Analytics in supply chain
demand forecasting
What is forecasting in
the supply chain?
The prediction of demand for an item or group of items for a
future period.
Factors to consider,
•
Past demand
•
Lead time of product replenishment
•
Planned advertisements
•
Planned price discounts/ promotions
•
Actions that competitors have taken
•
State of the economy
Forecasting
• Qualitative
Opinion from experts, decision makers or
customers
• Quantitative
Historical data from time series or
correlation information
Qualitative
• Executive opinion
• Delphi Method
• Sales force estimates
• Customer surveys
Quantitative
• Naive approach
• Moving average
• Exponential smoothing
• Trend Projections/Linear and multi regression models
Moving average – Exercise (3 year moving average)
calculate demand for 7th year
Year
Actual
demand(
At)
Forecast
Demand(
Ft)
Error
At-Ft
1
310
-
-
2
365
-
-
3
395
-
-
4
415
357
58
58
5
450
392
58
58
6
465
420
45
45
7
8
443
Absolute
Mean absolute deviation(MAD, MSE,MAPE)
Yea
r
Actual
demand
(At)
Forecast
Demand
Error
(At-Ft)
Absolute
MAD
Mean
Square
Error
(Ft)
Mean absolute
percentage
error(Error/actua
l demand*100)
1
310
-
-
2
365
-
-
3
395
-
-
4
415
357
58
58
(58)2=3364
58/415*100=13.97
5
450
392
58
58
(58)2 =3364
58/450*100=12.88
6
465
420
45
45
(45)2 =2025
8753/3
443
MAD
161/3 =
53.66= 54
54
2917
7
8
MSE
45/465*100=9.67
MAPE
13.9+12.8+9.67=3
6.52/3=12.17
Exponential smoothing(Constant ∝ is always given)
Year
Actual
demand
(At)
Forecast
demand
(Ft)
1
310
310
2
365
310
3
395
332
4
415
357
5
450
380
6
465
408
7
431
Ft+1=Ft+∝(At-1-Ft-1)
OR
F t+1= ∝At+(1- ∝)Ft
∝= 0.4
1- ∝=1- 0.4=0.6
8
Example:0.4(310)+0.6(310)
Stability- Actual demand
Responsiveness- Forecasted demand
Regression forecasting
methods
• Linear regression( One independent and a
dependent variable to predict)
• Multiple regressions( Many independent
variables and one or more dependent
variables )
Trend corrected exponential smoothing(Holt’s model)
Systemic component of demand =level +trend
By running a linear regression between demand Dt and time, Period t, of the form
Dt= at + b
Lt+1 = ∝ Dt+1 +(1-∝) (Lt +Tt)
Tt+1= β(Lt+1 - Lt) +(1 - β)Tt
Where ∝ (0 < ∝ <1) is smoothing constant for the level and β(0 < β <1) is the smoothing constant for
the trend.
(Source: Chopra & Kalra, 2019)
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