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As consumer appetite for greater insight into food authenticity grows, manufacturers need to ensure they are prepared so they can offer full transparency and traceability. The good news is that we are already seeing more evidence of processors trying to gain a deeper understanding of their supply chain and how data and AI can be used to provide actionable insight for more informed decisions.

As an industry, the quantity of product data individual companies generate is staggering. The challenge is how data from both internal and external sources can be combined in an efficient manner to create a true picture of the most significant risks to product integrity.

This is where the application of AI – underpinned by a sound risk taxonomy – comes to the fore, helping to identify the focus of risk mitigation efforts and intervention.

Whilst use of AI for greater insight into shopper habits and new product development is fairly common, it is still relatively new for most food companies when establishing transparency to mitigate product risk.

It is becoming more common that companies utilise data from product recalls, as well as their own product failure and supply network audits, to give them an edge when it comes to preventing risk.

However, transparency of critical supply risks using a company’s own data has its limits. A collaborative approach among industry stakeholders is a necessity, yet, there is some hesitance to open trusted networks with the supply chain. Some companies are reluctant to share data and information between businesses, but complete transparency cannot be obtained without trust being built.

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