Simulation for Business Planning
1. Advanced applications of simulation
Welcome to the final chapter of our course and congratulations of making it this far.2. Overview
In this chapter, we will first look at some advanced applications of simulation. We'll see how simulation is used in business planning. We'll then work through Monte Carlo integration and see how simulation is used for power analysis. Finally, we'll run a portfolio simulation as an introduction to financial applications. Let's first look at the business planning scenario.3. Simulation for business planning
As seen in the previous chapters, simulation is widely used in business for decision making. It's extremely useful in situations involving uncertainty. This makes it a wonderful tool for forecasting and planning. Let's investigate this application in the context of a real business. Suppose you manage a small corn farm and are interested in planning your costs for the upcoming season.4. Corn farm
First, let's identify the sources of uncertainty in your business. Your production of corn can be influenced by the amount of rainfall that season, your inputs into the farm like fertilizer, irrigation system, etc. The demand for corn could be influenced by numerous factors, for instance, how many people go to the movies and consume popcorn. All these factors will influence the price of corn, along with government regulation. To start understanding the profitability, we need to simplify this mental model and abstract away from the details. Let's build a very basic model of this business.5. Business profitability
At a very basic level, your input to the business is your cost. Production is determined by cost and rain, which is a random variable. Demand and price are externally determined. However, you might have historical distribution of demand and price. Your profitability then depends on how much of the corn you produced is actually sold.6. Business profitability
Given this basic model, you can then vary the cost input and track the impact on profitability. For instance, you could plug in a range of values for cost and select the one that results in the highest average profits. Once you have this basic model, you can begin tweaking it by adding more inputs or multiple sources of uncertainty. For example, you might be able to get local weather patterns from your weather station and get a more accurate estimate of how corn production is affected by the weather conditions. Thus, simulation gives you a very simple yet powerful framework for understanding the uncertainty inherent in business environments and for using this uncertainty to drive informed decisions.7. Let's practice!
Now let's actually code up the corn production example and see it in action.Create Your Free Account
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