{"id":239984,"date":"2026-08-26T10:00:04","date_gmt":"2026-08-26T08:00:04","guid":{"rendered":"https:\/\/www.alimentibevande.it\/uncategorized\/kaizenai-la-strada-pragmatica-per-portare-lai-nellimbottigliamento\/"},"modified":"2026-08-26T10:07:22","modified_gmt":"2026-08-26T08:07:22","slug":"kaizenai-the-pragmatic-approach-to-bringing-ai-to-the-bottling-process","status":"publish","type":"post","link":"https:\/\/www.alimentibevande.it\/en\/scientific-studies\/kaizenai-the-pragmatic-approach-to-bringing-ai-to-the-bottling-process\/","title":{"rendered":"KaizenAI: the pragmatic approach to bringing AI to the bottling process"},"content":{"rendered":"\n<p>The growing digitalisation of the manufacturing industry is accelerating the adoption of <strong>artificial intelligence<\/strong>, but transforming the data collected into predictive tools remains a challenge. This is where <strong>KaizenAI<\/strong> comes in: a methodology developed and tested in a <strong>bottling plant<\/strong> that integrates the principles of <strong>continuous improvement <\/strong>with <strong>machine learning<\/strong> techniques to facilitate a gradual and sustainable digital transition.<\/p>\n\n\n\n<p>The approach is based on integrating the Kaizen philosophy and uses artificial intelligence to support existing processes. The <strong>aim<\/strong> is to introduce predictive capabilities whilst maintaining staff engagement and minimising any organisational resistance.<\/p>\n\n\n\n<p>The process begins with an assessment of the company\u2019s digital maturity and the identification of areas for improvement, leading to the design of predictive tools tailored to users\u2019 actual needs. Validation was carried out using <strong>18 months<\/strong>\u2019 worth of operational data collected at a bottling plant, comprising <strong>over 40,000 records <\/strong>relating to minor production line stoppages. The company analysed had a good database but had not yet developed predictive capabilities.<\/p>\n\n\n\n<p>The <strong>results <\/strong>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.<\/p>\n\n\n\n<p>The <strong>conclusion<\/strong>: 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.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Bibliographical references<\/strong>: Soto-Chambilla, A., Fern\u00e1ndez-Del-Carpio, A., C\u00f3rdova-Silva, H., Luque-Mamani, E., &amp; Alatrista-Corrales, A. (2026).\u00a0<em>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<\/em>. 19(2), 288\u2013304.\u00a0<a href=\"https:\/\/doi.org\/10.3926\/jiem.9195\">https:\/\/doi.org\/10.3926\/jiem.9195<\/a><\/p>\n\n\n\n<p><\/p>\n\n\n\n<div class=\"su-button-center\"><a href=\"https:\/\/www.tecnichenuove.com\/riviste\/imbottigliamento\" class=\"su-button su-button-style-default su-button-wide\" style=\"color:#121010;background-color:#fab21d;border-color:#c88f18;border-radius:12px\" target=\"_blank\" rel=\"noopener noreferrer\"><span style=\"color:#121010;padding:0px 30px;font-size:22px;line-height:44px;border-color:#fcca61;border-radius:12px;text-shadow:none\">  <strong>Article taken from the September issue of the magazine \u201cImbottigliamento\u201d. To continue reading, click here and subscribe<\/strong><\/span><\/a><\/div>\n","protected":false},"excerpt":{"rendered":"<p>A study shows how to incorporate artificial intelligence into the drinks industry, making the most of existing data, people and processes<\/p>\n","protected":false},"author":3836,"featured_media":238279,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[21116,20580],"tags":[16973,21066,22577],"tipologia":[],"coauthors":[16423],"class_list":["post-239984","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-featured","category-scientific-studies","tag-ai","tag-bottling","tag-kaizenai"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>KaizenAI: the pragmatic approach to bringing AI to the bottling process - 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