Sales Forecasting

Problem 4

Listing out the provided data, S_t and F_t represent the sales and forecast, respectively, for period ‘t.’

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

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

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

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 give 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

References

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

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[u09a2] Unit 9 Assignment 2 Sales Forecasting

Complete problem 4 on page 309 of your textbook. For help on how to complete this problem, see the Solved Problems on pages 304–308.

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/or rationale useful in interpreting the sales forecasting results. 50%	Accurately summarizes salient points with supporting rationale for interpreting results.