SlideShare uma empresa Scribd logo
1 de 27
Baixar para ler offline
DEMAND FORECASTING
MEANING

IMPORTANTS

OBJECTIVES

METHODS
What is forecasting all about?
  Demand for Mercedes E                       We try to predict the
         Class                               future by looking back
                                                   at the past



                                                   Predicted
                                                    demand
                                                    looking
                                      Time         back six
    Ja   Fe Mar Apr May Jun Jul Aug                 months
    n    b
     Actual demand (past sales)
     Predicted demand
WHY DEMAND FORECASTING

•   Planning and scheduling production
•   Acquiring inputs
•   Making provisions for finances
•   Formulating pricing strategy
•   Planning Advertisement
OBJECTIVES

Short –term Forecasting

To evolve a suitable production policy
To reduce the cost of purchase
To determine appropriate price policy
To set sales targets and establish control
To forecast short-term financial
 requirements
OBJECTIVES

Long –term Forecasting
 Planning of a new unit or expansion of an
  existing unit
 Planning of long-term financial requirements
 Planning of man-power requirements
Levels of Forecasting
• Macro level
• Industry level
• Micro level
STEPS IN DEMAND FORECASTING

•   Determination of the objectives
•   Sub-dividing the task
•   Identifying of demand determinants
•   Selection of the method
•   Collection of Data
•   Estimation and interpretation of result
•   Reporting
METHODS OF DEMAND FORECASTING

QUALITATIVE                    QUANTITATIVE
  TECHNIQUES                     TECHNIQUES
 Survey Method                 Barometric Techniques
    Direct Interview Method    Time Series Analysis
    Collective opinion
                                Regression Method
 Delphi Method
 Controlled Experiments
SURVEY METHOD
• Surveys are conducted to collect information
  about the future plans of the potential
  consumers
• A firm may launch a new product, if the suvey
  indicates that there is a demand for that
  particular product in the market.
Direct Interview Method
Consumers are contacted directly to ask them what they
intend to buy in future
Collective opinion
The opinions of those who have the feel of the market, like
salesman, professional experts, market consultants etc.
Advantages
•Simple
•Quick
•Low cost
•Reliable
Disadvantages
Personal judgements may go wrong
Useful only foe short-term forecasting
DELPHI METHOD

• Applied to uncertain areas where past data or future
  data are not of much use
• Some expert in an area will be contacted with
  questionnaires
• A co-ordinator collect all the opinions
• Each expert will be supplied with responses of other
  experts without revealing their identity
• Expert may revise his opinion, if needed
• Process will be repeated so that all experts come to
  an agreement
CONTROLLED EXPERIMENTS
• Studies and experiments in consumers
  behavior are carried out under actual market
  conditions
• Three or four cities having similarity in
  population , income level, cultural and social
  background ,occupational distribution , taste
  etc.. are chosen
• Various demand determinants like price,
  advertisement , expenditure etc are changed
  one by one and these changes on demand are
  observed
QUANTITATIVE TECHNIQUES
BASED ON DATA AND ANALYTICAL TECHNIQUES
Barometric Techniques
Time series Analysis
Regression Method
BAROMETRIC FORECASTING
 based on the observed relationships between
   different economic indicators
It can be divided into three groups
 Leading indicators
 Coincident indicators
 Lagging indicators
Leading Indicators
• which run in advance of changes in demand for a particular
product
•an increase in the number of building permits
granted which would lead to an increase in demand for
building-related products such as wood, concrete and so on
Coincident Indicators
•occur alongside changes in demand
•an increase in sales would generate an increase in demand for
the manufacturers of the goods concerned
Lagging Indicators
•run behind changes in demand
New industrial investment by firms which will only invest in
new production facilities when demand is already firmly
established.
TIME SERIES ANALYSIS
• Used to predict the future demand for a
  product based on the past sales and demand.

