Post on 02-Dec-2015
description
Demand Forecastingin a Supply Chain
Supply Chain Management
© 2007 Pearson Education 7-2
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
© 2007 Pearson Education 7-3
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
© 2007 Pearson Education 7-4
Characteristics of Forecasts
Forecasts are always wrong. Should include expected value and measure of error.
Long-term forecasts are less accurate than short-term forecasts (forecast horizon is important)
Aggregate forecasts are more accurate than disaggregate forecasts
© 2007 Pearson Education 7-5
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
© 2007 Pearson Education 7-6
Components of an Observation
Observed demand (O) =
Systematic component (S) + Random component (R)
Level (current deseasonalized demand)
Trend (growth or decline in demand)
Seasonality (predictable seasonal fluctuation)
• Systematic component: Expected value of demand• Random component: The part of the forecast that deviates from the systematic component• Forecast error: difference between forecast and actual demand
© 2007 Pearson Education 7-7
Time Series ForecastingQuarter Demand Dt
II, 1998 8000III, 1998 13000IV, 1998 23000I, 1999 34000II, 1999 10000III, 1999 18000IV, 1999 23000I, 2000 38000II, 2000 12000III, 2000 13000IV, 2000 32000I, 2001 41000
Forecast demand for thenext four quarters.
© 2007 Pearson Education 7-8
Time Series Forecasting
0
10,000
20,000
30,000
40,000
50,000
97,2
97,3
97,4
98,1
98,2
98,3
98,4
99,1
99,2
99,3
99,4
00,1
© 2007 Pearson Education 7-9
Forecasting Methods
Static Adaptive
– Moving average
– Simple exponential smoothing
– Holt’s model (with trend)
– Winter’s model (with trend and seasonality)
© 2007 Pearson Education 7-10
Basic Approach toDemand 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
© 2007 Pearson Education 7-11
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
© 2007 Pearson Education 7-12
Static Methods
Estimating level and trend Estimating seasonal factors
© 2007 Pearson Education 7-13
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 (Table 7.1, Figure 7.1) p = 4
© 2007 Pearson Education 7-14
Time Series Forecasting (Table 7.1)
Quarter Demand Dt
II, 1998 8000III, 1998 13000IV, 1998 23000I, 1999 34000II, 1999 10000III, 1999 18000IV, 1999 23000I, 2000 38000II, 2000 12000III, 2000 13000IV, 2000 32000I, 2001 41000
Forecast demand for thenext four quarters.
© 2007 Pearson Education 7-15
Time Series Forecasting(Figure 7.1)
0
10,000
20,000
30,000
40,000
50,000
97,2
97,3
97,4
98,1
98,2
98,3
98,4
99,1
99,2
99,3
99,4
00,1
© 2007 Pearson Education 7-16
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 (Table 7.1, Figure 7.1) p = 4
© 2007 Pearson Education 7-17
Deseasonalizing Demand
[Dt-(p/2) + Dt+(p/2) + 2Di] / 2p 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
© 2007 Pearson Education 7-18
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
© 2007 Pearson Education 7-19
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
© 2007 Pearson Education 7-20
Time Series of Demand(Figure 7.3)
0
10000
20000
30000
40000
50000
1 2 3 4 5 6 7 8 9 10 11 12
Period
Dem
an
d
Dt
Dt-bar
© 2007 Pearson Education 7-21
Measures of Forecast Error
Forecast error = Et = Ft - Dt
Mean squared error (MSE)
MSEn = (Sum(t=1 to n)[Et2])/n
Absolute deviation = At = |Et|
Mean absolute deviation (MAD)
MADn = (Sum(t=1 to n)[At])/n
= 1.25MAD
© 2007 Pearson Education 7-22
Forecasting Demand at Tahoe Salt
Moving average Simple exponential smoothing Trend-corrected exponential smoothing Trend- and seasonality-corrected exponential
smoothing
© 2007 Pearson Education 7-23
Forecasting in Practice
Collaborate in building forecasts The value of data depends on where you are in the
supply chain Be sure to distinguish between demand and sales
© 2007 Pearson Education 7-24
Summary of Learning Objectives
What are the roles of forecasting for an enterprise and a supply chain?
What are the components of a demand forecast? How is demand forecast given historical data using
time series methodologies? How is a demand forecast analyzed to estimate
forecast error?