Like the chosen ingredients of a fine wine or the words used in a favorite poem, every course in the Online Master of Business Administration program at the University of Maryland’s Robert H. Smith School of Business serves a purpose. Over 54 credit hours broken down into foundation courses and selectives, students learn more about some of the key principles, concepts and leadership qualities that entrepreneurs use in their everyday lives — both inside and outside of the workplace.
One of these essential classes is Decision Modeling. A two-credit course, Decision Modeling is a concept-based focus of study that offers insight on how students can make smarter choices in business by predicting certain outcomes before they happen — physically mapping out all the possibilities. Decision models have risen in utilization in recent years, thanks in large measure to the vast repositories of data that business owners can leverage to improve accuracy and precision.
What industries take advantage of decision modeling? What are some examples in which decision models might be effective? What are the benefits to the strategy and are there any limitations? All these questions and more are further addressed and probed in this two-credit course.
What is a decision-making model?
A decision model is essentially a thought strategy that assists in forecasting actions taken. Every entrepreneur must make decisions to guide his or her business. While some are rather inconsequential and others far-reaching, these decisions frequently have more than one outcome. Through the use of spreadsheets and other computer-based systems, decision modeling allows business owners to physically map out all the possibilities of what could take place — similar in appearance to a scatterplot or family tree — which can help them make more informed decisions.
Decision modeling serves as a forecastable tool that provides a sneak peek into all the potential possibilities of chosen actions. Objective data is leveraged to more accurately depict those actionable scenarios based on scientific observation.
What are some examples in which decision models might be effective?
Decision models can come in handy in any industry. Perhaps the best example is something you see on the news virtually every day. Weather is an inexact science, and while state-of-the-art Doppler radars and computer-based models have helped meteorologists more accurately forecast storm systems, it’s virtually impossible to know for certain what the weather will bring. However, weather teams use forecast models to analyze and demonstrate what might happen if a storm system — such as a hurricane or Nor’easter — stalls or ventures in one direction versus another. Data and so-called “nowcasting” provides meteorologists with the insight they need to determine how likely it is that certain scenarios will or will not occur.
Predicting potential outcomes is nothing new, but thanks to the rapid development of technology and big data, business owners can make more informed decisions so there are no surprises. For example, retailers know customers can be price sensitive, yet supply and demand may necessitate shop or clothing and accessory store operators to raise their prices. Experimenting with price changes can provide store operators with insight on how customers respond to them and if sales are affected. The results gleaned can be used to predict behavior and thus make decision models more precise.
Banks and mortgage lenders learn from decision models as well. Mapping out various scenarios to extending a loan offer to someone with poor credit, for example, can be used to help them decide on what qualifications borrowers must satisfy to gain approval.
What are the benefits and limitations of decision modeling?
Social scientists and forensic psychologists would likely be the first to tell you that eyewitness accounts are among the most unreliable forms of evidence. Thanks to big data and sophisticated algorithms, decision models can more accurately depict certain scenarios by overcoming the inherent flaws and biases found in human observation.
At the same time, though, outcomes can’t always be guaranteed, no matter how much data is available. So while data can be useful and provide some level of certainty, there’s a risk of being too numbers focused, and some settings may not be conducive for decision models, like where choices need to be made immediately.
What students stand to learn from Decision Modeling
These are just a few of the concepts examined in this two-credit Online MBA course from the nationally ranked Robert H. Smith School of Business at the University of Maryland. Students will learn how to obtain and aggregate large sources of data so they can make the most of decision models when faced with complex managerial problems in business settings.
Find out more about how the hands-on Online MBA course curriculum can build your business skills by speaking to an academic advisor.