 Simple moving average
 Weighted moving average
 Exponential smoothing
Time series: simple moving average

In the simple moving average models the forecast value is


                       At + At-1 + … + At-n
                Ft+1 =
                                 n


   t    is the current period.
   Ft+1 is the forecast for next period
   n is the forecasting horizon (how far back we
   look),
   A    is the actual sales figure from each period.
Example: forecasting sales at Kroger

Kroger sells (among other stuff) bottled spring water


    Month          Bottles
     Jan            1,325
     Feb            1,353                  What will
     Mar            1,305                  the sales
     Apr            1,275                    be for
     May            1,210                    July?
     Jun            1,195
      Jul             ?
What if we use a 3-month simple moving average?



                         AJun + AMay + AApr
                FJul =                        = 1,227
                                 3



What if we use a 5-month simple moving average?



                 AJun + AMay + AApr + AMar + AFeb
       FJul =                                       = 1,268
                                 5
Time series: weighted moving average
We may want to give more importance to some of the
data…

             Ft+1 = wt At + wt-1 At-1 + … + wt-n At-n


                      wt + wt-1 + … + wt-n = 1

    t    is the current period.
    Ft+1 is the forecast for next period
    n    is the forecasting horizon (how far back we look),
    A    is the actual sales figure from each period.
    w    is the importance (weight) we give to each period
Time Series: Exponential Smoothing (ES)

 Main idea: The prediction of the future depends mostly on
the most recent observation, and on the error for the latest
                         forecast.


      Smoothi
          ng
      constan
                                          Denotes the
       t alpha                        importance of the past
          α                                   error
Exponential smoothing: the method

Assume that we are currently in period t. We calculated the
 forecast for the last period (Ft-1) and we know the actual
demand last period (At-1) …


                  Ft = Ft −1 + α ( At −1 − Ft −1 )

The smoothing constant α expresses how much our
forecast will react to observed differences…
If α is low: there is little reaction to differences.
If α is high: there is a lot of reaction to differences.
Linear regression in forecasting

Linear regression is based on
1. Fitting a straight line to data
2. Explaining the change in one variable through changes
   in other variables.


   dependent variable = a + b × (independent variable)


 By using linear regression, we are trying to explore which
   independent variables affect the dependent variable
Linear Regression Model

• Shows linear relationship between dependent
  & explanatory variables
  – Example: Diapers & # Babies (not time)
          Y-intercept    Slope


                  ^
                  Yi = a + b X i
 Dependent                         Independent (explanatory)
 (response) variable               variable
Example: do people drink more when it’s
cold?
     Alcohol Sales

                             Which line best
                              fits the data?




                             Average
                             Monthly
                           Temperature
The best line is the one that minimizes the
error
  The predicted line is …

                       Y = a + bX


  So, the error is …
                        εi = y i - Yi


  Where: ε is the error
        y is the observed value
        Y is the predicted value
Conclusion

• Accurate demand forecasting requires
  – Product knowledge
  – Knowledge about the customer
  – Knowledge about the environment

Mais conteúdo relacionado

Mais procurados

Inventory management
Inventory managementInventory management
Inventory managementUday Bansode
 
Cost volume profit analysis.
Cost volume profit analysis.Cost volume profit analysis.
Cost volume profit analysis.Varadraj Bapat
 
Demand Forecasting
Demand ForecastingDemand Forecasting
Demand Forecastingsoumya0896
 
Economic Order Quantity Models
Economic Order Quantity ModelsEconomic Order Quantity Models
Economic Order Quantity ModelsShashank Shekhar
 
law of variable proportions
law of variable proportionslaw of variable proportions
law of variable proportionsAreeb Syed
 
Cost output relationship
Cost  output relationshipCost  output relationship
Cost output relationshipsanjay kuamr
 
Production function ppt in economics
Production function ppt in economicsProduction function ppt in economics
Production function ppt in economicsMansi Tyagi
 
Material requirement planning, MRP.
Material requirement planning, MRP. Material requirement planning, MRP.
Material requirement planning, MRP. shoaibzaheer1
 
Presentation on Indifference Curve
Presentation on Indifference CurvePresentation on Indifference Curve
Presentation on Indifference CurveShuvongkor Barman
 
Models of inventory control
Models of inventory controlModels of inventory control
Models of inventory controlPriyanka Mangla
 
Demand forecasting
Demand forecastingDemand forecasting
Demand forecastingjyyothees mv
 

Mais procurados (20)

Demand forecasting
Demand forecastingDemand forecasting
Demand forecasting
 
Inventory management
Inventory managementInventory management
Inventory management
 
Cost volume profit analysis.
Cost volume profit analysis.Cost volume profit analysis.
Cost volume profit analysis.
 
