The growing digitalisation of the manufacturing industry is accelerating the adoption of artificial intelligence, but transforming the data collected into predictive tools remains a challenge. This is where KaizenAI comes in: a methodology developed and tested in a bottling plant that integrates the principles of continuous improvement with machine learning techniques to facilitate a gradual and sustainable digital transition.
The approach is based on integrating the Kaizen philosophy and uses artificial intelligence to support existing processes. The aim is to introduce predictive capabilities whilst maintaining staff engagement and minimising any organisational resistance.
The process begins with an assessment of the company’s digital maturity and the identification of areas for improvement, leading to the design of predictive tools tailored to users’ actual needs. Validation was carried out using 18 months’ worth of operational data collected at a bottling plant, comprising over 40,000 records relating to minor production line stoppages. The company analysed had a good database but had not yet developed predictive capabilities.
The results show that even organisations with an intermediate level of digitalisation can implement predictive systems without significant investment in infrastructure, by making the most of the data already available. The study also highlights how a well-established Kaizen culture is a decisive factor in facilitating the adoption of AI.
The conclusion: in bottling plants, artificial intelligence can become a natural evolution of continuous improvement programmes. KaizenAI offers the beverage sector a concrete path towards Industry 4.0, transforming data into operational decisions and decisions into greater efficiency.
Bibliographical references: Soto-Chambilla, A., Fernández-Del-Carpio, A., Córdova-Silva, H., Luque-Mamani, E., & Alatrista-Corrales, A. (2026). KaizenAI: Methodology for the Integration of Machine Learning in Manufacturing Processes Based on Kaizen Principles. Case Study: Bottling Industry. Journal of Industrial Engineering and Management. 19(2), 288–304. https://doi.org/10.3926/jiem.9195
