The predictive lifecycle is a way to handle projects that emphasises careful planning and documentation at the beginning, with a focus on being able to predict what will happen and sticking to a plan. It is often used in old-fashioned ways of managing projects, like the Waterfall model.
In the predictive lifecycle, a job is broken up into steps that are done one after the other. Before moving on to the next phase, each phase usually has certain deliverables and targets that must be met. Most of the time, the steps are:
1. Collecting and analysing requirements: During this phase, the project team works closely with partners to figure out and write down the project’s needs and goals.
Design: After the requirements have been set, the team moves on to the design phase, where they make thorough plans and specifications for the project’s parts or system.
3. Development: During this part, the team builds and codes the project based on the approved design specifications.
Testing: Once development is done, the project goes to the testing phase, where the team makes sure that the project meets the requirements and works as planned. This includes unit tests, integration tests, system tests, and user acceptance tests.
5.Deployment: The project is put into action in the production setting after it has been tested and passed. This includes installing and setting up the software, teaching end users, and moving from the development environment to the operational environment.
Maintenance and support: Once the project is up and running, there are ongoing maintenance and support tasks to deal with any problems, bugs, or changes that may come up during the project’s lifecycle.
The predictive lifecycle is based on the idea that objectives and scope can be set up front and that there will be few changes during the project. It works well for projects with stable needs that are easy to understand and where big changes are unlikely.
But it’s important to note that the predictive lifecycle may not work well for projects that are complicated or unsure, because it may not allow for freedom and adaptability to changing circumstances. In these situations, Agile or iterative methods are often better because they are more open to change and have cycles of planning, doing the work, and getting input.