Demand analysis
Demand analysisDemand analysis
Demand analysis
 
Demand Forecasting
Demand ForecastingDemand Forecasting
Demand Forecasting
 
Economic Order Quantity Models
Economic Order Quantity ModelsEconomic Order Quantity Models
Economic Order Quantity Models
 
law of variable proportions
law of variable proportionslaw of variable proportions
law of variable proportions
 
INVENTORY MODELS
INVENTORY MODELSINVENTORY MODELS
INVENTORY MODELS
 
Demand forecasting
Demand forecastingDemand forecasting
Demand forecasting
 
Cost output relationship
Cost  output relationshipCost  output relationship
Cost output relationship
 
Demand forecasting ppt
Demand forecasting pptDemand forecasting ppt
Demand forecasting ppt
 
Job costing
Job costingJob costing
Job costing
 
Production function ppt in economics
Production function ppt in economicsProduction function ppt in economics
Production function ppt in economics
 
Material requirement planning, MRP.
Material requirement planning, MRP. Material requirement planning, MRP.
Material requirement planning, MRP.
 
Presentation on Indifference Curve
Presentation on Indifference CurvePresentation on Indifference Curve
Presentation on Indifference Curve
 
Models of inventory control
Models of inventory controlModels of inventory control
Models of inventory control
 
Law of variable proportion
Law of variable proportionLaw of variable proportion
Law of variable proportion
 
Demand forecasting
Demand forecastingDemand forecasting
Demand forecasting
 
Law of demand
Law of demandLaw of demand
Law of demand
 
Demand Analysis
Demand  AnalysisDemand  Analysis
Demand Analysis
 

Destaque

Demand Forecasting
Demand ForecastingDemand Forecasting
Demand Forecastingyashpal01
 
Demand forecasting
Demand forecastingDemand forecasting
Demand forecastingAkshismruti
 
Demand Forecasting
Demand ForecastingDemand Forecasting
Demand ForecastingAnupam Basu
 
Forecasting Slides
Forecasting SlidesForecasting Slides
Forecasting Slidesknksmart
 
Mba 2 Sem Demand For Casting
Mba 2  Sem Demand For CastingMba 2  Sem Demand For Casting
Mba 2 Sem Demand For Castingkkiransoni
 
Forecasting Techniques
Forecasting TechniquesForecasting Techniques
Forecasting Techniquesguest865c0e0c
 
Forecasting and methods of forecasting
Forecasting and methods of forecastingForecasting and methods of forecasting
Forecasting and methods of forecastingMilind Pelagade
 
Demand forecasting
Demand forecastingDemand forecasting
Demand forecastingRahul Gupta
 
Demand forecasting
Demand forecastingDemand forecasting
Demand forecastingsuvarnapstpl
 
Demand forecasting
Demand  forecasting Demand  forecasting
Demand forecasting Rohit Parkar
 
Demand forecasting
Demand forecasting Demand forecasting
Demand forecasting Nithin Kumar
 
Attracting & Retaining Demand Planning & Forecasting Talent
Attracting & Retaining Demand Planning & Forecasting TalentAttracting & Retaining Demand Planning & Forecasting Talent
Attracting & Retaining Demand Planning & Forecasting TalentLifeWork_Search
 
Forecasting & time series data
Forecasting & time series dataForecasting & time series data
Forecasting & time series dataJane Karla
 
Demand forecasting.
Demand forecasting.Demand forecasting.
Demand forecasting.Akash Bharti
 
Class notes forecasting
Class notes forecastingClass notes forecasting
Class notes forecastingArun Kumar
 

Destaque (20)

