Question 36
A retail manager wants to analyze Daily Inventory Turnover for SKU A to identify days with possible stockout risk.
The IT team provides this dataset for the last month (30 days): ● Total units sold in the month: 3,000 ● Stock level on Day 1: 400 units ● Stock level on Day 30: 400 units ● No data on stock levels or sales by day in between
The manager suggests estimating Daily Inventory Turnover by: Daily Turnover = (3,000/30) / ((400+400)/2)
Why is this approach fundamentally flawed for detecting stockout risk?
It violates the standard inventory turnover formula, which must use the cost of goods sold instead of units sold.
It uses only opening and closing stock to compute an average, so any days with zero stock or very low stock in the middle of the month are hidden by the monthly smoothing.
It assumes demand is evenly spread across days, so days with sales spikes or stock-outs cannot be distinguished from normal days.
It is invalid because you cannot calculate a daily metric using any monthly data.