# Description of Operations Research

Operations research (OR), operational research, management science and decision science are just some of the names used for this discipline that seeks to arrive at answers to complex decisions faced by organizations of all sizes. OR is characterized by seeking efficiency in allocation of scarce resources. This means that OR applies to everything.

When trying to solve problems or make decisions, OR provides an approach that can be applied to deliver results. This is in contrast to the definition of Data Science which primarily focuses on gaining insight from data. Individuals can choose to make decisions based on this insight, but the process of improvement is not a definitive part of data science. OR incorporates the process of improvement and is commonly known as the “science of better”.

# Operations Research Methodology in (Data) Science Terms

The Operations Research Methodology approach to decision-making is familiar to the data scientist. The methodology is an application of the scientific method. The OR professional is presented with a problem, gathers data, formulates hypotheses, tests the hypotheses and generates useful solutions. There are three major disciplines or processes within the Operations Research framework.

Data Collection, Gathering and Exploration is an integral process in OR. An OR professional may not be given any raw data to begin with, but must seek data for input to define the problem mathematically.

Exploration of the data overlaps into the second discipline within OR. This is the Data Science portion of OR. Data Science’s main purpose is to gain additional insight into a process. Data Science is the practice of finding insights in the data.

In order to find insights in the data, the data scientist must formulate a hypothesis utilizing the data at hand. Manual or semi-automated modeling is used to formulate hypotheses. Combining the raw data and additional insights from the data exploration step, mathematical models can be built for simulation, optimization or other decision support purposes. These models become the hypotheses to be tested.

Hypothesis testing is regular practice in the data scientist’s day-to-day work. For effective hypothesis testing, the OR professional should utilize a mathematical model that is a simplification of the actual, real-world process. A test of these models can be functional tests, like validation used in regression or classification models, or alternately running simulations or implementing real-world tests.

The final key portion of OR is Decision and Outcome Management. The field of Decision Management offers methods and systems to track of decision points and the choices made. It is important to provide actionable information through data science, but it is equally important to track the recommendations or decisions available as well as the actual decision that is made, and the effectiveness and results of the decision outcome. In this way, the results can be reincorporated into data collection, and subsequently model improvements.

# Conclusion

Operations Research is composed of multiple fields and disciplines. Effective Operations Research leverages the skills of these different fields to improve processes and outcomes. The fields and terms used in OR are maturing, and this is leading to greater definition and understanding of the various components of an effective OR outcome. For example, Data Science is a key component of OR, but does not capture all of the components of that methodology. Statistics and business analytics are a key components of Data Science, but non-statistical methods like machine learning are key tools in the Data Science discipline. In order to find the most efficiency in allocation of scarce resources, all components of Operations Research should be employed.

# References

https://en.wikipedia.org/wiki/Data_science

https://www.informs.org/About-INFORMS/What-is-Operations-Research

http://www.scienceofbetter.org/

https://www.jstor.org/stable/4622744?seq=1#page_scan_tab_contents