Chapter Three “Customer Accommodation” Part Four “Forecasting” You will need to manually advance from slide-to-slide on this presentation.

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Presentation transcript:

Chapter Three “Customer Accommodation” Part Four “Forecasting” You will need to manually advance from slide-to-slide on this presentation.

To follow are explanations of certain quantitative forecasting techniques used to predict future sales. No audio on this slide.

Quantitative Methods Continuity extrapolation: incremental dollar or percentage increase is carried forward to the next year.

Quantitative Methods Continuity extrapolation: incremental dollar or percentage increase is carried forward to the next year. Growth Maturity Decline Introduction Product Life Cycle

Quantitative Methods (continued) Time series analysis Sales = T x C x S x I Where: T = trends or long-run changes C = cyclical changes S = seasonal variations I = irregular or unexpected factors.

Gasoline demand Time Series Analysis Time --Johnson, Kurtz, Scheuing 1994 “Sales Management”

Gasoline demand Time Series Analysis Time Cyclical changes Cyclical changes (economy) Cyclical changes Seasonal variation (summer, winter) Irregular or unexpected factors (political crisis) --Johnson, Kurtz, Scheuing 1994 “Sales Management”

Quantitative Methods (continued) Moving average YearSales2-year4-yearforecast

Quantitative Methods (continued) Moving average YearSales2-year4-year forecastforecast

Quantitative Methods (continued) Moving average YearSales2-year4-yearforecast

Quantitative Methods (continued) Moving average YearSales2-year4-yearforecast

Quantitative Methods (continued) Moving average YearSales2-year4-yearforecast

Quantitative Methods (continued) Moving average YearSales2-year4-yearforecast

Quantitative Methods (continued) Moving average YearSales2-year4-yearforecast

Quantitative Methods (continued) Moving average YearSales2-year4-yearforecast

Quantitative Methods (continued) Exponential smoothing: (alpha)(this period sales) + (1-alpha)(this period forecast) = forecast for next period Alpha is any number between 0 and 1. The higher the alpha, the more weight to recent sales.

Quantitative Methods (continued) Moving average YearSales2-year4-yearforecast …higher alpha means later data has more influence or weight All data contribute to forecast, but….

(alpha)(this period sales) + (1-alpha)(this period forecast) = forecast for next period

YearSalesAlphaAlphaAlpha =

(alpha)(this period sales) + (1-alpha)(this period forecast) = forecast for next period YearSalesAlphaAlphaAlpha =

(alpha)(this period sales) + (1-alpha)(this period forecast) = forecast for next period YearSalesAlphaAlphaAlpha =

(alpha)(this period sales) + (1-alpha)(this period forecast) = forecast for next period YearSalesAlphaAlphaAlpha (.2)(4410) + (.8)(4200) = 4242 (alpha)(this period sales) + (1-alpha)(this period forecast) = forecast for next period

YearSalesAlphaAlphaAlpha (.5)(4410) + (.5)(4200) = 4305 (alpha)(this period sales) + (1-alpha)(this period forecast) = forecast for next period

YearSalesAlphaAlphaAlpha (.8)(4410) + (.2)(4200) = 4368 (alpha)(this period sales) + (1-alpha)(this period forecast) = forecast for next period

YearSalesAlphaAlphaAlpha (.2)(4322) + (.8)(4242) = 4258

(alpha)(this period sales) + (1-alpha)(this period forecast) = forecast for next period YearSalesAlphaAlphaAlpha =

End of Program.