Automated timelines make delivering projects on time predictable and achievable. To do it, you need accurate durations and dependencies.
How would it feel to take a call from a client asking for a complex project – say a new site buildout – and be able to quickly build a plan to give them a delivery date that you believe in with absolute certainty.
Imagine yourself inputting the client’s particulars and working with an AI tool to populate a project plan with all the tasks, durations, and dependencies the work requires. The AI then checks everyone’s schedules, and calculates a completion date. This might sound like the MSP of the future, but it could easily be your MSP in a not-too-distant future.
One important step in getting to this level of futuristic, AI-enhanced efficiency – also known as automated timelines in Perfect Project – is to use accurate data, especially about task durations and dependencies.
If the AI in your project management tool knows how many hours it takes your people to complete specific work, how many days each task needs from start to completion, which tasks are dependent on others, as well as the team’s other time commitments, calculating a complex project with an accurate completion date is a matter of doing math. And AI tools are great at math.
Here’s how to start training the AI in Moovila Perfect Project to do precise and accurate project planning for you.
Perfect the durations
Durations are often a slippery piece of data when it comes to project plans. The duration of a task is the time – usually in days – that it takes to start a task and finish it.
The task – reviewing the scope of work, for example – might require only a few hours of actual labor. But that labor doesn’t happen all at once. Project managers need to gather information from the solutions architect or even the client; and the client needs time to respond to questions. None of these things are time consuming. But if you write a plan that allocates three hours for writing the scope over the course of one day, your schedule will quickly go south.
If you allow a three-day duration, with three hours of actual labor time, for the task, you are more likely to have a plan and timeline your team can deliver.
What often happens in project planning – when it comes to durations – is the project manager takes a wild guess at the task duration. That wild guess turns into an expectation. That expectation is written into the plan, and everyone eventually believes it to be a fact.
What if that wild guess is way off the mark? Would you ever know?
When you are creating automated timelines, wild guesses about durations mess up the plan. Fortunately, getting accurate data about them is easy in Perfect Project.
How to fine-tune duration and dependency data
Wild guesses are fast and, often, the only place you can start. But with a little patience, you can convert them into accurate data. In Perfect Project, this requires a small amount of detective work.
After a project is completed, plan a post-mortem to study the original plan to see how well reality matched your original estimates.
Was that scope completed in three days? If not, why? The answer is in the details of your plan. Pull up the task and see what happened. Did the client need more time to answer questions? Did your team need more time to gather information?
In your project post-mortem meeting, ask the team how well that estimate matched reality. Pull up the network upgrade template and alter it to reflect what you learn. Maybe the duration for writing the scope should have been four days. Maybe there are durations for other tasks that should be shortened or lengthened. Or maybe there are tasks that you didn’t realize are dependent on something other tasks being done first. Review everything you learn and use it to tweak your project plans for the future.
Use analytics to fine-tune data
If you use Template Analytics in Moovila Perfect Project, you can look back at a year or a few months of projects to quickly gather accurate data about how well your estimates match reality.
This tool allows you to examine a particular task and see how well it’s completion time – on average – matched your estimate. It is a quick and accurate way to update your templates.
You might, for example, look at that task for reviewing a scope of work. Let’s say you took a wild guess a year ago to allow a duration of three days. You have used that wild guess in every project – whether it was a network upgrade or something else – since.
The template analytics view shows you the duration average for that task over time. Was three days on the mark over the past year? Or were you allotting three days to a task most people finish in two? Or perhaps you were calculating a three-day duration for a task no one has completed in that time.
Template analytics will show you the variance from your estimate, the average time of this duration in reality – across all projects – so you can easily fine-tune your template to match the reality of what your team does in the real world.
Accurate durations = better project control
Once you have fine-tuned your durations and other important data that goes into your project templates, you will be closer to that futuristic MSP that can quickly deliver a realistic timelines with little effort.
When a client asks when you can deliver a network upgrade, for example, will know that an AI has used your real world data to do all the mind-bending information-hunting necessary to calculate an accurate delivery date. This date is a realistic estimate that reflects the work styles, history, and pace of your team and knows everyone’s current workload and schedule.
You might say, “We can get that done in three weeks.” Or, because you have recently booked a lot of work, two engineers are scheduled to go on vacation, and a Christmas holiday is coming up, you might say instead, “That will take us three months.”
Either way, the client will appreciate you told the truth.
Why your customers will love you for this
When your customers ask for delivery dates, it’s because they are trying to schedule their own projects. They want to know when they need staff, space, equipment, or time to interact with your team. A delivery date you can’t meet doesn’t help them do this.
If you are taking a wild guess at your backlog, your timeline is based on guesses, and you are using inaccurate duration data, you can’t estimate a delivery date with any certainty.
If you take the time to build accurate, automated timelines, though, you will be able to tell that client a realistic date and then, even more important, meet that date.
This will thrill your clients.
Read more about automated timelines – or schedule a demo to see them in action – at Moovila.com.
