Mathematics in project management

Did you love or hate maths at school as a child? Few subjects were as controversial as maths, with the possible exception of sport. But whether you were an athlete or a mathematical genius, whether you tore your hair out over sine curves and logarithms, a basic understanding of mathematics has undoubtedly come in handy. From calculating your mortgage to tipping your waiter, maths is at the heart of everyday life and plays an important role in project management.

Very few projects run smoothly from start to finish. This is the experience of many project managers. In many areas of project management, surprises and delays can have devastating and costly consequences. Therefore, it is necessary to plan the project accordingly and to make effective and energetic decisions. To do this, decision-makers need the right information, as complete and accurate as possible. 
This is where mathematical simulations come in to make reliable predictions. Machine learning analyses data from previous projects and uses the experience already gained. This is a great help in navigating an uncertain situation, especially when money, time and resources are at stake. However, questions remain as to why the simulation made a decision, whether that decision could have a negative impact on the project and, in the worst case, whether the project could fail.

Project managers involved in large projects need to think big. A good overview of the whole process is essential. And this is where mathematics comes in, because it influences the whole process. In addition to the past successes and failures that machine learning can capture, there are many other factors that influence decision-making. Machine learning cannot capture these and therefore cannot tell you how to optimise a strategy to make the project a success. If the simulation says that the probability of success is 90 %, then ideally you also want to know which strategies can be used to achieve this goal and how the 10 % failure can be avoided. A Monte Carlo "what-if" simulation would be one solution, as it can develop a winning strategy taking into account many variables. However, it cannot draw on past experience. 
To summarise: The machine learning algorithm will tell you if a strategy works, but not why. A Monte Carlo simulation will tell you how something can work based on the assumptions, but it does not take into account past experience. 
This is where Markov Chain Theory comes in. This model describes a sequence of possible events, with the probability of each event based on past experience.

The Markov Chain machine learning simulation tool is a kind of hybrid between predictive and retrospective techniques. It takes into account all the possible dependencies that can occur within the project management process. It shows exactly where problems can arise that, if not solved, will continue into subsequent project phases. It answers the most important question of why. This increases transparency, which enables a better understanding of the situation and thus makes decision making much easier. Delays are avoided and costs and disruption are minimised. Data intelligence has a positive impact on project management.

There is much more to mathematics than numbers and calculations. Particularly in the fields of machine learning and simulation, the calculation of probabilities means that mathematics, in its practical form, is part of many professional fields without being recognizable as such. This mix of retrospection and foresight, based on mathematical facts, is a powerful tool for making the right and best decisions in a project.