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Project Management using Event Chain Methodology

Estimations in Project Management

To find answers to these questions, let us review some issues related to estimations in project management. There are two types of uncertainties: aleatory and epistemic. Aleatory (alea is the Latin for die) uncertainties arise from possible variations and random errors in the values of the parameters and their estimates. These uncertainties can be objectively determined. Epistemic uncertainties are subjective and are related to the lack of knowledge of the particular process. For example, the duration of a task may be uncertain because this type of task has not been done before. In most cases, uncertainties related to estimations of durations, costs, and other project parameters are epistemic.

To explain the problem with estimations in project management, let us review the psychological aspects related to judgment and decision-making. In 2002, Daniel Kahneman was awarded the Nobel Prize in economics "for having integrated insights from psychological research into economic science, especially concerning human judgment and decision-making under uncertainty.” According to this theory, fundamental limitations in human mental processes cause people to employ various simplifying strategies or heuristics to ease the burden of mentally processing the information required to make judgments and decisions. In many cases, these heuristics or ‘rules of thumb” provide a correct judgment. However, under many circumstances, they lead to predictably faulty judgments or cognitive biases. According to the Availability heuristic, decision makers assess the probability of an event by the ease with which instances or occurrences can be brought to mind. For example, project managers sometimes estimate task duration based on similar tasks that have been previously completed. If they make judgments based on the most or least successful tasks they remember, it can cause inaccurate estimations. The Anchoring heuristic refers to the human tendency to remain close to the initial estimate. For example, you started thinking about the duration for an activity that had an original estimate of five days. Anchoring causes your analysis to stay close the original estimate, so that after your analysis the five days will remain the most likely or average duration with a range from three to four days. The Representative heuristic refers to how judgments concerning the probability of a scenario are influenced by the amount and nature of details in the scenario in a way that is unrelated to the actual likelihood of the scenario. Selective perception refers to instances where “you see what you want to see”. For example, this occurs when your estimate of a task’s cost are influenced by the intention to fit it into the project’s budget.

We can perform estimations related to epistemic uncertainties by analyzing historical data and by tracking the current project’s performance. The problem is both methods cannot change the subjective nature of epistemic uncertainties. Analysis of historical data is subjective and negatively affected by the aforementioned heuristics. What would happen if you kept accurate records? The answer depends on what type of tasks you are trying to estimate. In some industries, such as construction and manufacturing, these records are available. In these cases, project uncertainties are related to aleatory uncertainties. 

However, in many other industries, especially research and development projects, significant number of tasks have never been done before; therefore, historical records may not be available or very useful. Very often a similar, but not exact, task has been done before. Can you use this information about previous tasks as an analog for the estimation? Another problem with historical data is that if there was a problem with the activity before, project managers will avoid making the same mistake again. 

Because of these problems with historical data, the tracking of actual project performance remains one of the primary means of keeping projects on track. The goal is that by tracking actual performance, we can somehow reduce uncertainties during the course of an activity and derive better estimates of duration and cost. However, the problem of estimation remains for the reminder of the activity and project. 

Therefore, because we recognize that it is difficult to determine a single number associated with task duration and cost, the current practice is to overcome this deficiency by defining a range of numbers or a statistical distribution associated with this range for cost and duration. For example, the range for a task can be from 4 and 7 days. However, if historical records are unavailable, we will still have the same problem. These estimates will be as subjective as if they were defined by a single number (remember we still deal mostly with epistemic uncertainties). If the range estimations are as subjective as a single number estimate, then analysis by using ‘classic’ Monte Carlo simulation may not provide estimates that are any more accurate than deterministic project schedules. 

 

Introduction

Overview of Event Chain Methodology

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