## Tuesday, November 13, 2018

### Summary of Findings: Monte Carlo Simulation (4 out of 5 Stars)

Note: This post represents the synthesis of the thoughts, procedures and experiences of others as represented in the articles read in advance (see previous posts) and the discussion among the students and instructor during the Advanced Analytic Techniques class at Mercyhurst University in November 2018 regarding Monte Carlo Simulation as an Analytic Technique, specifically. This technique was evaluated based on its overall validity, simplicity, flexibility and its ability to effectively use unstructured data.

Description:
Monte Carlo Simulation is arguably both a method and a modifier by which analysts can pull together pieces of information in the form of ranges and, through random sampling of those ranges, produce an estimate. It is a highly flexible method due to its ability to handle uncertain data from virtually any discipline. The mental modeling required to piece apart the given problem decreases the simplicity of the method, but plugging in the ranges themselves is simple indeed. Monte Carlo Simulations have the capacity to produce a highly accurate distribution of estimates.

Strengths:
1. Applicable with simple ranges (exact numbers not required)
2. Increases confidence in estimate through statistical analysis
3. Simulations produce a visual statistical distribution
4. Allows analyst to identify collection needs to improve estimate
5. Allows analyst to create an estimate with uncertain data

Weaknesses:
1. Qualitative estimates require coding
2. Statistical data can be misleading
3. The result of a MC simulation is a numeric range, which is almost never where the analysis itself will stop
4. Challenging to explain to decision-makers

How-To:
1. Identify the variables that are needed to answer the question (See Application of Technique)
1. Can be ranges or exact numbers
1. Enter the variables into a software program capable of running a Monte Carlo simulation (Guesstimate, Microsoft Excel, @RISK, etc.)
2. Multiply average outputs of simulations together to produce estimate

Application of Technique:

Utilizing www.getguesstimate.com we presented the group with a question, “How many Big Mac’s will McDonald’s sell at locations in Erie in a week?”.  The group then created a model to determine the number of Big Mac’s sold.  The group developed a series of questions with estimate ranges to answer each question.  Pictured below is the model the group created to work through the question.  The arrows connecting boxes within the model indicate that a formula was used in order to produce an answer in the form of a range of possible outcomes (i.e. 4.9 McDonald’s in Erie * 400 customers per day = 2000 people visiting McDonald’s in Erie per day)

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