Artificial intelligence is at your mouth isn’t it near to Project management!
2 years ago, I decided to move towards a healthier style of living, and change to a gluten free bread with full grain. While searching on bread making machines that can accept full grain flour to help me by fast preparation and high quality bread delivery. I found a machine that you put flour, oil, water and it could do everything to me until it delivers hot full-grained bread on spot, in no mentioned time.
To let the machine start working I have to connect it to Wi-Fi, and then it will install the products’ configurations list.
Machine produces five types of bread; each type of bread has different thickness, and five levels of roasting.
Each type of bread can cost you one or two dough to get perfect bread with a machine message that recognizes the ingredients to make the bread type, and “I am getting informed by the message on the screen”.
After each delivery the machine requests an evaluation of each product, and lets me select why I am not satisfied. Actually, it is a learning machine; and never repeats’ the mistakes
A message from the machine appears on the screen “I am learning and I will enhance next dough” was a good message to me with hope and product trust, and gave me roasted puffy full grain bread at the end!
One day the manufacturer in a different far country added one more bread type, all what requested is to connect the machine to Wi-Fi and it will get the update of the new ingredients needed and the new type to prepare the operation.
“Internet of things and machine learning got in my personal life faster than I could expect!”
In fact, this situation reminded me many years ago while I was a financial and supply chain consultant for one of the famous manufacturing enterprise resources planning (ERP) solutions, where we were implementing the concept of material requirements planning MRP, and our client was requesting to implement the forecasting module.
It took us good time for the detailed setup, data models, and entering 5 years back data in different areas to enable the system to start producing the forecasting results.
That was the first machine learning exercise based on the data and accumulation machine can predict next plan of material requirements.
Artificial Intelligence “AI”
Artificial intelligence commonly known by machine simulation to the human intelligence process.
The process includes “information gathering, rules of using the information, reasoning, and self-correction, in addition to the ability to learn from the accumulation of data to different situations with computing power of analyzing many factors to have different possible solutions to the issues.
Artificial intelligence founded as an academic discipline in 1956. Sometimes known by machine intelligence, where machines became increasingly capable to help the decision makers to enhance the accuracy of reports and decision foundations elements.
Benefits of Machine learning
Machines, especially computers are famous of time deductions, higher rates of accuracy, visibility of on spot reports, decision-making support, and business enabler to meet the business challenges.
From my experience, a learning machine “is sincere assistant that lets you know more, work more accurately, and provide clear facts without subjective opinions and cover the objective part of the decision. Hence, eliminate the decision making time as a result”
Do we need Machine learning in project management?
When we mention project management, we must recognize that project management is the right arm of the strategist, and it is not limited to project it is a triangle of Portfolio, Programs, and Projects management practice.
Each level has a non-ignored need to automation. Projects not executed in vacuum; projects executed by organizations members though project management. Organizations need accumulative data learner, Reasoning, Analytics, intelligent decisions support.
The nature of Portfolio management is concerning with answering which project to accept or execute on the organization level.
The nature of Program management is concerned with achieving group of objectives from the strategy with different stakeholders’ interests and many dependencies between projects.
The nature of Project management is to care about the core production unit that is concerned with the real work to get things happen.
Each level needs machine learning aid at a point of time!
Projects management pain story
The story begins at the last quarter of each year when organizations starts collecting the New Year’s demand, create year objectives, and plans.
Portfolio manager is the man of this period, he/she is concerned with producing accurate prioritization matrix sheet to answer very critical questions:-
1) Is this the right project to execute?
2) Which projects will get seats in budget allocation?
3) Do we have enough human resources power to look after the projects?
To reply these questions, the project management office “PMO” should prepare the prioritization exercise. Also must do heavy hectic prediction process with technical teams and external vendors to put on the table two plates (How long this project will take, second how much that can cost us in high level of uncertainty in scope and future in addition to hopeful planning).
Those two answers are being carried inside hard efforts and many arguments and issues between teams with PMO members to reach good accepted answers that cannot annoy C suite!
Each year this exercise takes place without keeping accumulated records, that can help in building fundamental data to next year planning. All recipes are in the minds and isolated in administrative systems!
Project management issues are not only critical at the end of each year for planning next year’s projects, this is a result of not having and not keeping a project’s management historical data in systems, and “is considered wasted as long as it is not reprocessed to give indicators and learned lessons for organization excellence”.
What if the machine did it?
Now the machine have leaned many things about your organization and it can help you prepare good planning season, and enhance your operation management towards excellence.
What if you started with removing big effort results given and all what you need to do is enhancing the results!
The Machine has learned the following, for example and not limited to:-
- Prioritization fundamentals and its equations.
- Production rates per resource type.
- Resources behavior and delay reasons.
- Balancing the portfolio.
- Total load resources can carry across year quarters.
- Real capacity of the organization “how much it can deliver per year “.
- Optimistic, pessimistic and realistic performance historical data.
- Budget management average per project manager.
- Actual working hours in the projects type.
- Capacity equations.
This information can make initial planning exercise run with very good foundation.
Decisions proposals can be easily illustrated and visualized in the three dimensions or more through a group of proposals to the PMO to be ready to examination and add more scenarios to reflect the subjective perception and prepare the “C-suite” presentation for final decisions.
The Machine can smartly constitute a program by connecting all related projects’ objectives dividing the strategy portfolio of programs and projects in addition to helping the program manager to focus on managing his program instead of wasting time to check the plans and continuously update it on a daily base or weekly or fill documents. The Machine can help in master schedule, dependencies; risks effect on planning, issue CRs, match the requirements achievements and update stakeholders. Propose updated master planning with expected actuals and estimate to complete, calculates projects risk and cascade on the program schedule and master risk logs.
The value that the machine is learning is considered qualitative improvement to the project management practice, and enhancing the decision making process as well.
Finally, I guess that an AI project management system is a system that can accomplish daily project management activities without human directions, provide more insights, and make recommendations. In addition to producing the key performance indicators, provide learning curves indicators, conduct satisfaction surveys.
AI also can play major roles in projects data migration, mapping, cleansing, system integration tests, redistributes codes to the code writers who have bugs, can calculate the good time and bad time in projects and save projects budget with accurate change management, proposing rollout planning for large complex implementations.
Imagine an assistant making you more creative, productive than administrative, an assistant that can advise and advance your thinking by days ahead, amazing qualitative move!
I trust that project management practice needs more attention from practitioners to add values and transfer organizations’ forward than spending big time in administrative job that can be carried by your learned machine.
As practicing enhance the practice, project management practice will enhance with machine learning.
Published at pmmagazine.net with the consent of Walid Gamil