In these slides, why do we build a distribution from A/F ratios to be used in computing Q*?

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BlackTom AI · Accepted Answer

To approach this question, consider what building a distribution from A/F ratios accomplishes in the context of demand forecasting and inventory optimization.
Option 1: 'To ensure reorder points are always equal to average demand' — This is inaccurate. Building a distribution from A/F ratios is not about forcing reorder points to equal average demand; it’s about representative uncertainty, not a fixed value.
Option 2: 'To avoid using any historical data' — This is incorrect. Creating an A/F-based distribution relies on historical observations of arrivals and fills (A/F data) to characterize variability; it does not eliminate the use of history.
Option 3: 'To convert a point forecast into a calibrated probabilistic model of demand' — This is correct. Using A/F ratios to build a distribution allows us to move from a single point forecast to a probabilistic representation that captures demand uncertainty, enabling probabilistic service levels and risk-informed decisions.
Option 4: 'To remove the need for the critical ratio' — This statement is not true in general. The critical ratio is used to make ordering decisions; building a distribution does not inherently remove the need for considering the critical ratio.
So, option 3 is the key purpose: it turns a single forecast into a full probabilistic model of demand by capturing variability through the A/F-derived distribution.

In these slides, why do we build a distribution from A/F ratios to be used in computing Q*?

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