Chapter 3 - Forecasting

Question 1

**

Occurs when inventory is positioned in the supply chain processeses or entities to operate independently.

Answer 1

Decouping points

Question 2

The demand for a product or serviced caused by the demand for other products or services

Raw materials, component parts, sub-assemblies

Answer 2

Dependent Demand

Question 3

Demand that **cannot be delivered directly from that of other products **

Answer 3

Independent demand

Question 4

What are the 2 types of forecasting?

Answer 4

Qualitative and quantitative

Question 5

It is based on the idea that data relating to past demand can be used to predict the future

Answer 5

Time series analysis

Question 6

What are the 5 components of demand?

Answer 6

Trend, Seasonality, Cyclical variations, autocorrelation, random variation (TSCAR)

Question 7

Long term movement in data (growth in a business)

Answer 7

Trend

Question 8

Short-term regular and repetitive variations in data

Answer 8

Seasonality

Question 9

Long term, occasionally caused by unusual circumstances (war, economic downturn)

Answer 9

Cyclical valriation

Question 10

Denotes persistence of occurence (momentum driven)

Answer 10

Autocorrelation

Question 11

caused by a chance

Answer 11

Random variation

Question 12

Used mainly for tactical decision such as replenishing inventory - usually less than 3 months

Answer 12

Short-term forecasting

Question 13

Used to develop a strategy which will be implemented over the next 6 to 8 months such as meeting demand.

Answer 13

Medium term forecasting

Question 14

Useful for detecting general trandes and identifying major turning points - greater than 2 years

Answer 14

Long-term forecasting

Question 15

It is a forecasting method based on average demand over the most recent periods - useful when demand is not growing or declining rapidly, and no seasonality is present.

Answer 15

Simple moving average

Question 16

It is a a method where it allows unequal weighting of prior time periods and the sum of weights must be equal to 1

Answer 16

Weighted Moving Average

Question 17

This method is based on the importance of data that diminishes as the past becomes more distant and is the most logical and easiest method to use

Answer 17

Exponential Smoothing

Question 18

Functional relationship between 2 or more correlated values, usually from observed data.

Answer 18

Regression

Question 19

Predicted for a given values of the other variable (the independent variable||)

Answer 19

One variable

Question 20

Special case which assumes the relationship between the variables can be explained with a straight line

Answer 20

Linear Regression

Question 21

Past data and future projectsions are assumed to fall around straight line.

Answer 21

Linear Regression Forecasting

Question 22

Determines the parameters a and b such that errors is minimized the least squares

Answer 22

Least Squares Method

Question 23

Process of identifying and separating time series data into fundamental components: trend and seasonality.

Answer 23

Decomposition

Question 24

Difference between the forecast value and what actually occured

Answer 24

Forecast error

Question 25

When consistent mistake is made

Answer 25

Bias

Question 26

Errors that are not explained by the model being used

Answer 26

Random

Question 27

Scales the forecast error to the magnitude of demand

Answer 27

Mean Absolute Percentage Error (MAPE)

Question 28

Indicates whether forecast errors are accumulating over time (either psotive or negative errors)

Answer 28

Tracking Signal (TS)

Question 29

A forecasting uses indepedent variables other than the time to predict the demand.

Answer 29

Causal relationship Forecasting

Question 30

Generally used to take advantage of expert knowledge. For example, market research, panel consensus, dephi method and historical analogy.

Answer 30

Qualitative Forecasting