Demand Forecasting
Demand ForecastingDemand Forecasting
Demand Forecasting
 
Demand forecasting
Demand forecastingDemand forecasting
Demand forecasting
 
Demand Forecasting
Demand ForecastingDemand Forecasting
Demand Forecasting
 
Forecasting Slides
Forecasting SlidesForecasting Slides
Forecasting Slides
 
Mba 2 Sem Demand For Casting
Mba 2  Sem Demand For CastingMba 2  Sem Demand For Casting
Mba 2 Sem Demand For Casting
 
Demand forcasting
Demand forcasting Demand forcasting
Demand forcasting
 
Demand forecasting 12
Demand forecasting 12Demand forecasting 12
Demand forecasting 12
 
3...forecasting methods
3...forecasting methods3...forecasting methods
3...forecasting methods
 
Forecasting Techniques
Forecasting TechniquesForecasting Techniques
Forecasting Techniques
 
Forecasting and methods of forecasting
Forecasting and methods of forecastingForecasting and methods of forecasting
Forecasting and methods of forecasting
 
Demand forecasting
Demand forecastingDemand forecasting
Demand forecasting
 
Demand forecasting.
Demand forecasting.Demand forecasting.
Demand forecasting.
 
Demand forecasting
Demand forecastingDemand forecasting
Demand forecasting
 
Demand forecasting
Demand  forecasting Demand  forecasting
Demand forecasting
 
Demand forecasting
Demand forecasting Demand forecasting
Demand forecasting
 
Attracting & Retaining Demand Planning & Forecasting Talent
Attracting & Retaining Demand Planning & Forecasting TalentAttracting & Retaining Demand Planning & Forecasting Talent
Attracting & Retaining Demand Planning & Forecasting Talent
 
Forecasting & time series data
Forecasting & time series dataForecasting & time series data
Forecasting & time series data
 
Demand forecasting.
Demand forecasting.Demand forecasting.
Demand forecasting.
 
Class notes forecasting
Class notes forecastingClass notes forecasting
Class notes forecasting
 
Demand forecasting
Demand forecastingDemand forecasting
Demand forecasting
 

Semelhante a demand forecasting

FORECASTING ERRORS (2) (2).pptx
FORECASTING ERRORS (2) (2).pptxFORECASTING ERRORS (2) (2).pptx
FORECASTING ERRORS (2) (2).pptxPradipDulal2
 
Using Financial Forecasts to Advise Business - Method of Forecasting - Revised
Using Financial Forecasts to Advise Business - Method of Forecasting - RevisedUsing Financial Forecasts to Advise Business - Method of Forecasting - Revised
Using Financial Forecasts to Advise Business - Method of Forecasting - RevisedIrma Miller
 
Forecasting shivam
Forecasting shivamForecasting shivam
Forecasting shivamShivam Kumar
 
O M Unit 3 Forecasting
O M Unit 3 ForecastingO M Unit 3 Forecasting
O M Unit 3 ForecastingRASHMIPANWAR10
 
Demand Forecasting.pptx
Demand Forecasting.pptxDemand Forecasting.pptx
Demand Forecasting.pptxkarthigeyanl
 
demand forecasting
demand forecastingdemand forecasting
demand forecastingserveuuu
 
Es 08 forecasting topic final
Es 08 forecasting topic finalEs 08 forecasting topic final
Es 08 forecasting topic finalTim Arroyo
 
Ss sales forcasting
Ss sales forcastingSs sales forcasting
Ss sales forcastingCMPCERT
 
Forecasting
ForecastingForecasting
Forecastingconklij
 
Demand Forecasting and Market planning
Demand Forecasting and Market planningDemand Forecasting and Market planning
Demand Forecasting and Market planningAmrutha Raghu
 
Mba ii pmom_unit-1.3 forecasting a
Mba ii pmom_unit-1.3 forecasting aMba ii pmom_unit-1.3 forecasting a
Mba ii pmom_unit-1.3 forecasting aRai University
 
IFTA2020 Kei Nakagawa
IFTA2020 Kei NakagawaIFTA2020 Kei Nakagawa
IFTA2020 Kei NakagawaKei Nakagawa
 

