DEMAND FORECASTING IN A SUPPLY CHAIN
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DEMAND FORECASTING IN A SUPPLY CHAIN Moving from Supply Chain to Demand Management
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Transcript of DEMAND FORECASTING IN A SUPPLY CHAIN
DEMAND FORECASTING IN A
SUPPLY CHAIN
Moving from Supply Chain to
Demand Management
AGENDA
• INTRODUCTION (7.1)
• UNDERSTANDING (7.2 – 7.4)– Characteristics
– Components
– Basic Approach
AGENDA
• Methods (7.5)– Static
– Adaptive
• Measures of Forecast Error (7.6)
• Summary and Conclusions
INTRODUCTION
• Role in SCM– Move
• From Managing Supply
• To Managing Demand
– Basis of All Planning
INTRODUCTION
• Decision Areas (examples)– Production
• Scheduling
• Inventory
• Aggregate Planning
INTRODUCTION
• Decision Areas (examples)– Marketing
• Sales Force Allocation
• Promotion
• NPD
INTRODUCTION
• Decision Areas (examples)– Finance
• Capital
• Cash Flow
INTRODUCTION
• Decision Areas (examples)– Personnel
• Workforce Planning
• Hiring
• Layoffs
AGENDA
• UNDERSTANDING (7.2 – 7.4)– Characteristics
– Components
– Basic Approach
UNDERSTANDING
• Forecast Characteristics– Always Wrong
• Expected Value – Central Tendency
• Dispersion– Forecast Error– Long v Short– Aggregate v Disaggregate
UNDERSTANDING
• Components– Past as Predictor of Future
• Maybe
• Useful– Supply Chain Management– Demand Management (?)
UNDERSTANDING
• Components– Past as Predictor of Future
• Factors (examples)– Response Time– Demand– Marketing Actions– State of Economy– Etc.
UNDERSTANDING
• Components– Types
• Qualitative– Subjective– Judgment– Lacking
» Past » Expert Intelligence
UNDERSTANDING• Components
– Types• Time Series
– Historical Demand– No Change in Underlying Factors– Appropriate
» Stable
» Basic Patter Does Not Fluctuate
UNDERSTANDING• Components
– Types• Causal
– Past not Predictor of Future– Cause more Relevant than Correlation– Environmental Changes
UNDERSTANDING• Components
– Types• Simulation
– Imitate Stimulus, Response, Outcome– Consider
» Time Series
» Causal
» Qualitative
» Heuristics and Optimization
UNDERSTANDING
• Components– Past as Predictor of Future
• Factors (examples)– Response Time– Demand– Marketing Actions– State of Economy– Etc.
UNDERSTANDING
• Basic Approach– Objective
• Support Decision
• Link to Action
• Shared (CPFR)
• Relevant Horizon
UNDERSTANDING
• Basic Approach– Integrate Planning
• Capacity
• Production
• Promotion
• Purchasing
• Other
UNDERSTANDING• Basic Approach
– Identify Key Factors• Sales v Demand
• Nature of Relationship– Primary – Derived
• Covariates– Demand– Supply
UNDERSTANDING• Basic Approach
– Fundamentally• Service Output Demands (SOD)
• Service Output Supply (SOS)
UNDERSTANDING• Basic Approach
– Appropriate Techniques• Vary by
– Product– Service– Segment– Horizon
• Will be Required
UNDERSTANDING• Basic Approach
– Monitor Performance• Evaluate
– Accuracy– Timeliness– Value
UNDERSTANDING• Basic Approach
– Monitor Performance• Compare
