# Product Life Cycle

**Topics:**Time series, Time series analysis, Forecasting

**Pages:**13 (1433 words)

**Published:**March 27, 2013

Faculty of Financial & Administrative Sciences

O PERATIONS

M ANAGEMENT

B y: Dr. Ola E lgeuoshy

S pring 2013

C hapter (3)

F orecasting

F ORECASTING

“ a Statement about the future value of a variable of

i nterest .”

U ses of Forecasting:

Accounting

Cost/profit estimates

Finance

Cash flow and funding

Human Resources

Hiring/recruiting/training

Marketing

Pricing, promotion, strategy

MIS

IT/IS systems, services

Operations

Schedules, MRP, workloads

Product/service design

New products and services

F EATURES COMMON TO ALL FORECASTS

Assumes causal system

p ast ==> future

Forecasts rarely perfect because of

r andomness

Forecasts more accurate for

g roups vs. individuals

Forecast accuracy decreases

a s time horizon increases

I see that you will

get an A this semester.

E LEMENTS OF A GOOD FORECAST

Timely

Reliable

Accurate

Written

S TEPS IN THE FORECASTING PROCESS

“The forecast”

Step 6 Monitor the forecast

Step 5 Prepare the forecast

Step 4 Gather and analyze data

Step 3 Select a forecasting technique

Step 2 Establish a time horizon

Step 1 Determine purpose of forecast

A PPROACHES TO FORECASTING

Q ualitative methods:

c onsist mainly of s ubjective i nputs, which often d efy

p recise numerical description .

Quantitative methods:

I nvolve either the projection of h istorical data o r the d evelopment of a ssociative models t hat attempt to

u tilize c asual (explanatory ) variables to make a

forecast.

Quantitative techniques p ermit inclusion of soft

i nformation ( e.g.: human factors, personal opinions,

h unches) in the forecasting process.

F ORECASTING TECHNIQUES

C LASSIFICATIONS

Judgmental forecasts:

F orecasts that use s ubjective inputs s uch as opinions

f rom consumer surveys, sales staff, managers,

e xecutives and experts.

T ime series forecasts:

F orecasts that p roject patterns i dentified in recent

t ime series observations.

Associative forecasts:

F orecasting technique that uses e xplanatory variables

t o predict future demand.

T IME SERIES FORECASTS

Time series: a t ime - o rdered sequence of observations t aken a regular intervals.

The analysis of time series data requires the analyst

t o identify the u nderlying behavior o f the series .

Trend - l ong - term movement in data

Seasonality - s hort - term regular variations in data

Cycle – w avelike variations of more than one year’s d uration

Irregular v ariations - c aused by unusual circumstances Random v ariations - c aused by chance

F ORECAST VARIATIONS

Irregular

variation

Trend

Cycles

90

89

88

Seasonal variations

N ATIVE FORECAST

Native forecast i s a forecast for any period that equals the p revious period`s actual value.

D isadvantage i s a ccuracy issue.

Advantages:

Period

Simple to use

Virtually no cost

Quick and easy to prepare

t-2

Data analysis is nonexistent

t-1

Easily understandable

(next) t

Cannot provide high accuracy

Can be a standard for accuracy

Example:

Actual

Change

from

previous

value

Forecast

50

53

3

53+3= 56

T ECHNIQUES FOR AVERAGING

1 . Moving Average: t echnique that average a number of recent actual v alues, updated as new values become available.

E xample:

( A) Compute a t hree period m oving average forecast g iven demand for s hopping car ts for t he last five periods .

( B) (B) If t he actual demand in period 6 t urns to be 38, the m oving a verage forecast for period 7 would be

Period

Demand

1

42

2

40

3

43

4

40

5

41

The 3 most recent demands

S olution:

( A) F 6 = ( 43+40+41)/3= 41 .33

( B) F 7 = (40+41+38)/3= 3 9.67

T ECHNIQUES FOR AVERAGING

2 . Weighted M oving A verage: m ore recent values in a series are g iven more weight in computing a forecast.

E xample:

G iven the following demand data,

a . Compute a...

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