Quantitative Management

In this lecture, we introduce the basic concepts related to time series forecasting. If you feel you have a good conceptual grasp of the components that make up a time series, you can feel free to move on to the next lecture.

In this lesson, we learn to use the moving averages method to forecast a time series (gas prices) and how to use means squared to assess the accuracy of the forecast predictions.

When there is a large amount of instability in your data (e.g. gas prices), moving averages forecasting may not be the best solution. Weighted moving averages allows you to give more weight to more recent values, which are thought to be better predictors, and less weight to earlier data points.

Moving averages and weighted moving averages use a preset number of observations rather than the full range of observations. Single exponential smoothing takes into account all previous observations.

Trend projection is used when we want to forecast outside the range of values we currently have.

Deseasonalized forecasting is used to clarify an underlying trend when there are strong seasonal components to your data, such as in agriculture, sports, or the film industry.

In this optional video, I show you how to create a spreadsheet containing subscripts.

In this lecture, we setup and perform our first linear program in Excel. You will learn how to properly set up a linear program in Excel, how to enter the data, and how to interpret the output of a linear program that seeks to maximize profit.

In this lecture, we setup an Excel spreadsheet for maximizing profit, when that profit is limited by 3 constraint equations.

In this video, we will learn how to use linear programming to minimize cost in situations where constraints are involved.

The problem with linear programming is that it results in fractional solutions. This can be a significant issue when the product you're producing is extremely expensive (e.g., a 787-9 Dreamliner is $250,000,000). Integer programming is used in these situations and forces the programming to output an integer (non-fractional) solution.

In this lecture, we learn to use the transportation/distribution model to minimize shipping costs in order to maximize profits. Since the transportation/distribution model is a special case of linear programming, we learn to use solver to do all the heavy lifting!

In this lesson we learn the foundations of the Economic Order Quantity model of inventory control, which we will apply in the next lesson when we learn how to formulate an EOQ model using Excel.

In this lecture, we applied what we learned in the previous lecture to a real world problem.

This lesson gives you the necessary background formulae to enter PERT into Excel