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Forecasting
What is Forecasting?
Process of predicting a future event and it is a mere guess.
It is the estimating the future demand for products and services are commonly referred as a sales forecast
Underlying basis of all business decisions: Production Inventory Personnel Facilities
NEED OF DEMAND FORECASTING
New facility planningProduction planningWorkforce schedulingFinancial planning
Short-range forecastUp to 1 year (usually less than 3 months)Job scheduling, worker assignments, plan for
purchasingMedium-range forecast
3 months to 3 yearsSales & production planning, budgeting
Long-range forecast3 years, or moreNew product planning, facility location
Forecasts by Time Horizon
Types of Forecasts
Economic forecastsAddress the future business conditions
(e.g., inflation rate, money supply, etc.)Technological forecasts
Predict the rate of technological progressPredict acceptance of new products
Demand forecastsPredict sales of existing products
Features of demand forecasting
It generally assume the same underlying reasons
Forecasts are rarely perfectForecast for group items will be more
perfect than the individual itemsForecast accuracy decreases as the
time period covered by the forecast
Seven Steps in Forecasting
Determine the purpose of the forecastSelect the items to be forecastedDetermine the time horizon of the forecastSelect the forecasting model(s)Gather the dataMake the forecastValidate and implement results
Objectives of demand forecasting
Short range objectives• Formulation of production strategy and
policy• Formulation of pricing policies• Planning and control of sales• Financial planning
Objectives of demand forecasting
Medium or Long range objectives• Long range planning for production
capacity• Labour requirements• Restructuring the capital structure
Forecasting Approaches
Used when situation is stable & historical data existExisting productsCurrent technology
Involves mathematical techniquese.g., forecasting sales of
color televisions
Quantitative Methods Used when situation is
vague & little data existNew productsNew technology
Involves intuition, experiencee.g., forecasting sales on
Internet
Qualitative Methods
Qualitative MethodsJury of executive opinion
Pool opinions of high-level executives, sometimes augment by statistical models
Delphi method or judge mental method Panel of experts, queried iteratively
Sales force composite Estimates from individual salespersons are
reviewed for reasonableness, then aggregated Consumer (Market research) Survey
Ask the customer
Quantitative Approaches
Time series model(Trend, Seasonality, Cycles)
Naive approachMoving averageExponential smoothingCasual modelsTrend projectionLinear regression analysis
Set of evenly spaced numerical data Obtained by observing response variable at
regular time periods Forecast based only on past values
Assumes that factors influencing past and present will continue influence in future
ExampleYear: 19981999200020012002Sales: 78.763.589.7 93.292.1
Time Series Models
TrendTrend
SeasonalSeasonal
CycleCycle
RandomRandom
Time Series Components
Persistent, overall upward or downward pattern
Due to population, technology etc.Several years duration
Trend Component
Regular pattern of up & down fluctuations
Due to weather, customs, etc.Occurs within 1 year
Seasonal Component
Repeating up & down movementsDue to interactions of factors influencing
economyCan be anywhere between 2-30+ years
duration
Cyclical Component
Erratic, unsystematic, ‘residual’ fluctuationsDue to random variation or unforeseen events
Union strike Tornado
Short duration & non-repeating
Random Component
1.Naive Approach
Assumes demand in next period is equal to the actual demand in most recent period e.g., If May sales were 48, then June sales
will be 48Sometimes cost effective & efficient
Moving average uses a number of most recent historical actual data values to generate a forecast.
MA is a series of arithmetic means Used if little or no trend Used often for smoothing
Provides overall impression of data over time Equation:
MAMAnn
nn Demand inDemand in PreviousPrevious PeriodsPeriods
2.Moving Average Method
example
Forecast demand for 4 monthsd1+d2+d3 *4
3
3.Exponential Smoothing Method
• It requires only three items of data this periods forecast, the actual demand for this period and α which is referred to as a smoothing constant and having value between 0 and 1
• Next period’s forecast = This period forecast + α{this period’s actual dd – this periods forecast}
Ft = Ft-1 + (At-1 - Ft-1)
Ft = forecast for this period
Ft-1 = forecast for the previous period
At-1= Actual demand for the previous period
Smoothing constant (0 to 1)
Exponential Smoothing Equations
Used for forecasting linear trend lineAssumes relationship between
response variable, Y, and time, X, is a linear function
Estimated by least squares methodMinimizes sum of squared errors
iY a bX i
Linear Trend Projection
Answers: ‘how strong is the linear relationship between the variables?’
Coefficient of correlation Sample correlation coefficient denoted rRange: -1 < r < 1Measures degree of association
Used mainly for understanding
Correlation
Linear regression analysis
The demand or sales forecast is a dependent variable and other factors are independent variables
Factors to be considered in the selection of forecasting method
Cost and accuracyData availableTime spanNature of products and servicesImpulse response and noise dampening
You want to achieve:No pattern or direction in forecast error
Error = (Yi - Yi) = (Actual - Forecast)Seen in plots of errors over time
Smallest forecast errorMean Absolute Deviation (MAD), or Mean
Absolute Percentage Error (MAPE)Mean Squared Error (MSE)
Selecting a Forecasting Model
^
Which Model Is “Best” So Far?
The Naïve model has both the lowest MAD (1.91) and MSE (4.45) of the first five models tested
Therefore, the Naïve model is the “best”However, it may be that one model has
the lowest MAD or MAPE and another model has the lowest MSE…
So Which Model Do You Choose?
If you only require the forecast with the smallest average deviation, choose the model with the smallest MAD or MAPE
However, if you have a low tolerance for large deviations choose the model with the smallest MSE