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EP JOHN
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
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
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)
PAST DEMAND
 LEAD TIME OF PRODUCT
 PLANNED ADVERTISING
OR MARKETING EFFORTS
 STATE OF THE ECONOMY
 PLANNED PRICE DISCOUNTS
 ACTIONS THAT COMPETITORS HAVE
TAKEN
QUATITATIVE
TIME SERIES
  CASUAL
SIMULATION
1.    UNDERSTAND THE OBJECTIVE OF
     FORECASTING

2.    INTEGRATE DEMAND PLANNING AND
     FORECASTING THROUGH OUT THE SUPPLY
     CHAIN

3.    UNDERSTAND AND IDENTIFY CUSTOMER
     SEGMENTS
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
 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
 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,
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
 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
 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.
COLLABORATE IN BUILDING
FORECASTS

 SHARE ONLY THE DATA THAT TRULY
PROVIDE VALUE

 BE SURE TO DISTINGUISH BETWEEN
DEMAND AND SALES
 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
Forecasting in Supply Chain

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Forecasting in Supply Chain

  • 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