ARTIFICIAL INTELLIGENCE


AI algorithms can analyse equipment performance data to predict failures before they happen, helping minimise downtime and increase productivity. In quality control, computer vision systems powered by AI can identify defects in products more efficiently than manual inspection. However, the integration of AI does present several challenges. One primary obstacle is the initial cost of implementation, which may require significant investment in hardware, software, and training. Additionally, many


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confectionery companies may face difficulties accessing or analysing the vast amounts of data required for AI systems to function effectively – legacy systems and outdated infrastructure can hinder smooth AI integration, creating compatibility issues. To overcome these challenges, it is necessary to first identify areas where AI would have the most immediate impact – for example improving quality control or optimising production schedules. Gradually upgrading existing systems and adopting AI


Kennedy’s Confection December/January 2024/25


in incremental steps, rather than overhauling entire operations, can also reduce risk.


Starting small Offering his advice, Keith Thornhill, Head of Food and Beverage at Siemens Digital Industries in the UK, argues the importance of starting with a small project before expanding the use of AI across operations. Confectionery manufacturing processes can be highly complex and often involving multiple stages, so this advice is good – pick one part of the


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