Operation management 10e heizer render chapter 04

World  Forecasting Time Horizons  The Influence of Product Life Cycle... Outline – ContinuedRegression and Correlation Analysis  Using Regression Analysis for Forecasting  Standard

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World

 Forecasting Time Horizons

 The Influence of Product Life Cycle

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 Supply Chain Management

System

Outline – Continued

 Overview of Qualitative Methods

 Overview of Quantitative Methods

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 Seasonal Variations in Data

 Cyclical Variations in Data

Outline – Continued

Regression and Correlation Analysis

 Using Regression Analysis for

Forecasting

 Standard Error of the Estimate

 Correlation Coefficients for

Regression Lines

 Multiple-Regression Analysis

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Learning Objectives

When you complete this chapter you

should be able to :

 Understand the three time horizons and

which models apply for each use

 Explain when to use each of the four

qualitative models

 Apply the naive, moving average,

exponential smoothing, and trend methods

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 Develop seasonal indexes

 Conduct a regression and correlation

analysis

 Use a tracking signal

Forecasting at Disney World

 Global portfolio includes parks in Hong

Kong, Paris, Tokyo, Orlando, and Anaheim

 Revenues are derived from people – how

many visitors and how they spend their money

 Daily management report contains only

the forecast and actual attendance at each park

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Forecasting at Disney World

 Disney generates daily, weekly, monthly,

annual, and 5-year forecasts

 Forecast used by labor management,

maintenance, operations, finance, and park scheduling

 Forecast used to adjust opening times,

rides, shows, staffing levels, and guests admitted

Forecasting at Disney World

 20% of customers come from outside the

USA

 Economic model includes gross

domestic product, cross-exchange rates, arrivals into the USA

 A staff of 35 analysts and 70 field people

survey 1 million park guests, employees, and travel professionals each year

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Forecasting at Disney World

 Inputs to the forecasting model include

airline specials, Federal Reserve policies, Wall Street trends,

vacation/holiday schedules for 3,000 school districts around the world

 Average forecast error for the 5-year

forecast is 5%

 Average forecast error for annual

forecasts is between 0% and 3%

What is Forecasting?

 Process of

predicting a future event

 Underlying basis of

all business decisions

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 Short-range forecast

 Up to 1 year, generally less than 3 months

 Purchasing, job scheduling, workforce levels, job assignments, production levels

Distinguishing Differences

Medium/long range Medium/long range forecasts deal with

more comprehensive issues and support management decisions regarding

planning and products, plants and processes

Short-term Short-term forecasting usually employs

different methodologies than longer-term forecasting

Short-term Short-term forecasts tend to be more

accurate than longer-term forecasts

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Influence of Product Life

Cycle

 Introduction and growth require longer

forecasts than maturity and decline

 As product passes through life cycle,

forecasts are useful in projecting

Product Life Cycle

Best period to increase market share

R&D engineering is critical

Practical to change price or quality image

Strengthen niche

Poor time to change image, price, or quality

Competitive costs become critical Defend market position

Cost control critical

Introduction Growth Maturity Decline

CD-ROMs

3 1/2”

Floppy disks LCD & plasma TVs Analog TVs

iPods

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Product Life Cycle

Product design and

development critical

Frequent product and process design changes

Short production runs

High production costs

Limited models Attention to quality

Introduction Growth Maturity Decline

Product and process reliability Competitive product improvements and options Increase capacity Shift toward

product focus Enhance

distribution

Standardization Less rapid

product changes – more minor changes

Optimum capacity Increasing stability of process Long production runs

Product improvement and cost cutting

Little product differentiation Cost

minimization Overcapacity

in the industry Prune line to eliminate items not returning good margin Reduce

capacity

Figure 2.5

Types of Forecasts

 Economic forecasts

money supply, housing starts, etc.

