The NautilusNautilus Company, which is under contract to the U.S. Navy, assembles troop deployment boats.

1. Prepare a prediction of the total costs for producing the six PT109s for the Navy.

2. Compare the cost predictions from requirement 1 (incremental unit-time learning model) with the cost predictions based on a cumulative average-time learning model which has total costs of $ 5,831,039. Why are the predictions different? How should NautilusNautilus decide which model it should use?

Cumulative average-time learning model's cumulative average time per unit and cumulative total time:

Number of	Cumulative average time			Cumulative total time:
units	per unit: Labor-hours			Labor-hours
1		14,600			14,600
2		13,140			26,280
3		12,355			37,065
4		11,826			47,304
5		11,432			57,160
6		11,119			66,714
7		10,862			76,034

Direct material cost $ 206,000 2 Direct manufacturing labor time for first boat 14,600 labor-hours 3 Direct manufacturing labor rate $ 44 per direct manufacturing labor-hour Variable manufacturing overhead cost $ 22 per direct manufacturing labor-hour 5 Other manufacturing overhead 20% of direct manufacturing labor costs 6 Tooling costs $ 286,000 Learning curve for manufacturing labor time per boat 90% cumulative average | 8 "Tooling can be reused at no extra cost because all of its cost has been assigned to the first deployment boat In 0.90 Using the formula for a 90% learning curve, in 2 -0.105361 In 2 0 .693147 = -0.152004 Nautilus uses a 90% incremental unit-time learning model as a basis for predicting direct manufacturing labor-hours in its assembling operations. (A 90% learning curve means b = -0.152004.)