LEGACY SYSTEMS VS. MODERN SOLUTIONS Obstacle 3. Solutions
Nearly all (85%) of food and drink businesses who were part of the research programme are using automated business reporting, with 1 in 3 running initiatives to implement AI and data science solutions. But, while there is clearly growing demand, there is a wide variety of maturity in the industry. Some companies use digital twins and simulation to model processes and create capacity to allow for growth, while, at the other end, some struggle to obtain accurate measurements. Implementation of new technology can be challenging in any industry, but it is particularly so in food and drinks manufacturing. Food products are fragile, meaning any machinery needs to handle delicately 17 . The industry is highly regulated, with production processes being tightly controlled. Machinery must adhere to strict health and safety policy 18 . There is huge variability in food items compared to other manufacturing industries that are successfully using robotics and autonomous systems 19 . In some cases, automation has already been implemented in what is a mature manufacturing sector, so the challenge then becomes how to modernise factories with legacy assets. Specialised food and drink manufacturing technology is also harder to come by than in other industries 20 . The off-the shelf technology products that do exist require complex customisation which often don’t easily integrate into existing, legacy machinery and data systems.
Many technology products are not in a “hand able” state, believes Dario Riccomini from Aldomak: “Thesoftware I have engaged with has unnecessary conversions to fit a standard which I then have to convert back to something that makes sense,” he says, and he’s frustrated. “We keep talking about Industry 4.0 and 5.0 but we’ve not even made it to 2.5 as a sector.” What these firms are experiencing is not unusual, according to Bhavnita Patel from the Manufacturing Technology Centre. “Off-the-shelf products to help across manufacturing do exist, but they’re not always being used properly or to their potential,” she says. “Many organisations have invested in Enterprise Resource Planning (ERP) systems, for instance, but few are using them effectively. Getting the basics right is essential but, quite often, I see businesses still running on basic spreadsheets.” Companies know they need to be outcomes-focussed with automation and AI initiatives, looking for value on the profit and loss sheet. But the technology often depends on the foundation of existing infrastructure. Solutions can’t be built on quicksand and accelerating AI opportunities relies on solid data and systems.
17. Onyeaka, Helen, Phemelo Tamasiga, Uju Mary Nwauzoma, Taghi Miri, Uche Chioma Juliet, Ogueri Nwaiwu, and Adenike A. Akinsemolu. 2023. "Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review" Sustainability 15, no. 13: 10482. 18. Dora, M., Kumar, A., Mangla, S. K., Pant, A., & Kamal, M. M. (2021). Critical success factors influencing artificial intelligence adoption in food supply chains. International Journal of Production Research, 60(14), 4621– 4640.https://doi.org/10.1080/00207543.2021.1959665
19. Dora, M., Kumar, A., Mangla, S. K., Pant, A., & Kamal, M. M. (2021). Critical success factors influencing artificial intelligence adoption in food supply chains. International
Journal of Production Research, 60(14), 4621–4640. https://doi.org/10.1080/00207543.2021.1959665
20. Lloyd, Caroline, Payne Jonathan, Food for thought: Robots, jobs and skills in food and drink processing in Norway and the UK, New Technology, Work and Employment (2021)
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