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Discussion board postings.

 

1.Respond to at least two of your classmates’ posts in a substantive manner. Some ways could include examples, current events, and/or possible outcomes. 

1.The answer to this question depends on the firm. An advantage of many suppliers is that it allows for competition between several suppliers ultimately driving down the price.  Additionally, having many suppliers eases the stress if one company cannot produce the widget for the firm because there are several more that can.  A disadvantage is that some competitors might not want to enter into the supply chain in order to protect their company from the competitors. One advantage, which could be a disadvantage also, is that with few suppliers a long term partnership could be created improving quality and costs of the product. A disadvantage with dealing with few suppliers is that the firm normally will be contractually obligated to pay a set price. Additionally single source contracts tend to be lengthy and if there are quality issues with the product, it could affect the firm.  An issue we deal with a lot in the Air Force is what the textbook refers to as the bullwhip effect which is an unanticipated shift in demand.  Because of this flux, contracts regularly expire that provide necessary parts for the aging fleet.  If there is no demand, contracting does not question the expired supply/repair/refurb facility.  Everything is cyclical however and eventually a part would be the new “hot” item that every aircraft is grounded for.

References

Vonderembse, M.A. & White, G.P. (2013). Operations Management. San Diego, CA: Bridgepoint Education, Inc.

2. When a firm looks at how many suppliers to use it must look at the advantages and disadvantages of each. If they choose to use many suppliers it allows competition in regards to cost, quality, and delivery. The biggest advantage to numerous suppliers is that if one supplier is out of a product or goes out of business the impact is mitigated due to the availability to readily use another supplier. There is also a disadvantage to numerous suppliers, and it is harder to forge a long-term partnership arrangement. The numerous suppliers would feel no loyalty toward the company. Using only a few or even one supplier would enable both parties to work together for greater integration of the supply chain and for development of methods that can improve quality and lower costs (Vonderembse & White, 2013). Firms that are inventory driven may need to use multiple suppliers, so that they can service their customers in a timely manner. Other firms that do and supply the same things would probably be better served with fewer or even one supplier, so that they would have that synergy and partnership. Lastly, there will also be the firms that use a mixture of suppliers throughout the production process.

 

Vonderembse, M.A. & White, G.P. (2013). Operations Management. San Diego, CA: Bridgepoint Education, Inc.

 

 

Respond to at least two of your classmates’ posts to identify some of their recommended forecasting methods.  Give additional advice and alternative solutions that might be used as well. 

 

3. a. The 5-year moving average used to forecast the number of mergers for 2012 is shown below: (Verma, 2010):

f2012 = (123+97+186+225+240)/5 = 174.2

b. The moving average technique used to determine the forecast for 2005 to 2011 is shown using MSE and MAD.

f2005 = (64+45+62+46+46)/5 = 52.6

f2006 = (61+64+45+62+46)/5 = 55.6

f2007 = (83+61+64+45+62)/5 = 63

f2008 = (123+83+61+64+45)/5 = 75.2

f2009 = (97+123+83+61+64)/5 = 85.6

f2010 = (186+97+123+83+61)/5 = 110

f2011 = (225+186+97+123+83)/5 = 142.8

 

Year

Actual Mergers

Forecasted Mergers

Error

Squared Error

2005

61

52.6

8.4

70.56

2006

83

55.6

27.4

750.76

2007

123

63

60

3,600

2008

97

75.2

21.8

475.24

2009

186

85.6

100.4

10,080.16

2010

225

110

115

13,225

2011

240

142.8

97.2

9,447.84

Total

  

430.2

37,649.56

 

MSE = 37,649.56/7 = 5,378.51

MAD = 430.2/7 = 61.46

c. The 5-year weighted moving average is used to forecast the number of merg­ers for 2012. Using weights of 0.10, 0.15, 0.20, 0.25, and 0.30, with the most recent year weighted being the largest (Verma, 2010).

