A CASE STUDY: THE SUPER GRAIN CORP. ADVERTISING
The Case Study is found in chapter 3, P.65 3.1 A CASE STUDY: THE SUPER GRAIN CORP. ADVERTISING-MIX PROBLEM After carefully reviewing the case,
A CASE STUDY: THE SUPER GRAIN CORP. ADVERTISING-MIX PROBLEM
The Case Study is found in chapter 3, P.65
3.1 A CASE STUDY: THE SUPER GRAIN CORP. ADVERTISING-MIX PROBLEM
After carefully reviewing the case, write a minimum two page paper answering all three questions at the end of the case.
Review Questions
Firstly, what is the problem being addresse d in this case study?
Secondly, what overall measure of performance is being use d?
Also, what are the assumptions of linear programming that need to be checke d to evaluate the adequacy of using a linear programming model to represent the problem under consideration?
Also, the paper should have 1″ margins around. Cover sheets are fine but they do not count toward the two page minimum total.
The paper must be a full two pages (or more) in order to receive full credit.
This means you will need to think through each question deeply, as a cursory, one or two sentence response will be inadequate.
More Details:
Optimization is the way of life. We all have finite resources and time and we want to make the most of them.
From using your time productively to solving supply chain problems for your company – everything uses optimization. It’s an especially interesting and relevant topic in data science.
It is also a very interesting topic – it starts with simple problems, but it can get very complex.
For example, sharing a bar of chocolate between siblings is a simple optimization problem. We don’t think in mathematical terms while solving it.
On the other hand, devising inventory and warehousing strategy for an e-tailer can be very complex.
Millions of SKUs with different popularity in different regions to be delivere d in define d time and resources – you see what I mean!
Linear programming (LP) is one of the simplest ways to perform optimization. It helps you solve some very complex optimization problems by making a few simplifying assumptions.
As an analyst, you are bound to come across applications and problems to be solve d by Linear Programming.