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Forecasting

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Title: Forecasting


1
Forecasting
2
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.

3
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.

4
  • 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.

5
Forecasts
  • Forecasts affect decisions and activities
    throughout an organization
  • Accounting, finance
  • Human resources
  • Marketing
  • MIS
  • Operations
  • Product / service design

6
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
7
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

8
Elements of a Good Forecast
9
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)
10
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

11
MAD, MSE, and MAPE
?
?
Actual
forecast
MAD


n
12
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)

13
Example 1
14
Ans Example 1
15
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
16
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)

17
Quantitative method
  • Naïve approach
  • Moving average
  • Exponential smoothing
  • Trend projection
  • Linear regression

Time series models
Associative model
18
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

19
Forecast Variations
Year 1
Year 2
Year 3
Seasonal variations
Month
20
Naive Forecasts
The forecast for any period equals the previous
periods actual value.
21
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

22
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))

23
Techniques for Averaging
  • Moving average
  • Weighted moving average
  • Exponential smoothing

24
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.

25
Simple Moving Average
Actual
MA5
MA3
26
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.

27
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

28
Example 3 - Exponential Smoothing
29
Picking a Smoothing Constant
30
Example 3 - Exponential Smoothing
31
Common Nonlinear Trends
Figure 3.5
32
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

33
Calculating a and b
34
Linear Trend Equation Example
35
Linear Trend Calculation
36
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.

37
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

38
Linear Model Seems Reasonable
A straight line is fitted to a set of sample
points.
39
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

40
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

41
Sources of Forecast errors
  • Model may be inadequate
  • Irregular variations
  • Incorrect use of forecasting technique

42
Tracking Signal
  • Tracking signal
  • Ratio of cumulative error to MAD

Bias Persistent tendency for forecasts to
be Greater or less than actual values.
43
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

44
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

45
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)

46
Exponential Smoothing
47
Linear Trend Equation
48
Simple Linear Regression
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