1. FORECASTING_Introduction.ppt

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Operations Management

ForecastingChapter 4

ADM 3301 ~ Rim Jaber

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Outline• Global Company Profile: Walt Disney Parks and Resorts• WHAT IS FORECASTING?

– Forecasting Time Horizons– The Influence of Product Life Cycle

• TYPES OF FORECASTS• THE STRATEGIC IMPORTANCE OF FORECASTING

– Human Resources– Capacity– Supply-Chain Management

• SEVEN STEPS IN THE FORECASTING SYSTEM


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Outline - Continued• FORECASTING APPROACHES

– Overview of Qualitative Methods– Overview of Quantitative Methods

• TIME-SERIES FORECASTING– Decomposition of Time Series– Naïve Approach– Moving Averages– Exponential Smoothing– Exponential Smoothing with Trend Adjustment– Trend Projections– Seasonal Variations in Data and Cyclic

Variations in Data (Decomposition Models)ADM 3301 ~ Rim Jaber

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Outline - Continued• ASSOCIATIVE FORECASTING METHODS:

REGRESSION AND CORRELATION ANALYSIS– Using Regression Analysis to Forecast– Standard Error of the Estimate– Correlation Coefficients for Regression Lines

• MONITORING AND CONTROLLING FORECASTS– Adaptive Smoothing– Focus Forecasting

• FORECASTING IN THE SERVICE SECTOR


Learning ObjectivesWhen you complete this chapter you should be able to :

1. Understand the three time horizons and which models apply for each use

2. Explain when to use each of the four qualitative models

3. Apply the naive, moving average, exponential smoothing, trend methods, and time series multiplicative decomposition model

5ADM 3301 ~ Rim Jaber

Learning ObjectivesWhen you complete this chapter you should be able to :

4. Compute measures of forecast accuracy5. Develop seasonal indices6. Conduct a regression and correlation analysis7. Use a tracking signal


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WHAT IS FORECASTING?• Process of predicting a

future event• Underlying basis of

all business decisions– Production– Inventory– Personnel– Facilities

• Forecasting is as much of an art as science

Sales will be $200 Million!


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THINKING CHALLENGEIn 1968, Switzerland had a 65% market share of the worldwide watch market. Their market share had increased steadily for 60 years. They had done this partly by continuously improving their watches. In 1968, what market share would you have forecast for 1978?


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SOLUTION

10% Japan had competed with a new technology, electronic quartz watches. In 1978, Japan’s market share was about 33%. Yet, Switzerland had invented the electronic quartz movement.

The bottom line: Forecasting methods are based on past behaviour.If future behaviour is significantly different from that of the past, forecasting methods will not work well.

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Forecasting at Walt Disney Parks & Resorts

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

Copyright © 2014 Pearson Canada Inc.


Disney generates daily, weekly, monthly, annual, and five-year forecasts

Forecast used by labour management, maintenance, operations, finance, and park scheduling

Forecast used to adjust opening times, rides, shows, staffing levels, and guests admitted



20% of customers come from outside the USA Economic model includes gross domestic

product (GDP), 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



Inputs to the forecasting model include airline specials, Federal Reserve policies, Wall Street trends, vacation/holiday schedules for 3000 school districts around the world

Average forecast error for the five-year forecast is 5%

Average forecast error for annual forecasts is between 0% and 3%


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FORECAST TERMINOLOGY

Forecasts are to be made through time, into the future:

• The period is the basic time unit (week, month, quarter, year).

• The horizon is the number of periods to be covered by the forecast.

• The interval determines when a forecast is to be updated.


