forecasting methods
Transcript of forecasting methods
SECTION-C PGDM-III rd Trimester
FORECASTING METHODS
Presented By GROUP 5 :Asha Jaikishan (126)Neha Agarwal (136)Shyamashree Das (160)S.Pramoth (148)Swaroop Saha (167)
2/26/2009
Forecasting as Planning tool
Managerial decision making is complicated. Examples:
Production facility: Demand Hospital : Specialty health wing
Forecasting: branch of operations management Estimates of timing and magnitude of occurrence of future
events . Important tool in public policy decisions as well.
Functions Estimation tool Method of addressing the complex and uncertain
environment surrounding business decision making. Tool for predicting events related to operations
planning and control. Vital prerequisite for the planning process in
organizations.
Forecasting Time HorizonCriterion Short-term Medium-term Long -term
Typical duration 1-3 months 12-18 months 5-10 years
Nature of decisions Purely tactical Tactical as well as strategic
Purely strategic
Key considerations Random effects Seasonal and cyclical effect
L-T trends and business cycles
Nature of data Mostly quantitative Subjective & qualitative
Largely subjective
Degree of uncertainty
Low Significant high
Examples Revising quarterly production plans
New business development
New product introduction
Design Of Forecasting Systems
• 3 Stage Process:
Develop A Forecasting Logic By Identifying The Purpose, data And Models To Be Used.
Establish Control Mechanisms To Obtain Reliable Forecasts.
Incorporate Managerial Considerations In Using The Forecasting System.
Sources Of Data
Sales force estimatesPoint of sales data systemForecast from supply chain partnersTrade/industry association journalsB2B portals/Market placesEconomic surveys and indicatorsSubjective knowledge
TIME SERIES METHODS
MOVING AVERAGES
EXPONENTIAL SMOOTHING
TREND PROJECTION
METHODS OF FORECASTING
Time Series
It Is A Collection Of Data At Fixed Time Intervals Over Several Years.
Forecasting Time Series Implies That Predictions About The Future Values Are Made Only From Past Data.
EXAMPLE
YEAR SALES OF FIRM A (‘000 UNITS)
1987 401988 421989 471990 411991 431992 481993 651994 41
COMPENTS OF TIME SERIES
• Secular Trend• Seasonal Variation• Cyclical Movements• Random Movements
SECULAR TREND
• The General Tendency Of The Data To Grow Or Decline Over A Long Period Of Time. For Example :
YEAR SALES OF TV SETS
YEAR SALES OF TV SETS
2000 2000 2004 4000
2001 2500 2005 4567
2002 3097 2006 5000
2003 3568 2007 5500
SEASONAL VARIATIONS
Fluctuations That Occur Periodically- Movements Recurring Within A Definite Period, May Be A Every Week Or Month- With Reasonably High Degree Of Predictability.
Example ;For A Soft Drink Manufacturer, Yearly Sales May Be Increasing But Sales Are Likely To Be High Every Summer And Low Every Winter.
Cyclical movements
These are caused by business cycles or trade cycles. These movements are of more than a year.Example:Sales of a companyHigh- because of prosperity phase of business cycleLow- because of depression
RANDOM MOVEMENTS
They Are Residual Or Erratic Movements That Do Not Have Any Set Pattern And Are Usually Caused By Some Unpredictable Reasons.Example :Flood, Wars, Strikes, Earthquakes Etc
TIME SERIES MODELS OF FORECASTING
• Moving Averages
• Exponential Smoothing
• Trend Projection
MOVING AVERAGE
•It Attempts To Forecast Values On The Basis Of The Average Of The Values Of Past Few Periods.
•Successive Values Are Calculated By Considering New Value And Dropping The Old One.
