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Roll Up Sales Forecast for Gonzales Electronic Company

Please see the attached file.

The Gonzales Electronic Company's 12 products are further grouped into four product families. Product A, B, and C compose family 1; D, E, and F compose family 2; and product s G, H, and I make up family 3; and products J, K, and L make up family 4. Dick Gonzales has the following forecasts of monthly demand for each product.

Family Product Forecast $/unit
1 A
C 10
20 1,000
2 D
F 5
2 5,000
3 G
I 100
220 250
4 J
L 2
3 10,000

Gonzales's sales force has also come up with the monthly forecasts of sales for each product family.

Family $ Sales
1 50,000
2 50,000
3 75,000
4 75,000

a. Top management has independently set a $300,000 overall monthly sales goal for the company. Roll up the individual product forecasts and compare them with the family data. Use a spreadsheet and the family forecasts to revise the individual item forecasts (in both dollars and units).

b. Roll up the family forecasts to the top level, compare these to the overall forecasts, and roll the forecasts back down to families and to individual unit forecasts (dollars units).

c. Suppose a major customer order has just been received for 10 units of product J. This order wasn't expected and it in addition to any other forecasts for product J. The company still wants to plan a total monthly sales volume of $300,000. Use the family forecasts (revised) to roll the forecasts to and down to get revised individual product forecasts.


Solution Preview

See attached for answers.

The question is essentially a weighted average type of problem where you figure new units to produce based upon a new sum, but using the same make up or the same percentages of product. In other words, if ...

Solution Summary

The problem sets up by listing the necessary components of a process and how many of each type are used (for example, part A is made up of 2 part C's and 3 part D's, etc.) Monthly sales forecast increases that effect demand, and the problem asks for you to roll up and down new component demand forecasts.