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Business Forecasting


Business Forecasting Used to try to predict the future Uses two main methods: Qualitative seeking opinions on which to base decision making Consumer panels, focus ... – PowerPoint PPT presentation

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Title: Business Forecasting

Business Forecasting
  • Used to try to predict the future
  • Uses two main methods
  • Qualitative seeking opinions on which to base
    decision making
  • Consumer panels, focus groups, etc
  • Quantitative using statistical data to help
    inform decision making
  • Identifying trends
  • Moving averages seasonal, cyclical, random
  • Extrapolation - simple

Types of Forecasts
  • Qualitative (Judgmental)
  • Quantitative
  • Time Series Analysis
  • Causal Relationships
  • Simulation

Forecasting Horizons
  • Long Term
  • 5 years into the future
  • RD, plant location, product planning
  • Principally judgement-based
  • Medium Term
  • 1 season to 2 years
  • Aggregate planning, capacity planning, sales
  • Mixture of quantitative methods and judgement
  • Short Term
  • 1 day to 1 year, less than 1 season
  • Demand forecasting, staffing levels, purchasing,
    inventory levels
  • Quantitative methods

Types of Forecasting Models
  • Types of Forecasts
  • Qualitative --- based on experience, judgment,
  • Quantitative --- based on data, statistics
  • Methods of Forecasting
  • Formal Methods --- systematically reduce
    forecasting errors
  • time series models
  • Moving Average
  • Exponential smoothing
  • Least square method
  • causal models (e.g. regression).
  • Focus here on Time Series Models Assumptions
    of Time Series Models
  • There is information about the past
  • This information can be quantified in the form of
  • The pattern of the past will continue into the

Forecasting Examples
  • Examples from student projects
  • Demand for tellers in a bank
  • Traffic on major communication switch
  • Demand for liquor in bar
  • Demand for frozen foods in local grocery
  • Example from Industry
  • Production
  • Sales
  • Profit
  • Loss

Analysis of time series
  • Time Series Data Data collected on the same
    element for the same variable at different points
    in time or for different periods of time are
    called time-series data. E.g. daily stock price
  • A data arranged in chronological order is called
    a time series.
  • A set of observation of a variable collected at
    regular interval of time is called Time series.

Time series
  • Time Series A time series process is an ordered
    sequence of values of a variable at equally
    spaced time intervals, and usually represented
    by e.g. daily, weekly, monthly, or
    annually) and can take the form of, say, Gross
    Domestic Product each quarter monthly
    profitsannual rainfall or daily Stock Market

Objective of time series
  • Analyze the past present behaviour of the
    series and predict for future.
  • i.e Identify the pattern and isolate the
    influencing factors for prediction purposes as
    well as for future planning and control.
  • Evaluation of progress made on the basis of a
  • are done on the basis of time series data.
  • Ex-The progress of our five year plans is judged
    by the annual growth rate.

Goal of Time series
  • 1. Describe the data using summary statistics or
    graph methods. A time series plot is particularly
  • 2. Modeling to find a suitable statistical model
    to describe the data.
  • 3. Forecasting that is to predict the future
    values of the time series variable.
  • 4. Control that is to control a given process.
    Note good forecast enable the analyst to take

Specification of a Forecasting Model
  • The process of specifying a forecasting model
  • 1. selecting the variable to be included,
  • 2. selecting the form of the equation of
  • 3. estimating the values of the parameters in
    that equation,
  • 4. verifying its performance characteristics by
    comparison of its
  • forecast with historical data.

The Need to Forecast
  • When the result of an action is of consequences
    but cannot be known in advance with precision,
    forecasting may reduce decision risk by supplying
    additional information about the possible
    outcome. The potential benefits of forecasting
    lies in the realm of decision making to exert
    control over someprocess.

Applications of Time Series Analysis
  • Economic forecasting
  • Sales forecasting
  • Budgetary analysis
  • Stock market analysis
  • Census analysis

Component of time series
  • Trend
  • Cyclic
  • Seasonal
  • Irregular

  • Trend is a long term movement in a time series.
    It is the underlying direction(an upward or
    downward tendency) and rate of change in a time
    series, when allowance has been made for the
    other components. A simple way of detecting trend
    in seasonal data is to take averages over a
    certain period and if these averages change with
    time we can say that there is evidence of a trend
    in the series, indicating that the population
    mean is time dependent.

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Excel in Action
  • The linear component

  • Seasonality is the component of variation in a
    time series which is dependent on the time of
    year. It describes any regular fluctuations with
    a period of less than one year. For example, the
    costs of various types of fruits and vegetables,
    unemployment figures and average daily rainfall,
    all show marked seasonal variation.

The Seasonal Component
Other irregular/Random Noise
  • After trend and cycle variations have been
    removed from a data set, we are left with a
    series of residuals (error). Random variation
    usually makes the pattern difficult to identify
    in a series. Most time series techniques involve
    removing noise in order to make the pattern

The Random Component
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  • Cycle refers to patterns, or waves, in the data
    that are repeated after approximately equal
    intervals with approximately equal intensity. For
    e.g. some economists believe that business
    cycle repeat themselves every four
  • or five years. In weekly or monthly data, the
    cyclical component describes any regular

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Multiplicative Model
  • The actual value of a time series, represented by
    Y can be found by multiplying four component at a
    particular time period. The effect of four
    components on the time series is independent. The
    multiplicative time series model is defined as
  • y T X C X S X I

Additive model
  • It is assumed that the effect of various
    components can be estimated by adding the various
    components of a time series
  • Y T C S I

Component of time series data
Measurement of Trend
  • (a) Freehand drawing
  • (b) Semi averages
  • (c) Least square regression
  • (d) Moving average
  • (e) Exponential smoothing

Freehand Drawing
  • Freehand drawing is a quick and simple method to
    measure trend. It involves first plotting the
    data and joining the successive points with
  • smooth curve. Looking at the graph closely will
    enable you to get a feel for the direction of
    trend and thereby drawing a line that best
    reflects this
  • trend. There is also no reason why the freehand
    drawing should be a straight line, although it
    may be easier to achieve.

Method of least square
When n odd number i.e. we shift the origin to
the middle time period. When n even number i.e.
we shift the origin to the mean of two middle
time Periods.
Linear Time-Series Forecasting Model
Relationship between response variable Y time X
is a linear function
The Quadratic Trend Model
The Exponential Trend Model