If your role involves making frequent critical decisions, you're probably familiar with the pressure that often comes with many of your job responsibilities. Now, more than ever, decision-makers rely on tools that offer practical solutions and easy implementation to avoid decision fatigue.
One tool that is becoming more and more popular is Monte Carlo Simulation (MCS). Unlike traditional methods that require complex mathematical or statistical expertise, MCS simplifies decision-making by using probabilities and random sampling to explore various scenarios and assess risks. With a user-friendly interface and intuitive functionality, professionals across different industries use Monte Carlo Simulation to make more informed, smarter decisions. Not only does this mean that anyone on your team can learn how to use MCS, but it also means that professionals at all levels can seamlessly implement MCS into their weekly workflow. Best of all, it is conveniently located within Minitab Workspace, backed by the over 50 years of statistical authority from Minitab.
In this post, we’ll explore a use case to experience a practical example from an IT department, but applications extend well beyond IT into industries such as manufacturing, healthcare, human resources and many, many more.
A use case
Let’s consider an IT team at a mid-sized company. The IT team was generally meeting management expectations, but the leadership team decided that it was time for new goals as the company grows. The IT director asked the IT team lead for individual metrics to gauge how many daily tickets each team member could handle. They received high and low benchmarks.
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After the team lead delivered the metrics to the director, they entered the data into Minitab Workspace and ran a Monte Carlo Simulation. In this instance, Monte Carlo Simulation could show the director how frequently the IT team could likely hit their daily goals based on pre-determined metrics. Here is the data they entered for each person on the team: 
After entering the X variables, Y variables, the equation and a LSL (lower specification limit) of 25, it was time to run the actual simulation to see how this team might perform with an average goal of 25 issues resolved per day. There was no USL (upper specification limit) entered since any number of tasks completed over 25 per day would be considered a success. Completing at least 25 tasks per day would reduce the backlog of IT requests. Here are the results: 
The simulation results showed that the team would likely be successful roughly 72% of the time with the new goal.
While this is a stretch, the team lead did not want the team to start feeling burnt out or unsuccessful since they would likely miss their goal 1-2 times each week. Most importantly, the team lead did not want to deal with costly employee turnover that could result from burnout, or an endless backlog of tickets that could lead to customer dissatisfaction. Instead of changing the goal, leadership decided to support the team in other tangible ways given the nature of the data.
The director asked the team lead what resources they need to be successful operating within this framework. The team lead mentioned that having one or two more junior specialists might be helpful, especially considering that the 72% metric represented a normal week when nobody was out of office nor sick.
So, another junior specialist was entered into the equation based on the output of past specialists. Here is the updated model: 
Now, the team would have two specialists, three mid-level team members and one senior team member. Here are the results for this potential scenario: 
For a typical day with this team structure, the team could expect to be successful roughly 99% of the time. That leaves room to exceed expectations and some cushion for when people call out or if someone ends up leaving the team.
Equally important, it also demonstrated that a third specialist would likely not be a necessary addition to the team at this point.
The team lead was more comfortable with this scenario and felt as though it would give their team a better chance of success with their new goals.
Other IT Applications for Monte Carlo Simulation
The possible uses for this tool in the IT field extend well beyond staffing. In fact, integrating Monte Carlo Simulation into your long-term decision-making process can yield better results, help to set more accurate goals, and produce stronger cybersecurity. Here are a few specific examples:
- Analyze Risk: Monte Carlo simulation can be used to assess and quantify risks in IT projects, such as predicting budget overruns or resource shortages.
- System Performance Evaluation: Monte Carlo simulation can simulate different scenarios to assess the performance of IT systems, applications, or databases under various conditions, helping identify optimization opportunities.
- Cybersecurity: It can help in evaluating cybersecurity risks by simulating various cyberattack scenarios, estimating the number of breaches, data leaks, or service disruptions, and assessing the effectiveness of security measures.
Data-Driven Decisions Can Protect Your Bottom Line and Boost Team Morale
Regardless of the decisions you make, being able to back them up with data will not only preserve your budget and help you run a supported team, but also gain buy-in from your employees.
Tools like Monte Carlo Simulation take the guesswork and bias out of your most important decisions. With Minitab, you can harness the power of your data and make better, smarter decisions.
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