{"id":1220,"date":"2026-03-02T14:12:20","date_gmt":"2026-03-02T14:12:20","guid":{"rendered":"https:\/\/www.tessanalytics.eu\/?p=1220"},"modified":"2026-03-25T16:22:58","modified_gmt":"2026-03-25T16:22:58","slug":"artificial-intelligence-case-study-for-inventory-optimisation-and-forecasting","status":"publish","type":"post","link":"https:\/\/www.tessanalytics.eu\/en\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\/","title":{"rendered":"Case Study: Artificial Intelligence for Stock Optimisation and Forecasting"},"content":{"rendered":"<p>In a market characterised by extreme volatility, anticipating consumer needs has become a critical success factor.<br \/>\nIn this case study, we illustrate how <strong>us<\/strong> We use machine learning algorithms to transform historical data into accurate forecasts, reducing waste and optimising the supply chain.<\/p>\n<h2>1. Context and Challenge (Problem)<\/h2>\n<p>Many companies in the Retail and Large-Scale Retail (GDO) sectors have to face the problem of \u201cout of stock\u201d or, conversely, of excess inventory.<br \/>\nThe main challenge is uncertainty: traditional forecasting models fail to consider dynamic external variables (such as sudden seasonal trends, weather variations, or macroeconomic changes), leading to logistical inefficiencies and significant economic losses.<\/p>\n<h2>2. Approach and Methodology (Solution)<\/h2>\n<p><strong>We<\/strong> We have implemented an advanced forecasting system based on neural networks and predictive statistical models.<\/p>\n<h3>Our analysis methodology<\/h3>\n<ul>\n<li><strong>Multisource Data Integration:<\/strong> We combine historical customer sales data with exogenous variables such as holidays, weather, and macroeconomic data.<\/li>\n<li><strong>Machine Learning Algorithms:<\/strong> We use regression models and time series to identify recurring patterns and market anomalies.<\/li>\n<li><strong>Scientific Validation<\/strong> We constantly test the model's accuracy (backtesting) to refine forecasts and reduce the margin of error.<\/li>\n<\/ul>\n<h2>3. Results<\/h2>\n<p>The application of Artificial Intelligence to business processes has enabled the achievement of:<\/p>\n<ul>\n<li><strong>Predictive Accuracy<\/strong> A significant increase in the accuracy of sales forecasts on a weekly and monthly basis.<\/li>\n<li><strong>Process Automation:<\/strong> Automatic generation of forecast reports ready for integration into company management systems (ERP).<\/li>\n<li><strong>Cost Reduction<\/strong> Optimising warehouse stock levels, drastically reducing storage costs and the risk of goods spoilage.<\/li>\n<\/ul>\n<h2>4. Impact and operational value<\/h2>\n<p>The value that <strong>we provide<\/strong> lies in the transformation of reactive management into a proactive strategy.<br \/>\nThanks to our forecasting models, companies can plan production and logistics with a clear vision of the future, improving profit margins and ensuring a consistently punctual and reliable service for the end customer.<\/p>","protected":false},"excerpt":{"rendered":"<p>In un mercato caratterizzato da estrema volatilit\u00e0, anticipare le esigenze dei consumatori \u00e8 diventato un fattore critico di successo. In questo caso studio, illustriamo come noi utilizziamo algoritmi di machine learning per trasformare i dati storici in previsioni accurate, riducendo gli sprechi e ottimizzando la catena di approvvigionamento. 1. Contesto e la sfida (Problem) Molte [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":1425,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_angie_page":false,"page_builder":"","footnotes":""},"categories":[17],"tags":[],"class_list":["post-1220","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-case-study"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Case Study AI e Forecasting per Retail e GDO | TESSA<\/title>\n<meta name=\"description\" content=\"Scopri come l&#039;AI ottimizza la catena di approvvigionamento. Un caso studio su Machine Learning e modelli predittivi per ridurre gli sprechi e prevedere la domanda.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.tessanalytics.eu\/en\/artificial-intelligence-case-study-for-inventory-optimisation-and-forecasting\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Case Study AI e Forecasting per Retail e GDO | TESSA\" \/>\n<meta property=\"og:description\" content=\"Scopri come l&#039;AI ottimizza la catena di approvvigionamento. Un caso studio su Machine Learning e modelli predittivi per ridurre gli sprechi e prevedere la domanda.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.tessanalytics.eu\/en\/artificial-intelligence-case-study-for-inventory-optimisation-and-forecasting\/\" \/>\n<meta property=\"og:site_name\" content=\"Tessa Analitycs\" \/>\n<meta property=\"article:published_time\" content=\"2026-03-02T14:12:20+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-25T16:22:58+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.tessanalytics.eu\/wp-content\/uploads\/2026\/03\/data-analysis-scaled.