1. Using Machine Learning to Improve Project Planning Accuracy

Machine Learning can guide project managers to create more accurate project schedules and estimates by providing suggestions using ML from completed projects, based on project types with similar Tasks and Project Assets.  Machine Learning provides a new capability for Project Intelligence to analyze project history and improve project planning and resourcing accuracy. 

Over time, Project ML can build a significant Project knowledge base to help Project Managers improve their project scheduling and budgeting accuracy, reducing costly project budget overruns.  Other ML benefits include using the identified Tasks which are running late to identify any associated Assets (from like Asset assignments, etc.) to identify the specific Project Assets which are impacted by these late tasks. 

For Capital Projects, Projects can have multiple CIP Assets assigned at the Task-level to capture project-related Asset costs.  These Task-level Asset assignments can be used to identify the impacted Asset(s) for a Project. When PM’s know which Project Assets are impacted by a Late Tasks or Project Change Orders is going to be important information for Project Managers and Stakeholders to prepare remediation plans.

Project ML can also help identify other tasks which reference these same Project assets to identify other impacts (in addition to the task predecessor-successor relationships).  Note: each Project Asset will have estimated In-Service dates, as well.

  

For example, in the case of a Project Task that is running late, Machine Learning capabilities can be used to analyze other Projects with similar tasks (and assigned Project Assets for common asset types) to compare Task effort and resources assigned to see if the late task was estimated / resourced correctly.  Projects ML can also alert a Project Manager if they missed a Project Task dependency, such as a facility permitting step or a construction funding approval gate.  

ML may be able to determine if a predecessor relationship for a prerequisite task was not identified.  Other factors can include long lead project equipment delivery schedules for engineered/major equipment that requires fabrication.   Other Projects ML opportunity areas include -  

• Provide Task estimating and Resourcing suggestions 

• Provide Supplier Lead times for Major/Engineered Equipment (long lead equipment) based on prior projects (with similar Assets)

• Provide shortest and longest project durations for similar Projects based on prior project history.

• For Tasks running late, use ML to identify any associated Assets (Asset assignments) to identify the specific Project Assets which are impacted by these late tasks.