{"id":3846,"date":"2025-11-15T19:43:37","date_gmt":"2025-11-15T19:43:37","guid":{"rendered":"https:\/\/iwis.io\/?post_type=portfolio&#038;p=3846"},"modified":"2025-12-29T07:42:09","modified_gmt":"2025-12-29T07:42:09","slug":"new-products","status":"publish","type":"portfolio","link":"https:\/\/iwis.io\/en\/results\/new-products\/","title":{"rendered":"Production forecasting model: ML solution for the company New Products Group"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"","protected":false},"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"portfolio_category":[77,112],"portfolio_tag":[114,113],"class_list":["post-3846","portfolio","type-portfolio","status-publish","hentry","portfolio_category-machine-learning","portfolio_category-business-intelligence-bi","portfolio_tag-business-intelligence-bi","portfolio_tag-data-engineering"],"acf":{"portfolio_project_image":3848,"portfolio_project_description":"The Novi Produkty Group is one of the largest producers of beverages and snacks in Ukraine, operating on the market for over 21 years and included in the Register of the country's largest taxpayers.","portfolio_project_tags":[114,113],"portfolio_project_url":"","portfolio_categories":[77,112],"portfolio_featured_project":false,"portfolio_about_label":"About the project","portfolio_about_title":"Forecast that balances demand and spending","portfolio_about_description":"The Novi Produkty Group of Companies is one of the largest producers of beverages and snacks in Ukraine, operating on the market for over 21 years and included in the Register of the country's largest taxpayers. The company exports its products to 23 countries worldwide, has over 1,500 employees, and serves over 106,000 retail outlets. \r\nThe brand portfolio includes more than 220 items, including SHAKE, NON STOP, PIT BULL, REVO, KING\u2019S BRIDGE, APPS, EAT ME, and PRYRODNIE DZERELO. The company has implemented a certified food quality and safety management system. The group is also actively involved in charitable activities.  \r\n","portfolio_about_problem_title":"The client's problem","problem_item_text_default":"<span style=\"font-weight: 400;\">With the growth in production volumes and expansion of sales markets, the company faced the risk of an imbalance between demand and production volumes. Intuitive planning led to either overproduction or product shortages: both scenarios resulted in financial losses and lost market opportunities. <\/span>\r\n\r\n<strong>Key challenges:<\/strong>","portfolio_about_problem_items":[{"portfolio_about_problem_item_text":"excess production created illiquid inventories and write-off costs;"},{"portfolio_about_problem_item_text":"product shortages led to a loss of sales volume;"},{"portfolio_about_problem_item_text":"The lack of a forecasting tool complicated planning in conditions of fluctuating demand."},{"portfolio_about_problem_item_text":"historical sales data was of poor quality, in particular due to the absence of zero balances;"},{"portfolio_about_problem_item_text":"It was necessary to reduce losses without compromising production flexibility."}],"problem_item_text_default_second":"<span style=\"font-weight: 400;\">To solve the problem, the company sought a tool that would allow it to forecast production volumes taking into account demand, seasonality, and data limitations.<\/span>","portfolio_about_problem_image":3848,"portfolio_details_label":"Project details:","portfolio_details_title":"Key facts about the project","portfolio_details_cards":[{"portfolio_details_card_type":"text","portfolio_details_card_title":"12 months","portfolio_details_card_image":null,"portfolio_details_card_text":"Project duration"},{"portfolio_details_card_type":"text","portfolio_details_card_title":"Medium","portfolio_details_card_image":null,"portfolio_details_card_text":"Project size:"},{"portfolio_details_card_type":"image","portfolio_details_card_title":"","portfolio_details_card_image":3540,"portfolio_details_card_text":"Project complexity"},{"portfolio_details_card_type":"text","portfolio_details_card_title":"Completed","portfolio_details_card_image":null,"portfolio_details_card_text":"Project status:"}],"portfolio_team_label":"Our Team:","portfolio_team_members":[{"portfolio_team_member_text":"Project Manager"},{"portfolio_team_member_text":"Data Analyst"},{"portfolio_team_member_text":"Data Science Engineer"},{"portfolio_team_member_text":"DevOps Engineer"}],"portfolio_solution_items":[{"media_type":"image","gif_image":null,"static_image":3847,"solution_label":" Case information","solution_title":"Built a predictive model that synchronizes demand with production","solution_points":[{"point_text":"<span style=\"font-weight: 400;\">The tool has become part of the company's operational and strategic planning.<\/span>","point_has_bullet":false},{"point_text":"<strong>Our approach:<\/strong>","point_has_bullet":false},{"point_text":"<span style=\"font-weight: 400;\">\u200b\u200b<\/span>\r\n\r\n<span style=\"font-weight: 400;\">It all started with participation in a tender: we built the first version of the model based on a limited set of features, which, despite its simplicity, showed the highest accuracy among all participants.<\/span>\r\n\r\n<span style=\"font-weight: 400;\">After launch, we focused on adapting to real business: we held a series of workshops with the client, identified features that influence demand, and tested several ML architectures. As a result, we implemented an ensemble model with an accuracy of over 80%. <\/span>\r\n\r\n<span style=\"font-weight: 400;\">The difficulty arose due to unrecorded zero balances at some retail outlets: the system confused the absence of sales with the absence of demand. Approximation methods corrected these distortions and significantly improved the forecast. <\/span>","point_has_bullet":false}],"button_text":"","button_url":""},{"media_type":"image","gif_image":null,"static_image":3849,"solution_label":"","solution_title":"Work results:","solution_points":[{"point_text":"<span style=\"font-weight: 400;\">We have implemented a forecasting model that allows us to plan production more accurately:<\/span>","point_has_bullet":false},{"point_text":"<span style=\"font-weight: 400;\">thanks to the ensemble ML model, a prediction accuracy of 80% has been achieved; <\/span>","point_has_bullet":true},{"point_text":"<span style=\"font-weight: 400;\">reduced risks of overproduction and shortages;<\/span>","point_has_bullet":true},{"point_text":"<span style=\"font-weight: 400;\">data inaccuracies corrected: absence of zero balances taken into account;<\/span>","point_has_bullet":true},{"point_text":"<span style=\"font-weight: 400;\">the model helps determine how much to produce and when;<\/span>","point_has_bullet":true},{"point_text":"<span style=\"font-weight: 400;\">The foundation has been laid for scaling to other product categories.<\/span>","point_has_bullet":true},{"point_text":"<span style=\"font-weight: 400;\">Instead of reacting to shortages or surpluses after the fact, the company began planning volumes in advance, thus moving from a reactive approach to proactive production management.<\/span>","point_has_bullet":false}],"button_text":"","button_url":""}],"portfolio_results_slider_type":"mobile","portfolio_results_mobile_images":null,"portfolio_results_desktop_images":[{"portfolio_results_desktop_image":4296}],"portfolio_show_feedback_button":true,"portfolio_feedback_preview_image":"","portfolio_feedback_video_url":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - 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