Sales forecasting
Download
1 / 12

Sales Forecasting - PowerPoint PPT Presentation


  • 144 Views
  • Uploaded on

Sales Forecasting. MKT 311 Instructor: Dr. James E. Cox, Ph.D. The Forecasting Process. Set the objective of the forecast. Select Possible Forecasting Technique(s). Data Collection and Preparation. Parameterize the technique(s).

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Sales Forecasting' - avent


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Sales forecasting

Sales Forecasting

MKT 311

Instructor:

Dr. James E. Cox, Ph.D


Sales forecasting

The Forecasting Process

Set the objective of the forecast

Select Possible Forecasting Technique(s)

Data Collection and Preparation

Parameterize the technique(s)

Select Technique(s) to Be used:Technique Evaluation and Selection

Application of Technique(s) and Forecast Revision

Evaluation of technique Performance


Step 4 parameterize the techniques
Step 4: Parameterize the Techniques

  • Basic Procedure

  • Error Measurement

    • ME = mean error

    • MSE = mean squared error

    • MAD = mean absolute deviation

    • MPE = mean percentage error

    • MAPE = mean absolute percentage error

    • SD = standard deviation

      (or RMSE = root mean squared error)

    • SSE = signed square error


Questions to ask regarding which error to use
Questions to Ask Regarding Which Error to Use

  • Is the manager looking for a long-term perspective; i.e. more interested in final result then by period-by-period accuracy? Is the period-by-period accuracy more important than ultimate accuracy?

  • Would the manager have trouble comprehending unless “regular” units are used to express error (accuracy ) ?


Sales forecasting

  • Is the manager willing to accept more error if the (sales) base is larger?

  • Would extreme error be very costly so that manager would be willing to take lower overall accuracy if extreme error could be avoided for any one period?

  • Does the direction (sign) of error makes a difference?


Characteristics of error measures
Characteristics of Error Measures

  • Mean Error (ME)

    - shows direction of error

    - does not penalize extreme deviations

    - errors cancel out (no idea of how much)

  • Mean Absolute Deviation (MAD)

  • - shows magnitude of overall error

  • - does not penalize extreme deviations

  • - errors do not cancel out

  • - no idea of direction of error


Sales forecasting

  • Mean Squared Error (MSE)

  • - penalizes extreme errors

  • - errors do not offset one another

  • - not in original units

  • - does not show direction of error

  • Standard Deviation (SD)

    - penalizes extreme errors

    - errors do not offset one another

    - in original units


Sales forecasting

Squared and Keep Sign (SSE)

- penalize extreme errors

- errors can offset one another

- shows direction of error

- not in original units


Sales forecasting

  • Mean Percentage Error (MPE)

  • - takes percentage of actual sales

  • - does not penalizes extreme error

  • - errors can offset one another

  • - assumes more sales can absorb more error in units

  • Mean Absolute Percentage Error (MAPE)

    - takes percentage of actual sales

    - does not cancel offsetting errors

    - no penalty for extreme errors

    - assumes more sales can absorb more error in units