Operations Research (OR) is a discipline that applies advanced analytical methods and state-of-the-art mathematical techniques to help decision-makers make better decisions and optimize performance in complex systems and environments.
There are many forms of analysis available to support decision makers. Often times, analysis seeks to answer known questions. Most questions can be placed under one of three major categories: "What?", "Why?", and "How?"
What?
The question of "What?" seeks to characterize the behavior of a system. It is perhaps the most common analytical question individuals first want to answer. These questions include, "What happened?", "What will happen?", "Which system performs better?", or "Do we see improvement when implementing this policy?" The ultimate goal of these questions is to paint a descriptive picture of system.
Why?
The question of "Why?" extends beyond the question of "What?" by seeking to obtain an understanding of the causal relationships within a system. These questions are much more difficult to answer and require close coordination with subject-matter expertise. Mathematics hold tremendous capability to recognize patterns or represent systems, making it a useful tool to support subject-matter expertise in answering the question of "Why?"
How?
The question of "How?" aims to identify courses of action with the most favorable outcomes. Operations Research lives to answer these types of questions. Common questions may include, "How should we allocate these limited resources?", "How should we manage the incoming flow of patients?", "How should we schedule these events?", or "What is the recommended course of action?" This form of analysis can be incredibly fruitful when applied correctly.
Although we recommend maintaining human oversight over decision-making processes, many decision-based tasks can be automated and handled by a computer. A computer can handle decision-making processes spanning from simple tasks (e.g., handling rules-based tasks) to very complex tasks (e.g., real-time decision-making in dynamic and uncertain environments). Interestingly, the complexity of tasks are not always intuitive. Tasks that are easily handled by a human may be difficult for a computer, and tasks that humans find difficult may be easy for a computer. Decision & Information Based Automation may benefit those seeking any of the following capabilities:
Obtain recommended courses of action (augment human capabilities)
Free up human resources from time-consuming tasks
Apply decision-making to environments that are too complex or fast for humans to respond effectively
Complete tasks faster than any human can operate
Autonomous systems and process automation
Minimize costs
Maximize profits or efficiency
Allocate resources effectively
Improve planning and scheduling
Supply Chain & Logistics
Manufacturing & Production
Finance & Risk Management
Telecommunication and Networks