This document outlines demand forecasting methods for supply chain planning. It discusses the role of forecasting in production scheduling, inventory management, sales planning and other supply chain functions. Several forecasting techniques are described, including qualitative, time series and causal methods. Time series methods use historical demand patterns to forecast future demand. The document explains how to decompose time series data, estimate trend and seasonal factors, and generate forecasts. Measures of forecast accuracy like mean squared error, mean absolute deviation and mean absolute percentage error are also defined. An example of demand forecasting for Tahoe Salt is presented to illustrate the techniques.

1.  Chapter 7
Demand Forecasting
in a Supply Chain
 Outline
 The role of forecasting in a supply chain
 Characteristics of forecasts
 Components of forecasts and forecasting methods
 Basic approach to demand forecasting
 Time series forecasting methods
 Measures of forecast error
 Forecasting demand at Tahoe Salt
 Forecasting in practice
 Role of Forecasting
in a Supply Chain
 The basis for all strategic and planning decisions in a supply chain
 Used for both push and pull processes
 Examples:
 Production: scheduling, inventory, aggregate planning
 Marketing: sales force allocation, promotions, new production
introduction
 Finance: plant/equipment investment, budgetary planning
 Personnel: workforce planning, hiring, layoffs
 All of these decisions are interrelated
 Characteristics of Forecasts
 Forecasts are rarely perfect because of randomness.

2.  Beside the average, we also need a measure of variations– Standard
deviation.
 Forecasts are more accurate for groups of items than for individuals.
 Forecast accuracy decreases as time horizon increases.
 Forecasting Methods
 Qualitative: primarily subjective; rely on judgment and opinion
 Time Series: use historical demand only
 Static
 Adaptive
 Causal: use the relationship between demand and some other factor to
develop forecast
 Simulation
 Imitate consumer choices that give rise to demand
 Can combine time series and causal methods
 Components of an Observation
Observed demand (O) =
Systematic component (S) + Random component (R)
 Example: Tahoe Salt
 Tahoe Salt Scattered Graph
 Forecasting Methods
 Static
 Adaptive
 Moving average
 Simple exponential smoothing
 Holt’s model (with trend)

3.  Winter’s model (with trend and seasonality)
 Basic Approach to
Demand Forecasting
 Understand the objectives of forecasting
 Integrate demand planning and forecasting
 Identify major factors that influence the demand forecast
 Understand and identify customer segments
 Determine the appropriate forecasting technique
 Establish performance and error measures for the forecast
 Time Series
Forecasting Methods
 Goal is to predict systematic component of demand
 Multiplicative: (level)(trend)(seasonal factor)
 Additive: level + trend + seasonal factor
 Mixed: (level + trend)(seasonal factor)
 Static methods
 Adaptive forecasting
 Static Methods
 Assume a mixed model:
Systematic component = (level + trend)(seasonal factor)
Ft+l = [L + (t + l)T]St+l
= forecast in period t for demand in period t + l
L = estimate of level for period 0
T = estimate of trend
St = estimate of seasonal factor for period t
Dt = actual demand in period t
Ft = forecast of demand in period t
 Static Methods

4.  Estimating level and trend
 Estimating seasonal factors
 Estimating Level and Trend
 Before estimating level and trend, demand data must be deseasonalized
 Deseasonalized demand = demand that would have been observed in
the absence of seasonal fluctuations
 Periodicity (p)
 the number of periods after which the seasonal cycle repeats itself
 for demand at Tahoe Salt p = 4
 Deseasonalizing Demand
[Dt-(p/2) + Dt+(p/2) ]/2p +  Di] / p for p even
Dt = (sum is from i = t+1-(p/2) to t+1+(p/2))
 Di / p for p odd
(sum is from i = t-(p/2) to t+(p/2)), p/2 truncated to lower
integer
 Time Series Forecasting
 Deseasonalizing Demand
For the example, p = 4 is even
For t = 3:
D3 = {D1 + D5 + Sum(i=2 to 4) [2Di]}/8
= {8000+10000+[(2)(13000)+(2)(23000)+(2)(34000)]}/8
= 19750
D4 = {D2 + D6 + Sum(i=3 to 5) [2Di]}/8
= {13000+18000+[(2)(23000)+(2)(34000)+(2)(10000)]/8
= 20625
 Deseasonalized Demand
 Deseasonalizing Demand
Then include trend
Dt = L + tT
where Dt = deseasonalized demand in period t