                                f2012 = (.30) 240 + (.25)225 + (.20)186 + (.15)97 + (.10)123

                                         = 72 + 56.25 + 37.2 + 14.55 + 12.3 = 192.3

d. Regression analysis used to forecast the number of mergers in 2012 is shown as follows:

Year

Coded Value for Year

Mergers

XY

X2

Y2

2000

1

46

46

1

2,116

2001

2

46

92

4

8,464

2002

3

62

186

9

3,844

2003

4

45

180

16

2,025

2004

5

64

320

25

4,096

2005

6

61

366

36

3,721

2006

7

83

581

49

6,889

2007

8

123

984

64

15,129

2008

9

97

873

81

9,409

2009

10

186

1,860

100

34,596

2010

11

225

2,475

121

50,625

2011

12

240

2,880

144

57,600

SUM

78

1,278

10,843

650

198,514

 

Ƅ = 12(10,843) – 78(1,278) / 12(650) – 782

    = 130,116 – 99,684 / 7,800 – 6,084

                                                    = 30,432 / 1,716

                                                    = 17.73

 a = 1,278/12 – 17.73(78)/12

    = 106.5 – 115.25

                                                    = -8.75

 r = 12(10,843) – 78(1,278) / √{12(650)-782}{12(198,514)-1,2782}

    = 130,116-99,684/√{7,800-6,084}{2,382,168-1,633,248}

    = 130,116-99,684/√(1,716)(748,920)

    = 30,432/√1,285,146,720

    = 30,432/35,848.94

    = 0.85

The moving average was used instead of another forecasting method based on its simplicity and emphasizing trends. The use of moving average method evens out the peaks and valleys in the data (Vonderembse & White, 2013).

In its place we could use the exponential smoothing method. This method also looks at historical data and attempts to average out the historical information in order to forecast the future with some measure of reliability (Vonderembse & White, 2013). In this case we have past values to smooth out the average data.

References:

Verma, S. (2010). Comparison of Six Traditional Forecasting Techniques for Sales Demand. International Journal Of Statistics & Systems5(2), 223-227. 
Retrieved from 
http://eds.a.ebscohost.com.proxy-library.ashford.edu/eds/pdfviewer.          

Vonderembse, M.A. & White, G.P. (2013). Operations Management . San Diego, CA: Bridgepoint Education, Inc.

 

4. 

  1. The five year moving average forecast for 2012 is as follows:

2012 = (240 + 225 + 186 + 97 +123) / 5

         = 871 / 5

         = 174.2

 

  1. The forecasts for 2005 to 2011 using 5 year moving average are:

The MAD value = Sum of deviation/ 7

The MSE value = Sum of squared error/ 7

 

Year

Mergers

5 Yr. Moving Avg.

MAD

MSE

2000

46

 

 

 

2001

46

 

 

 

2002

62

 

 

 

2003

45

 

 

 

2004

64

 

 

 

2005

61

52.6

8.4

70.56

2006

83

55.6

27.4

750.76

2007

123

63

60

3600

2008

97

75.2

21.8

475.24

2009

186

85.6

100.4

10,080.16

2010

225

110

115

13,225

2011

240

142.8

97.2

9,447.84

MAD

 

 

61.46

 

MSE

 

 

 

5,378.51

 

  1. The five year weighted moving average forecast for 2012 is as follows:

2012 = (0.3*240 +0.25* 225 + 0.2*186 +0.15*97 +0.1*123)

         = 192.3

 

  1. The regression analysis for 2012 merger forecasts are:

 

Coded Value for Yr.

Mergers

XY

X2

Y2

1

46

 46

1

2,116

2

46

92

4

8,464

3

62

 186

9

3,844

4

45

 180

16

2,025

5

64

 320

25

4,096

6

61

366

36

3,721

7

83

581

49

6,889

8

123

984

64

15,129

9

97

873

81

9,409

10

186

1,860

100

34,596

11

225

2,475

121

50,625

12

240

2,880

144

57,600

78

1,278

10,843

650

198,514

 

 

b = [12(10,843) – 78(1,278)] / 12(650) – 782

   = 130116- 99684 / 7800-6084

   = 30432 / 1716

   = 17.73

 

a = 1,278 – 17.73(78) / 12

   = 106.5 – 138.94 / 12

   = 106.5 – 115.25

   = -8.75

 

 r = [12(10,843) – 78(1,278)]

   = √[12(650) – 782] [12(192,166 – 1,2782]

   = -8.75 + 17.73(13)

   = -87.5 + 230.49

   = .90

 

2012 Mergers = -8.75 + 17.73(13)

                       = 221.74

 

The advantage of moving average is that it smooth the data and provides a clearer visual picture of the current trend and can give a precise answer as to what the trend is.

 

Exponential smoothing could also be used because it averages the data as well.  The last year is assigned a greater weight than the more recent years making it more sensitive and reducing the lag effect, which can be experienced with moving average.

 

 


Discussion board postings. was first posted on August 2, 2020 at 1:50 am.
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