Lecture Principle of inventory and material management - Lecture 17

Lecture 17

Forecasting

Books
•
Introduction to Materials Management, Sixth Edition, J. R. Tony Arnold, P.E., CFPIM, CIRM, Fleming 
College, Emeritus, Stephen N. Chapman, Ph.D., CFPIM, North Carolina State University, Lloyd M. 
Clive, P.E., CFPIM, Fleming College
•
Operations Management for Competitive Advantage, 11th Edition, by Chase, Jacobs, and Aquilano, 2005, 
N.Y.: McGraw­Hill/Irwin.
•
Operations Management, 11/E, Jay Heizer, Texas Lutheran University, Barry Render, Graduate School of 
Business, Rollins College, Prentice Hall


Learning Objectives
When you complete this chapter you
should be able to :
Understand the three time horizons and
which models apply for each use
þ
Explain when to use each of the four
qualitative models
þ
Apply the naive, moving average,
exponential smoothing, and trend
methods
þ

Forecasting at Disney World
þ

þ

þ

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


Forecasting at Disney World
þ

þ

þ

Disney generates daily, weekly, monthly, annual, 
and 5­year 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
þ
þ

þ

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


Forecasting at Disney World
þ

þ

þ

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 5­year forecast is 
5%
Average forecast error for annual forecasts is 

between 0% and 3%


What is Forecasting?
þ

þ

Process of
predicting a future
event
Underlying basis of
all business
decisions
þ
þ
þ
þ

Production
Inventory
Personnel
Facilities

??


Forecasting Time Horizons
þ

Short­range forecast
þ
þ

þ

Medium­range forecast
þ
þ

þ

Up to 1 year, generally less than 3 months
Purchasing, job scheduling, workforce levels, job 
assignments, production levels
3 months to 3 years
Sales and production planning, budgeting

Long­range forecast
þ
þ

3+ years
New product planning, facility location, research and 
development


Distinguishing Differences
þ

þ

þ

Medium/long range forecasts deal with more 
comprehensive issues and support management 
decisions regarding planning and  products, plants 
and processes
Short­term forecasting usually employs different 
methodologies than longer­term forecasting
Short­term forecasts tend to be more accurate than 
longer­term forecasts


Influence of Product Life Cycle

Introduction – Growth – Maturity – Decline
þ

þ

Introduction and growth require longer forecasts 
than maturity and decline
As product passes through life cycle, forecasts 
are useful in projecting
þ
þ
þ

Staffing levels

Inventory levels
Factory capacity


Product Life Cycle
Introduction

Growth

Maturity

Practical to change
price or quality
image

Poor time to change
image, price, or
quality

R&D engineering is
critical

Strengthen niche

Competitive costs
become critical
Defend market
position

Company Strategy/Issues

Best period to
increase market
share

Internet search engines
Drive-through
restaurants

LCD & plasma TVs
Sales

Decline

Cost control
critical

CD-ROMs
Analog TVs

iPods
Xbox 360

3 1/2”
Floppy
disks
Figure 2.5


Product Life Cycle

Introduction

OM Strategy/Issues

Product design and 
development 
critical
Frequent product 
and process design 
changes
Short production 
runs
High production 
costs
Limited models
Attention to quality

Growth

Forecasting critical
Product and process 
reliability
Competitive product 
improvements and 
options
Increase capacity
Shift toward product 
focus
Enhance distribution

Maturity

Standardization
Less rapid product 
changes – more 
minor changes
Optimum capacity
Increasing stability 
of process
Long production 
runs
Product 
improvement and 
cost cutting

Decline

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
þ

Economic forecasts
þ

þ

Technological forecasts
þ
þ

þ

Address business cycle – inflation rate, money 
supply, housing starts, etc.
Predict rate of technological progress
Impacts development of new products

Demand forecasts
þ

Predict sales of existing products and services


Strategic Importance of Forecasting

Human Resources – Hiring, training, laying

off workers
þ
Capacity – Capacity shortages can result in
undependable delivery, loss of
customers, loss of market share
þ
Supply Chain Management – Good
supplier relations and price advantages
þ


Seven Steps in Forecasting
þ
þ
þ
þ
þ
þ
þ

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


The Realities!

Forecasts are seldom perfect
þ
Most techniques assume an underlying
stability in the system
þ
Product family and aggregated
forecasts are more accurate than
individual product forecasts
þ


Forecasting Approaches
Qualitative Methods
þ

Used when situation is vague
and little data exist
þ
þ

þ

New products
New technology

Involves intuition, experience
þ

e.g., forecasting sales on Internet

Forecasting Approaches
Quantitative Methods
þ

Used when situation is ‘stable’ and
historical data exist
þ
þ

þ

Existing products
Current technology

Involves mathematical techniques
þ

e.g., forecasting sales of color
televisions


Overview of Qualitative Methods

þ

þ

Jury of executive opinion
þ Pool opinions of high­level experts, sometimes 

augment by statistical models
Delphi method
þ Panel of experts, queried iteratively


Overview of Qualitative Methods

þ

þ

Sales force composite
þ Estimates from individual salespersons are 
reviewed for reasonableness, then aggregated 
Consumer Market Survey
þ Ask the customer


Jury of Executive Opinion
þ

þ

þ

þ
þ

Involves small group of high-level experts
and managers

Group estimates demand by working
together
Combines managerial experience with
statistical models
Relatively quick
‘Group-think’
disadvantage


Sales Force Composite
þ

þ

þ
þ

Each salesperson projects his or
her sales
Combined at district and national
levels
Sales reps know customers’ wants
Tends to be overly optimistic


Delphi Method
þ

þ

Iterative group process, 
continues until consensus 
is reached
3 types of participants
þ Decision makers
Staff
þ Staff
(Administering
þ Respondents

Decision Makers
(Evaluate
responses and
make decisions)

survey)

Respondents
(People who can
make valuable
judgments)


Consumer Market Survey
þ

þ

þ

Ask customers about purchasing
plans
What consumers say, and what
they actually do are often different
Sometimes difficult to answer


Overview of Quantitative Approaches

4.

Naive approach
Moving averages
Exponential smoothing
Trend projection

5.

Linear regression

1.
2.
3.

Time-Series
Models

Associative
Model