– Forecast v Actual– When Available v When Needed– Cost v Benefit
AGENDA
• (Time Series) Methods (7.5)– Static
– Adaptive
METHODS• Static
– Mixed
seasonaltrendlevelSysematic )(
METHODS• Static
– Forecast
tperiodfor demandforecast
tperiodin observed demand actual
tperiodin factor seasonal of estimate
trendof estimate
0 periodfor level of estimate
])([ 1
t
t
t
tlt
F
D
S
T
L
where
STltLF
METHODS• Static
– Level and Trend• Deseasonalize Demand
• Periodicity
METHODS• Static
– Level and Trend• Periodicity
– P Even
pDDDDpt
ptiiptptt 22
)2/(1
)2/(1)2/()2/(
METHODS• Static
– Level and Trend• Periodicity
– P Odd
pDDpt
ptiit
)2/(
)2/(
METHODS• Static
– Forecast Linear Relationship
trend
demand izeddeseasonal ofgrowth of rate
0 periodin demand izeddeseasonal
level
periodin demand izeddeseasonal
T
L
tD
where
tTLD
t
t
METHODS• Adaptive
– Mixed
seasonaltrendlevelSysematic )(
METHODS• Adaptive
– Basic Forecast
t
tt
t
t
t
t
t
t
tlt
AMAD
EtA
tE
tF
tD
tS
tT
tL
where
SlTLF
ave.deviation absolutemean
periodfor deviation absolute
periodin error forecast
periodfor demandforecast
periodin observed demand actual
periodin factor seasonal of estimate
period of endat trendof estimate
period of endat level of estimate
][ 1
METHODS
• Adaptive– Basic Forecast Steps
• Initialize
• Forecast
• Measure Error
• Adapt
METHODS
• Adaptive– Basic Forecast Steps
tF
tD
where
DFE
t
t
ttlt
periodfor demandforecast
periodin observed demand actual
11
METHODS
• Adaptive– Basic Forecast Steps
down revisefor
up revisefor
11
11
tt
tt
DF
DF
METHODS• Adaptive
– Moving Average
111
1
11
ErrorForecast
,
Forecast
/)...(
Level Estimate
ttt
tnttt
Ntttt
DFE
LFLF
NDDDL
METHODS• Adaptive
– Simple Exponential Smoothing
constant smoothing10
)1(
Forecast Revised
,
Forecast
1
Level Estimate Initial
11
1
10
where
LDL
LFLF
Dn
L
ttt
tnttt
n
ii
METHODS• Adaptive
– Trend Corrected (Holt)
periodper change of rate
of estimate0 periodin forecast
Trend and Level of Estimate Initial
Trend Level Systematic
0
a
Lb
where
batDt
METHODS• Adaptive
– Trend Corrected (Holt)
for trendconstant smoothing10
levelfor constant smoothing10
)1()(
))(1(
Forecast Revised
and,
Forecast
11
11
t1
where
TLLT
TLDL
nTLFTLF
tttt
tttt
tntttt
METHODS• Adaptive
– Trend Season Corrected (Winter)
nttnttttt SnTLFSTLF
staticper as
)( and,)(
Forecast
Seasonal and Trend,Level, of Estimate Initial
Season X Trend) (Level Systematic
t11
METHODS• Adaptive
– Trend Season Corrected (Winter)
seasonfor constant smoothing10
for trendconstant smoothing10
levelfor constant smoothing10
)1()/(
)1()(
))(1()/(
Forecast Revised
1111
11
111
where
SLDS
TLLT
TLSDL
tttpt
tttt
ttttt
AGENDA
• Forecast Error (7.6)
FORECAST ERROR
• Why– Accuracy of Systematic Component
– Contingency Planning
FORECAST ERROR
• Measures
n
ttn E
nMSE
1
21
FORECAST ERROR
• Measures
tt
n
ttn
EA
where
An
MAD
1
1
FORECAST ERROR
• Measures
MADr 25.1
FORECAST ERROR
• Measures
n
DE
MAPE
n
t t
t
n
1001
FORECAST ERROR
• Measures
n
ttn Ebias
1
FORECAST ERROR
• Measures
t
tt MAD
biasTS
SUMMARY AND CONCLUSIONS
