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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