Forecasting Demand

Forecasting Methods Qualitative – Judgmental, Executive Opinion - Internal Opinions - Delphi Method - Surveys Quantitative - Causal, Extrinsic Factors - Time Series

Causal Methods Seek Relation between Sales and Economic Indicators (Especially Leading Indicators) Example: Door Lock Demand & Housing Starts MonthHousing StartsDoor Locks January February March April May

Scatter Diagram of Door Lock Sales vs. Housing Starts

Time Series Forecasts Based on Past Demand Patterns or Components: Average or Level Trend Seasonal Cyclical (Omit) Random (Cannot Forecast)

Time Series: Level Demand Simple or Arithmetic Mean E.g. F 5 = ( ) / 4 = 126 Moving Average – Discard Old Data Weighted Average F t+1 =  t D t +  t-1 D t-1 + Etc.  = Weight between 0 and 1,   i  D = Actual Demand t = Current Time Period (t=4) E.g. F 5 =.4(150)+.3(130)+.2(121)+.1(103) = 133.5

Time Series: Level Demand Exponential Smoothing Weighted Average F t+1 =  D t + (1-  )F t F t is Old Forecast from Last Period E.g. F 5 = (.2)(150) + (.8)(115) = 122

Time Series: Trends Trend is Predictable Long Term Increase or Decrease in Demand E.g.January103 February121 March130 April 150 If Trend Continues, Averages are Too Low Forecasting Techniques: - Regression (Least Squares) - Adjusted Exponential Smoothing

Scatter Diagram of Demand vs. Month Number

Time Series: Trends Simple Regression: One Independent Variable E.g. F t = a + bt (t is Time, a & b are Constants) F 5 = (15)(5) = Multiple Regression: Multiple Independent Variables E.g. F t = a + b 1 t + b 2 i (i is base index) F 5 = 81 + (12.83)(5) + (16.67)(1.05) = We Can Use Excel to Get a & b’s

Time Series: Trends & Exponential Smoothing 1.F t+1 =  D t + (1-  )F t = Trend Factor = (F t+1 – F t ) = = 7 T t+1 =  (F t+1 – F t ) + (1-  ) T t  = Weight between 0 and 1, Often =  T t = Old Trend, Use Trend Line Slope at First E.g. T t+1 =.2(7) +.8(15) = 13.4

Time Series: Trends & Exponential Smoothing 1.F t+1 = T t+1 =.2(7) +.8(15) = A F t+1 = F t+1 + (Lag)(T t+1 ) Lag Can be (1/  ) = (1/.2) = 5 E.g. A F t+1 = (5)(13.4) =189 Can You Do a Forecast for June?

Time Series: Seasonal Demand Seasonal Demand: Definite, Dependable Reason for Heavy Demand at One Time, Light Demand at Another 1.Construct Base Series or Index from Historical Demand 2.Divide All Demand by Appropriate Base 3.Forecast Using Any Method 4.Adjust Forecast by Multiplying by Appropriate Base

Evaluating Forecasts: MAD MAD is Mean Absolute Deviation Smaller the MAD, the Better MAD =  | D t – F t | / n D t = Actual Demand F t = Forecast n = Number of Periods

Evaluating Forecasts: MAD Example of MAD for May and June: MonthD t F t | D t – F t | May June MAD = 110 / 2 = 55