Chapter 4

Question 1

the art and science of predicting future events

Answer 1

forecasting

Question 2

the length of time on which a forecast is based

Answer 2

planning horizon

Question 3

systems that integrate marketing, inventory, sales, operations planning, and financial data to synchronize the supply chain

Answer 3

demand planning software

Question 4

: planning indications that are valuable in helping organizations prepare medium- to long-range forecasts

Answer 4

economic forecasts

Question 5

projections of a company’s sales for each time period in the planning horizon

Answer 5

demand forecasts

Question 6

Forecasts are rarely ______ because of randomness

Forecasts more accurate for groups vs. individuals
Forecast accuracy _______ as time horizon increases

Answer 6

perfect

decreases

Question 7

: forecasts that incorporate such factors as the decision maker’s intuition, emotions, personal experiences and value system

Answer 7

Qualitative forecasts

Question 8

a forecasting technique that uses the opinion of high-level managers to form a group estimate of demand

Answer 8

Jury of executive opinion

Question 9

: a forecasting technique using a group process that allows experts to make forecasts

Answer 9

delphi method

Question 10

a forecasting technique based on salespersons’ estimates of expected sales

Answer 10

sales force composite

Question 11

: a forecasting method that solicits input from customers or potential customers regarding future purchasing plans

Answer 11

consumer market survey

Question 12

: a forecasting technique that uses a series of past data points to make a forecast

Analyze by breaking down into components and projecting them forward – there are four components:

Answer 12

time series

Question 13

the underlying pattern of growth or decline

Answer 13

trend

Question 14

regular patterns in a data series that take place over long periods of time

Answer 14

Cyclical patterns

Question 15

unexplained deviation from a predictable pattern

Answer 15

Random variation (or noise)

Question 16

repeatable periods of ups and downs over short periods of time

Answer 16

seasonal patterns

Question 17

: The forecast for any period equals the previous period’s actual value

Answer 17

Naïve forecast

Question 18

: more recent values in a series are given more weight in computing the forecast

Answer 18

Weighted Moving Average (WMA)

Question 19

a weighted moving average forecasting technique in which data points are weighted by an exponential function

Answer 19

Exponential Smoothing

Question 20

is the difference between the observed value of the time series and the forecast, or At – Ft

Answer 20

Forecast error

Question 21

a straight-line mathematical model to describe the functional relationships between independent and dependent variables

Answer 21

Regression analysis:

Question 22

A measure of the strength of the linear relationship between independent and dependent variables
Varies between -1.00 and + 1.00

Answer 22

Correlation Coefficient (r):

Question 23

The percentage of the variation in the dependent variable that results from the independent variable
Varies between 0 and 1

Answer 23

Coefficient of Determination (r2):