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ERP Glossary

ABC Analysis

ABC analysis is an inventory categorization method that divides products into three groups based on their value contribution. A-items (typically 20% of SKUs generating 80% of value) receive the most attention, B-items are moderate, and C-items (often 50% of SKUs generating 5% of value) receive the least.

Understanding ABC Analysis

ABC analysis applies the Pareto principle (80/20 rule) to inventory management. The insight is that not all inventory items are equally important, and treating them as if they are wastes resources. A business with 10,000 SKUs cannot give equal attention to every product. ABC analysis provides a rational framework for allocating management effort. A-items are your vital few. They represent a small percentage of your SKU count but drive the majority of revenue or margin. These items deserve tight inventory control: frequent cycle counts, accurate demand forecasting, carefully managed safety stock, and close supplier relationships. A stockout of an A-item has a disproportionate impact on revenue. B-items fall in the middle. They deserve reasonable attention but not the same intensity as A-items. Standard reorder points and periodic review cycles work well for this group. C-items are the trivial many. Individually, each C-item contributes little to revenue. Ironically, C-items often consume the most management time because there are so many of them. The best approach for C-items is simplification: higher reorder quantities to reduce ordering frequency, less precise forecasting, and higher safety stock in percentage terms (because the absolute cost is low). Some businesses extend the model to include D-items (dead stock that should be liquidated or scrapped) and occasionally E-items (new products not yet classifiable). The classification should be refreshed periodically because products shift categories as demand patterns change. A product launched as a C-item may become an A-item if it gains market traction.

How Yukti Handles This

Yukti performs automatic ABC classification based on configurable criteria (revenue, margin, or movement velocity) and refreshes the analysis periodically. AI-driven recommendations adjust inventory policies by category, ensuring A-items get tight control while C-items get streamlined handling.

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