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Simulation Modeling

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Market Models, Financial and Risk Modeling, Monte Carlo Simulation

Appian Analytics can assist your business with using simulation modeling on a variety of industry and business scenarios using sophisticated computer and statistical methods. Making simulation models is a highly sophisticated analytical challenge that involves both an business and industry expertise and also knowledge of specialized software and analytical approaches used for simulation. In fact, Appian Analytics has analysts experienced in the formulation, design, and modeling of business research questions for a wide range of industries. It is likely that a staff Analyst has experience in your specific industry and business problem.

By leveraging staff that are 100% dedicated to solving your challenging data analysis and business analytics problems you can get the job done quickly and cost effectively.

Simulation Modeling Examples:

  • Monte Carlo Simulation - This form of simulation modeling uses probability distributions to model the potential and impact of key variables in the model. The model itself is then "run" thousands of times to learn the result of all the inter-related variables affect on the bottom line result (such as profit or cost). The result itself is then expressed as a probability distribution which thus gives the probability associated with a range of results.
  • Marketing Models - These are models that measure and estimate the success of various marketing methods given real input data from your business. With a custom developed marketing simulation model you can estimate the impact on sales from expenditures on TV, Radio, Internet, Out of Home, direct mail, email and other methods. What's more the models can break down the effect of creative factors in the marketing media/copy.
  • "What If" Analysis - Any business decision can be modeling based on the key variables involved. When this is completed a key aspect is testing various scenarios with the model to estimate or forecast the impact.
  • Probability Trees - When choice to be made from a variety of business opportunities a probability tree can be developed to assist in better understanding the relationship between the choices and the result.
  • Decision, Risk, and Option Models - Similar to probability trees but more details, this involves the quantification of risk and other decision factors on business economics. Often times, formal financial option pricing theory can be applied to more accurately estimate the probability and impact.
  • Sensitivity Analysis - Similar to "What If" analysis but involves testing ranges of values to see how they affect the model across a variety of financial, productivity or other economic values. The question answers is how sensitive are various economic results to changes in the key input variables and which input variables have the greatest impact and consequently which have the least.
  • Market Models - Markets for transactions of good and services can be modeled and better understood. This is especially useful when a market participant is looking to make a change in the marketplace such as introducing a new product or service. Using a combination of simulation modeling with primary data collection can provide deep insight into what might happen after the new product or service is launched. This approach is often used in conjunction with conjoint style analysis.
  • Financial Modeling - Using Generally Accepted Accounting Principles (GAAP), Financial Statements can be modeled for the impact of import decisions. All items in the financial statements are modeled and analyzed from the result of the simulation model. This can be used in conjunction with generalized simulation approaches such as Monte Carlo.
  • Healthcare Models - This highly specialized area of modeling has to do with estimating results of healthcare interactions. For example, models are used to estimate the expected cost of treatment for patients presenting to the physician or hospital with certain conditions or risk factors.
  • Optimization - Optimizing an output variable for multiple input variables is a special type of operations research challenge that uses specialized software. Additionally, setting up a special add-in called, "Solver" for Microsoft Excel can be a very effective way to solve certain problems.
  • Forecasting - Forecasting is a special case of simulation modeling that is commonly performed in business. In some cases the forecasting technique can be quite simple such as exponential smoothing but in other cases a more quantitatively exhaustive approach is preferred using a multi-variety statistical model to forecast based on a variety of input variables.

Specialized Software for Simulation Modeling

Often times in simulation modeling specialized software is used to assist in the development. The Appian team has used a number of off-the-shelf tools for simulation such as @Risk software which is a highly Monte Carlo style simulation software with a wide range of probability distribution for use in modeling the real world. Additionally, more common types of software can be leveraged to build quite sophisticated simulations including Microsoft Excel or Microsoft Access. The benefit of using more common software for simulation modeling includes the price but also being able to more easily distribute the model, and also the ability to store the results of the permutations for further analysis.

In addition to specialized software and use of Microsoft Office applications for simulation modeling, open source languages such as Python and R have become outstanding choices for this work. In particular, Python has a very active community using the SimPy library.

Appian Analytics can help you select the best software and approach to use in your simulation modeling. Whether you have a one time requirement for a project or software application or if you have ongoing need, you can outsource your challenging simulation modeling to experts.

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