Generative scheduling: Saving time and money in capital projects

With both infrastructure spending and inflation on the rise, optimizing scheduling and work sequencing can accelerate projects and unlock valuable savings.
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Between now and 2027, investment in infrastructure and sustainability capital projects is expected to hit $130 trillion. Effective spending at this level will require a solid delivery strategy to mitigate the schedule and cost overruns that so often plague capital projects.

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Part of this mitigation can be found by increasing the data-driven nature of decision making on capital projects with advanced analytics. The innovative combination of new ways of working and powerful advanced-analytics tools creates an opportunity to reduce project costs and overruns by as much as 30 to 50 percent. A wide variety of advanced solutions are being deployed by projects today to solve complex, nonlinear problems in resourcing, sequencing, design, and construction and engineering processes, opening new options for consideration by project teams, and more rapid decision-making during execution.

For example, with skillful problem solving and a structured approach, project teams can leverage generative-scheduling tools to test millions of schedule configurations and find the optimal work sequence and resources for a construction project in a matter of minutes. This output offers a counterintuitive approach—when human-led planning might otherwise simply add labor to accelerate delivery—and guides fast decision making to change how projects are delivered. In a similar manner to navigation tools that plot the fastest route to a destination, generative scheduling can generate optimized sequencing and resourcing plans that can save both time and money.

A generative-scheduling approach works by building a model that considers the physical and spatial constraints that govern how work needs to be done on-site. Once the model is created, structured problem solving is used to identify targeted “what if” questions about subjects including labor, equipment, installation rates, access, start-up sequence, and productivity. An advanced-analytics algorithm then produces hundreds of thousands of scenarios for how the work could be completed, and rapidly tests these scenarios to explore different options to generate a resource-loaded schedule that forecasts the costs and timing of the project, allowing project owners to select the option that best delivers their objectives. With a robust analysis in hand, project teams can work differently at every level of the project, making faster decisions and avoiding the pitfalls of simply reacting to deviations as they happen.

To be successful, these advanced-analytics approaches that provide the ability to rapidly interrogate decisions in a data-driven way, must be paired with a transformation in the way that project teams work. Tools alone will not capture the opportunity. Changing what questions are asked, how decisions are made, and how decisions are executed, prevent projects from missing the opportunity to revolutionize their cost and schedule outcomes.

Learn more about generative scheduling in this short video, which explains some of the challenges that project owners today are facing, as well as the opportunities that exist for generative-scheduling tools to help reduce costs and minimize schedule overruns.

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