Turning a Schedule Into a Model
The goal of creating a schedule is to help companies work smarter and smoother, but many scheduling tools are actually the bane of efficiency. Scheduling features are often complex and time-consuming to learn and leverage their potential. And when adjustments or changes are made, these changes can upset subsequent actions to create bottlenecks and other inefficiencies throughout the remaining timeline.
There’s a growing need to transform schedules into predictive models to align timing, resources, and production for an optimal outcome. Here’s what the process looks like and why it’s important:
Schedules are Transactional
By definition, a schedule simply places tasks at a point in time and establishes an order in which tasks will be completed. Schedules are commonly resource-based, such as setting a schedule for a drilling rig or crew.
The scheduling process is largely transactional — you’re trading time for resources, where one event happens because of another event. When the event, or transaction, is over, there’s no understanding of its larger impact on a project, no progress to be shown, and no projection of completion for the project as a whole.
Models are Predictive
Modeling is proving to be a viable alternative to traditional scheduling. A model uses data to show how a single task affects the entire system or project. When tasks are shown as models instead of transactions, you can see its larger impact on a project, gauge any progress made, and better predict the completion of a project.
By connecting the tasks on a schedule with dependencies, a schedule can become a model in the following ways:
- Dependencies can link tasks to dates. For example, a task must start or end by a certain date.
- Dependencies can link tasks to each other. For example, a task must begin or end at least x number of days after another task begins or ends.
When tasks contain other parameters, such as the cost of revenue, the model now predicts the effects of timing on these parameters.
Why Modelling Your Project is Essential for Informed Decision Making
There’s no doubt that data drives decision making, and predictive models rely on your project data to help you keep your project moving forward. This means you gain the advantage of two key benefits:
- Comparing the results of models with different assumptions to guide decision making
- Timely identifying future conflicts in a project to provide accurate lead time and find the most efficient solution
When you can rely on real data versus gut instinct or hunches, you can provide your customers and employees with a better overall experience.
ForeSource was Designed with These Principles in Mind
We developed ForeSource to achieve the benefits of informed decision making. ForeSource allows users to build as many dependencies as necessary for a task. From there, a simplex solver analyzes all dependencies and re-orders tasks to meet requirements. Using the dependency dashboard, dependencies can be edited, ignored, or deleted to see the impacts on the model.
See ForeSource in action — schedule a demo today and discover how data-driven predictions can help you solve the inefficiencies of traditional scheduling.