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Business, Coursework

Sales Forecasting

Sales Forecasting

Problem 4

Listing out the provided data, St and Ft represent the sales and forecast for period’ t.’

I am taking t = 1,2,3,4,5 and 6 for January, February, March, April, May, and June, respectively.

S1 = 45 units

S2 = 30 units

S3 = 40 units

S4 = 50 units

S5 = 55 units

S6 = 47 units

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Calculate the forecast for March.

The following formula gives the sales forecast for a given period:

F_(t+1)=S_t

The following formula gives the forecast using the two-period moving average:

F_(t+1)=(S_t+S_(t-1))/2

We are applying the above formulas to calculate the forecasts.

Month Actual Sales Naïve Method 2-Period Moving Average
January 45
February 30
March 40 30 37.5
April 50 40 35
May 55 50 45
June 47 55 52.5

 

From the table, using the naive method, the forecasts for March, April, May, and June are approximately 30, 40, 50, and 55 units, respectively.

From the table, using the two moving average method, the forecasts for March, April, May, and June are approximately 38, 35, 45, and 53 units, respectively.

To evaluate the forecasts using the exponential smoothing method, apply the exponential smoothing formula below:

For the initial month of March, assuming the forecast for March is equal to the actual demand for February.

Applying the above formula to evaluate the forecast:

Month Actual Sales Exponential Smoothing Method
January 45
February 30
March 40 30
April 50 32
May 55 35.6
June 47 39.48

 

From the table, using the exponential method, the forecasts for March, April, May, and June are approximately 30, 32, 36, and 40 units, respectively.

The Mean Absolute Deviation (MAD) is evaluated using the following formula:

MAD=(∑▒〖|Actual-Forecast|〗)/n

Where ‘n’ is the number of forecasts.

Applying the above formula to the three sets of forecasts gives the table below.

Month Actual Sales Naïve Method Absolute Error 2-Period Moving Average Absolute Error Exponential Smoothing Method Absolute Error
January 45
February 30
March 40 30 10 37.5 2.5 30 10
April 50 40 10 35 15 32 18
May 55 50 5 45 10 35.6 19.4
June 47 55 8 52.5 5.5 39.48 7.52
Total     33   33   54.92

I am calculating the MAD for each method.

MAD=(∑▒〖|Actual-Forecast|〗)/n

  • Naïve method.

MAD=33/4=8.25

  • Two Moving Averages Method.

MAD=33/4=8.25

  • Exponential Smoothing Method.

MAD=54.92/4=13.73

From the calculations above, the naïve and two-moving average methods give the lowest value of MAD. Therefore, these methods provide the most accurate forecast.

The two-moving average method gives the most accurate results. The forecast for July using this method.

F_7=(55+47)/2=51

Therefore, the sales forecast for July is 51 units.

Sales Forecasting Worksheet

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References

Reid, R. D., & Sanders, N. R. (2016). Operations Management, Binder Ready Version: An Integrated Approach. John Wiley & Sons.

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Question 

[u09a2] Unit 9 Assignment 2 Sales Forecasting

Complete problem four on page 309 of your textbook. For help completing this problem, see the Solved Problems on pages 304–308.

Sales Forecasting

Sales Forecasting

When completed, submit your answers as an attachment in the assignment area. Be sure to include your work with the answer.

Resources

  • Sales Forecasting Scoring Guide
  • Due Date: Unit 9
    Percentage of Course Grade: 2%.
CRITERIA DISTINGUISHED
Accurately solves all computation aspects of the sales forecasting problem.
50%		 Accurately solves all computation aspects of the problem and shows work.
Provides summary and rationale helpful in interpreting the sales forecasting results.
50%		 Accurately summarizes salient points with supporting rationale for interpreting results.