, all key personnel can quickly note any potential risks, and implement a real-time strategy to reduce or eliminate just for your specific setting. How? Because this dashboard combines AI models that combine three key data sources:
- Data from your factory and equipment (building design, management systems, equipment design and surface specifications etc.)
- Data from your products (QA data, ingredient specifications, processing data, shelf life studies etc.)
- Microbial database
What insights or foresights can this give me?
Companies that are learning how to use this data are discovering insights that enable them to identify what is happening in their factory, predict and rank future risks so that you can implement early preventative action.
Further value can be achieved by feeding genomic data into food safety predictive algorithms. These algorithms use many data sources, including incoming material specification, temperature and humidity in the factory and historic data. Combining these data sources greatly enhances the output and value to the users.