SIMPLE MOVING AVERAGE METHOD
FT = DT-1 + DT-2 +……..+ DT-n nFT = Moving Average Forecast For Period TD = Actual DemandN = No. Of Periods For Moving Average
For 3 Yearly Moving Average = D1 +D2 +D3 3
SIMPLE MOVING AVERAGE EXAMPLE
MONTH DEMAND 3-MONTHLY MOVING AVERAGE
1 280 -
2 288 -
3 266 -
4 295 278
5 302 283
6 310 287.7
7 303 302.3
WEIGHTED MOVING AVERAGE
FT = DT-1 WT-1+ DT-2 WT-2+……..+ DT-n WT-n
WT-1+WT-2+ WT-3
Earlier Example : WEIGHTED MOVING AVERAGE = 266*3 + 288*2 + 280*1 = 275.5 3 + 2 + 1
EXPONENTIAL SMOOTHING
•In This Method, The Forecast For Next Period Is Calculated As Weighted Average Of All The Previous Values.•It Is Based On The Premise That The Most Recent Value Is The Most Important For Predicting Future Value.•Also It Presumes That Values Prior To Current Value Are Also Relevant But In Declining Importance As We Go Back In Time.•The Weights Decline Exponentially As We Consider The Older Values.Symbolically, FT+1 = α y T+ α(1-α)yt-1 + α(1-α)2 Y T-2 +……………..
F T+1 = FT + α( YT – FT)
CHOICE OF SMOOTH CONSTANT IS IMPORTANT
MEAN ABSOLUTE DEVIATION(MAD)= ∑ {FORECAST ERROR} N
EXPONENTIAL SMOOTHING
Calculate forecasted values and MAD using α =0.2 and 0.5 assuming initial forecast as 208
MONTH (t)
DEMAND (YT)
α = 0.2F T │YT -FT│
α = 0.5F T │YT-FT │
1 213 208 5 208 5
2 201 209 8 210.5 9.5
3 198 207.4 9.4 205.75 7.75
4 207 205.5 1.5 201.87 5.13
TOTAL 23.9 27.38
MAD 5.98 6.845
CAUSAL METHOD
It Is The Method to Construct A Forecasting Logic Through A Process Of Identifying The Factors That Cause Some Effect On The Forecast And Building A Functional Form Of The Relationship Between The Identified Factors.
Simple Regression(Trend Projection) & MultipleRegression on excel sheets
ECNOMETRIC MODEL(EM)
• Macro-economic Performance Is Predicted For A Variety Of Planning Purposes Using A Large No. Of Variables.
• With The Help Of The Relationship Between These Variables & Dependent Variable Several Predictions Are Made At The Macro-economic Level & Planning Exercises Are Undertaken.
DEMERITS
• Developing Such Causal Model Is Time Consuming & Also Very Expensive.
• Demand Specialized Skills Of Model Building & Analysis.
• Requires Use Of Powerful Computing Environment To Handle Complex & Numerous Mathematical Relationships & Regression Analysis.
INPUT- OUTPUT ANALYSIS
• It Takes Into Consideration The Interdependence Of The Different Sectors In The Economy.
• For E.G.- An Input From The Steel Sector Might Give Rise To An Output From The Electricity Sector, Which In Itself Is An Input To The Steel Sector.
MERITS
It Takes Into Account
All The Intricate Relationships In The Economy.
DEMERITS
Utility Is Restricted To Economic Analysis , Not Considering The Other Business, Government, Technological, & Internal Factors.
Limited Analysis.
END USE ANALYSIS
• It Thoroughly Considers All The Different Uses To Which A Product Will Be Put & Traces The Entire Chain Of Uses In Order To Arrive At A Forecast.
DEMERITS
• Limited Approach Since It Considers Only The Demand Side Picture & Not The Supply Side Picture.
• It Does Not Consider Explicitly The Various Other Economic Factors Influencing The Demand Of A Product.
Qualitative Models Of Forecasting
• DELPHI METHOD: It Is An Iterative Group Process And It Employs A Group Of Experts To Obtain Forecasts.
• SALES FORCE COMPOSITE: Each Of The Members Comprising Sales Force Of A Company Are Asked To Estimate The Likely Sales In Their Respective Areas.
• CONSUMER PANEL SURVEY: Here A Consumer Panel Is Maintained And Consumers On Such A Panel Are Questioned About Their Purchase Plans.
Accuracy Of Forecasts
Wrong forecasts could create several problems in the organization as forecasting forms a key input to the planning function.
Forecast Error
• Forecast error for period t, Et denotes the difference between the demand Dt and the forecast Ft for the period.
Et =Dt - Ft
• Sum of errors is merely the sum of errors during the period of consideration which is given by,
SFE=∑ Ei
...Cont
• Mean Absolute Percentage Error(MAPE) MAPE=1/n*∑|Ei|/Di *100
• Mean Squarred Error(MSE) MSE=1/n*∑ Ei
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