Which of the following is an example of Semi-Additive Facts?

Semi-additive facts are metrics that can be aggregated across certain dimensions but are not additive across all dimensions. This means they can be summed in some contexts but not in others.

In this case, daily inventory levels serve as a practical example of semi-additive facts. Inventory can be accurately calculated for specific time periods, such as viewing total inventory levels at the end of each day, allowing for appropriate calculations when looking at time series data over specific points in time (e.g., daily, weekly). However, summing inventory levels across all days to produce a total across a longer time frame (like a year) would not make sense since it would not represent an accurate overall value of inventory at any given time.

The other options don't fit the definition of semi-additive facts in the same way. Total sales can be summed across any dimension, such as time or product category, so it is fully additive. Average customer satisfaction scores can also be calculated in a straightforward manner and summed across populations. Lastly, population growth rate represents a scalar measure that can be summed across different sectors or regions without the same limitations, therefore making it fully additive as well.

Thus, daily inventory levels stand out as a definitive example of semi-additive facts, showcasing