Semelhante a demand forecasting (20)

Forecasting
ForecastingForecasting
Forecasting
 
Forecasting
ForecastingForecasting
Forecasting
 
Forecasting
ForecastingForecasting
Forecasting
 
FORECASTING ERRORS (2) (2).pptx
FORECASTING ERRORS (2) (2).pptxFORECASTING ERRORS (2) (2).pptx
FORECASTING ERRORS (2) (2).pptx
 
Using Financial Forecasts to Advise Business - Method of Forecasting - Revised
Using Financial Forecasts to Advise Business - Method of Forecasting - RevisedUsing Financial Forecasts to Advise Business - Method of Forecasting - Revised
Using Financial Forecasts to Advise Business - Method of Forecasting - Revised
 
Sales Ch 3-7.pptx
Sales Ch 3-7.pptxSales Ch 3-7.pptx
Sales Ch 3-7.pptx
 
Forecasting shivam
Forecasting shivamForecasting shivam
Forecasting shivam
 
O M Unit 3 Forecasting
O M Unit 3 ForecastingO M Unit 3 Forecasting
O M Unit 3 Forecasting
 
Demand Forecasting.pptx
Demand Forecasting.pptxDemand Forecasting.pptx
Demand Forecasting.pptx
 
demand forecasting
demand forecastingdemand forecasting
demand forecasting
 
Es 08 forecasting topic final
Es 08 forecasting topic finalEs 08 forecasting topic final
Es 08 forecasting topic final
 
Ss sales forcasting
Ss sales forcastingSs sales forcasting
Ss sales forcasting
 
Varunapriya
VarunapriyaVarunapriya
Varunapriya
 
forecasting methods
forecasting methodsforecasting methods
forecasting methods
 
Forecasting
ForecastingForecasting
Forecasting
 
Demand Forecasting and Market planning
Demand Forecasting and Market planningDemand Forecasting and Market planning
Demand Forecasting and Market planning
 
UNIT - II.pptx
UNIT - II.pptxUNIT - II.pptx
UNIT - II.pptx
 
Mba ii pmom_unit-1.3 forecasting a
Mba ii pmom_unit-1.3 forecasting aMba ii pmom_unit-1.3 forecasting a
Mba ii pmom_unit-1.3 forecasting a
 
IFTA2020 Kei Nakagawa
IFTA2020 Kei NakagawaIFTA2020 Kei Nakagawa
IFTA2020 Kei Nakagawa
 
Forecasting
ForecastingForecasting
Forecasting
 

Último

The_Chronological_Life_of_Christ_Part_93_Fear God
The_Chronological_Life_of_Christ_Part_93_Fear GodThe_Chronological_Life_of_Christ_Part_93_Fear God
The_Chronological_Life_of_Christ_Part_93_Fear GodNetwork Bible Fellowship
 
Deerfoot Church of Christ Bulletin 3 24 24
Deerfoot Church of Christ Bulletin 3 24 24Deerfoot Church of Christ Bulletin 3 24 24
Deerfoot Church of Christ Bulletin 3 24 24deerfootcoc
 
DP & Nostradamus-Fatima-Bailey-Branham-Ford - Short vers
DP & Nostradamus-Fatima-Bailey-Branham-Ford - Short versDP & Nostradamus-Fatima-Bailey-Branham-Ford - Short vers
DP & Nostradamus-Fatima-Bailey-Branham-Ford - Short versBengt & Maarit de Paulis
 
SERPENT COIL: THE AWAKENING OF KUNDALINI
SERPENT COIL: THE AWAKENING OF KUNDALINISERPENT COIL: THE AWAKENING OF KUNDALINI
SERPENT COIL: THE AWAKENING OF KUNDALINISantanu Das
 
The Mormon & Quaker Moons of Lancashire: Stories of Religious Conversion & Mi...
The Mormon & Quaker Moons of Lancashire: Stories of Religious Conversion & Mi...The Mormon & Quaker Moons of Lancashire: Stories of Religious Conversion & Mi...
The Mormon & Quaker Moons of Lancashire: Stories of Religious Conversion & Mi...Cometan
 