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• Short-range forecast– Up to 1 year; usually less than 3 months– Purchasing, job scheduling, workforce levels, job

assignments, production levels• Medium-range forecast

– 3 months to 3 years– Sales & production planning, budgeting

• Long-range forecast– 3+ years– New product planning, facility location, research and

development

TYPES OF FORECASTS BY TIME HORIZON


Distinguishing Differences

• Medium/long range forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants and processes

• Short-term forecasting usually employs different methodologies than longer-term forecasting

• Short-term forecasts tend to be more accurate than longer-term forecasts


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

– Staffing levels– Inventory levels– Factory capacity

Introduction – Growth – Maturity – DeclineIntroduction – Growth – Maturity – Decline


Types of Forecasts

Economic forecasts– Address business cycle – inflation rate, money

supply, housing starts, etc. Technological forecasts

– Predict rate of technological progress– Impacts development of new products

Demand forecasts– Predict sales of existing products and services


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


STEPS IN THE FORECASTING PROCESS

Step 1 Determine purpose of forecast

Step 3 Establish a time horizonStep 4 Select a forecasting technique(s)

Step 5 Gather and analyze dataStep 6 Prepare the forecast

Step 7 Monitor the forecast

“The forecast”

Step 2 Select the items to be forecasted

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The Realities! Most techniques assume an underlying stability

in the system Forecasts are seldom perfect Product family and aggregated forecasts are

more accurate than individual product forecasts(e.g., product line versus individual products)

Forecast accuracy decreases as the time horizon increases (flexible organizations with short response time benefit from more accurate forecasts than their less flexible competitors)


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• Used when situation is ‘stable’ & historical data exist– Existing products– Current technology

• Involves mathematical techniques

Quantitative Methods

FORECASTING APPROACHES

• Used when situation is vague & little data exist– New products– New technology

• Involves intuition, experience

Qualitative Methods


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BREAKDOWN OF FORECASTING TECHNIQUES

Qualitative Methods

Time Series Methods

Causal Methods

*Sales force composite *Jury of executive opinion *Consumer Market Survey *Delphi Method

*Moving Average *Exponential Smoothing *Trend Projections *Multiplicative/ Additive Model *Box Jenkins

*Regression Analysis *Econometric Models *Life-Cycle Analysis *Input/Output Models


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EMPIRICAL RESULTS• Depending upon the situation, judgmental or

quantitative forecasts may be best• Causal (explanatory, associative, econometric)

methods are not necessarily more accurate than extrapolative (time series) methods

• More complex or statistically sophisticated methods are not necessarily more accurate than simpler methods

• The more information available about the future, the better


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ACCURACY• The decision maker needs a measure of accuracy:

– To know how far off a forecast might be. – To use as a basis for comparison when choosing among

different alternatives. – To take corrective action if the forecast errors are not

within reasonable bounds.• Two aspects of forecast accuracy when deciding

among forecasting alternatives:– Historical error performance of a forecast– The ability for a forecast to respond to changes


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HOW TO MEASURE ACCURACY (Historical Error Performance)

• At = Actual observed value at time t• Ft = Forecasted value at time t• Et = At - Ft = forecast Error• t = At/Ft = relative Error• For n periods:

– ME = ( Et)/n = Mean Error;– MAD = (Et)/n = Mean Absolute Deviation;– MSE = ( Et

2)/n = Mean Squared Error.ADM 3301 ~ Rim Jaber

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EXAMPLE 1(Forecaster number 1)

FORECAST ERROR

RELATIVE ERROR

ACTUAL A FORECAST F A - F A/F 1,410 1,940 1,660 1,140 1,200 1,550

1,500 1,625 1,225 1,375 1,850 1,450

-90 315 435 -235 -650 100

0.94 1.19 1.36 0.83 0.65 1.07

ME = -20.83 MAD = 304.17 MSE = 130,712.51


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EXAMPLE 1(Forecaster number 2)

FORECAST ERROR

RELATIVE ERROR

ACTUAL A FORECAST F A - F A/F 1,570 2,000 1,330 1,250 1,780

1,425 1,625 1,400 1,100 1,500

145 375 -70 150 280

1.10 1.23 0.95 1.14 1.19

ME = 176 MAD = 204 MSE = 53,4901


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Quantitative Forecasting Methods

• Naïve approach• Moving averages• Exponential smoothing• Trend projection• Multiplicative/Additive

Models

• Linear regression

Time-series Models

Associative/Causal models


1. FORECASTING_Introduction.ppt

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