Evaluating an Organizational Initiative

Prior to beginning work on this assignment, review Chapter 13: Introduction to Optimization Modeling and Chapter 14: Optimization Models.
Now is the time to celebrate what you have learned in the course and apply it in practice. Imagine you are a business analyst who is writing an article for
Forbes magazine evaluating a recent initiative by a prominent organization of your choice and the data-based decision-making processes they implemented.
Your editor has asked you to include the below bullet points in your evaluation.
In your final paper,
– Select an organization.
– Describe the industry and business model.
– Examine a recent initiative that the organization implemented.
– Assess the success of the initiative.
– Describe the organization’s decision support systems and their decision-making process.
– Identify the decision support tools used by the organization to make various structured, semi-structured, and unstructured decisions.
– Outline their processes and tools the organization used following the decision tree model.
– Evaluate how their decision support system contributed to either the success or failure of the initiative.
– Assess how the organization could have approached the initiative differently to realize greater success.

 

 

Sample Solution

regards to the osmosis of pieces into lumps. Mill operator recognizes pieces and lumps of data, the differentiation being that a piece is comprised of various pieces of data. It is fascinating to take note of that while there is a limited ability to recall lumps of data, how much pieces in every one of those lumps can change broadly (Miller, 1956). Anyway it’s anything but a straightforward instance of having the memorable option huge pieces right away, somewhat that as each piece turns out to be more natural, it very well may be acclimatized into a lump, which is then recollected itself. Recoding is the interaction by which individual pieces are ‘recoded’ and allocated to lumps. Consequently the ends that can be drawn from Miller’s unique work is that, while there is an acknowledged breaking point to the quantity of pieces of data that can be put away in prompt (present moment) memory, how much data inside every one of those lumps can be very high, without unfavorably influencing the review of similar number

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