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"1707\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Ilaria Benedetti\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Ilaria Benedetti\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimated reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.tessanalytics.eu\\\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.tessanalytics.eu\\\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\\\/\"},\"author\":{\"name\":\"Ilaria Benedetti\",\"@id\":\"https:\\\/\\\/www.tessanalytics.eu\\\/#\\\/schema\\\/person\\\/415085c629cac1ab93477df5b0ee02f5\"},\"headline\":\"Caso Studio: Intelligenza Artificiale per l&#8217;Ottimizzazione delle Scorte e Forecasting\",\"datePublished\":\"2026-03-02T14:12:20+00:00\",\"dateModified\":\"2026-03-25T16:22:58+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.tessanalytics.eu\\\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\\\/\"},\"wordCount\":322,\"commentCount\":0,\"image\":{\"@id\":\"https:\\\/\\\/www.tessanalytics.eu\\\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.tessanalytics.eu\\\/wp-content\\\/uploads\\\/2026\\\/03\\\/data-analysis-scaled.jpg\",\"articleSection\":[\"Case Study\"],\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/www.tessanalytics.eu\\\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.tessanalytics.eu\\\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\\\/\",\"url\":\"https:\\\/\\\/www.tessanalytics.eu\\\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\\\/\",\"name\":\"Case Study AI e Forecasting per Retail e GDO | TESSA\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.tessanalytics.eu\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/www.tessanalytics.eu\\\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/www.tessanalytics.eu\\\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.tessanalytics.eu\\\/wp-content\\\/uploads\\\/2026\\\/03\\\/data-analysis-scaled.jpg\",\"datePublished\":\"2026-03-02T14:12:20+00:00\",\"dateModified\":\"2026-03-25T16:22:58+00:00\",\"author\":{\"@id\":\"https:\\\/\\\/www.tessanalytics.eu\\\/#\\\/schema\\\/person\\\/415085c629cac1ab93477df5b0ee02f5\"},\"description\":\"Scopri come l'AI ottimizza la catena di approvvigionamento. Un caso studio su Machine Learning e modelli predittivi per ridurre gli sprechi e prevedere la domanda.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.tessanalytics.eu\\\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\\\/#breadcrumb\"},\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.tessanalytics.eu\\\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\\\/\\\/www.tessanalytics.eu\\\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\\\/#primaryimage\",\"url\":\"https:\\\/\\\/www.tessanalytics.eu\\\/wp-content\\\/uploads\\\/2026\\\/03\\\/data-analysis-scaled.jpg\",\"contentUrl\":\"https:\\\/\\\/www.tessanalytics.eu\\\/wp-content\\\/uploads\\\/2026\\\/03\\\/data-analysis-scaled.jpg\",\"width\":2560,\"height\":1707},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.tessanalytics.eu\\\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/www.tessanalytics.eu\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Caso Studio: Intelligenza Artificiale per l&#8217;Ottimizzazione delle Scorte e Forecasting\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/www.tessanalytics.eu\\\/#website\",\"url\":\"https:\\\/\\\/www.tessanalytics.eu\\\/\",\"name\":\"Tessa Analitycs\",\"description\":\"\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/www.tessanalytics.eu\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-GB\"},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/www.tessanalytics.eu\\\/#\\\/schema\\\/person\\\/415085c629cac1ab93477df5b0ee02f5\",\"name\":\"Ilaria Benedetti\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/6fdc289a865cc6349987dd6bc5ea229f1204f676f92a85df445e64c669c60b37?s=96&d=mm&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/6fdc289a865cc6349987dd6bc5ea229f1204f676f92a85df445e64c669c60b37?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/6fdc289a865cc6349987dd6bc5ea229f1204f676f92a85df445e64c669c60b37?s=96&d=mm&r=g\",\"caption\":\"Ilaria Benedetti\"},\"sameAs\":[\"https:\\\/\\\/www.tessanalytics.eu\\\/\"],\"url\":\"https:\\\/\\\/www.tessanalytics.eu\\\/en\\\/author\\\/ilaria\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"AI and Forecasting Case Studies for Retail and Large-Scale Retail | TESSA","description":"Learn how AI optimises the supply chain. A case study on Machine Learning and predictive modelling to reduce waste and predict demand.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.tessanalytics.eu\/en\/artificial-intelligence-case-study-for-inventory-optimisation-and-forecasting\/","og_locale":"en_GB","og_type":"article","og_title":"Case Study AI e Forecasting per Retail e GDO | TESSA","og_description":"Scopri come l'AI ottimizza la catena di approvvigionamento. Un caso studio su Machine Learning e modelli predittivi per ridurre gli sprechi e prevedere la domanda.","og_url":"https:\/\/www.tessanalytics.eu\/en\/artificial-intelligence-case-study-for-inventory-optimisation-and-forecasting\/","og_site_name":"Tessa Analitycs","article_published_time":"2026-03-02T14:12:20+00:00","article_modified_time":"2026-03-25T16:22:58+00:00","og_image":[{"width":2560,"height":1707,"url":"https:\/\/www.tessanalytics.eu\/wp-content\/uploads\/2026\/03\/data-analysis-scaled.jpg","type":"image\/jpeg"}],"author":"Ilaria Benedetti","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Ilaria