According to the 3rd edition of Organizational Management and Leadership: A Christian Perspective (Saterlee, 2018) quantitative management is the use of mathematical and scientific processes to assist in the decision making process where resources are concerned (Saterlee). These processes allow for a more data driven approach to fiscal planning and strategic decision making that lead to the overall success of an organization (Saterlee, 2018). There are different concepts that quantitative management revolves around, but all of them rely on collected data to model what should happen in the future of an organization to allow leaders and followers alike to better plan for what lies ahead.
Concept 1: Operations Research
Operations research is a product of operational aspects of the military during World War II failing to keep up with the technological advancements of the time (Saterlee, 2018). Operations management is an inherently analytics centered field that exists to collect data that allows an organization to forecast their possible future outcomes based on decisions made in the present and past (Hazen et al, 2016). This opens the door for the use of predictive modeling techniques that drive innovation in decision making processes in every industry (Hazen et al, 2016). Predictive models, however, do not determine what decisions will be made on their own, they are tools that require decision makers to analyze the data they provide (Hazen et al, 2016). They also do not solve every problem all at the same time, for instance if the solution that a model suggests to a problem in turn creates another problem for the organization in the long term, the predictive model will likely not take that into account and thus the decision maker in charge must take all effects into account (Hazen et al, 2016).
Concept 2: Simulations
A product of operations research, simulations can allow decision makers to mitigate risks that are found in an everyday operating environment by testing those decisions in a space were they wont suffer real consequences for mistakes (Erana-Diaz et al, 2020). These simulations can be as simple as a flow chart on a dry erase board or as complex as artificial intelligence software; However, as stated in Concept 1, these decisions that are a result of a simulation meant to solve a specific issue in the present have a chance of resulting in the creation of additional issues down the road. Because of this, simulations are generally built with a set of safeguards in place to minimize the effect of risk factors that the simulation itself creates (Erana-Diaz et al, 2020). Examples of risk factors that might need to be taken into account when creating a management simulation are industry regulations, the organization’s long term finances, and the organization’s ability to implement the proposed solution (Erana-Diaz et al, 2020).
Concept 3: Queuing Theory
Queuing theory is how management professionals and researchers decrease the time that customers must wait and thus the costs that come with providing customer service for longer amounts of time (Satterlee, 2018). In simpler terms, when a person enters a line that they must wait in to receive a product or service, they calculate how long they are willing to stand in that line (physically or virtually) before they no longer consider the product or service worth the amount of time they must wait (Afolalu et al, 2019). Queuing theory attempts to put a mathematical model to this phenomenon, and managers of organizations can use the results of these models to improve the overall outward facing experience that they provide (Afolalu et al, 2019). Better overall customer experience generally leads to more returning customers and thus higher profit margins.
Proverbs 1:5: “A wise man will hear and increase learning, And a man of understanding will attain wise council”(King James Bible, 2014).
This verse demonstrates the importance of using scientific processes and research in management. Without looking to what others have done in the past (wise council) leaders cannot hope to improve their organizations in the future and will struggle with decisions that others have likely made in the past.