- The Gatson manufacturing company has estimated the following components for anew product.
Fixed cost = 50,000
Material cost per unit = 2.15
Labor cost per unit = 2.00
Revenue per unit = 7.50
Note that fixed cost is incurred regardless of the amount produced. Per-unit material and labor cost together make up the variable cost per unit. Profit is calculated by subtracting the fixed cost and total variable cost from total revenue, assuming the company sells all its produced goods.
- Build an influence diagram that illustrates how to calculate profit.
- Using mathematical notation, give a mathematical model for calculating profit.
- Implement your model from part (b) in Excel using the principles of good spreadsheet design.
- If the company decides to make 70,000 units of the new product, what will be the resulting profit?
Q. 2Use the influence diagram given below to answerquestions
Q. 3
- Which of the following data patterns best describes the scenario shown in the given time series plot?
Q. 4po9m19KilmUuvmDU51e3RSsjCAEAA
AAmEukHbnMCkLRgiAEAAAAmEukHbn
CMwg9bmvrbxtWBi2JIAQAAACYS6Qd
ucIyCA1cf39z2dkhw7gFEYQAAAAAc
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 width=367 height=254 />
- Linear trend and cyclical pattern
- Linear trend and horizontal pattern
- Seasonal and cyclical patterns
- Seasonal pattern and linear trend
"
Q. 5
- Which of the following data patterns best describes the scenario shown in the given time series plot?
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