Free eBook ~Short Inspirational Stories - The Benefits.pdf
Free eBook ~Short Inspirational Stories - The Benefits.pdfFree eBook ~Short Inspirational Stories - The Benefits.pdf
Free eBook ~Short Inspirational Stories - The Benefits.pdfOH TEIK BIN
 
Easter Apocalypse_Palm Sunday_Rev. 7.pptx
Easter Apocalypse_Palm Sunday_Rev. 7.pptxEaster Apocalypse_Palm Sunday_Rev. 7.pptx
Easter Apocalypse_Palm Sunday_Rev. 7.pptxStephen Palm
 
Old Age but fruitful and meaningful.pptx
Old Age but fruitful and meaningful.pptxOld Age but fruitful and meaningful.pptx
Old Age but fruitful and meaningful.pptxInnovator Marbun
 
All About Zakah for Muslim Americans - Dr. Main Alqudah [https://www.guidance...
All About Zakah for Muslim Americans - Dr. Main Alqudah [https://www.guidance...All About Zakah for Muslim Americans - Dr. Main Alqudah [https://www.guidance...
All About Zakah for Muslim Americans - Dr. Main Alqudah [https://www.guidance...iqra tube
 
365 Days of Thanking God_ Cultivating a Heart of Thanksgiving Everyday (Revis...
365 Days of Thanking God_ Cultivating a Heart of Thanksgiving Everyday (Revis...365 Days of Thanking God_ Cultivating a Heart of Thanksgiving Everyday (Revis...
365 Days of Thanking God_ Cultivating a Heart of Thanksgiving Everyday (Revis...Eizijesu Obahaiye
 
Islamic Finance 101 - Dr. Main Alqudah [https://www.guidancecollege.org/]
Islamic Finance 101 - Dr. Main Alqudah [https://www.guidancecollege.org/]Islamic Finance 101 - Dr. Main Alqudah [https://www.guidancecollege.org/]
Islamic Finance 101 - Dr. Main Alqudah [https://www.guidancecollege.org/]iqra tube
 
DP & Jesus Marriage - 2 potential candidates
DP & Jesus Marriage - 2 potential candidatesDP & Jesus Marriage - 2 potential candidates
DP & Jesus Marriage - 2 potential candidatesBengt & Maarit de Paulis
 

Último (13)

The_Chronological_Life_of_Christ_Part_93_Fear God
The_Chronological_Life_of_Christ_Part_93_Fear GodThe_Chronological_Life_of_Christ_Part_93_Fear God
The_Chronological_Life_of_Christ_Part_93_Fear God
 
Deerfoot Church of Christ Bulletin 3 24 24
Deerfoot Church of Christ Bulletin 3 24 24Deerfoot Church of Christ Bulletin 3 24 24
Deerfoot Church of Christ Bulletin 3 24 24
 
DP & Nostradamus-Fatima-Bailey-Branham-Ford - Short vers
DP & Nostradamus-Fatima-Bailey-Branham-Ford - Short versDP & Nostradamus-Fatima-Bailey-Branham-Ford - Short vers
DP & Nostradamus-Fatima-Bailey-Branham-Ford - Short vers
 
SERPENT COIL: THE AWAKENING OF KUNDALINI
SERPENT COIL: THE AWAKENING OF KUNDALINISERPENT COIL: THE AWAKENING OF KUNDALINI
SERPENT COIL: THE AWAKENING OF KUNDALINI
 
The Mormon & Quaker Moons of Lancashire: Stories of Religious Conversion & Mi...
The Mormon & Quaker Moons of Lancashire: Stories of Religious Conversion & Mi...The Mormon & Quaker Moons of Lancashire: Stories of Religious Conversion & Mi...
The Mormon & Quaker Moons of Lancashire: Stories of Religious Conversion & Mi...
 
