Design of experiments and data analytics for optimizing the defrosting process in Bolton Food

This thesis focuses on optimizing Bolton Food's defrosting process, a critical stage in the production of high-quality tuna products. The study aims to identify optimal operational configurations to minimize temperature variability in defrosted loins, ensuring consistency between core and surface temperatures and enhancing downstream processing performance. By addressing inefficiencies in this process, the project aligns with Bolton Food’s strategic goals of operational excellence and yield optimization. The research involved an in-depth analysis of the current defrosting process, combining on-site observations, stakeholder consultations, and statistical data analysis using R. Key variability sources, such as trolley positioning, shelf levels, and cooker types, were identified and targeted for improvement. To address these challenges, a systematic framework based on the Design of Experiments (DoE) was developed, leveraging full factorial and space-filling designs configured on Minitab and JMP. These designs allow for a detailed exploration of critical factors and their interactions while balancing the constraints of time and resources. The preparatory phase also encompassed the procurement of advanced equipment, such as new probes and data loggers, and the training of personnel to ensure consistent execution of future experimental runs. This groundwork enables Bolton Food to systematically investigate process parameters and determine optimal settings for improved efficiency and product quality. In conclusion, this thesis provides a robust methodology for enhancing the defrosting process, laying the foundation for significant short-term and long-term improvements. The findings and framework developed can guide future optimizations, not only for defrosting but also for subsequent production stages, supporting Bolton Food’s broader commitment to sustainability and excellence in the global seafood industry.