Blending is probably the most pressing challenge managers must face in the political consulting industry. In most circumstances, resources are finite and the goal is to produce x amount of either content, merchandise, data, and media to give our client the best chance at winning. While this may not be necessarily unique to my industry, it does pose an interesting challenge as the resources presented to us are regulated and monitored by the Federal Elections Commission (FEC) and it is this reason that in many cases blending what needs to be accomplished in order for us to deliver for the client.

I must admit I personally have never seen a matrix be used in our industry however after reviewing the chapter it is clear that on some level, linear optimization techniques are used to determine what projects need to be prioritized for what candidates. The best example from the text can be seen in Table 9.14 in relation to the "Product Specifications of the Grand Stand Blending Problem" primarily because it showcases the linear programing needed to determine the most optimal solution while still considering the operational constraints. This is nearly identical as it relates to the FEC filings and limitations imposed on us by campaign finance laws. This aspect of the industry is something that I previously had not even considered as being an example of linear programing prior to this chapter.

Do you agree with my post. Please explain.