Free eBook ~Short Inspirational Stories - The Benefits.pdf
Free eBook ~Short Inspirational Stories - The Benefits.pdfFree eBook ~Short Inspirational Stories - The Benefits.pdf
Free eBook ~Short Inspirational Stories - The Benefits.pdf
 
Easter Apocalypse_Palm Sunday_Rev. 7.pptx
Easter Apocalypse_Palm Sunday_Rev. 7.pptxEaster Apocalypse_Palm Sunday_Rev. 7.pptx
Easter Apocalypse_Palm Sunday_Rev. 7.pptx
 
Old Age but fruitful and meaningful.pptx
Old Age but fruitful and meaningful.pptxOld Age but fruitful and meaningful.pptx
Old Age but fruitful and meaningful.pptx
 
All About Zakah for Muslim Americans - Dr. Main Alqudah [https://www.guidance...
All About Zakah for Muslim Americans - Dr. Main Alqudah [https://www.guidance...All About Zakah for Muslim Americans - Dr. Main Alqudah [https://www.guidance...
All About Zakah for Muslim Americans - Dr. Main Alqudah [https://www.guidance...
 
365 Days of Thanking God_ Cultivating a Heart of Thanksgiving Everyday (Revis...
365 Days of Thanking God_ Cultivating a Heart of Thanksgiving Everyday (Revis...365 Days of Thanking God_ Cultivating a Heart of Thanksgiving Everyday (Revis...
365 Days of Thanking God_ Cultivating a Heart of Thanksgiving Everyday (Revis...
 
Islamic Finance 101 - Dr. Main Alqudah [https://www.guidancecollege.org/]
Islamic Finance 101 - Dr. Main Alqudah [https://www.guidancecollege.org/]Islamic Finance 101 - Dr. Main Alqudah [https://www.guidancecollege.org/]
Islamic Finance 101 - Dr. Main Alqudah [https://www.guidancecollege.org/]
 
DP & Jesus Marriage - 2 potential candidates
DP & Jesus Marriage - 2 potential candidatesDP & Jesus Marriage - 2 potential candidates
DP & Jesus Marriage - 2 potential candidates
 
English - The 1st Book of Adam and Eve.pdf
English - The 1st Book of Adam and Eve.pdfEnglish - The 1st Book of Adam and Eve.pdf
English - The 1st Book of Adam and Eve.pdf
 

