This document introduces IBM Decision Optimization to a data science audience. The goals are to identify possible uses of IBM Decision Optimization in data science projects and demonstrate where additional value can be captured by adding an optimization step.

Overview

IBM® Decision Optimization products use advanced mathematical and artificial intelligence techniques to support decision analysis and identify the best available solutions to a complex problem. After reading this document, you should understand some of the benefits that decision optimization can bring to a data science project.


What is decision optimization?

Decision optimization is concerned with solving decision-making problems where there is an objective to be attained (such as maximizing profit or minimizing cost), but where there are also restrictions (such as limited resources, budget, or time).

If you could simply list all the possible feasible decisions (those that satisfy all the restrictions) and measure how good they are with respect to the objective, you would not need decision optimization. It would be easy to find the optimal solutions and make decisions.

In reality, for most decision-making problems, listing all the possible candidate solutions might require days, years, or even centuries of computation time on the most powerful computers.

Decision optimization applies advanced mathematical and artificial intelligence techniques to eliminate large quantities of candidate solutions without analyzing them in detail. Thus, it can focus quickly on analyzing the feasible and most promising candidate solutions to provide you with the optimal ones.

With IBM Decision Optimization tools, you can combine mathematical models that represent real world decision making problems with large data sets. This allows you to rapidly discover the best possible solutions. Such solutions are often formulated in a way that prescribes a course of action, which is why decision optimization is part of the broader technology category known as prescriptive analytics.

You often have control of certain factors that you can change to achieve a better solution. For example, you can hire more resources, increase budget or time, run a machine at a different speed, or enforce a certain level for a key performance indicator. In these situations, you want to perform what-if analysis: What would happen if I change certain values in my problem? With decision optimization, you can explore different scenarios by running the same model using different sets of data.

Consider a problem where you want to determine the optimal sizing of staff in a retail store, given the current roles and skills of staff and the level of service that you want to offer. A decision optimization solution can determine the best possible staff sizing. If you want to further increase the staff utilization rate, you might consider introducing a training program to enable staff to acquire new skills. However, how can you be sure that investing in such a program will increase the utilization rate? You can simply rerun the same decision optimization model including the new skills to find the answer. You can even create several scenarios of skill changes and then compare the utilization rate of the optimal staffing. There are endless possibilities to what you can investigate with what-if analysis, and decision optimization makes this fast and easy for decision makers.

When linear programming techniques are used, IBM Decision Optimization tools automatically provide you with sensitivity analysis. This gives you insight into problem data and its sensitivity with respect to the achievement of your goals. Sensitivity analysis can be particularly useful when there is uncertainty about some of the data used in the model. It can indicate how the optimal decision is affected by minor changes to the problem data. It can also enable you to see what further gains can be made with just slight changes to the model (such as a small increase of a particular resource).

However, future scenarios often involve data that cannot be known for certain in advance. For example, the average oil price in one year can be a decisive parameter of the model. However, you can still use decision optimization.

If you can measure the uncertainty by estimating probability distributions for this uncertain data, you can use Monte Carlo simulation together with IBM Decision Optimization tools to solve those scenarios. Data analysis and data visualization tools can then be used to provide insight from the results of those simulations. For example, if you observe that decision optimization prescribes the same course of action for 95% of the scenarios, you can be confident that the prescribed decisions are likely to be the optimal ones even within the presence of this data uncertainty.

Most real-world situations can benefit from decision optimization and these techniques have been successfully applied to a wide range of industries.

You can read about some successful deployments of decision optimization in the links at the end of this article.


What can IBM Decision Optimization tools do for your organization?

By following the prescribed actions and insights that IBM Decision Optimization tools provide, you can accomplish these goals: