Fact Table Design for Sales and Inventory Analytics

Abstract

Fact table design is important for sales and inventory analytics because enterprises need measurable, well-structured transaction data to analyze revenue, stock movement, product demand, and operational performance. A fact table stores quantitative business measures such as sales amount, quantity sold, discount, cost, stock level, reorder quantity, and inventory turnover, linked with dimensions such as product, customer, store, supplier, and time. Existing literature highlights transaction fact tables, periodic snapshot fact tables, accumulating snapshot fact tables, grain definition, surrogate keys, foreign key relationships, and additive measures as major elements of analytical warehouse design. However, many organizations still face challenges such as unclear fact table granularity, duplicated measures, inconsistent product hierarchies, mismatched sales and inventory records, slow analytical queries, and difficulty tracking stock changes over time. This research is important because poorly designed fact tables can reduce reporting accuracy, weaken demand forecasting, and affect inventory planning decisions. This article discusses fact table design for sales and inventory analytics, focusing on grain selection, measure definition, dimension linkage, sales transaction modeling, inventory snapshot design, aggregation support, and query performance. The study concludes that effective fact table design improves analytical accuracy, strengthens sales and inventory visibility, supports faster reporting, and enables better enterprise decision-making.