Assume the same information for Blanda Brothers as in Problem 11-29. Due to increased competition among grocery stores, the cost to Blanda of selling a low-quality apple to the grocery store as an acceptable-quality apple is now $0.75 instead of $1.05 for each low-quality apple.

Date from Problem 11-29:    

Blanda Brothers is a produce processing company that specializes in fruits, with apple sales representing the majority of their revenue. Their main facility receives a daily shipment of apples, which are then sorted and shipped to grocery stores and to producers of apple jelly and juice, depending on the apple quality (sweetness, taste, color, etc.). The acceptable-quality apples are sold to grocery stores and low-quality apples are sold to apple processors. If a low-quality apple is sold to a grocery store, the apples are returned and Blanda must pay the stores a fee to compensate the store for lost sales and for the extra work of shipping the apples back. Blanda’s reputation also suffers, affecting future business. Blanda has an algorithm to determine apple quality, but the algorithm is not perfect and misclassifies apples. The data science team presents their work to Cindy Hansen, the management accountant at Blanda, to help them choose between two cutoff prediction probabilities for low-quality apples of 0.50 and 0.30. Apples with a predicted probability above the cut-off probability are classified as low quality, and apples below the cut-off probability are classified as acceptable quality apples. The following confusion matrices are based on the validation sample.

Required:
1. Cindy realizes that the change in the cost of selling a low-quality apple to the grocery store as an acceptable-quality apple may change the optimal model cutoff. Re-evaluate the two cutoffs based on the new payoff matrix. How does this compare to the decision in Problem 11-29, requirement 2?
2. Why is it important for Cindy as the management accountant and the data science team to partner with each other?

Transcribed Image Text:

Actual Outcomes Confusion Matrix (0.30) Predicted Outcomes Low Acceptable Quality 20 620 Quality Low Quality 130 230 Acceptable Quality Actual Outcomes Low Quality Acceptable Quality Confusion Matrix (0.50) Predicted Outcomes Acceptable Quality 50 730 Low Quality 100 120