- Define the problem – Defining the problem is necessary. Defining the “right” problem is absolutely critical, otherwise you’re just wasting everyone’s time. However, the customers you are working for may not know how to express the real problem or may not know what the problem really is. The Operations Research Analyst must ask the right questions and draw the right problem out form where it may be hiding.
- Define the Business case – Once you identify the real problem, you have to help answer the question, “Is the problem worth solving (or at least worth the OR analyst’s involvement)?”. That may sound odd, but if the problem statement is not translated in to a business case, then the business does not need the problem solved badly enough to warrant the time and effort to solve it. We have to ask, “What is it worth to the business?” Increased profit? Improved production? Savings of marketing dollars?
- Define the Model Objective – It takes a well-defined problem and solid business case to ensure that we build the right model or provide the right solution. In my earlier years, I often found myself halfway through a project before realizing I was not building the right model. A model objective that answers the call of the business case is essential. It will keep you on course.
- Determine the requirements – We have a well-defined problem, a business case with a supporting model objective, so now let’s go and build the model. Slow down! What are the requirements? Do you have the tools you need, the team you need, and the budget to support it? If data is to be used, do you have access to it, has it been processed, and is it in the right format? We have to determine the requirements or we may fail due to some oversight.
- Gather the Data – If data is necessary, it will probably not be handed to you on a silver platter ready for consumption. It may be “dirty”, in the wrong format, incomplete, of just not right. If you have fully identified the requirements, you will already know what has to be accomplished at this stage.
- Process the Data – This is where you may spend an abundant amount of time. If you do not have clean, complete, and reliable data, you are doomed. You may have to remove inconsistencies, impute missing values, and so on. Then you have to analyze the data, perform data reduction, and integrate the data so that it is ready for use. Modeling with “bad” data results in a “bad” model!
- Build the Model – So, you have done the hard part and it’s time to have fun. Building the model is an iterative process. I doubt that anyone ever uses the first model they build. If they do, they probably should not. There is as much art as there is science in model development. Modeler judgment is as important as p-values, and Chi-square tests. If the model does not make sense to you—is not intuitive—then it is probably not the final model.
- Interpret the Model Results – Here is where we often have the most difficulty as analysts. We understand the mathematical and statistical interpretation of results, but we have to go beyond that and translate it to the customer’s language. They are not going to get the significance of the odds ratio of variable X. If you start thinking about that here, it will make life less painful later on.
- Validate the Model for Production – This is purely a scientific task. The model must be documented every step of the way and then stand up to the scrutiny of stress testing, peer review, and so on. This is where you find out if you really did build the right model, and you built it right.
- Perform an Economic Analysis – Here is where you answer the call of the business case. You must translate the results to dollars, the economic benefit of the solution, or another metric if savings or profit are not objectives. For instance, the military OR may be saving lives instead of dollars.
- Present the Results – If you bothered to interpret the results and translate them to “customer speak”, this part is easy. But, you are not just presenting results, you are selling the solution. If your work and your recommendations are not implemented, you just wasted a lot of time, effort and money.
- Follow-up – Too often we fix the problem and go back to our lives or onto another problem, but following up with the customer to evaluate the effectiveness of the solution you provided is just as important as the solution that you provided. It is especially important if you want to ever see that customer again.
So there is my view of model building. I have built just about every kind of model: linear programs, nonlinear programs, machine learning models, statistical models, Markov transition models, and so on. I have built models of unmanned aerial vehicles, space launch vehicles, communications networks, software systems, propensity to buy a product, call center operations, combat phenomena, and so on. And I have, more than I would like to admit, built the wrong model. Hopefully, this will help the modeler step out on the right foot, and help the model recipient know what to look for when asking for a modeling solution.
Jeffrey Strickland, Ph.D.
Jeffrey Strickland, Ph.D., is the Author of Predictive Analytics Using R and a Senior Analytics Scientist with Clarity Solution Group. He has performed predictive modeling, simulation and analysis for the Department of Defense, NASA, the Missile Defense Agency, and the Financial and Insurance Industries for over 20 years. Jeff is a Certified Modeling and Simulation professional (CMSP) and an Associate Systems Engineering Professional (ASEP). He has published nearly 200 blogs on LinkedIn, is also a frequently invited guest speaker and the author of 20 books including:
- Operations Research using Open-Source Tools
- Discrete Event simulation using ExtendSim
- Crime Analysis and Mapping
- Missile Flight Simulation
- Mathematical Modeling of Warfare and Combat Phenomenon
- Predictive Modeling and Analytics
- Using Math to Defeat the Enemy
- Verification and Validation for Modeling and Simulation
- Simulation Conceptual Modeling
- System Engineering Process and Practices