 Technological forecasts

 Demand forecasts

services

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Strategic Importance of

Forecasting

 Human Resources – Hiring, training,

laying off workers

 Capacity – Capacity shortages can

result in undependable delivery, loss

of customers, loss of market share

 Supply Chain Management – Good

supplier relations and price advantages

Seven Steps in Forecasting

 Determine the use of the forecast

 Select the items to be forecasted

 Determine the time horizon of the

forecast

 Select the forecasting model(s)

 Gather the data

 Make the forecast

 Validate and implement results

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The Realities!

underlying stability in the system

forecasts are more accurate than individual product forecasts

Forecasting Approaches

 Used when situation is vague Used when situation is vague

and little data exist

 New products New products

 New technology New technology

 Involves intuition, experience Involves intuition, experience

 e.g., forecasting sales on Internet e.g., forecasting sales on Internet

Qualitative Methods

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Forecasting Approaches

historical data exist

 Existing products

 Current technology

 e.g., forecasting sales of color

televisions

Quantitative Methods

Overview of Qualitative

Methods

 Pool opinions of high-level experts,

sometimes augment by statistical models

 Panel of experts, queried iteratively

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Overview of Qualitative

Methods

 Estimates from individual

salespersons are reviewed for reasonableness, then aggregated

 Ask the customer

 Involves small group of high-level experts

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Sales Force Composite

her sales

levels

Decision Makers

(Evaluate responses and make decisions)

Respondents (People who can make valuable judgments)

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Consumer Market Survey

plans

they actually do are often different

Associative

Model

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 Obtained by observing response

variable at regular time periods

no other variables important

 Assumes that factors influencing

past and present will continue influence in future

Time Series Forecasting

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Seasonal peaks

Trend component

Actual demand

Random variation

Figure 4.1

 Persistent, overall upward or

downward pattern

technology, age, culture, etc.

duration

Trend Component

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down fluctuations

Seasonal Component

Number of Period Length Seasons

 Repeating up and down movements

political, and economic factors

associative relationships

Cyclical Component

0 5 10 15 20

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 Erratic, unsystematic, ‘residual’

Naive Approach

period is the same as demand in most recent period

 e.g., If January sales were 68, then February sales will be 68

efficient

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 Provides overall impression of data over time

Moving Average Method

Moving Average Example

10 12 13

( (10 10 + 12 + 12 + 13 + 13 )/3 = 11 2 / 3

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Graph of Moving Average

Moving Average Forecast

 Used when trend is present

 Older data usually less important

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[(3 x 16) + (2 x 13) + (12)]/6 = 14 1 / 3 [(3 x 19) + (2 x 16) + (13)]/6 = 17 [(3 x 23) + (2 x 19) + (16)]/6 = 20 1 / 2

Weighted Moving Average

10 12 13

[(3 x

[(3 x 13 13 ) + (2 x 12 ) + (2 x 12 ) + (10 ) + ( 10 )]/6 = 12 1 / 6

Weights Applied Period

1 Three months ago

6 Sum of weights

 Increasing n smooths the forecast

but makes it less sensitive to changes

Potential Problems With

Moving Average

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Moving Average And Weighted Moving Average

Moving average

Weighted moving average

Figure 4.2

 Form of weighted moving average

 Weights decline exponentially

 Most recent data weighted most

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Exponential Smoothing

New forecast = Last period’s forecast

+ α (Last period’s actual demand

– Last period’s forecast)

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Effect of Smoothing Constants

Weight Assigned to

Most 2nd Most 3rd Most 4th Most 5th Most Recent Recent Recent Recent Recent Smoothing Period Period Period Period Period Constant (α) α(1 - α) α(1 - α) 2 α(1 - α) 3 α(1 - α) 4

α = 1 1 09 081 073 066

α = 5 5 25 125 063 031

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 Chose high values of α

when underlying average

is likely to change

 Choose low values of α

when underlying average

is stable

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Common Measures of Error

MAD = ∑ |Actual - Forecast|

n

MSE = ∑ (Forecast Errors)

2

n

Common Measures of Error

MAPE = ∑100|Actual i - Forecast i |/Actual i

n

n

i = 1

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Comparison of Forecast

Error

Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded α = 10 α = 10 α = 50 α = 50

Comparison of Forecast

Error

Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded α = 10 α = 10 α = 50 α = 50

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Comparison of Forecast

Error

Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded α = 10 α = 10 α = 50 α = 50

Comparison of Forecast

Error

Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded α = 10 α = 10 α = 50 α = 50

i = 1

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Comparison of Forecast

Error

Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded α = 10 α = 10 α = 50 α = 50

Exponential Smoothing with

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Exponential Smoothing with

Trend Adjustment

Step 1: Compute F t Step 2: Compute T t Step 3: Calculate the forecast FIT t = F t + T t

