Exam 2 - Chapter 9

Question 1

___ is the process of projecting the values of one or more
variables into the future.
(he said read this over, most likely don need to study it, prob will be a multiple choice answer)

Answer 1

forecasting

Question 2

Collaborative Planning, Forecasting and Replenishment (CPFR)
at Colgate-Palmolive

(read over this, teacher said make sure you understand cpfr)

Answer 2

• Colgate-Palmolive is a global consumer products company that
manufactures such products as toothpaste, laundry detergents, pet
foods, and soap, and it operates in over 200 countries. To reduce
supply chain costs, Colgate-Palmolive implemented a supply chain
planning process with its suppliers and customers to manage
promotional demand, improve forecasts, and synchronize activities
along the supply chain. These initiatives have improved on-time
order performance from 70 to 98 percent for vendor managed
inventories, reduced total inventories by 10 percent, and improved
customer order fulfillment rates to 95 percent.

Question 3

Example of Collaborative Planning, Forecasting and Replenishment (CPFR)

Answer 3

◦for example, Walmart and Colgate will work together to forecast

Question 4

The ______ _______ is the length of time on which a forecast is
based. This spans from short-range forecasts with a planning horizon
of under 3 months to long-range forecasts of 1 to 10 years.

Answer 4

planning horizon

Question 5

Which one is more accurate Short range or Long range

Answer 5

Short Range

Question 6

Two Basic Forecast Approaches

Answer 6

1. Judgmental Forecast: this does not include the data

2. Statistical Forecast (Quantitative)

Question 7

A _____ _______ is a set of observations measured at successive points
in time or over successive periods of time.

Answer 7

time series

Question 8

A time series pattern may

have one or more of the following five characteristics:

Answer 8

1. Judgmental Forecast
2. Statistical Forecast (Quantitative)
3. Trend
4. Seasonal patterns
5. Cyclical patterns
6. Random variation (or noise) & Irregular (one time) variation

Question 9

What is the keywords for time series?

Answer 9

Over a time period

Question 10

Seasonal patterns

Answer 10

are characterized by repeatable
periods of ups and downs over
short periods of time, usually
within a year.

Question 11

Cyclical patterns

Answer 11

are regular patterns in a data series that take place over long periods of time, usually more than a year.

Question 12

Difference between seasonal pattern and cyclical pattern

Answer 12

◦Seasonal pattern is within a year

◦Cyclical pattern is more than a year

Question 13

____ ______ (sometimes called noise) is the unexplained
deviation of a time series from a predictable pattern, such as a trend,
seasonal, or cyclical pattern. Because of these random variations,
forecasts are never 100% accurate.
( not sure if this is on exam or not)

Answer 13

random variation

Question 14

___ ______ is a one-time variation that is explainable. For
example, a hurricane can cause a surge in demand for building
materials, food, and water.
( not sure if this is on exam or not)

Answer 14

Irregular variation

Question 15

_________ ______ (_______) relies upon opinions and expertise
of people in developing forecasts. When no historical data is available,
only judgmental forecasting is possible.

Answer 15

Judgmental forecasting (Qualitative)

Question 16

Delphi method

(test question definitely on the delphi method

Answer 16

The Delphi method consists of forecasting by expert opinion by gathering
judgments and opinions of key personnel based on their experience and
knowledge of the situation.

Question 17

Another common approach to gathering data is a _____. Sample sizes are
usually much larger than with Delphi, but the cost of such surveys can be high.

Answer 17

survey

Question 18

Statistical forecasting (Quantitative)

Answer 18

Statistical forecasting (Quantitative) is based on the assumption that the future
will be an extrapolation of the past.

Question 19

A moving average (MA) forecast

Answer 19

A moving average (MA) forecast is an average of the most recent “k”
observations in a time series. MA methods work best for short planning horizons
when there is no major trend, seasonal, or business cycle pattern. As the value of
“k” increases, the forecast reacts slowly to recent changes in the time series data.

Question 20

Single Exponential Smoothing (SES)

Answer 20

Single Exponential Smoothing (SES) is a forecasting technique that uses a
weighted average of past time-series values to forecast the value of the time
series in the next period. The forecast “smoothes out” the irregular fluctuations in
the time series.

Question 21

Linear Regression analysis

Answer 21

Linear Regression analysis is a method for building a statistical model that defines
a linear relationship between a single dependent variable and one or more
independent variables, all of which are numerical.
• Simple Linear Regression – one independent variable
• Multiple Linear Regression – more than one independent variables

Question 22

Make sure you know the difference between simple and multiple linear regression

Answer 22

Simple Linear Regression – one independent variable

• Multiple Linear Regression – more than one independent variables

Question 23

____ is best for single moving average

Answer 23

seasonal

Question 24

Make sure you go over the math on slide 12

math question on he test

Answer 24

-

Question 25

Forecast error (E)

Answer 25

Forecast error (E) is the difference between the observed value (A) of the time
series and the forecast (F), i.e., Et = At – Ft.

Question 26

[test question] how to explain a bias to someone

Answer 26

• On AVERAGE our forecast it is too low (if its positive)

* ON average our forecast it is too high (if its negative)

Question 27

[test question] bias

• 1.93 units for measure

Answer 27

◦on average my forecast is off by 1.93 units and its little bit too low

Question 28

A major problem of Bias error measure is that it

Answer 28

A major problem of Bias error measure is that it
ignores the sign (+/-) of
errors. For example, +9 – 9 = 0 (no error).

Question 29

A major difference between MSE and MAD

Answer 29

A major difference between MSE and MAD is that MSE is influenced much
more by large forecasts errors than by small errors.

Question 30

MAPE

Answer 30

MAPE is different than the other two error measures in that the measurement scale factor is eliminated by dividing the absolute error by the time-series data value. This makes the measure easier to interpret.

Question 31

The selection of the best measure of forecast accuracy is not a _____
matter; indeed, forecasting experts often disagree on which measure
should be used.

Answer 31

simple

Question 32

1. How would you interpret a BIAS = 2 units?
2. How would you interpret a MAD = 2 units?
3. How would you interpret a MAPE = 2%?

Answer 32

1. On average, the fx is too low.
2. On average, the fx is off by 2 units.
3. On average, the fx is off by 2%.