11. Cause-and-Effect Models can have multiple
independent variables.

12. The
difference between a simple regression forecast and a multiple regression
forecast is that simple regression is used when there is only one explanatory
(or independent) variable, while multiple regression is used when there are
numerous explanatory variables.

13. Examples
of forecasting accuracy measures are Mean Absolute Deviation, Mean Absolute
Percentage Error, and Mean Square Error.

14. According
to the text, the ultimate goal of any forecasting endeavor is to have an
accurate and unbiased forecast.

15. As
tighter control limits are instituted for the tracking signal, there is a
greater probability of finding exceptions that require no action, but it also
means catching changes in demand earlier.

16. The
true value of CPFR comes from the sophisticated forecasting algorithms that
provide companies with highly accurate forecasts, not from the exchange of
forecasting information.

17. The
objective of CPFR is to optimize the supply chain by improving demand forecast
accuracy, delivering the right product at the right time to the right location,
reducing inventories across the supply chain, avoiding stock-outs, and
improving customer service. As a result of the many benefits attributed to
CPFR, this business practice has been widely adopted.

18. Some
of the benefits of CPFR include strengthening partner relationships, providing
an analysis of sales and order forecasts both upstream and downstream, and
allowing collaboration on future requirements and planning.

19. Some
of the important steps involved in implementation of a CPRF process model
include developing a collaborative arrangement, creating a sales forecast,
creating an order forecast, and generating those orders.

20. Some
of the leading suppliers of CPFR solutions mentioned in the textbook are JDA
Software Group, i2 Technologies, and Oracle.