Everyone knows that project planning is an art that requires the planner to cast a spell over everyone involved so they will believe in a myth that’s not only farfetched but that has been repeatedly disproven. And there is no bigger fantasy in this mythology than the Holy Grail of resource and capacity management.
Especially when calculating a project’s most important resource – people – most plans are based, largely, on creative guesswork. “If people’s time is calculated at all,” says Mike Psenka, Moovila’s president and CEO. “It is done very crudely.”
Because in almost every project planning tool or methodology, calculating people is so complex it approaches impossible. The only way to do it is to let smart people make calculated guesses and check in constantly to see how it’s going.
Moovila, though, has created an embedded artificial intelligence that is not only capable of making this enormously complex calculation but will also recalculate it every time the project changes. It has access to the data and can do the intensely complex math that no human has the time or mental capacity to do.
The persistent myth of human resource capacity
Let’s start with an example.
Say you are building a project where Susan is a key resource. She isn’t working full time on this project. She has a team to lead and responsibilities elsewhere. So, you assign her at 40 percent time for 40 hours, four weeks in the future. She is essential to the project and one of only three engineers with the requisite skills in the company. Everyone okays this plan without hesitation even though, “this rough swag is the root of all evil when it comes to capacity conflict delays,” warns Psenka.
Let’s unpack what’s wrong with this calculation. First, humans don’t work at 40 percent. Susan knows how long this task will take and, when she gets the data she needs, will work on it for that long in a normal, human way. She okays this plan based on her calendar, which says she will have that much time four weeks in the future.
But what if the data she needs is late or something else pushes the project back?
“What happens,” says Psenka “is that the team says ‘we're on time, we're on time, we’re on time’ at those constant check-ins until the next milestone arrives. And then the plan goes off the rails.”
There is a delay. There were supply problems, a contractor doesn’t make a deadline, or something else comes up that puts the project back a week or three. And that’s when Susan has a vacation, is committed to another project, or has scheduled knee surgery because she was told her commitment to this project would be over by then. Now, too late to hire someone, the project is pushed back until after she returns. Delays pile on top of delays.
What if, though, an artificial intelligence had noticed, weeks earlier when a contractor missed a deadline, all the delays this small one would cause through the project plan and, within seconds, flag the need for someone with Susan’s skills weeks later?
“Right now,” says Psenka. “It’s like a hurricane is coming and we don’t know about it. But we could know about it. We could prepare.”
How Moovila’s AI tells you the hurricane is coming
Unlike a human with a project planning tool, the AI in Moovila has instant access to all the data as well as infinite time and the ability to calculate the complexities that go into determining how much of Susan’s time – and everyone else’s – the project will need and when, even if things change. And, since it can calculate every time shift the first missed deadline will cause, it can see – long before Susan or the project planner does – that there will be a resource deficit in the future.
While humans are forced to take a wild, educated, guess, the AI makes its calculations based on real, up-to-date data, pulled from the source.
“I've got access to Susan’s calendar,” says Psenka, explaining how the AI engine thinks. “I know Susan has to do these twelve things across three projects. I know how long those things will take and that they depend on other things being completed. I also have access to everyone’s calendars and commitments so I can analyze all the availability of everyone involved, on all the days in the future. My time estimates are based on when these actions need to start and how much time these people have told me they will need to do them. I can see if what is scheduled is possible, mathematically.”
Feeding the engine this data is easy. It’s all entered somewhere. It’s in a spreadsheet, on someone’s calendar, or in a project.
A human can see that the project is going off the rails and push it back, hoping Susan will be able to right things when she returns. But Moovila’s AI engine can see, long before anything goes off the rails that Susan is never going to get this done, that no one else with that skill is available either, and will tell you, with plenty of time to do it, that you need to hire a contractor with comparable skills for forty hours, five weeks in the future.
Myths are often fantastical ways of explaining something that’s too complex for humans to yet grasp. But the tools are here to turn this myth into math. It’s just a matter of using the right one.