Forecasting and Production Planning, Essay Example

Abstract

This paper addresses the different types of forecasting techniques that are applied in my business.  These provide a breakdown by forecast method and type used.  Analysis examines the effectiveness of these methods with specific examples. Forecasts are an important process of problem solving and decision making in the financial control of a business.

Forecasting techniques

My organization uses the following types of forecasting techniques:

Qualitative Methods

• Executive Opinions – Based upon meetings and projecting sales forecasts;
• Market Surveys – Uses interviews to assess customer satisfaction and demand;
• Salesforce Composition – Each Area sales manager predicts a sales forecast for that area;

Quantitative Methods

• Time Series Models – Creation of statistical models to look at trends to ascertain future predictions of sales and production levels;
• Causal Models – Examination of correlations between associated variables to see if they create specific trends

Time Series Models

• Simple Moving Average graphs
• Weighted Moving average graphs
• Trend Projections
• Seasonal Indexing (Armstrong, J.S.2001)

Effective methods

Forecasts are effective measure because they are mathematical based projections (quantitative) and experience based estimates that can be measured against both Budget and Actual results in order to produce variance analysis.  These variance provide indications, over time, of movements and trends in projected data like sales and marketing forecasts. These can also be plotted onto graphs in order to illustrate a visual picture of events e.g. Forecast of Sales by Area for the next 12 months shown as monthly forecasts. (Harris, E. 1997).

Whilst all of the Forecasts have useful places in the organization I would keep the quantitative forecasts and perhaps move the more subjective qualitative forecasts i.e. those based upon observation, personal feelings and guesswork.  The more mathematics and scientific analysis you can apply then the greater the degree of accuracy in the forecasts.

An adoption of the Level Strategy is adopted in order to maintain average production levels.  This using the inventory as a buffer.  This shows those months where the demand is low and production exceeding demand with the balance going into the inventory. Fig 1 illustrates a typical analysis column for this purpose.

Figure 1 : Example of Columnar Analysis.   (Students Notebook, 2012)

Month | Month of Production

Forecast Demand | Monthly forecast of hard drives

Cumulative Demand |  Month + Month to date

Working Days | Number of working days in the month

Cumulative Days | Number of cumulative days to date

Production/ Day | Production of units per day.

Conclusion

The user of forecast information is an important psart of any budgetary financial control system.  They provide a mathematical basis for the production of variance analysis i.e. comparative analysis to Actual and Budget data.  This facilitates both problem solving and decision making in the business.

References

Armstrong, J. (2001). Principles of Forecasting: A Handbook for Researchers and Practitioners. New York: Springer.

Harris, C. W. (1997). Variance Analysis. London: CIMA.

Students Notebook. (2012, 10 12). Strategies for Production Planning. Retrieved from Students Notebook: http://adonai4-5.tripod.com/id19.html