Decision-Making Model Analysis

Decision-Making Model Analysis

General assumptions create the foundation of a person's reasoning. Imperfections with a supposition can create the opportunity for a skewed perspective in a person's reasoning process (Paul & Elder, 2002). The process of choosing one course of action over another is commonly known as decision making. Consciously or unconsciously, people make decisions on a daily basis founded on one or more of the various decision-making models (Sullivan, n.d.). This paper examines how I apply various decision-making models in the workplace to generate accurate workload estimations in my career.

The Qualitative Choice Theory also known as analogous reasoning uses past experience to help an individual make decisions. A resolution is derived by looking at what has occurred historically and basing the decision on the expected outcome (Arsham, 1994). The Program Evaluation and Review Technique (PERT) methodology is a decision-making model that uses a mathematical formula established on realistic, pessimistic, and optimistic estimates to provide an accurate estimate of the most likely amount of time to complete a project ("Critical path analysis & pert charts," n.d.). Building on the analogous methodology, the parametric decision-making model looks at one small piece of a project, estimates the amount of time required to complete the particular section of the project, and multiplies the smaller piece times the number of total pieces (International Society of Parametric Analysts, 1999). The Monte Carlo simulation is a technique that makes use of computer models to aid in making decisions in intricate circumstances (Grambow, n.d.).

My duties at work often require that I provide estimations for the level of effort required for projects I am working on and I employ all three of the aforementioned decision-making methodologies. More often than not, I rely on the analogous model drawing on my experience as a subject matter expert to create time estimates for small projects. Drawing on more than 5 years of programming experience, I am able create realistic estimates of the level of effort required to complete a small project. I use this methodology when asked to estimate simple text changes to the user interface for Web pages I maintain. For example, a client has just requested a change to the welcome page of his Web site and has committed to provide text copy of the changes. I know from past experience with this client these types of exchanges execute without issues, and I issue a one hour estimate to complete the task.

I normally make use of the parametric model when estimating a large project. I look at one piece of the project and create an estimate of the level of effort needed to complete the piece, and multiply the estimate by the number of pieces that comprise the project. When I bid the creation of the initial website for the aforementioned client, I determined there would be only two Web pages requiring Active Server Pages (ASP) programming due to database interaction; the remaining 20 pages could be coded using static Hypertext Markup Language (HTML) and JavaScript. Drawing on parametric model I estimated the time needed to code a single HTML page and multiplied that estimate by 20. I created individual time estimates for the 2 ASP pages due to the unique nature of the pages. The total estimate delivered was approximately 30 hours.

As of late, my supervisor has encouraged me to begin using the PERT technique to provide a more accurate time estimate. The formula used to calculate the PERT is: ((realistic * 4) + pessimistic + optimistic) / 6. Using the 30-hour estimate issued to the aforementioned client and accounting for every situation that could create difficulties, I created a pessimistic estimate of 47 hours. Assuming flawless execution with no complications, I have created an optimistic estimate of 25 hours. The PERT formula calculates the estimated level of effort at 32 hours.

The Monte Carlo simulation assumes