demand forecasting

  • 2. What is forecasting all about? Demand for Mercedes E We try to predict the Class future by looking back at the past Predicted demand looking Time back six Ja Fe Mar Apr May Jun Jul Aug months n b Actual demand (past sales) Predicted demand
  • 3. WHY DEMAND FORECASTING • Planning and scheduling production • Acquiring inputs • Making provisions for finances • Formulating pricing strategy • Planning Advertisement
  • 4. OBJECTIVES Short –term Forecasting To evolve a suitable production policy To reduce the cost of purchase To determine appropriate price policy To set sales targets and establish control To forecast short-term financial requirements
  • 5. OBJECTIVES Long –term Forecasting  Planning of a new unit or expansion of an existing unit  Planning of long-term financial requirements  Planning of man-power requirements
  • 6. Levels of Forecasting • Macro level • Industry level • Micro level
  • 7. STEPS IN DEMAND FORECASTING • Determination of the objectives • Sub-dividing the task • Identifying of demand determinants • Selection of the method • Collection of Data • Estimation and interpretation of result • Reporting
  • 8. METHODS OF DEMAND FORECASTING QUALITATIVE QUANTITATIVE TECHNIQUES TECHNIQUES  Survey Method  Barometric Techniques  Direct Interview Method  Time Series Analysis  Collective opinion  Regression Method  Delphi Method  Controlled Experiments
  • 9. SURVEY METHOD • Surveys are conducted to collect information about the future plans of the potential consumers • A firm may launch a new product, if the suvey indicates that there is a demand for that particular product in the market.
  • 10. Direct Interview Method Consumers are contacted directly to ask them what they intend to buy in future Collective opinion The opinions of those who have the feel of the market, like salesman, professional experts, market consultants etc. Advantages •Simple •Quick •Low cost •Reliable Disadvantages Personal judgements may go wrong Useful only foe short-term forecasting
  • 11. DELPHI METHOD • Applied to uncertain areas where past data or future data are not of much use • Some expert in an area will be contacted with questionnaires • A co-ordinator collect all the opinions • Each expert will be supplied with responses of other experts without revealing their identity • Expert may revise his opinion, if needed • Process will be repeated so that all experts come to an agreement
  • 12. CONTROLLED EXPERIMENTS • Studies and experiments in consumers behavior are carried out under actual market conditions • Three or four cities having similarity in population , income level, cultural and social background ,occupational distribution , taste etc.. are chosen • Various demand determinants like price, advertisement , expenditure etc are changed one by one and these changes on demand are observed
  • 13. QUANTITATIVE TECHNIQUES BASED ON DATA AND ANALYTICAL TECHNIQUES Barometric Techniques Time series Analysis Regression Method
  • 14. BAROMETRIC FORECASTING  based on the observed relationships between different economic indicators It can be divided into three groups  Leading indicators  Coincident indicators  Lagging indicators
  • 15. Leading Indicators • which run in advance of changes in demand for a particular product •an increase in the number of building permits granted which would lead to an increase in demand for building-related products such as wood, concrete and so on Coincident Indicators •occur alongside changes in demand •an increase in sales would generate an increase in demand for the manufacturers of the goods concerned Lagging Indicators •run behind changes in demand New industrial investment by firms which will only invest in new production facilities when demand is already firmly established.
  • 16. TIME SERIES ANALYSIS • Used to predict the future demand for a product based on the past sales and demand.  Simple moving average  Weighted moving average  Exponential smoothing
  • 17. Time series: simple moving average In the simple moving average models the forecast value is At + At-1 + … + At-n Ft+1 = n t is the current period. Ft+1 is the forecast for next period n is the forecasting horizon (how far back we look), A is the actual sales figure from each period.
  • 18. Example: forecasting sales at Kroger Kroger sells (among other stuff) bottled spring water Month Bottles Jan 1,325 Feb 1,353 What will Mar 1,305 the sales Apr 1,275 be for May 1,210 July? Jun 1,195 Jul ?
  • 19. What if we use a 3-month simple moving average? AJun + AMay + AApr FJul = = 1,227 3 What if we use a 5-month simple moving average? AJun + AMay + AApr + AMar + AFeb FJul = = 1,268 5
  • 20. Time series: weighted moving average We may want to give more importance to some of the data… Ft+1 = wt At + wt-1 At-1 + … + wt-n At-n wt + wt-1 + … + wt-n = 1 t is the current period. Ft+1 is the forecast for next period n is the forecasting horizon (how far back we look), A is the actual sales figure from each period. w is the importance (weight) we give to each period
  • 21. Time Series: Exponential Smoothing (ES) Main idea: The prediction of the future depends mostly on the most recent observation, and on the error for the latest forecast. Smoothi ng constan Denotes the t alpha importance of the past α error
  • 22. Exponential smoothing: the method Assume that we are currently in period t. We calculated the forecast for the last period (Ft-1) and we know the actual demand last period (At-1) … Ft = Ft −1 + α ( At −1 − Ft −1 ) The smoothing constant α expresses how much our forecast will react to observed differences… If α is low: there is little reaction to differences. If α is high: there is a lot of reaction to differences.
  • 23. Linear regression in forecasting Linear regression is based on 1. Fitting a straight line to data 2. Explaining the change in one variable through changes in other variables. dependent variable = a + b × (independent variable) By using linear regression, we are trying to explore which independent variables affect the dependent variable
  • 24. Linear Regression Model • Shows linear relationship between dependent & explanatory variables – Example: Diapers & # Babies (not time) Y-intercept Slope ^ Yi = a + b X i Dependent Independent (explanatory) (response) variable variable
  • 25. Example: do people drink more when it’s cold? Alcohol Sales Which line best fits the data? Average Monthly Temperature
  • 26. The best line is the one that minimizes the error The predicted line is … Y = a + bX So, the error is … εi = y i - Yi Where: ε is the error y is the observed value Y is the predicted value
  • 27. Conclusion • Accurate demand forecasting requires – Product knowledge – Knowledge about the customer – Knowledge about the environment