Supply Chain Management Analysis


Supply Chain Management Analysis

  1. Consider using a simple moving average model.  Experiment with models using five weeks’ and three weeks’ past data. The past data in each region is given below  (week  -1 is the week before week  1 in the table,  -2 is two weeks before week  1, ).  
Week 5 4 3 2 1 Mean  

Average for the previous period

Mean absolute deviation Mean absolute percentage error Analysis
Atlanta 45 38 30 58 37 41.6 40 7.92 4% Increasing
Boston 62 18 48 40 35 40.6 42 11.52 -3.33% Decreasing
Chicago 62 22 72 44 48 49.6 47 13.92 5.53% Increasing
Dallas 42 35 40 64 43 44.8 48 7.68 -6.67% Decreasing
LA 43 40 54 46 35 43.6 46 5.12 -5.33% Decreasing
Total 254 153 244 252 198 220.2 222 35.76 -0.81% Decreasing


2    Next, consider using a simple exponential smoothing model.

In your analysis,   test two alpha values,  .2  and  .4. Use the same criteria for evaluating the model as in question J. Assume that the initial previous forecast for the modeling an alpha value of .2 is the past three-week average.  For the model using. an alpha of .4, assume that the previous forecast is the past five-week average.

α = 2, an analysis of the three weeks using a simple exponential smoothing model

Week 3 2 1
Atlanta 30 58 37
Boston 48 40 35
Chicago 72 44 48
Dallas 40 64 43
LA 54 46 35
Total 244 252 198

t=1, s1=30

t=2, st=αxt – 1 +(1-α)st- 1

s2=αx2 – 1 + (1-α)s2 – 1

s2=2(58) +(-1)30


t=3, s3=2(37) + (-1)86

s3= -12

Using the same analogy the rest of the table will be completed as follows

Week 3 2 1 Mean α=2
Atlanta 30 58 37 41.7 S1=30, s2=86, s3=-12
Boston 48 40 35 41 S1=48,s2=32,s3=38
Chicago 72 44 48 54.7 S1=72,s2=16,s3=80
Dallas 40 64 43 49 S1=40,s2=88,s3=-2
LA 54 46 35 45 S1=54,s2=38,s3=32
Total 244 252 198 231.3

When α=4, for the five periods, the model will look like this;

Week 5 4 3 2 1 Mean α=4
Atlanta 45 38 30 58 37 41.6 S1=45,s2=95.86,s3=139.58
Boston 62 18 48 40 35 40.6 S1=62,s2=114,s3=534,s4=1442,s5=4466
Chicago 62 22 72 44 48 49.6 S1=62,s2=-98,s3=582,s4=1570,s5=4902
Dallas 42 35 40 64 43 44.8 S1=42,s2=14,s3=118,s4=-98,s5=466
LA 43 40 54 46 35 43.6 S1=43,s2=31,s3=123,s4=5,s5=695
Total 254 153 244 252 198 220.2


Series 1= α=2

Series 2= α=4

3    Altavox   is considering a new option for distributing   the
Model VC202 where, instead of using five distributors, only a single distributor would be used.  Evaluate this option by analyzing how accurate the forecast would be based on the demand aggregated across all regions.

The best method is the simple exponential smooth model using α=2. This is because it is easy to calculate and using a smaller alpha makes the chances of making an error minimal. The main reason for forecasting before entering into a new market is to be able to budget for the endeavor. At the same time, it gives a clear picture of the viability of the idea of the company selling its products in Thai using a single distributor.

Some of the advantages of using exponential smooth model are;
  • It applies the previous predictions to what happened on the ground and finally paints a clear picture of the next forecast using the difference between the previous two.
  • It creates an account for the difference between the previous forecast and the results on the ground. This aids in avoiding certain mistakes (Chou, 1989).
Some of the disadvantages of the method include;
  • It is based on baseline forecasts that easily rise and fall with the change in time.
  • The method does not account for other changes that are a result of other factors in the business that are qualitative in nature.
  • It is based on using previous sales to forecast future sales without considering changes in the time that affect sales (Bradley, 2007).

Some of the advantages of aggregating demand include;

-it aids in planning as budgeting for time and other resources can easily be established.

-it reduces overhead because it costs less to have and maintain aggregate inventory.

-it reduces employment fluctuations as a business can easily predict their staff requirements over a fiscal year.

Some of the disadvantages are;

-aggregate planning can reduce the flexibility of the company as there is no specific nature of dealing with one particular factor in the production process (Lewis, 1963).

-as much as there is an increase in production rate, aggregate demand can lead to a business experiencing some diseconomies of scale that will reduce the profitability of the company.



Bradley, T. (2007). Essential Statistics for Economics, Business, and Management, Random House Publishers

Chou, L. Y. (1989). Statistical Analysis: with Business and Economic Application, Thai Publishers

Lewis, E. E. (1963). Methods of Statistical Analysis in Economics and Business, Orthodox Print Press, New York


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