25 May, 2023

How to Use Machine Learning in Project Estimating, Scheduling, & Planning

Machine learning is one form of Artificial Intelligence. And it is starting to be used in project estimating, scheduling, and planning.

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Machine Learning

Machine learning is a generic term for programs that can use data to form their own rulesets and therefore imitate the way humans learn.

Therefore, Machine learning can be a powerful tool for project estimating, scheduling, and planning.

Machine Learning for Estimating

Machine learning can help improve the accuracy of project estimates.

By analyzing past projects, machine learning models can learn to identify the factors that impact project cost and duration, such as the:

  • number of team members
  • complexity of the project
  • number of tasks involved.

It can use this information to create more accurate estimates for new projects. The challenge, of course, is to build a database of accurate data.

Machine Learning for Scheduling

Machine learning can be used to optimize project schedules by analyzing a wide range of factors, such as resource availability, task dependencies, and project constraints. Alongside this, it will consider options for parallel working, fast-tracking, or bringing in additional resources.

Associated with this, ML tools can also optimize resources to make project delivery more efficient and therefore make cost savings.

This is no different from what project managers do now, with basic software. The difference is that ML tools can search through simulations of hundreds of options to find the best results. They can assess them against the criteria we offer them.

Machine Learning for Project Planning

Planning goes beyond estimating budgets and durations, and creating a schedule. By analyzing past projects, machine learning models can also identify which project components tend to lead to greater levels of risk. They can then use this information to enhance project planning with contingencies and confidence levels.

Available Software Tools that Use Machine Learning

While there are a good number of Project-focused automation tools that use AI, there are fewer that can use AI for prediction, estimation, scheduling, and planning.

Tools like ClickUp and Wrike are using AI and ML for reporting, forecasting, prioritization, and search. But they do not yet offer estimation and scheduling tools. (2023 Q1).

Another tool, Ayanza, has a collaboration tool that looks very like ChatGPT embedded.

However, in the construction sector, we can see maturing use of Machine Learning to provide useful advice on how to plan, schedule and execute projects. A good example is Alice Technologies, headquartered in the US.

For mainstream project management, another example is Greyfly.ai. Their Intelligent Project Prediction (IPP) tool can predict project outcomes and identify underlying risks. It uses limited data points and Artificial Intelligence (AI) to make estimates, optimize resources, predict outcomes, and streamline processes.

Implementing Machine Learning Tools

As always, getting the software working is the easy part. The tougher part is transforming your project management organization. The key steps will be:

  1. Create the data sources, tailor your data model, and start collecting data
  2. Implement the Machine Learning software
  3. Configure the tools you need, to fit your own project processes and governance. 
  4. Train your teams and communicate with stakeholders
  5. Maintain the system and its operation under constant review, evaluation, and improvement

Machine learning is likely to become a valuable tool for project managers. It will help us make better decisions and improve the accuracy of our project estimating, scheduling, and planning.

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Mike Clayton

About the Author...

Dr Mike Clayton is one of the most successful and in-demand project management trainers in the UK. He is author of 14 best-selling books, including four about project management. He is also a prolific blogger and contributor to ProjectManager.com and Project, the journal of the Association for Project Management. Between 1990 and 2002, Mike was a successful project manager, leading large project teams and delivering complex projects. In 2016, Mike launched OnlinePMCourses.
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