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Unformatted text preview: ffects • Smooth seasonality forecasts using γ by assuming m
periods per season Forecast Ft+1 = (Bt + Tt )St+1
Ft+k = (Bt + kTt )St+k m
m Algorithm 4: Exponential Smoothing
with Trend & Seasonality
• How to construct seasonality:
• Smooth base forecast Bt
! • Dt
Bt = ↵
St m ↵)(Bt + Tt 1) Smooth trend forecasts Tt
! Tt = (Bt Bt 1) + (1 !
• 1 Smooth seasonality forecasts St Dt
Bt )St m )Tt 1 Measuring forecast accuracy
• Mean Forecast Error (MFE or Bias): Measures
average deviation of forecast from actuals. • Mean Absolute Deviation (MAD): Measures average
absolute deviation of forecast from actuals. • Mean Absolute Percentage Error (MAPE): Measures
absolute error as a percentage of the forecast. • Mean Squared Error (MSE): Measures variance of
forecast error. Part 10: Pooling Strategies Some pooling we have seen
• Capacity Pooling (economy of scale)
! • Process Pooling (ﬂow design) Economy of Scale in Queueing
Wq vs. n with load being constant 1.80 Waiting time in queue 1.35 Beneﬁts of pooling
or economies of
scale in queueing!
is huge 0.90 0.45 0.00 0.000 12.500 25.000 n 37.500 50.000 Inventory Pooling
• Current operations:
• • • Each sales representative has her
own inventory to serve demand in
her own territory. Inventory Territory 1 Inventory Territory 2 e.g., 3 territories, 3 stockpiles of
inventory Inventory Territory 3 The pooling strategy:
• • • A single inventory is used by
several sales reps. (How to
Inventory is automatically
replenished at the pooled location
as depleted by demand.
e.g., 3 pooled territories, 1 stockpile
of inventory Territory 1
Inventory Territory 2
Territory 3 Capacity Pooling class 1
For ﬂexibility Lead Time Pooling
Store Store Supplier Retail
Store Probabilistic view
• Joint distribution two random variables X and Y • coefﬁcients of variation for X+Y
75 50 25 0
0 25 50 When to use pooling
• Almost always better • But comes with some cost Pooling No Pooling...
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This document was uploaded on 01/28/2014.
- Fall '14