Benedetti","Estimated reading time":"2 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.tessanalytics.eu\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\/#article","isPartOf":{"@id":"https:\/\/www.tessanalytics.eu\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\/"},"author":{"name":"Ilaria Benedetti","@id":"https:\/\/www.tessanalytics.eu\/#\/schema\/person\/415085c629cac1ab93477df5b0ee02f5"},"headline":"Caso Studio: Intelligenza Artificiale per l&#8217;Ottimizzazione delle Scorte e Forecasting","datePublished":"2026-03-02T14:12:20+00:00","dateModified":"2026-03-25T16:22:58+00:00","mainEntityOfPage":{"@id":"https:\/\/www.tessanalytics.eu\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\/"},"wordCount":322,"commentCount":0,"image":{"@id":"https:\/\/www.tessanalytics.eu\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\/#primaryimage"},"thumbnailUrl":"https:\/\/www.tessanalytics.eu\/wp-content\/uploads\/2026\/03\/data-analysis-scaled.jpg","articleSection":["Case Study"],"inLanguage":"en-GB","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/www.tessanalytics.eu\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/www.tessanalytics.eu\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\/","url":"https:\/\/www.tessanalytics.eu\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\/","name":"AI and Forecasting Case Studies for Retail and Large-Scale Retail | TESSA","isPartOf":{"@id":"https:\/\/www.tessanalytics.eu\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.tessanalytics.eu\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\/#primaryimage"},"image":{"@id":"https:\/\/www.tessanalytics.eu\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\/#primaryimage"},"thumbnailUrl":"https:\/\/www.tessanalytics.eu\/wp-content\/uploads\/2026\/03\/data-analysis-scaled.jpg","datePublished":"2026-03-02T14:12:20+00:00","dateModified":"2026-03-25T16:22:58+00:00","author":{"@id":"https:\/\/www.tessanalytics.eu\/#\/schema\/person\/415085c629cac1ab93477df5b0ee02f5"},"description":"Learn how AI optimises the supply chain. A case study on Machine Learning and predictive modelling to reduce waste and predict demand.","breadcrumb":{"@id":"https:\/\/www.tessanalytics.eu\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\/#breadcrumb"},"inLanguage":"en-GB","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.tessanalytics.eu\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\/"]}]},{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/www.tessanalytics.eu\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\/#primaryimage","url":"https:\/\/www.tessanalytics.eu\/wp-content\/uploads\/2026\/03\/data-analysis-scaled.jpg","contentUrl":"https:\/\/www.tessanalytics.eu\/wp-content\/uploads\/2026\/03\/data-analysis-scaled.jpg","width":2560,"height":1707},{"@type":"BreadcrumbList","@id":"https:\/\/www.tessanalytics.eu\/caso-studio-intelligenza-artificiale-per-lottimizzazione-delle-scorte-e-forecasting\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.tessanalytics.eu\/"},{"@type":"ListItem","position":2,"name":"Caso Studio: Intelligenza Artificiale per l&#8217;Ottimizzazione delle Scorte e Forecasting"}]},{"@type":"WebSite","@id":"https:\/\/www.tessanalytics.eu\/#website","url":"https:\/\/www.tessanalytics.eu\/","name":"Tessa Analitycs","description":"","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.tessanalytics.eu\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-GB"},{"@type":"Person","@id":"https:\/\/www.tessanalytics.eu\/#\/schema\/person\/415085c629cac1ab93477df5b0ee02f5","name":"Ilaria Benedetti","image":{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/secure.gravatar.com\/avatar\/6fdc289a865cc6349987dd6bc5ea229f1204f676f92a85df445e64c669c60b37?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/6fdc289a865cc6349987dd6bc5ea229f1204f676f92a85df445e64c669c60b37?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/6fdc289a865cc6349987dd6bc5ea229f1204f676f92a85df445e64c669c60b37?s=96&d=mm&r=g","caption":"Ilaria Benedetti"},"sameAs":["https:\/\/www.tessanalytics.eu\/"],"url":"https:\/\/www.tessanalytics.eu\/en\/author\/ilaria\/"}]}},"_links":{"self":[{"href":"https:\/\/www.tessanalytics.eu\/en\/wp-json\/wp\/v2\/posts\/1220","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tessanalytics.eu\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.tessanalytics.eu\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.tessanalytics.eu\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tessanalytics.eu\/en\/wp-json\/wp\/v2\/comments?post=1220"}],"version-history":[{"count":4,"href":"https:\/\/www.tessanalytics.eu\/en\/wp-json\/wp\/v2\/posts\/1220\/revisions"}],"predecessor-version":[{"id":1420,"href":"https:\/\/www.tessanalytics.eu\/en\/wp-json\/wp\/v2\/posts\/1220\/revisions\/1420"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.tessanalytics.eu\/en\/wp-json\/wp\/v2\/media\/1425"}],"wp:attachment":[{"href":"https:\/\/www.tessanalytics.eu\/en\/wp-json\/wp\/v2\/media?parent=1220"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tessanalytics.eu\/en\/wp-json\/wp\/v2\/categories?post=1220"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tessanalytics.eu\/en\/wp-json\/wp\/v2\/tags?post=1220"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}