# Supply Chain Planning And Control Essay

3010 words - 13 pages

University of Hull |
Supply Chain Planning and Control-Individual assignment: Pony Group |
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4/13/2013 |

56130
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Student number: 201100320
Word count: 2,662

Contents

1. Introduction 2

2. Section 1: Demand forecast 2
2.1. Moving average 2
2.2. Simple Exponential Smoothing 3
2.3. Holt’s Model 4
2.4. Winter’s Model 5
2.5. Demand forecast for XYZ 8

3. Section 2: Aggregate planning 9
3.1. Aggregate planning Question 1 10
3.2. Aggregate planning Question 2 11

4. Section 4: Pricing and revenue management 12

Appendix 15

1. Introduction
This assignment is based on the market information of Pony ...view middle of the document...

Then according to the formula Lt, , to calculate the level results for period 12, which is displayed in figure 2. And the level for period 12 is the forecast for period 13 (figure 3), therefore, L12 = F13. Then it is easy to solve levels and forecasts for all periods. The next step is to calculate the forecast errors for this technique.
There are three measures to judge the forecast error which includes MAD (Mean absolute deviation), MSE (Mean squared error) and MAPE (Mean absolute percentage error). The calculation formulas are showed as below:
Forecast error:
Absolute Forecast error:
MAD = Sum of deviations / number of forecasts
Squared error:
MSE = Sum of squared errors / number of forecasts
Absolute percentage error:
MAPE = (Sum of absolute percentage errors / number of forecasts)*100%
On the basis of the formulas above, it is easy to calculate the results out, which is showed as figure 4.
MAD = 4,175 MSE = 23,584,490.74 MAPE = 78.51%

3.2. Simple Exponential Smoothing
The exponential smoothing forecasting method modifies the moving average method by placing the greatest weight on the last value in the time series and then progressively smaller weights on the older values, it also uses parameter ‘level’ for the forecast, and uses α to represent the smoothing constant for level. Convert the words to formula will be easier to understand:

α = smoothing constant of level
The initial estimate of level L0 is the average of all historical data:

The forecast for next period is also the level for this period:

According to the formula L0, L0 is the average of demand for all the periods, and the result is came out in figure 5, L0 = 8,523.81. In this paper, we assume α = 0.1, so L1 = 0.1*20,000 + (1-0.1)*8,523.81 = 9,671.42, which is the result for level 1. The forecast for demand is the same as moving average method, the forecast of next period is the level of previous period, just as figure 7 illustrates. Then we can figure out level and forecast for all the periods, in excel is simply drag down the data, and the results will come out. The next step is to estimate forecast error for exponential smoothing method, and the measures are also the same as mentioned in moving average: MAD, MSE and MAPE. On the basis of those formulas in moving average part, the forecast results are easily to figure out:
MAD = 4,987.18 MSE = 32,935,626.85 MAPE = 94.89% (figure 8)

3.3. Holt’s Model
Holt’s model is a trend-corrected exponential smoothing method. It has two parameters for the forecast: level and trend. The initial estimate of level and trend are based on the results by running a linear regression, the formulas are demonstrated as below:
Dt
T0 = a
L0 = b
When we do the regression analysis we need to input demand for all periods as dependent variable (Y range) and input periods as independent variable (X range) (figure 9) and the result will be calculated out automatically, which is showed in figure 10, the...

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