# Product Life Cycle

Topics: Time series, Time series analysis, Forecasting Pages: 13 (1433 words) Published: March 27, 2013
Pharos University
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

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.

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...