From the image you uploaded, it appears that a public utility needs to determine the optimal number of spare parts (specifically part no. X135) to order for a turbine expansion. Here’s a breakdown of the key points and the process for solving the questions based on the table provided (Table C) and the problem description:

Problem Information:

• Cost of spare part: $170 each
• Carrying and disposal costs: 190% of purchase price (i.e., 1.9 × $170 = $323 per part)
• Stockout cost: $110,000 due to downtime, ordering, and special factors.
• Poisson distribution: Mean demand of 4.4 parts for the turbine's useful life.
• Table C: Provides cumulative probabilities for different demand values under Poisson distribution.

Part a) Optimal number of spares to order

The goal is to minimize the total cost (which includes both carrying and stockout costs). Using the Poisson distribution and values from Table C, the optimal number of spare parts will likely balance the holding (carrying) costs with the risk of a shortage, which would lead to stockout costs.

For determining the optimal number of spares, we need to reference the cumulative probabilities (P) from Table C corresponding to different spare numbers and calculate the expected total costs for different numbers of spares, ultimately choosing the number that minimizes the cost.

Part b) Range of shortage cost

This question asks for the range of shortage costs where carrying no spare parts is the best strategy. We will need to reference the Poisson distribution cumulative probabilities (P) from Table C for the demand of spares and calculate for which range of stockout costs, based on the probabilities, carrying no spares is optimal.

Since the image contains the data and tables for these calculations, would you like me to:

1. Work through the detailed step-by-step calculation for each question?
2. Provide further context on the use of Poisson distribution for spare parts optimization?

Here are five follow-up questions to consider:

1. How is the total cost calculated for different numbers of spare parts based on carrying and stockout costs?
2. What is the role of the Poisson distribution in calculating expected demand for spare parts?
3. How would the results change if the stockout cost of $110,000 is altered?
4. Why are carrying and stockout costs crucial in determining the number of spares?
5. How does the cumulative probability from Table C affect the decision-making process?

Tip: When balancing carrying and stockout costs, it's crucial to minimize the expected total cost, which is a combination of holding too many parts (carrying cost) and the potential shortage (stockout cost).