5. L = level (deseasonalized demand at period 0)
T = trend (rate of growth of deseasonalized demand)
Trend is determined by linear regression using deseasonalized demand as
the dependent variable and period as the independent variable (can be
done in Excel)
In the example, L = 18,439 and T = 524
 Deseasonalized Demand
 Deseasonalizing Demand
Then include trend
Dt = L + tT
where Dt = deseasonalized demand in period t
L = level (deseasonalized demand at period 0)
T = trend (rate of growth of deseasonalized demand)
Trend is determined by linear regression using deseasonalized demand as
the dependent variable and period as the independent variable (can be
done in Excel)
In the example, L = 18,439 and T = 524
 Time Series of Demand
 Estimating Seasonal Factors
Use the previous equation to calculate deseasonalized demand for each
period
St = Dt / Dt = seasonal factor for period t
In the example,
D2 = 18439 + (524)(2) = 19487 D2 = 13000
S2 = 13000/19487 = 0.67
The seasonal factors for the other periods are calculated in the same
manner
 Estimating Seasonal Factors
 Estimating Seasonal Factors
 Estimating Seasonal Factors
The overall seasonal factor for a “season” is then obtained by averaging all
of the factors for a “season”
If there are r seasonal cycles, for all periods of the form pt+i, 1<i<p, the
seasonal factor for season i is
Si = [Sum(j=0 to r-1) Sjp+i]/r
In the example, there are 3 seasonal cycles in the data and p=4, so
S1 = (0.42+0.47+0.52)/3 = 0.47
S2 = (0.67+0.83+0.55)/3 = 0.68

6. S3 = (1.15+1.04+1.32)/3 = 1.17
S4 = (1.66+1.68+1.66)/3 = 1.67
 Estimating the Forecast
Using the original equation, we can forecast the next four periods of
demand:
F13 = (L+13T)S1 = [18439+(13)(524)](0.47) = 11868
F14 = (L+14T)S2 = [18439+(14)(524)](0.68) = 17527
F15 = (L+15T)S3 = [18439+(15)(524)](1.17) = 30770
F16 = (L+16T)S4 = [18439+(16)(524)](1.67) = 44794
Error: difference between predicted value and actual value
 Mean Absolute Deviation (MAD)
 Mean Square Error (MSE)
 Mean Absolute Percentage Error (MAPE)
 Tracking Signal (TS)


 Measures of Forecast Error
 Forecast error = Et = Ft - Dt
 Mean squared error (MSE)
MSEn = (Sum(t=1 to n)[Et
2
])/n
 Absolute deviation = At = |Et|
 Mean absolute deviation (MAD)
MADn = (Sum(t=1 to n)[At])/n
 = 1.25MAD
 = Standard Deviation of forecast
 Measures of Forecast Error

7.  Mean absolute percentage error (MAPE)
MAPEn = (Sum(t=1 to n)[100|Et/ Dt|])/n
 Bias
 Shows whether the forecast consistently under- or overestimates
demand; should fluctuate around 0
biasn = Sum(t=1 to n)[Et]
 Tracking signal
 Should be within the range of +6
 Otherwise, possibly use a new forecasting method
TSt = bias / MADt
 Case 1: TahoeSalt
In a very well designed excel work book, repeat the computations explained
in this lecture. Also compute MSE, MAD, MPE, and TS for all possible
periods