  Collaborative forecasting involves exchanging information on how much of an
item will be needed by a buyer and how much can be supplied by a seller or
manufacturer in a supply chain. This exchange allows parties to plan their
operations based on the needs and limitations of their supply chain partner.
The success of this system critically depends on the healthy flow of
information. This paper focuses on methods to easily analyze and visualize this
process. To understand how the information travels on this network and how
parties react to new information from their partners, this paper proposes a
Gaussian Graphical Model based method, and finds certain inefficiencies in the
system. To simplify and better understand the update structure, a Continuum
Canonical Correlation based method is proposed. The analytical tools introduced
in this article are implemented as a part of a forecasting solution software
developed to aid the forecasting practice of a large company.
