The document discusses demand forecasting and push/pull processes in supply chains. It notes that push processes anticipate customer demand while pull processes respond to it. Forecasts will always contain errors that should be measured. Long term forecasts are generally less accurate than short term ones. Aggregate forecasts tend to be more accurate than disaggregate ones due to error reduction. The bullwhip effect causes demand distortion as information moves up a supply chain.

1. EP JOHN

2. ALL PUSH PROCESSES IN SUPPLY CHAIN ARE
PERFORMED IN ANTICIPATION OF
CUSTOMER DEMAND
ALL PULL PROCESSES IN SUPPLY CHAIN ARE
PERFORMED IN RESPONSE TO
CUSTOMER DEMAND

3. FORECASTS ARE ALWAYS WRONG AND SHOULD
THUS INCLUDE THE EXPECTED VALUE AND A
MEASURE OF FORECAST ERROR
LONG TERM FORECASTS ARE USUALLY LESS
ACCURATE THAT SHORT TERM FORECAST
i.e. LONG TERM FORECASTS HAVE A
LARGER STANDARD DEVIATION
OF ERROR RELATIVE TO THE
MEAN THAN SHORT TERM
FORECAST

4. AGGREGATE FORECASTS ARE USUALLY MORE
ACCURATE THAN DISAGGREGATE FORECASTS
AS THEY TEND TO HAVE A SMALLER
STANDARD DEVIATION OF ERROR
RELATIVE TO THE MEAN
THE FARTHER UP A SUPPLY CHAIN, OF A
COMPANY, GREATER IS THE DISTORTION OF
INFORMATION IT RECEIVES.
(BULLWHIP EFFECT)

5. PAST DEMAND
LEAD TIME OF PRODUCT
PLANNED ADVERTISING
OR MARKETING EFFORTS
STATE OF THE ECONOMY
PLANNED PRICE DISCOUNTS
ACTIONS THAT COMPETITORS HAVE
TAKEN

6. QUATITATIVE
TIME SERIES
CASUAL
SIMULATION

7. 1. UNDERSTAND THE OBJECTIVE OF
FORECASTING
2. INTEGRATE DEMAND PLANNING AND
FORECASTING THROUGH OUT THE SUPPLY
CHAIN
3. UNDERSTAND AND IDENTIFY CUSTOMER
SEGMENTS

8. 4. IDENTIFY THE MAJOR FACTORS THAT
INFLUENCE THE DEMAND FORECAST
5.DETERMINE THE APPROPRIATE FORECASTING
TECHNIQUE
6.ESTABLISH PERFORMANCE AND ERROR
MEASURES FOR THE FORECAST

9.  GOAL – SYSTEMATIC COMPONENET &
RANDOM COMPONENT
 SYSTEMATIC COMPONENT – LEVEL, TREND
AND SEASONAL FACTOR
 MULTIPLICATIVE
LEVEL X TREND X SEASONAL FACTOR
 ADDITIVE
LEVEL + TREND + SEASONAL FACTOR
 MIXED
(LEVEL + TREND) X SEASONAL FACTOR

10.  A GOOD FORECASTING METHOD SHOULD
CAPTURE THE SYSTEMATIC COMPONENT OF
DEMAND BUT NOT THE RANDOM COMPONENT.
 THE RANDOM COMPONENT MANIFESTS ITSELF
IN THE FORM OF A FORECAST ERROR THAT
CONTAIN VALUABLE INFORMATION AND MUST
BE ANALYSED FOR THE TWO REASONS,

11. MANAGERS USE ERROR ANALYSIS TO
DETERMINE WHETHER THE CURRENT
FORECASTING METHOD IS PREDICTING THE
SYSTEMATIC COMPONENT OF DEMAND
ACCURATELY.
ALL CONTIGENCY PLANS MUST ACCOUNT
FOR FORECAST ERROR.
Et = Ft - Dt

12.  DEMAND PLANNING MODULE
(COMMERCIAL DEMAND PLANNING MODULES)
 ALSO USED FOR PRODUCTS AND CATEGORIES
 CUSTOMER SALES INFORMATION
 FACILITATE SHAPING OF DEMAND
THESE TOOLS HELP ANALYSE THE IMPACT OF
PROMOTIONS ON DEMAND AND CAN BE USED TO
DETERMINE THE EXTENT AND TIMING OF
PROMOTIONS

13.  FIRMS WITH A FEW CUSTOMERS OFTEN
EXPERIENCE VERY LUMPY DEMAND THAT IS
HARDER TO FORECAST THAN DEMAND
FROM MANY SMALL CUSTOMERS, THAT
TENDS TO BE SMOOTHER.
 TWO STRATEGIES USED TO MITIGATE
FORECAST RISK WITH RIGHT BALANCE ARE
 INCREASEING THE RESPONSIVENESS
 UTILISING OPPURTUNITIES FOR POOLING OF
DEMAND.

14. COLLABORATE IN BUILDING
FORECASTS
SHARE ONLY THE DATA THAT TRULY
PROVIDE VALUE
BE SURE TO DISTINGUISH BETWEEN
DEMAND AND SALES

15.  UNDERSTAND THE ROLE OF FORECASTING
FOR BOTH AN ENTERPRISE AND A SUPPLY
CHAIN
 IDENTIFY THE COMPONENTS OF A
DEMAND FORECAST
 FORECAST DEMAND IN A SUPPLY CHAIN
GIVEN HISTORICAL DATA USING TIME-
SERIES METHGODOLOGIES
 ANALYSE DEMAND FORECASTS TO
ESTIMATE FORECAST ERROR