Gary Fischer closes the 12th Annual PPI Symposium by distilling the day’s common themes: PPM’s impact on speed, the necessity of integrating product and process design, and how production thinking provides a framework that, once seen, permanently changes how you view projects and the world.
Creature, a vertically integrated design-build firm, shares how mapping their production system produced a 23% cycle time reduction, and applying controls to specific phases yielded up to 60% improvements, while extending PPM principles from construction trades to their in-house architecture team.
A Texas A&M Next-Gen capstone project applies production system mapping to a chemical plant turnaround, identifying pipe fitter bottlenecks, enabling real-time change analysis, and shortening a four-day shutdown by one day to save nearly $1 million in lost revenue.
Texas A&M research reframes risk management through production systems, showing how WIP measures exposure to design change risk and how carrying design alternatives creates quantifiable value, while the university’s certification program with PPI teaches professionals to apply these concepts to real projects.
Research from Stanford demonstrates how computer vision and graph neural networks can automatically capture granular production data from construction video footage, enabling real-time detection of workflow interruptions and closing the information gap between field execution and management decisions.
Stanford CIFE and Mortenson apply Little’s Law and operations science to the submittal process, revealing that delays on a $1.3 billion project weren’t caused by slow reviewers but by system overload, and showing how AI can enable production control rather than just task automation.
Stanford’s Martin Fischer shares how operations science and PPM concepts are being integrated into graduate and professional education, alongside research on design change management and multitask construction robots that directly address the industry’s demand and supply challenges.
Mark Spearman and James Choo explain the science behind optimal buffering strategy, revealing why OEE metrics can be counterproductive and demonstrating that capacity buffer and variability reduction are mathematically equivalent paths to faster, more predictable project delivery.
