Kesla Inc. manufactures a unique fuel injection system that yields dramatic cost savings through improved fuel efficiency. The effectiveness of the system is dependent upon the quality of a specialized sensor. These sensors are purchased from an external supplier for $21 each.

For the past few years, an average of 10% of the sensors purchased from the supplier have not met Keslas quality requirements. The number of unusable sensors has ranged from 5% to 25% of the total number purchased and has resulted in failures in meeting

production schedules. In addition, Kesla has incurred additional costs to replace the defective units because the rejection rate of the units is within the range agreed upon in the contract.

Kesla is considering a proposal from its engineering department to manufacture the sensors internally. Kesla has the facilities and equipment to produce the components. The engineering department has designed a manufacturing system that will produce the sensors with a defect rate of 5% of the number of units produced. The following schedule presents the engineers estimates of the probabilities that different levels of variable manufacturing cost per sensor will be incurred under this system. Additional annual fixed costs incurred by Kesla, if it manufactures the sensor, will amount to

$42,500.

Estimated variable manufacturing cost per sensor	Probability of occurrence
$12	10%
$15	30%
$18	40%
$21	20%

Kesla will need 18,000 sensors to meet its annual demand requirements.

Required:

1. Prepare an expected-value analysis to determine whether Kesla should manufacture the sensors.
2. The engineers used internal costing records of similar internal processes when estimating the probabilities of variable manufacturing costs. However, they realized the estimates of the probability did not take into account that the new manufacturing system consisted of newer, more efficient machines and a different gauge of steel.

Identify other sources of data that could reduce the estimated range of variable manufacturing cost per sensor.