Enhancing productivity and inventory management in aquatic processing through a comprehensive lean manufacturing framework

Purpose In today’s global competition, every industry continuously seeks better ways to improve productivity and product quality, reduce production costs, eliminate waste, and enhance customer satisfaction. Lean Manufacturing (LM) is a great tool for this continuous improvement. Studies in the aquatic processing industry using LM techniques are still limited, with a few single LM tools utilized. Hence, this study aims to propose a comprehensive methodology using several LM tools such as Value Stream Mapping (VSM), Standard time (i.e. Designing Workstation Standardization and Standardizing operations), Lean layout, Rebalancing line via Arena simulation, Kanban cards, and Total Quality Management process to increase productivity, quality, and efficiency. Design/methodology/approach The process begins with selecting the primary product family and systematically collecting production data. Subsequently, existing issues are scrutinized by utilizing VSM and a fishbone diagram. The third stage entails the proposition of Lean tools for enhancement, followed by developing a future VSM in the fourth step to achieve continuous flow and process optimization. Finally, the last step involves the comprehensive evaluation of processes before and after improvements, with the implementation of necessary modifications. A case study of pangasius fillet products at a seafood company in Vietnam was implemented to prove the effectiveness of the proposed model. Findings The results showed that significant improvements to the production line had been achieved. Lead time was improved by 53%, while value-added time increased by 25% from 3.82 to 4.76 h. Inventory time decreased dramatically by more than 80%, from 10.27 to 1.72 h, whereas there has been a noticeable improvement in the process cycle efficiency ratio, jumping from 27.1 to 73.4%. Research limitations/implications The study’s findings underscore the need for ongoing tracking of productivity metrics to maximize workflow efficiency. However, it is essential to acknowledge the limitations of relying solely on these metrics, as they may not capture the full complexity of the production process. Additionally, implementing data-driven standards may require significant resources and organizational changes. Originality/value Further research is warranted to explore these potential challenges and assess such measures’ long-term sustainability in diverse operational contexts.