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Using a weighted moving average with weights of 0.60

22/11/2021 Client: muhammad11 Deadline: 2 Day

Operations Forecasting

Problem 18-4

Here are the data for the past 21 months for actual sales of a particular product:

LAST YEAR

THIS YEAR

January

365

300

February

455

375

March

400

375

April

430

455

May

432

440

June

505

380

July

400

385

August

320

305

September

395

365

October

500

November

590

December

490

Develop a forecast for the fourth quarter using a three-quarter, weighted moving average. Weight the most recent quarter 0.50, the second most recent 0.25, and the third 0.25. Do the problem using quarters, as opposed to forecasting separate months. (Round your answer to 2 decimal places.)

Forecast for the fourth quarter

Explanation:

Third most recent quarter 300 + 375 + 375 = 1,050

Second most recent quarter 455 + 440 + 380 = 1,275

Most recent quarter 385 + 305 + 365 = 1,055

WMA = (0.25 × 1,050) + (0.25 × 1,275) + (0.50 × 1,055) = 1,108.75

References

WorksheetDifficulty: Challenge

Problem 18-4Learning Objective: 18-02 Evaluate demand using quantitative forecasting models.

Problem 18-7

The following table contains the demand from the last 10 months:

MONTH

ACTUAL DEMAND

1

32

2

35

3

36

4

38

5

41

6

39

7

39

8

41

9

44

10

40

a.

Calculate the single exponential smoothing forecast for these data using an α of 0.30 and an initial forecast (F1) of 32. (Round your answers to 2 decimal places.)

Month

Exponential Smoothing

1

2

3

4

5

6

7

8

9

10

b.

Calculate the exponential smoothing with trend forecast for these data using an α of 0.30, a δ of 0.20, an initial trend forecast (T1) of 1.00, and an initial exponentially smoothed forecast (F1) of 31. (Round your answers to 2 decimal places.)

Month

FITt

1

2

3

4

5

6

7

8

9

10

c-1.

Calculate the mean absolute deviation (MAD) for the last nine months of forecasts. (Round your answers to 2 decimal places.)

MAD

Single exponential smoothing forecast

Exponential smoothing with trend forecast

c-2.

Which is best?

Exponential smoothing with trend forecast

Single exponential smoothing forecast

Explanation:

a. to c.

Month

Demand

Exponential Smoothing

Absolute Deviation

Tt

Ft

FITt

Absolute Deviation

1

32

32.00

1.00

31.00

32.00

2

35

32.00

3.00

1.00

32.00

33.00

2.00

3

36

32.90

3.10

1.12

33.60

34.72

1.28

4

38

33.83

4.17

1.20

35.10

36.30

1.70

5

41

35.08

5.92

1.30

36.81

38.11

2.89

6

39

36.86

2.14

1.47

38.98

40.45

1.45

7

39

37.50

1.50

1.38

40.02

41.40

2.40

8

41

37.95

3.05

1.24

40.68

41.92

0.92

9

44

38.87

5.13

1.18

41.64

42.82

1.18

10

40

40.41

0.41

1.25

43.17

44.42

4.42

MAD

3.16

2.03

Based upon the MAD of each forecast, the exponential smoothing with trend is the better forecasting model.

References

WorksheetDifficulty: Challenge

Problem 18-7Learning Objective: 18-02 Evaluate demand using quantitative forecasting models.

Problem 18-10

The number of cases of merlot wine sold by the Connor Owen winery in an eight-year period is as follows:

YEAR

CASES OF MERLOT WINE

2005

291

2006

377

2007

419

2008

477

2009

379

2010

521

2011

431

2012

397

Using an exponential smoothing model with an alpha value of 0.40, estimate the smoothed value calculated as of the end of 2012. Use the average demand for 2005 through 2007 as your initial forecast for 2008, and then smooth the forecast forward to 2012. (Round your intermediate calculations and final answer to the nearest whole number.)

Forecast for 2012

Explanation:

Year

Demand

F(t)

2005

291

2006

377

2007

419

2008

477

362

2009

379

408

2010

521

396

2011

431

446

2012

397

440

References

WorksheetDifficulty: Challenge

Problem 18-10Learning Objective: 18-02 Evaluate demand using quantitative forecasting models.

Problem 18-15

Historical demand for a product is

DEMAND

January

16

February

14

March

18

April

16

May

19

June

18

a.

Using a weighted moving average with weights of 0.50 (June), 0.20 (May), and 0.30 (April), find the July forecast. (Round your answer to 1 decimal place.)

July forecast

b.

Using a simple three-month moving average, find the July forecast. (Round your answer to 1 decimal place.)

July forecast

c.

Using single exponential smoothing with α = 0.20 and a June forecast = 12, find the July forecast. (Round your answer to 1 decimal place.)

July forecast

d.

Using simple linear regression analysis, calculate the regression equation for the preceding demand data. (Do not round intermediate calculations. Round your intercept value to 1 decimal place and slope value to 2 decimal places.)

