{"id":1385786,"date":"2024-02-29T09:10:57","date_gmt":"2024-02-29T08:10:57","guid":{"rendered":"https:\/\/www.supplyvalueconsulting.com\/whitepaper-implementation-of-reusable-transport-packaging-rtp-into-fashion-supply-chains-copy\/"},"modified":"2024-04-11T12:18:55","modified_gmt":"2024-04-11T10:18:55","slug":"whitepaper-best-practices-fur-die-anwendung-von-maschinellem-lernen-auf-nachfrageprognosen","status":"publish","type":"post","link":"https:\/\/www.supplyvalueconsulting.com\/de\/whitepaper-best-practices-for-applying-machine-learning-to-demand-forecasts\/","title":{"rendered":"Whitepaper: Best Practices f\u00fcr die Anwendung von maschinellem Lernen auf Nachfrageprognosen"},"content":{"rendered":"\r\n<p><strong>Applying machine learning to improve your forecast, is it possible? <\/strong><strong>This whitepaper, a result of quantitative research by Supply Value with key clients, evaluates three forecasting models\u2014Prophet, Neuralprophet, and LightGBM\u2014in a real-life retail business context. The importance of a mature data architecture is emphasized, with pointers on drivers and barriers towards achieving this goal. Companies with mature data workflows demonstrate higher-quality datasets, emphasizing the significance of accessing the right data at the right time for accurate predictions.<\/strong><\/p>\r\n<p>The power and limitations of machine learning are explored through the pilot study, showcasing the promise of increased model complexity. However, a balance is crucial as complexity reduces interpretability. Your business context also determines model performance, with neural networks suitable for erratic demand patterns and LightGBM effective in rich datasets.<\/p>\r\n<p>Are you interested in unlocking the benefits of Machine Learning driven forecasts through mature data management? 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Companies [&hellip;]<\/p>\n","protected":false},"author":55,"featured_media":1385788,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_et_pb_use_builder":"off","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[120,115],"tags":[],"class_list":["post-1385786","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications","category-supply-chain"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.4) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Whitepaper: Best practices for applying machine learning to demand forecasts - Supply Value Consulting<\/title>\n<meta name=\"description\" content=\"Do you want to learn more about the best practices for applying machine learning to demand forecasts? 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