*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:**

- Applicable
with simple ranges (exact numbers not required)
- Increases
confidence in estimate through statistical analysis
- Simulations
produce a visual statistical distribution
- Allows
analyst to identify collection needs to improve estimate
- Allows
analyst to create an estimate with uncertain data

**Weaknesses:**

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

**How-To:**

- Identify
the variables that are needed to answer the question (See Application of
Technique)

- Can be ranges
or exact numbers
- Enter
the variables into a software program capable of running a Monte Carlo
simulation (Guesstimate, Microsoft Excel, @RISK, etc.)
- Multiply
average outputs of simulations together to produce estimate
- Continue
adding variables and running simulations until question is answered

**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)

**For Further Information:**