Y = + t

e.

Using the regression equation in d, calculate the forecast for July. (Do not round intermediate calculations. Round your answer to 1 decimal place.)

July forecast

Explanation:

a.

FJuly = 0.50(18) + 0.20(19) + 0.30(16) = 17.6

b.

FJuly = (18 + 19 + 16)/3 = 17.7

c.

FJuly = FJune + α(AJune – FJune) = 12 + 0.20(18 − 12) = 13.2

d.

t

y

ty

t2

January

1

16

16

1

February

2

14

28

4

March

3

18

54

9

April

4

16

64

16

May

5

19

95

25

June

6

18

108

36

Total

21

101

365

91

Average

3.5

16.8

= 3.5

= 16.833

Y = a + bt = 14.5 + 0.66t

e.

FJuly, where July is the 7th month.

Y = a + bt = 14.5 + 0.66(7) = 19.1

References

WorksheetDifficulty: Challenge

Problem 18-15Learning Objective: 18-02 Evaluate demand using quantitative forecasting models.

Problem 18-22

Your manager is trying to determine what forecasting method to use. Based upon the following historical data, calculate the following forecast and specify what procedure you would utilize.

MONTH

ACTUAL DEMAND

1

61

2

64

3

67

4

66

5

71

6

70

7

73

8

74

9

74

10

83

11

84

12

86

a.

Calculate the simple three-month moving average forecast for periods 4–12. (Round your answers to 3 decimal places.)

Month

Three-Month Moving Average

4

5

6

7

8

9

10

11

12

b.

Calculate the weighted three-month moving average for periods 4–12 using weights of 0.40 (for the period t−1); 0.40 (for the period t−2), and 0.20 (for the period t−3). (Do not round intermediate calculations. Round your answers to 1 decimal place.)

Month

Three-Month Weighted Moving Average

4

5

6

7

8

9

10

11

12

c.

Calculate the single exponential smoothing forecast for periods 2–12 using an initial forecast (F1) of 66 and an α of 0.30. (Do not round intermediate calculations. Round your answers to 3 decimal places.)

Month

Single Exponential Smoothing Forecast

2

3

4

5

6

7

8

9

10

11

12

d.

Calculate the exponential smoothing with trend component forecast for periods 2–12 using an initial trend forecast (T1) of 1.70, an initial exponential smoothing forecast (F1) of 65, an α of 0.30, and a δ of 0.20. (Do not round intermediate calculations. Round your answers to 3 decimal places.)

Month

Exponential Smoothing with Trend

2

3

4

5

6

7

8

9

10

11

12

e-1.

Calculate the mean absolute deviation (MAD) for the forecasts made by each technique in periods 4–12. (Do not round intermediate calculations. Round your answers to 3 decimal places.)

Mean Absolute Deviation

Three-month moving average

Three-month weighted moving average

Single exponential smoothing forecast

Exponential smoothing with trend

e-2.

Which forecasting method do you prefer?

Three-month moving average

Three-month weighted moving average

Single exponential smoothing forecast

Exponential smoothing with trend forecast

*******Explanation: see second page for chart

a. to e.

Single Exponential Smoothing

Exponential Smoothing with Trend

Month (t)

Demand

Three-Month Moving Average

Absolute Deviation

Three-Month Weighted Moving Average

Absolute Deviation

Ft

Absolute Deviation

Ft

Tt

FITt

Absolute Deviation

1

61

66.000

65.000

1.700

66.700

2

64

64.500

64.990

1.358

66.348

3

67

64.350

65.644

1.217

66.861

4

66

64.000

2.000

64.6

1.400

65.145

0.855

66.903

1.225

68.128

2.128

5

71

65.667

5.333

66.0

5.000

65.402

5.598

67.490

1.098

68.587

2.413

6

70

68.000

2.000

68.2

1.800

67.081

2.919

69.311

1.243

70.554

0.554

7

73

69.000

4.000

69.6

3.400

67.957

5.043

70.388

1.209

71.597

1.403

8

74

71.333

2.667

71.4

2.600

69.470

4.530

72.018

1.294

73.311

0.689

9

74

72.333

1.667

72.8

1.200

70.829

3.171

73.518

1.335

74.853

0.853

10

83

73.667

9.333

73.8

9.200

71.780

11.220

74.597

1.284

75.881

7.119

11

84

77.000

7.000

77.6

6.400

75.146

8.854

78.016

1.711

79.727

4.273

12

86

80.333

5.667

81.6

4.400

77.802

8.198

81.009

1.967

82.976

3.024

MAD

4.407

3.933

5.599

2.495

Based upon MAD, the exponential smoothing with trend forecast component appears to be the best method.

References

WorksheetDifficulty: Challenge

Problem 18-22Learning Objective: 18-02 Evaluate demand using quantitative forecasting models.

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