What might be a consequence of underestimated data cardinality in inventory management?

What might be a consequence of underestimated data cardinality in inventory management?

• A. Improved system speed
• B. Inaccurate sales and supply analysis
• C. Reduced need for database maintenance
• D. Maximized resource utilization

Answer: (B)

Underestimating data cardinality in inventory management can lead to inaccurate sales and supply analysis. Data cardinality refers to the uniqueness of data values within a dataset, which is particularly important in inventory contexts where precise data is essential for forecasting, ordering, and managing stock levels.

When organizations fail to accurately assess the uniqueness and volume of their inventory data—such as product types, quantities, and transaction frequencies—they risk drawing incorrect conclusions about customer demand and supply levels. This miscalculation may result in overstocking or stockouts, ultimately impacting sales and customer satisfaction. For instance, if a business underestimates the number of different products or variations in demand, it may not order enough stock to meet customer needs or may order too much, leading to waste and increased holding costs.

The other options would not logically follow from underestimated data cardinality. Improved system speed is generally associated with optimized database querying and indexing rather than directly linked to cardinality. Reduced need for database maintenance implies a lower operational workload, which is not a benefit of erroneous data estimates. Maximized resource utilization refers to effectively using available resources, which can actually suffer if analysis is flawed due to incorrect cardinality assessments. Each of these options does not align with the fundamental issues caused by misjud