DATA-DRIVEN MANUFACTURING


systems that can replace offline or manual processes and will show a faster ROI than a plant-wide rollout. Another important step is to treat the


use of data as an organisational challenge as much as a technical one. Schneider Electric research points to the skills gaps in AI and data science as the single biggest barrier to scaling data-driven projects. “To


close this gap, we recommend training production and maintenance staff in practical data and AI skills and building cross-functional teams that bring together AI, quality and production specialists so adoption is shared and owned across the business,” says Neil. Importantly, he also argued that overcoming industrial data challenges requires transparency about


what the technology does and does not do, and showing operators that solutions such as open, software-defined automation or Industrial AI can make their jobs easier as well as make the plant more productive. Making transformation work Confectionery manufacturers who are


investing in connectivity and real-time data will usually be chasing the same outcomes – higher overall equipment effectiveness (OEE), shorter cycle times, better quality, and lower energy use. “Dashboards get built, but decisions will often still rely on gut feel and spreadsheets,” says Dr Nuri Abukhshim, Digital Manufacturing Consultant, OMRON Industrial Automation Europe. “Budgets are spent, and a year later the KPIs look much the same. The technology worked, but the transformation did not.” According to Nuri, the reasons for digital transformation disappointment


tend to


cluster around the following recurring issues, and have little to do with the technology chosen: •


Process standardisation, or the lack of it: Connecting a machine to a data platform does not make the process


JULY 2026 • KENNEDY’S CONFECTION • 27


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