Впровадження автоматичного підбору асортиментної матриці для категоріального аналізу у Power BI
Альтернативна назва
Implementation of automatic selection of assortment matrix for category analysis in Power BI
Ескіз недоступний
Дата
2025
Науковий керівник
Укладач
Редактор
Назва журналу
ISSN
2413-9998
E-ISSN
Назва тому
Видавець
Одеський національний університет імені І. І. Мечникова
Анотація
У статті представлено методологію формування мультикатегорійної асортиментної матриці та комплексний підхід до дослідження автоматизації підбору асортиментної матриці для торгових точок, що мають обмежений торговий простір. Використовуючи інструменти Power BI та мову DAX, розроблено алгоритм для визначення оптимального набору SKU з урахуванням рейтингу товару, категоріальної структури та обмежень поличного простору клієнта. У роботі докладно описано логіку впровадження стратегічного бачення компанії, математичну модель, побудову таблиць, логіку DAX-обчислень, а також представлено дві ключові DAX-міри, що реалізують різні підходи до обмежень асортименту. Стаття демонструє можливості Power BI як інструмента оптимізаційної аналітики та описує практичні аспекти впровадження рішень для категорійного менеджменту.
The article presents a methodology for the formation of a multicategory assortment matrix and a comprehensive approach to automating the selection of assortment matrices for retail outlets with limited shelf space. Using Power BI tools and the DAX language, an algorithm was developed to identify the optimal set of SKUs considering product ranking, category structure, and client space constraints. The study details the implementation logic of the strategic vision of the company, the mathematical model, the construction of tables, and the DAX calculations, including two key DAX measures designed to apply different assortment constraints. The first measure evaluates the status of each SKU within the category, taking into account its ranking and category limit, the available shelf space, and actual assortment allocation, thereby providing decisions on whether an item should be included, excluded, or marked for review. The second measure assesses overall SKU status at the store level, considering aggregated category values and adjusted limits, ensuring that high-priority items are retained while low-priority or restricted-status SKUs are not proposed. The study illustrates Power BI capabilities as an optimization analytics platform and demonstrates practical aspects for implementing data-driven decision-making in category management, including rules for excluding items with special statuses, such as NPI or DEL, from the assortment matrix, ensuring both efficiency and strategic alignment with company goals. Furthermore, the article highlights the integration of demand data, ranking logic, and categorical constraints into simple, but effective dashboards, enabling managers and analysts to proactively monitor decision outcomes and adjust assortments according to dynamic market conditions. The approach proposed provides an innovative model for balancing economic performance with operational constraints, offering a practical framework for retailers and distributors facing limited shelf capacity while maximizing customer choice and store profitability. The paper also considers methodological implications for designing adaptive data-driven tools for assortment optimization, illustrating how mathematical formulations can be translated into intuitive and actionable DAX metrics. Power BI thus serves as a powerful platform for implementing automated assortment strategies, enhancing decision quality, and increasing managerial efficiency in category management applications across different retail contexts. The study contributes both practically and theoretically by taking a classic problem of assortment planning and addressing it through systematic automation, providing examples of DAX calculations and model structure that can be replicated in a multi-category retail environment. The findings demonstrate that the integration of ranking, category structure, and space constraints into the decision logic can improve assortment effectiveness, reduce managerial workload, and ensure that priority SKUs are made available to shoppers while restricted or low-performing products are appropriately managed. This research provides a novel framework for applied retail analytics and category management, bridging mathematical modeling, data-driven decision-making, and practical implementation through Power BI and DAX-based tools.
The article presents a methodology for the formation of a multicategory assortment matrix and a comprehensive approach to automating the selection of assortment matrices for retail outlets with limited shelf space. Using Power BI tools and the DAX language, an algorithm was developed to identify the optimal set of SKUs considering product ranking, category structure, and client space constraints. The study details the implementation logic of the strategic vision of the company, the mathematical model, the construction of tables, and the DAX calculations, including two key DAX measures designed to apply different assortment constraints. The first measure evaluates the status of each SKU within the category, taking into account its ranking and category limit, the available shelf space, and actual assortment allocation, thereby providing decisions on whether an item should be included, excluded, or marked for review. The second measure assesses overall SKU status at the store level, considering aggregated category values and adjusted limits, ensuring that high-priority items are retained while low-priority or restricted-status SKUs are not proposed. The study illustrates Power BI capabilities as an optimization analytics platform and demonstrates practical aspects for implementing data-driven decision-making in category management, including rules for excluding items with special statuses, such as NPI or DEL, from the assortment matrix, ensuring both efficiency and strategic alignment with company goals. Furthermore, the article highlights the integration of demand data, ranking logic, and categorical constraints into simple, but effective dashboards, enabling managers and analysts to proactively monitor decision outcomes and adjust assortments according to dynamic market conditions. The approach proposed provides an innovative model for balancing economic performance with operational constraints, offering a practical framework for retailers and distributors facing limited shelf capacity while maximizing customer choice and store profitability. The paper also considers methodological implications for designing adaptive data-driven tools for assortment optimization, illustrating how mathematical formulations can be translated into intuitive and actionable DAX metrics. Power BI thus serves as a powerful platform for implementing automated assortment strategies, enhancing decision quality, and increasing managerial efficiency in category management applications across different retail contexts. The study contributes both practically and theoretically by taking a classic problem of assortment planning and addressing it through systematic automation, providing examples of DAX calculations and model structure that can be replicated in a multi-category retail environment. The findings demonstrate that the integration of ranking, category structure, and space constraints into the decision logic can improve assortment effectiveness, reduce managerial workload, and ensure that priority SKUs are made available to shoppers while restricted or low-performing products are appropriately managed. This research provides a novel framework for applied retail analytics and category management, bridging mathematical modeling, data-driven decision-making, and practical implementation through Power BI and DAX-based tools.
Опис
Ключові слова
категоріальний аналіз, асортиментна матриця, оптимізація SKU, Power BI, DAX, категорійний менеджмент, обмеження торгового простору, автоматизоване прийняття рішень, рітейл-аналітика, categorical analysis, assortment matrix, SKU optimization, categorical management, retail space constraints, automated decision making, retail analytics
Бібліографічний опис
Чайковська М. П., Стоянов В. М. Впровадження автоматичного підбору асортиментної матриці для категоріального аналізу у Power BI. Ринкова економіка: сучасна теорія і практика управління : зб. наук. пр. Одеса : Одес. нац. ун-т ім. І. І. Мечникова, 2025. Т. 24, вип. 3(61). С. 160–171.
УДК
658.8:004.89