Chapter 1 question:

- Why is it important to match supply and demand? If a manager believes that supply and demand will not be equal, what actions could the manager take to increase the probability of achieving a match?

Chapter 3 questions:

- National Scan, Inc sells radio frequency inventory tags. Monthly sales for a seven-month period were as follows:

Month Sales (000 units)

Feb. 20

Mar. 19

Apr. 16

May 21

Jun. 19

Jul. 23

Aug. 21

- Plot the monthly data on a sheet of graph paper.
- Forecast September sales volume using each of the following:

(1) The naive approach

(2) A five month moving average

(3) A weighted average using .60 for August, .30 for July, and .10 for June

(4) Exponential smoothing with a smoothing constant equal to .20, assuming a a March forecast of 19(000)

(5) A linear trend equation

- Which method seems least appropriate? Why? (Hint: Refer to your plot from part a.)
- What does use of the term sales rather than demand presume?

- a. Obtain the linear trend equation for the following data on new checking accounts at Fair Savings Bank and use it to predict expected new checking accounts for periods 16 through 19.

Period | New Accounts | Period | New Accounts | Period | New Accounts |

1 | 199 | 6 | 231 | 11 | 280 |

2 | 213 | 7 | 247 | 12 | 274 |

3 | 210 | 8 | 251 | 13 | 279 |

4 | 227 | 9 | 252 | 14 | 287 |

5 | 234 | 10 | 266 | 15 | 309 |

b. Use trend-adjusted smoothing with α = .3 and β = .2 to smooth the new account data in part a. What is the forecast for period 16?

- The following equation summarizes the trend portion of quarterly sales of condominiums over a long cycle. Sales also exhibit seasonal variations. Using the information given, prepare a forecast of sales for each quarter of next year (not this year), and the first quarter of the year following that.

= 40 – 6.5t + 2t^2

where

=Unit Sales

t=0 at, the first quarter of last year

Quarter Relative

1 1.1

2 1.0

3 .7

4 1.2

- Two independent methods of forecasting based on judgment and experience have been prepared each month for the past 10 months. The forecasts and actual sales are as follows:

Month | Sales | Forecast 1 | Forecast 2 |

1 | 660 | 661 | 659 |

2 | 679 | 675 | 677 |

3 | 684 | 680 | 682 |

4 | 670 | 674 | 688 |

5 | 658 | 660 | 664 |

6 | 662 | 658 | 660 |

7 | 650 | 651 | 649 |

8 | 665 | 661 | 665 |

9 | 676 | 674 | 678 |

10 | 680 | 678 | 678 |

- Compute the MSE and MAD for each forecast. Does either forecast seem superior? Explain.
- Compute MAPE for each forecast.
- Prepare a naive forecast for periods 2 through 11 using the given sales data. Compute each of the following; (1) MSE, (2) MAD, (3) tracking signal at month 10, and (4) 2s control limits. How do the naive results compare with the other two forecasts?

- Lovely Lawns, Inc., intends to use sales of lawn fertilizer to predict lawn mower sales. The store manager estimates a probable six-week lag between fertilizer sales and mower sales. The pertinent data are:

Period | Fertilizer Sales (tons) | Number of Mowers Sold (six-week lag) |

1 | 1.9 | 13 |

2 | 1.6 | 11 |

3 | 2.1 | 14 |

4 | 2.3 | 15 |

5 | 2.5 | 15 |

6 | 1.9 | 12 |

7 | 1.8 | 11 |

8 | 1.6 | 10 |

9 | 2 | 13 |

10 | 1.5 | 9 |

11 | 2.2 | 14 |

12 | 1.7 | 11 |

13 | 2 | 13 |

14 | 1.9 | 12 |

- Determine the correlation between the two variables. Does it appear that a relationship between these variables will yield reasonable predictions? Explain.
- Obtain a linear regression line for the data.
- Predict expected lawn mower sales for the first week in August, given fertilizer sales six weeks earlier of 2 tons.

Chapter 5 question:

- A manager must decide which type of machine to buy, A, B, or C. Machine costs are as follows:

Machine | Cost |

A | $50,000 |

B | $40,000 |

C | $90,000 |

Product forecasts and processing times on the machines are as follows:

PROCESSING TIME PER UNIT (minutes) | ||||

Product | Annual Demand | A | B | C |

1 | 18,000 | 3 | 4 | 2 |

2 | 14,000 | 4 | 4 | 3 |

3 | 8,000 | 5 | 6 | 4 |

4 | 32,000 | 2 | 2 | 1 |

- Assume that only purchasing costs are being considered. Which machine would have the lowest total cost, and how many of that machine would be needed? Machines operate 10 hours a day, 250 days a year.
- Consider this additional information: The machines differ in terms of hourly operating costs: The A machines have an hourly operating cost of $10 each, B machines have an hourly operating cost of $11 each, and C machines have an hourly operating cost of $12 each. Which alternative would be selected, and how many machines, in order to minimize total cost while satisfying capacity processing requirements?

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