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# Chapter 3 - PowerPoint PPT Presentation

Chapter 3 . Forecasting. Chapter 3: Learning Objectives. You should be able to: 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

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### Chapter 3

Forecasting

Instructor Slides

• You should be able to:

• 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

• Know definition of time-series behaviors.

• Describe averaging techniques (moving average, weighted moving average, and exponential smoothing) and linear regression analysis, and solve typical problems

• Explain and calculate three measures of forecast accuracy (MAD, MSE, MAPE)

• Assess the major factors and trade-offs to consider when choosing a forecasting technique

3-2

• 2. Techniques for seasonality (knowing definition of seasonality is enough)

• 3. Techniques for cycles (knowing definition of cycles is enough)

• 4. For linear regression calculation (a and b, correlation), formula will be provided in exam (if there’re questions), but you need to memorize three accuracy measures, and three averaging technique formula.

• 5. Monitoring the forecast (just ignore this)

Instructor Slides

• Forecast – a statement about the future value of a variable of interest

• We make forecasts about such things as weather, demand, and resource availability

• Forecasts are an important element in making informed decisions

3-4

Instructor Slides

• Expected level of demand (or other variable)

• The level of demand may be a function of some structural variation such as trend or seasonal variation

• Accuracy

• Related to the potential size of forecast error

3-5

Instructor Slides

Techniques assume some underlying causal system that existed in the past will persist into the future

Forecasts are not perfect

Forecasts for groups of items are more accurate than those for individual items (cancelling effect)

Forecast accuracy decreases as the forecasting horizon increases

Features Common to All Forecasts

3-6

Instructor Slides

The forecast in the past will persist into the future

should be timely

should be accurate

should be reliable

should be expressed in meaningful units

should be in writing

technique should be simple to understand and use

should be cost effective

Elements of a Good Forecast

3-7

Instructor Slides

Determine the purpose of the forecast in the past will persist into the future

Establish a time horizon

Obtain, clean, and analyze appropriate data

Select a forecasting technique

Make the forecast

Monitor the forecast

Steps in the Forecasting Process

3-8

Instructor Slides

Forecast Accuracy and Control in the past will persist into the future

• Forecasters want to minimize forecast errors

• It is nearly impossible to correctly forecast real-world variable values on a regular basis

• So, it is important to provide an indication of the extent to which the forecast might deviate from the value of the variable that actually occurs

• Forecast accuracy should be an important forecasting technique selection criterion

• Error = Actual – Forecast

• If errors fall beyond acceptable bounds, corrective action may be necessary

3-9

Instructor Slides

Forecast Accuracy Metrics in the past will persist into the future

MSE weights errors according to their squared values

MAPE weights errors according to relative error

Instructor Slides

3-10

Forecast Error Calculation in the past will persist into the future

Instructor Slides

3-11

Qualitative Forecasting in the past will persist into the future

Qualitative techniques permit the inclusion of soft information such as:

Human factors

Personal opinions

Hunches

These factors are difficult, or impossible, to quantify

Quantitative Forecasting

Quantitative techniques involve either the projection of historical data or the development of associative methods that attempt to use causal variables to make a forecast

These techniques rely on hard data

Forecasting Approaches

3-12

Instructor Slides

Forecasting in the past will persist into the futureApproaches (another classification)*

• Judgmental forecasts: forecast that use subjective inputs such as opinions from consumer surveys, sales staff, managers, executives, and experts.

• Time-series forecasts: forecasts that project patterns identified in recent time-series observations.

• Associative model: forecasting technique that uses explanatory variables to predict future demand.

Instructor Slides

Time-Series Forecasts in the past will persist into the future

• Forecasts that project patterns identified in recent time-series observations

• Time-series - a time-ordered sequence of observations taken at regular time intervals

• Assume that future values of the time-series can be estimated from past values of the time-series

3-14

Instructor Slides

Trend-long-term upward of downward movement in the past will persist into the future

Seasonality-short-term regular variations related to the calendar or time of day

Cycles-wavelike variations lasting more than one year

Irregular variations-caused by unusual circumstances, not reflective of typical behavior

Random variation-residual variations after all other behaviors are accounted for

Time-Series Behaviors

3-15

Instructor Slides

Time-Series Behaviors in the past will persist into the future

3-16

Instructor Slides

Naïve Forecast in the past will persist into the future

Uses a single previous value of a time series as the basis for a forecast

The forecast for a time period is equal to the previous time period’s value

Can be used with

a stable time series

seasonal variations

trend

Time-Series Forecasting - Naïve Forecast

3-17

Instructor Slides

Averaging techniques smooth variations in the data

They can handle step changes or gradual changes in the level of a series

Techniques

Moving average

Weighted moving average

Exponential smoothing

Time-Series Forecasting – Averaging (Smoothing)

3-18

Instructor Slides

Moving Average an average

• Technique that averages a number of the most recent actual values in generating a forecast

Instructor Slides

3-19

As new data become available, the forecast is updated by adding the newest value and dropping the oldest and then re-computing the average

The number of data points included in the average determines the model’s sensitivity

Fewer data points used-- more responsive

More data points used-- less responsive

Moving Average

3-20

Instructor Slides

Weighted Moving Average adding the newest value and dropping the oldest and then re-computing the average

• The most recent values in a time series are given more weight in computing a forecast

• The choice of weights, w, is somewhat arbitrary and involves some trial and error

Instructor Slides

3-21

Exponential Smoothing adding the newest value and dropping the oldest and then re-computing the average

• A weighted averaging method that is based on the previous forecast plus a percentage of the forecast error

Instructor Slides

3-22

Linear trend equation adding the newest value and dropping the oldest and then re-computing the average

Non-linear trends

Techniques for Trend

3-23

Instructor Slides

Linear Trend adding the newest value and dropping the oldest and then re-computing the average

• A simple data plot can reveal the existence and nature of a trend

• Linear trend equation

3-24

Instructor Slides

Slope and intercept can be estimated from historical data adding the newest value and dropping the oldest and then re-computing the average

Estimating slope and intercept

3-25

Instructor Slides

Seasonality – regularly repeating movements in series values that can be tied to recurring events

Expressed in terms of the amount that actual values deviate from the average value of a series

Models of seasonality

Seasonality is expressed as a quantity that gets added to or subtracted from the time-series average in order to incorporate seasonality

Multiplicative

Seasonality is expressed as a percentage of the average (or trend) amount which is then used to multiply the value of a series in order to incorporate seasonality

Techniques for Seasonality*

3-26

Instructor Slides

Models of Seasonality values that can be tied to recurring events

3-27

Instructor Slides

Seasonal Relatives values that can be tied to recurring events

• Seasonal relatives

• The seasonal percentage used in the multiplicative seasonally adjusted forecasting model

• Using seasonal relatives

• To deseasonalize data

• Done in order to get a clearer picture of the nonseasonal (e.g., trend) components of the data series

• Divide each data point by its seasonal relative

• To incorporate seasonality in a forecast

• Obtain trend estimates for desired periods using a trend equation

• Add seasonality by multiplying these trend estimates by the corresponding seasonal relative

Instructor Slides

3-28

Explanatory approach

Search for another variable that relates to, and leads, the variable of interest

Housing starts precede demand for products and services directly related to construction of new homes

If a high correlation can be established with a leading variable, an equation can be developed that describes the relationship, enabling forecasts to be made

Techniques for Cycles*

3-29

Instructor Slides

Associative techniques are based on the development of an equation that summarizes the effects of predictor variables

Predictor variables - variables that can be used to predict values of the variable of interest

Home values may be related to such factors as home and property size, location, number of bedrooms, and number of bathrooms

Associative Forecasting Techniques

3-30

Instructor Slides

Simple linear regression - the simplest form of regression that involves a linear relationship between two variables

The object of simple linear regression is to obtain an equation of a straight line that minimizes the sum of squared vertical deviations from the line (i.e., the least squares criterion)

Simple Linear Regression

3-31

Instructor Slides

Least Squares Line points

Instructor Slides

3-32

Correlation Coefficient points

• Correlation, r

• A measure of the strength and direction of relationship between two variables

• Ranges between -1.00 and +1.00

• r2, square of the correlation coefficient (only in 2 variable case)

• A measure of the percentage of variability in the values of y that is “explained” by the independent variable

• Ranges between 0 and 1.00

Instructor Slides

3-33

• Variations around the line are random

• Deviations around the average value (the line) should be (approximately) normally distributed

• Predictions are made only within the range of observed values

3-34

Instructor Slides

The data may be time-dependent.

If they are

use analysis of time series

use time as an independent variable in a multiple regression analysis

A small correlation may indicate that other variables are important

Issues to consider*

3-35

Instructor Slides

Choosing a Forecasting Technique appropriate

• Factors to consider

• Cost

• Accuracy

• Availability of historical data

• Availability of forecasting software

• Time needed to gather and analyze data and prepare a forecast

• Forecast horizon

3-36

Instructor Slides

Using Forecast Information appropriate

3-37

Instructor Slides

• Reactive approach

• View forecasts as probable future demand

• React to meet that demand

• Proactive approach

• Seeks to actively influence demand