Retail Giant Sales Forecasting using Machine Learning
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Abstract
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Author information unavailable
Abstract
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Sales forecasting is widely recognized and plays a major role in an organization’s decision making. It is an integral part in business execution of retail giants, so that they can change their strategy to improve sales in the near future. This helps in better management of their resources like machine, money and manpower. Forecasting the sales will help in managing the revenue and inventory accordingly. This paper proposes a model that can forecast most profitable segments at granular level. As most retail giants have many branches in different locations, consolidation of sales are hard using data mining. Instead using machine learning model helps in getting reliable and accurate results. This paper helps in understanding the sales trend to monitor or predict future applicable on different types of sales patterns and products to produce accurate prediction results.
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Sales forecasting is widely recognized and plays a major role in an organization’s decision making. It is an integral part in business execution of retail giants, so that they can change their strategy to improve sales in the near future. This helps in better management of their resources like machine, money and manpower. Forecasting the sales will help in managing the revenue and inventory accordingly. This paper proposes a model that can forecast most profitable segments at granular level. As most retail giants have many branches in different locations, consolidation of sales are hard using data mining. Instead using machine learning model helps in getting reliable and accurate results. This paper helps in understanding the sales trend to monitor or predict future applicable on different types of sales patterns and products to produce accurate prediction results.
Key concepts: Sales forecasting, Sales management, Consolidation (business), Revenue, Retail sales, Demand forecasting, Revenue management, Computer science