August 25, 2025
Validating a bid schedule isn’t just about double-checking the schedule for dates, milestones, resources and other items.
It’s about pressure-testing the entire plan from the scope of the project, milestones, sequencing of construction activities, resource availability and allocation, as well as productivity, to ensure you’re submitting a proposal you can stand behind or identify an opportunity to improve the schedule.
In one of our recent webinars, Director of Customer Success Diana Su walked through a structured approach to doing exactly that, using ALICE’s generative construction platform. The session followed a fictional infrastructure demo project, the Gotham City Water Mains Installation, a multi-phase underground utility project involving shafts, tunneling, chambers, and commissioning. She also showcased two of ALICE’s newest features in the webinar: the Schedule Insights Agent and the Schedule Difference Report.
Both features support a wide range of use cases across bidding, planning, and execution. You can watch the full webinar here.
One of the first steps in validating a bid schedule is confirming that the scope and high-level sequencing logic actually reflect the intent of your plan. Using ALICE Plan’s 2D canvas, Diana demonstrated how to visualize imported P6 schedules to quickly spot:
She then used critical path filtering, block creation, and a 2D+time playback feature to validate that the schedule flowed as expected and aligned with the project’s phasing strategy.
A common challenge during bidding is confirming whether critical milestones—such as phase completions—can realistically be achieved under the proposed schedule. This is a standard requirement in many tenders, and validating feasibility early can build confidence or break a proposal.
In the demo, the focus was on Phase One Completion, a critical milestone tied to contractual incentives. The question was simple: Can the schedule built in P6 actually meet the phase one completion milestone as planned?
To test that, Diana ran a ‘What-if’ scenario in ALICE using the built-in optimization presets feature that prioritized the milestone above all else. All labor constraints were removed to simulate an ideal condition, just to see if hitting the date was even theoretically possible.
The result? The AI simulation showed that the scheduling was still hitting exactly 592 workdays compared to the original P6 schedule with a one day of delay for the Phase 1 Completion.
Use case: Test Feasibility of Milestones and Durations
This revealed a key insight.
Even with unlimited labor resources available, ALICE couldn’t accelerate the schedule to meet the Phase One milestone.
Why? Because the underlying schedule structure was too rigid.
One simple step is to make the schedule more flexible—specifically by giving flexibility to how many resources can perform each task.
Instead of assigning a fixed number of crews (e.g., always one per activity), the demo showed how to define a range—say, between 3 and 6 crews. This gives ALICE the ability to decide what’s most effective based on the optimization objective, available labor pool, and sequence logic.
To support this flexibility, the original fixed durations were replaced with parametric formulas. The scope stays the same; the schedule just becomes more adaptable.
A new ‘what-if’ scenario in ALICE with this flexibility led to a powerful result: the Phase One Completion milestone was not only achieved, but exceeded by 26 days, without increasing the overall labor pool.
The optimized scenario preserved the original headcount but reallocated crews more intelligently across the schedule. The final bid schedule reflected a plan that was not just buildable—it was defensible, achievable, and backed by data.
Validating a bid also means stress-testing the schedule against realistic risks, especially around labor productivity.
Using ALICE’s Insights Agent, Diana asked:
“What are the highest risks to meeting the milestone?”
The AI surfaced key risks such as:
To explore these further, she ran scenarios simulating 20% slower tunneling productivity, using parametric productivity indices in the duration formulas.
The impact was clear: Phase One Completion slipped by 212 days.
By simulating both best- and worst-case productivity outcomes, teams can identify which crews or scopes were most sensitive—and proactively address them before submitting the bid.
Finally, ALICE's Explore page provides users with a visual landscape of all tested scenarios. Each one—a “dot” on the scatter plot—represented a version of the bid schedule with different constraints or resource strategies.
This made it easy to:
For even deeper insight, the demo leveraged ALICE’s new Schedule Difference Report—a feature that automatically generates a written summary comparing two schedule scenarios. Instead of manually scanning Gantt charts to understand what changed, teams get a clear, narrative explanation of key differences and their impact. This is especially valuable for internal reviews, cross-functional discussions, and executive reporting, where time and clarity matter most.
Introduce flexibility into your bid schedule and validate it through scenario-based simulation. ALICE makes it possible to do this in hours—not weeks—by enabling teams to: