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TM 745 Forecasting for Business

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TM 745 Forecasting for Business & Technology. Dr. Frank Joseph Matejcik ... Air Travel Forecast. 1) Judgmental (Expert survey) 2) Extrapolation (time series) ... – PowerPoint PPT presentation

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Title: TM 745 Forecasting for Business


1
TM 745 Forecasting for Business TechnologyDr.
Frank Joseph Matejcik
7th Session 6/28/07 Chapter 8 Combining
Forecast Results Chapter 9 Forecast
Implementation
  • South Dakota School of Mines and Technology,
    Rapid City

2
Tentative Schedule
Chapters Assigned 17-May 1 e-mail,
contact problems 1,4, 8 24-May 2
problems 4, 8, 9 31-May 3,4 problems
ch3(1,5,8,11) ch4(6,10) 07-June 5 problems
5,8 14-June 6 start Test (Covering chapters 1-4)
Study Guide is on the class website. problems 4,
7 21-June 6 finish, 7 problems 3, 4, 5(series A
only), and 7B 28-June 8, 9 problem 6 in chapter
8 05-July Final Traveling in July and
August Dates on board
3
Web Resources
  • Class Web site on the HPCnet system
  • http//sdmines.sdsmt.edu/sdsmt/directory/courses/2
    007su/tm745001
  • Streaming video http//its.sdsmt.edu/Distance/
  • Answers at http//www.hpcnet.org/what63
  • The same class session that is on the DVD is on
    the stream in lower quality. http//www.flashget.c
    om/ will allow you to capture the stream more
    readily and review the lecture, anywhere you can
    get your computer to run.

4
Agenda New Assignment
  • Chapter 8 problem 6, Chapter 9 no problems
  • Final is next week
  • Study guide is posted
  • Chapter 8 Combining Forecast Results
  • Chapter 9 Forecast Implementation

5
Combining Forecast Results
  • Intro
  • Bias
  • Ex. What can be combined?
  • How to get the weights?
  • Three techniques
  • Delfield
  • About ForecastX comnbining

6
Introduction
  • 83 of experts believe that combining forecasts
    will produce more accurate forecasts than
    originals Collopy Armstrong (1992)
  • Bates Granger (1969) 1st idea
  • Why the best forecast may not be
  • 1) Some variables may be missing
  • 2) Discarded forecast may use a type of relation
    ship ignored in the best forecast

7
Bias
  • Unbiased here not used strictly as in Statistics
  • Statistics term unbiased
  • 1) a strong property of a statistics
  • 2) excludes reasonable statistics
  • Forecasters believes may influence forecasts
  • Try to ignore preconceived ideas
  • Fresh employees may help

8
An Example
  • Output indexes of Gas, Electricity, Water
  • Linear Fit

9
An Example Exponential fit
  • Uses a transform to fit it

10
An Example Combined fit
  • Combined Improvements
  • 1) Optimal weights yield considerable
    improvements
  • 2) combined forecasts statement 2)page 395 is
    not quite correct. It happens. See table 8-1
    (Elmo)

11
What Forecasts Are Combined?
  • Actual Practice try very different models
  • 1) Extract different predictive factors
  • a) transforms
  • b) model format
  • 2) Models use different variables
  • Air Travel Forecast
  • 1) Judgmental (Expert survey)
  • 2) Extrapolation (time series)
  • 3) Segmentation (Customer survey)
  • 4) Econometric (Causal regression)

12
What Forecasts Are Combined?
13
Choosing Weights for Combined Forecasts
  • Armstrong likes equal weights (ex ante)
  • MAPEs reduced 6.6
  • Better if gt2 forecasts
  • Bates Granger weight more accurate more heavily
  • In general combined forecast have smaller errors
    (exs bw)
  • Book suggests weight more accurate more heavily

14
3 Techniques for Selecting Weights
  • 1) Minimize variances of forecasts
  • 2) Adaptive weights based on each error
  • 3) Use regression on the forecasts. (Optimal
    linear composite)

15
Minimize variances of forecasts
16
Adaptive weights based on each error
17
Optimal linear composite
18
Optimal linear composite procedure
  • Constant term is found and tested, if tested to
    be in the model dont apply it.
  • Comment b1 b2 about 1, b1, b2 gt 1
  • Comment F(1) F(2) will likely haveconstant
    terms. So?

19
Regression for Combining Household Cleaner,
application
  • Sales by Sales Force Composite
  • Sales by Winters Method

20
Regression for Combining Household Cleaner,
application
  • Run usual regression
  • Force constant to be zero
  • Improved

21
Forecasting THS with a Combined Forecast
  • Time Series Decomposition chapter 6
  • Multiple regression chapter 5THS106.31
    10.78(MR) - 0.45(ICS)
  • THS -2.540.06(THSRF)0.97(THSDF) (-1.2)
    (1.53) (31.8)
  • THS 0.03(THSRF) 0.97(THSDF)
  • RMSE combined 3.54
  • RMSE THSDF Winters 3.55

22
Forecasting THS with a Combined Forecast
23
Forecasting DCS with a Combined Forecast
24
Forecasting DCS with a Combined Forecast
25
Comment from the field
  • Delfield Company Food Service Co.
  • 4th edition table below

26
Integrative Case The Gap 4th
27
Integrative Case The Gap 4th
28
Integrative Case The Gap 4th
29
Using ForecastXTM to Combine Forecasts
  • It is just the regression that was given, so just
    remember to check the no intercept box.

30
9 Forecast Implementation
  • Keys (a list)
  • Forecast Process (steps)
  • Choosing the right forecast
  • New Product
  • Artificial Intelligence

31
Keys to Obtaining Better Forecasts
  • 1. Understand what forecasting is is not
  • Focus on management processes controls, not
    computers Establish forecasting group
  • Implement management control systems before
    selecting forecasting software
  • Derive plans from forecasts
  • Distinguish between forecasts and goals
  • Forecasting is acknowledged as a critical
  • Accuracy emphasized not game-playing

32
Keys to Obtaining Better Forecasts
  • 2. Forecast demand, plan supply
  • Dont use shipments as actual demand
  • Identify sources of demand information
  • Build systems to capture key demand data
  • Get improved customer service capital planning

33
Keys to Obtaining Better Forecasts
  • 3. Communicate, cooperate, collaborate
  • Avoids duplication Mistrust of "official
    forecast
  • Creates understanding of impact throughout
  • Establish a cross-functional approach to
    forecasting

34
Keys to Obtaining Better Forecasts
  • 3. Communicate, cooperate, collaborate
  • Establish an independent forecast group that
    sponsors cross-functional collaboration
  • All relevant information used to generate
    forecasts
  • Forecasts trusted by users
  • More accurate relevant forecasts

35
Keys to Obtaining Better Forecasts
  • 4. Eliminate islands of analysis
  • Mistrust inadequate information leading
    different users to create their own forecasts
  • Build 1 "forecasting infrastructure"
  • More accurate, relevant, credible forecasts
  • Provide training for both users developers of
    forecasts
  • Optimized investments in information
    communication systems

36
Keys to Obtaining Better Forecasts
  • 5. Use tools wisely
  • Relying solely on qualitative or quantitative
  • Integrate quantitative qualitative methods
  • Identify sources of improved accuracy increased
    error
  • Provide instruction
  • Process improvement in efficiency effectiveness

37
Keys to Obtaining Better Forecasts
  • 6. Make it important
  • Have accountability for poor forecasts
  • So developers can understand forecast uses
  • Training developers to understand implications of
    poor forecasts
  • Include forecast performance in performance
    plans reward systems
  • Striving for accuracy credibility

38
Keys to Obtaining Better Forecasts
  • 7. Measure, measure, measure
  • Know if the firm is getting better
  • Measure accuracy at relevant levels of
    aggregation
  • Ability to isolate sources of forecast error
  • Establish multidimensional metrics
  • Incorporate multilevel measures
  • Measure accuracy whenever wherever forecasts
    are adjusted

39
Keys to Obtaining Better Forecasts
  • 7. Measure, measure, measure
  • Forecast performance can be included in
    individual performance plans
  • Sources of errors can be isolated and targeted
    for improvement
  • Achieve greater confidence in forecast process

40
The Forecast Process
  • 1. Specify objectives
  • Articulate role of forecast in decisions
  • If forecasts dont effect decisions, Why?
  • 2. Determine what to forecast
  • Sales revenue or units?
  • weekly, annually, quarterly?
  • Communicate with user

41
The Forecast Process
  • 3. Identify time dimensions
  • Horizon
  • Frequency
  • Urgency
  • 4. Data considerations
  • Internal needs database management
    disaggregation time, unit, region
  • External govt, trade association

42
The Forecast Process
  • 5. Model selection (next section)
  • 6. Model evaluation
  • Less important for subjective methods
  • Use holdout method if quantitative
  • Go back to step five if a problem
  • 7. Forecast preparation
  • Try for multiple multiple types

43
The Forecast Process
  • 8. Forecast presentation
  • Management must understand be confident
    (corporate culture)
  • Oral written
  • same time same level
  • be generous with charts etc.
  • 9. Tracking results
  • process continues
  • reviews open, objective, positive

44
Choosing the Right Forecasting Techniques
  • Few hard and fast rules (guidelines)
  • Focus on data, time, personnel
  • Subjective Methods
  • Sales force composite
  • short to medium term
  • Preparation time is quick once set up
  • Customer surveys
  • medium to long term, take 2-3 months
  • survey research is a profession

45
Choosing the Right Forecasting Techniques
  • Subjective Methods
  • Jury of Executive Opinion
  • Requires Expertise
  • Is relatively quick to prepare
  • Delphi
  • long to medium term
  • useful for new products
  • can be slow computers help
  • alternatives are better

46
Choosing the Right Forecasting Techniques
  • Objective Methods
  • Naive (little data, sometimes good)
  • Moving Averages (easy, little data)
  • Exponential Smoothing Simple
  • Need to establish weight
  • Easy to compute, quick
  • Adaptive response ES
  • short term, no seasonality
  • Users need little background

47
Choosing the Right Forecasting Techniques
  • Objective Methods
  • Holt's ES
  • short term, no seasonality, trend included
  • Users need little background
  • Winters ES
  • short term, seasonality, trend included
  • Need 4 or 5 observations per season
  • Need computer for updates
  • Users need little background (tell them about
    weighted dates)

48
Choosing the Right Forecasting Techniques
  • Objective Methods
  • Regression-Based
  • Trend (10 observations, quick to develop, easy
    for users, modest developer skills)
  • Trend with Seasonality (Need 4 or 5 observations
    per season, short to medium term, need a
    computer, usually little sophistication)
  • Causal (10 observations per independent
    variable, short, medium, or long term,
    developers need regression skills.)

49
Choosing the Right Forecasting Techniques
  • Objective Methods
  • Time-Series Decomposition (two peaks and two
    troughs per cycle, 4 to 5 seasons of data, can
    use turning points, short to medium range, modest
    sophistication, managers like it.)
  • ARIMA (managers dont like it, it takes a
    skillful developer, Need a computer to do ACF
    and PACF plots)

50
New Product Forecasting
  • Product Life Cycle (PLC) curve

51
New Product Forecasting
  • Analog forecasts
  • Similar products
  • Think Christmas movie toys
  • Test marketing
  • Pick a smaller representative place
  • Ex. given is Indianapolis
  • Product clinics (panel lab study)
  • Type of Product Affects NPF

52
Artificial Intelligence and Forecasting
  • Expert systems
  • Neural Networks

Summary
  • Difficult task many considerations
  • New opportunities

53
Using ProCastTM in ForecastXTM to Make Forecasts
  • It is okay now that you know what you are doing.
  • You understand that a selection method is
    choosing the best of things that you already know.
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