$1.$ Summarize the stages associated with an effective forecasting process. What are ways that forecasting error can be introduced in each of the stages? Cite any reference used to support the description of the forecasting process.

$2.$ When deciding on a suitable model for time series forecasting, it is useful to be able to identify particular features in the past data so that the series can be decomposed effectively into its parts $($or components$).$ Provide an example of a times series with each of the following component behaviors:

long$-$term trend

cyclical effects,

outlier time periods and$/$or extreme short$-$term shifts

large amounts of predictable short$-$term variation

How do these impact the ability to achieve forecasts that are usable for business and operational planning?

$3.$ Obtain at least one article or source on forecasting demand in supply chains. An original article on the Bullwhip Effect in Supply Chains is provided as a link in your Learning Resources.

Why is forecasting demand so difficult in supply chains?

Explain the two common issues in supply chain forecasting dealing with:

the propagation of variation $--$ i$.$e$.,$ the impact of upstream variability on downstream demand processes $--$ and, consequently, the "bullwhip effect";

event$-$based disruptions

$4.$ How are artificial intelligence and other advanced "big data" methods expected to improve demand forecasting accuracy in supply chains?