Forecasting

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Forecasting
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Learning Objectives

• List the elements of a good forecast.
• Outline the steps in the forecasting process.
• Describe at least three qualitative forecasting
  techniques and the advantages and disadvantages
  of each.
• Compare and contrast qualitative and quantitative
  approaches to forecasting.

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Learning Objectives

• Briefly describe averaging techniques, trend and
  seasonal techniques, and regression analysis, and
  solve typical problems.
• Describe two measures of forecast accuracy.
• Describe two ways of evaluating and controlling
  forecasts.
• Identify the major factors to consider when
  choosing a forecasting technique.

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• FORECAST
• The art and science of predicting future (It may
  involve using statistics and mathematical model,
  or may be a subjective prediction).
• Forecasting is used to make informed decisions.
• Short-range (up to 1 Yr) planning purchasing,
  job scheduling, workforce levels, job assignment.
• Medium-rang (3 Mth 3 Yr) sales planning,
  production planning and budgeting.
• Long-range (more than 3 Yr) planning for new
  products, facility location or expansion, and RD.

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Forecasts

• Forecasts affect decisions and activities
  throughout an organization
• Accounting, finance
• Human resources
• Marketing
• MIS
• Operations
• Product / service design

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Uses of Forecasts
Accounting Cost/profit estimates
Finance Cash flow and funding
Human Resources Hiring/recruiting/training
Marketing Pricing, promotion, strategy
MIS IT/IS systems, services
Operations Schedules, MRP, workloads
Product/service design New products and services
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Features of Forecasts

• Assumes causal systempast gt future
• Forecasts rarely perfect because of randomness
• Forecasts more accurate forgroups cf. (compared
  to) individuals
• Forecast accuracy decreases as time horizon
  increases

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Elements of a Good Forecast
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6 Steps in the Forecasting Process
The forecast
Step 6 Monitor the forecast (modify, revise)
Step 5 Make the forecast
Step 4 Obtain, clean and analyze data (eliminate
outliers, incorrect data)
Step 3 Select a forecasting technique (Moving
AVG, Weighted AVG, etc)
Step 2 Establish a time horizon (How long?)
Step 1 Determine purpose of forecast (How/when
it will be used?, Resources)
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Forecast Accuracy

• Error - difference between actual value and
  predicted value
• Mean Absolute Deviation (MAD)
• Average absolute error
• Mean Squared Error (MSE)
• Average of squared error
• Mean Absolute Percent Error (MAPE)
• Average absolute percent error

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MAD, MSE, and MAPE
?
?
Actual
forecast
MAD


n
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MAD, MSE and MAPE

• MAD
• Easy to compute
• Weights errors linearly
• MSE
• Squares error
• More weight to large errors
• MAPE
• Puts errors in perspective (the errors are
  presented as percentage)

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Example 1
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Ans Example 1
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Types of Forecasts

• Judgmental - uses subjective inputs
• Time series - uses historical data assuming the
  future will be like the past
• Associative models - uses explanatory variables
  to predict the future

Qualitative method
Quantitative method
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Qualitative method (Judgmental
forecast)

• Executive opinions (long-range planning, new
  product development)
• Sales force opinions (direct contact with
  customers however, sales staff are overly
  influenced by recent experience)
• Consumer surveys (specific information but money
  and time-consuming)

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Quantitative method

• Naïve approach
• Moving average
• Exponential smoothing
• Trend projection
• Linear regression

Time series models
Associative model
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Time Series Forecasts

• Trend - long-term movement in data
• Seasonality - short-term regular variations in
  data
• Cycle wavelike variations of more than one
  years duration
• Random variations - caused by chance and unusual
  circumstances

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Forecast Variations
Year 1
Year 2
Year 3
Seasonal variations
Month
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Naive Forecasts
The forecast for any period equals the previous
periods actual value.
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Naïve Forecasts

• Simple to use
• Virtually no cost
• Quick and easy to prepare
• Data analysis is nonexistent
• Easily understandable
• Cannot provide high accuracy
• Can be a standard for accuracy

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Uses for Naïve Forecasts

• Stable time series data
• F(t) A(t-1)
• Seasonal variations
• F(t) A(t-n)
• Data with trends
• F(t) A(t-1) (A(t-1) A(t-2))

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Techniques for Averaging

• Moving average
• Weighted moving average
• Exponential smoothing

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Moving Averages

• Moving average A technique that averages a
  number of recent actual values, updated as new
  values become available.
• Weighted moving average More recent values in a
  series are given more weight in computing the
  forecast.

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Simple Moving Average
Actual
MA5
MA3
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Exponential Smoothing
Ft Ft-1 ?(At-1 - Ft-1)

• Premise--The most recent observations might have
  the highest predictive value.
• Therefore, we should give more weight to the more
  recent time periods when forecasting.

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Exponential Smoothing
Ft Ft-1 ?(At-1 - Ft-1)

• Weighted averaging method based on previous
  forecast plus a percentage of the forecast error
• A-F is the error term, ? is the feedback

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Example 3 - Exponential Smoothing
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Picking a Smoothing Constant
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Example 3 - Exponential Smoothing
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Common Nonlinear Trends
Figure 3.5
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Linear Trend Equation

• Ft Forecast for period t
• t Specified number of time periods
• a Value of Ft at t 0
• b Slope of the line

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Calculating a and b
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Linear Trend Equation Example
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Linear Trend Calculation
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Techniques for Seasonality

• Seasonal variations
• Regularly repeating movements in series values
  that can be tied to recurring events.
• Seasonal relative
• Percentage of average or trend
• Centered moving average
• A moving average positioned at the center of the
  data that were used to compute it.

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Associative Forecasting

• Predictor variables - used to predict values of
  variable interest
• Regression - technique for fitting a line to a
  set of points
• Least squares line - minimizes sum of squared
  deviations around the line

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Linear Model Seems Reasonable
A straight line is fitted to a set of sample
points.
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Linear Regression Assumptions

• Variations around the line are random
• Deviations around the line normally distributed
• Predictions are being made only within the range
  of observed values
• For best results
• Always plot the data to verify linearity
• Check for data being time-dependent
• Small correlation may imply that other variables
  are important

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Controlling the Forecast

• Control chart
• A visual tool for monitoring forecast errors
• Used to detect non-randomness in errors
• Forecasting errors are in control if
• All errors are within the control limits
• No patterns, such as trends or cycles, are present

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Sources of Forecast errors

• Model may be inadequate
• Irregular variations
• Incorrect use of forecasting technique

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Tracking Signal

• Tracking signal
• Ratio of cumulative error to MAD

Bias Persistent tendency for forecasts to
be Greater or less than actual values.
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Choosing a Forecasting Technique

• No single technique works in every situation
• Two most important factors
• Cost
• Accuracy
• Other factors include the availability of
• Historical data
• Computers
• Time needed to gather and analyze the data
• Forecast horizon

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Operations Strategy

• Forecasts are the basis for many decisions
• Work to improve short-term forecasts
• Accurate short-term forecasts improve
• Profits
• Lower inventory levels
• Reduce inventory shortages
• Improve customer service levels
• Enhance forecasting credibility

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Supply Chain Forecasts

• Sharing forecasts with supply can
• Improve forecast quality in the supply chain
• Lower costs
• Shorter lead times
• Gazing at the Crystal Ball (reading in text)

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Exponential Smoothing
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Linear Trend Equation
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Simple Linear Regression