Exponential Smoothing with

Trend Adjustment Example

Forecast Actual Smoothed Smoothed Including Month(t) Demand (A t) Forecast, F t Trend, T t Trend, FIT t

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Exponential Smoothing with

Trend Adjustment Example

Forecast Actual Smoothed Smoothed Including Month(t) Demand (A t) Forecast, F t Trend, T t Trend, FIT t

Exponential Smoothing with

Trend Adjustment Example

Forecast Actual Smoothed Smoothed Including Month(t) Demand (A t) Forecast, F t Trend, T t Trend, FIT t

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Exponential Smoothing with

Trend Adjustment Example

Forecast Actual Smoothed Smoothed Including Month(t) Demand (A t) Forecast, F t Trend, T t Trend, FIT t

Exponential Smoothing with

Trend Adjustment Example

Forecast Actual Smoothed Smoothed Including Month(t) Demand (A t) Forecast, F t Trend, T t Trend, FIT t

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Exponential Smoothing with

Trend Adjustment Example

Trend Projections

Fitting a trend line to historical data points

to project into the medium to long-range

Linear trends can be found using the least

squares technique

y ^ = a + bx

the variable to be predicted (dependent variable)

^

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Least Squares Method

Least Squares Method

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Least Squares Method

Equations to calculate the regression variables

b = Σxy - nxy

Σx2 - nx2

y ^ = a + bx

a = y - bx

Least Squares Example

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Least Squares Example

Least Squares Example

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Seasonal Variations In Data

The multiplicative

seasonal model

can adjust trend

data for seasonal

variations in

demand

Seasonal Variations In Data

season

seasons

number of seasons, then multiply it by the seasonal index for that season

Steps in the process:

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Seasonal Index Example

Seasonal Index Example

Seasonal index = average 2005-2007 monthly demand

average monthly demand

= 90/94 = 957

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Seasonal Index Example

Seasonal Index Example

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Seasonal Index Example

San Diego Hospital

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San Diego Hospital

San Diego Hospital

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Associative Forecasting

Used when changes in one or more independent variables can be used to predict

the changes in the dependent variable

Most common technique is linear

regression analysis

We apply this technique just as we did

in the time series example

Associative Forecasting

Forecasting an outcome based on predictor

variables using the least squares technique

y ^ = a + bx

the variable to be predicted (dependent variable)

though to predict the value of the dependent variable

^

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Associative Forecasting

Example

4.0 – 3.0 – 2.0 – 1.0 –

Standard Error of the

Figure 4.9

4.0 – 3.0 – 2.0 – 1.0 –

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Standard Error of the

Standard Error of the

Estimate

Computationally, this equation is

considerably easier to use

We use the standard error to set up prediction intervals around the

point estimate

S y,x = ∑y2 - a∑y - b∑xy

n - 2

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Standard Error of the

Estimate

4.0 – 3.0 – 2.0 – 1.0 –

 How strong is the linear

relationship between the variables?

imply causality!

measures degree of association

 Values range from -1 to +1

Correlation

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Correlation Coefficient

r = nΣxy - ΣxΣy

[nΣx2 - (Σx) 2 ][nΣy2 - (Σy) 2 ]

r = -1

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measures the percent of change in

y predicted by the change in x

 Values range from 0 to 1

Multiple Regression

Analysis

If more than one independent variable is to be

used in the model, linear regression can be

extended to multiple regression to accommodate several independent variables

Computationally, this is quite complex and generally done on the

computer

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means this model does a better job of predicting

the change in construction sales

 Measures how well the forecast is

predicting actual values

 Ratio of running sum of forecast errors

(RSFE) to mean absolute deviation (MAD)

forecast has a bias error

Monitoring and Controlling

Forecasts

Tracking Signal

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Monitoring and Controlling

Forecasts

Tracking signal = RSFE MAD

Tracking signal =

∑(Actual demand in

period i - Forecast demand

in period i)

(∑|Actual - Forecast|/n)

Tracking Signal

Tracking signal +

0 MADs

–

Upper control limit

Lower control limit

Time

Signal exceeding limit

Acceptable range

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Tracking Signal Example

Cumulative Absolute Absolute Actual Forecast Forecast Forecast Qtr Demand Demand Error RSFE Error Error MAD