Supply Chain Management
Dr. Eng. Muhammad Rusman, ST.,MT.
Demand Forecas8ng in a Supply Chain
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Introduc@on to forecas@ng
• What is forecas@ng?
– Primary Func@on is to Predict the Future using (@me series related or other) data we have in hand
• Why are we interested?
– Affects the decisions we make today
Supply Chain Management – @uh 2015
What Makes a Good Forecast?
• It should be @mely
• It should be as accurate as possible
• It should be reliable
• It should be in meaningful units
• It should be presented in wri@ng
• The method should be easy to use and understand in most cases.
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Forecast Horizons in Operation
Planning
SCM LG EIN Process
Inventory Company Level
Factory (Dom Produc8on)
Import
CDC RDC
DEALER
Start Coun@ng Company Level for Dom
Start Coun@ng Company Level for
Import
Start Coun@ng Warehouse Level
St
1. Lead time: Unloading, Loading, Move, Queue
2. Handling cost:
Operator, Tools/Equip, WH space, Defect risk
3. Inv. carrying cost 4. Transportation from CDC to
RDC
1. Lead time: Unloading, Loading, Move, Queue
2. Handling cost:
Operator, Tools/Equip, WH space, Defect risk
3. Inv. carrying cost 4. Transportation from RDC to
Dealers
1. Lead 8me: Unloading
1. Lead 8me: Loading
2. Transporta8on from Factory to CDC
Saving:
§ Reduce lead @me unloading & loading at CDC (2-5 days)
§ Eliminate transporta@on cost from factory to CDC
§ Eliminate handling cost at CDC
§ Avoid high inventory/aging risk at CDC and RDC
Saving:
§ Reduce lead @me unloading & loading at CDC (2-5 days) and RDC (2-3 days)
§ Eliminate transporta@on cost: from factory to CDC and from CDC to Dealers
§ Eliminate handling cost at CDC and RDC
§ Avoid high inventory/aging risk at CDC and RDC
REQUIREMENT:
ü Strong Forecast Accuracy by Weekly S&OP with BM
ü Align Prod. Plan to Cust. Demand with “Ship To” code for each branches.
ü Sales leveling
Supply Chain Management – @uh 2015
Subjective Forecasting Methods
• The methods mostly are theory-based which involved mathema@cal models with parameters that must be calibrated.
• With liYle or no historical dataà The Delphi method
• The Delphi Method
– Rely on experts qualita@ve assessment or ques@onnaires o develop forecast
– Individual opinions are compiled and considered. These are anonymously shared among group. Then opinion request is repeated un@l an overall group consensus is (hopefully) reached.
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Demand process
• Trends, demand consistently increase or decrease over @me
• Seasonality, Demand shows peaks and valleys at consistent intervals
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Supply Chain Management – @uh 2015
Pola Data
• Pola horisontal (H) terjadi bilamana data
berfluktuasi disekitar nilai rata-rata yg konstan. Suatu produk yg penjualannya tdk meningkat atau menurun selama waktu tertentu.
• Pola musiman (S) terjadi bilamana suatu deret dipengaruhi oleh faktor musiman (misalnya kuartal tahun tertentu, bulanan, atau hari-hari pada minggu tertentu).
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Pola Data
• Pola siklis (C) terjadi bilamana datanya dipengaruhi oleh fluktuasi ekonomi jangka
panjang seper@ yang berhubungan dengan siklus bisnis.
Supply Chain Management – @uh 2015
Supply Chain Management – @uh 2015
Moving Average
• The moving average method calculates the average amount of demand over given @me
period and uses this average to predict the future demand
16 1
1 t
t i
i t N
y D
N
−
= −
=
∑
Let D1, D2, . . . Dn, . . . be the past values of the series to be predicted
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3 month MA: (oct+nov+dec)/3=258.33 6 month MA: (jul+aug+…+dec)/6=249.33 12 month MA: (Jan+feb+…+dec)/12=205.33
Example
Period (t) Sales (y)
1 5.3
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Example
Supply Chain Management – @uh 2015
Summary of Moving Averages
• Advantages of Moving Average Method
– Easily understood
– Easily computed
– Provides stable forecasts
• Disadvantages of Moving Average Method
– Requires saving lots of past data points: at least the N periods used in the moving average computa@on
– Lags behind a trend
– Ignores complex rela@onships in data
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Exponential Smoothing Method
• Exponen@al smoothing is the technique that uses a weighted moving average of the past data as the basis for the forecast.
• α is the weight placed on the demand
observa@on and 1- α is the weight placed on last forecast
1
(1
)
1t t t
y
=
α
D
−+
−
α
y
−Supply Chain Management – @uh 2015
Exponential Smoothing Method
In symbols:
yt+1 = α Dt + (1 - α ) yt
= α Dt + (1 - α ) (α Dt-1 + (1 - α ) yt-1)
= α Dt + (1 - α )(α )Dt-1 + (1 - α)2 (α )D
t - 2 + . . . • Hence the method applies a set of exponen@ally
declining weights to past data. It is easy to show that the sum of the weights is exactly one.
{Or : yt + 1 = yt - α (yt - Dt) }
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Effect of
α
value on the Forecast
• Small values of
α
means that the forecastedvalue will be stable (show low variability
– Low α increases the lag of the forecast to the actual
data if a trend is present
• Large values of
α
mean that the forecast willmore closely track the actual @me series
Supply Chain Management – @uh 2015
Supply Chain Management – @uh 2015
Valida@on
• Aker the model specified, its performance
characteris@cs should be verified or validated by comparison of its forecast with historical data for the process it was designed to forecast.
• We can use the error measures such as MAPE (Mean absolute percentage error), MSE (Mean square error) or RMSE (Root mean square error)
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1
1
.100%
n i
i i
e MAPE
n = y
=
∑
2 1
1 n i i
MSE e
n =
=
∑
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Comparison of MA and ES
•
Similari@es
– Both methods are appropriate for sta@onary series
– Both methods depend on a single parameter
– Both methods lag behind a trend
– One can achieve the same distribu@on of forecast error by seong:
α = 2/ ( N + 1) or N = (2 - α)/ α
Comparison of MA and ES
•
Differences
– ES carries all past history (forever!)
– MA eliminates “bad” data aker N periods
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Linear Regression
• In linear regression, the model specifica@on assumes that the independent variable, Y, is linear combina@on of the independent variables.
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0 1
Y
=
β
+
β
X
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Linear Regression
Supply Chain Management – @uh 2015
Case
• A hospital receives regular shipments of liquefied oxygen, which it converts to oxygen gas that is used for the life support. The company that sells the
oxygen to hospital wishes to forecast the amount of the liquefied oxygen the hospital will use tomorrow. The number of liters of liquefied oxygen used by the hospital in each of the past 30 days is reported in the oxygen.xlsx.
a. Using a moving average with N=7, forecast tomorrow’s demand
b. Using single exponen@al smoothing with α=0.1, forecast tomorrow’s demand
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