Fourth Annual
Technical Conference

Held 29 July 2026

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The Project Production Institute hosted its Fourth Annual Technical Conference on Wednesday 29 July 2026. This annual event brought together industry experts, academics and thought leaders to explore cutting-edge solutions, methodologies, and technologies driving technical advancements and innovation in project delivery.

The primary objective of the conference is to discuss and address the root cause of major capital project cost and schedule overruns via research, discussion and dissemination of Project Production Management (PPM) and its foundation of Operations Science. Specifically, this conference focuses on the research, development and application of methods and technologies used to optimize the delivery of civil, digital and energy infrastructure projects. 

In support of this, PPI invited practitioners and academics to submit technical abstracts for consideration and presentation at the upcoming conference. Selected papers are presented at the conference. Papers focus on one of the following research categories: Theory (specifically related to Operations Science, PPM, Lean etc.), Model (the application of simulations, digital twins, robotics, autonomous, IoT, AI / ML) or Control (the use of various systems, protocols, methods and tools that are used to control Project Production Systems). Learn more about PPI’s research here.

Access presentation recordings from past Technical Conferences

3rd Annual Technical Conference Presentations
2nd Annual Technical Conference Presentations
1st Annual Technical Conference Presentations

Presentations and Papers

Opening Remarks

The next five years will commit more capital to data centers, energy and infrastructure than the industry has ever had to deliver at once. Gary Fischer of the Project Production Institute opened the Fourth Annual Technical Conference by setting the day’s research against that number. Estimates reach $3 to $7 trillion for data centers, $17 to $19 trillion for energy, and $22 to $25 trillion for global infrastructure. Meeting expectations at that scale requires doing more with less, and Fisc...
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The Next Era of Nuclear Energy: Why the Future of Nuclear Power Depends on Industrializing the Production Systems That Deliver Nuclear Power Plants

The obstacle to nuclear power at scale is no longer reactor engineering; it is the delivery model. Todd Zabelle, author of Built to Fail, argued that one-off projects cannot supply the nuclear capacity now demanded, and that the answer sits outside project management altogether. The stakes: three to four trillion dollars of capital investment, in a sector where indirects run to 41 percent of cost against 17 percent for equipment. Zabelle’s diagnosis: projects are managed as organizations, ...
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Opportunities and Challenges of Nuclear-powered AI Data Centers

The technology for nuclear-powered AI data centers exists; a nuclear industry that learns from one reactor to the next does not. Tony Roulstone of the University of Cambridge set out to bring realism to the demand question, not to dampen the enthusiasm but to locate the obstacles. Demand is real: 70 gigawatts of data centers by 2030, some say 200 by 2035. Against it stands a delivery record of ten-to-fifteen-billion-dollar gigawatt reactors, planning and construction each taking eight to ten yea...
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Six Fundamental Production System Measurements

Existing project metrics do not predict project performance; production measurements can. Roberto Arbulu of Strategic Project Solutions argued that project outcomes emerge from the behavior of production systems, not from the schedules, budgets, and management reports used to observe them. The paper defines six fundamental production system measurements, four rates (demand, capacity, throughput, and load) and two ratios (rework and scrap), alongside work in process and capacity utilization, two ...
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A Technical Decomposition of Project Schedules and Production System Models

A resource-loaded schedule is not a production system model, and the difference is measured in weeks. Chet Carlson of Factory Physics and Strategic Project Solutions built his session around the question project teams ask most, what a model adds that the schedule cannot, taking both apart: inputs, assumptions, and what each can predict. The test case: 620 pipe spools across five areas of a pipe rack, planned at six weeks on a resource-loaded schedule with adequate resources and material. The sam...
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Beyond Variability: Designing and Controlling Capital Project Production Systems

Ask why capital projects are hard to control and the answer is usually one word: uncertainty. James Choo of the Project Production Institute argued that the word covers six distinct behaviors and using it loosely is costly: one label draws one remedy, more contingency, more planning, more pressure, then longer durations, higher cost, and more tied-up cash. The paper names six sources of demand-capability mismatch: uncertainty, randomness, statistical variability, structural heterogeneity, rate i...
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Inventory-Time Buffers vs. Capacity Buffers Tradeoff in Project Production Systems

Every project buffers variability somewhere; few choose where deliberately. Iris Tommelein of UC Berkeley, presenting with James Choo of the Project Production Institute on work co-authored with Gregory Saragih, used the Parade of Trades to turn that choice into a measurable trade-off between trade time on site and total project duration. Five trades pass 100 units at rates set by dice. With no variability, an all-five die, the work takes 24 time units; replace it with a 3-7 die, same average of...
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The Capable Owner: Why Owner Capability Is the Primary Determinant of Capital Project Outcomes

Owner capability, not contractor skill or contract form, is the primary determinant of capital project outcomes. Gary Fischer of the Project Production Institute made that bold claim and asked every owner: are you a buyer, configuring options, or a builder, living in the thousands of decisions a custom asset requires? The core case: an LNG development whose five-partner owner consortium limited its team to answering questions and paying bills. The winning EPC’s claimed standard, proven des...
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From Static Control to Adaptive Intelligence: Introducing the Adaptive Project Delivery Framework

Stage-gate governance reviews a project quarterly; risk arrives daily. Keith Magowan, a major capital projects executive, argued that the capital value process governing oil and gas megaprojects was designed for simpler supply chains and slower information; in Magowan’s framing, “governance latency is the root cause of drift.” Magowan cited the record: 80 percent of major capital projects exceed sanctioned cost or schedule, and 1.6 trillion dollars of value is destroyed annuall...
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Proper Selection of a Project Delivery Method: Reconciling Deterministic Frameworks with Operations Science and Project Production Management

Delivery method selection is still treated as commercial risk transfer. Victor Viteri of CONEXIG argued it decides whether the production system can be optimized. Risk moved on paper does not change how work flows, yet traditional frameworks evaluate commercial characteristics and largely ignore production physics. The empirical base is Construction Industry Institute analysis of 351 projects: delivery methods produce statistically different outcomes, with construction management at risk 4.5 per...
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Advanced Project Delivery (APD): A Coordinated Push For Better Projects

Lean, AWP, and PPM share one origin and push in uncoordinated directions. John Strickland of Collaborative Flow presented Advanced Project Delivery as the reconnection. APD is a forum, not a merger: each institute keeps its mission while the three learn from each other and combine their influence. Strickland traced the fifteen-year divergence: lean construction began from the tenet that projects are production systems, its planning practices fed Alberta’s workface planning, which became Ad...
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Real-Time Production Measurement through IoT: Steps Towards Enabling Automated Production Control

Manual production reporting is too slow and too subjective to trust. Dan Rahill of Pirimid asked whether fabrication data can collect itself. Owners re-check every layer of manually reported progress until monthly reports land six weeks late; advanced manufacturing runs on continuous, objective data from a sensor and an algorithm. The test case was simple: one week at an oil and gas fabrication facility in Texas, a three-inch filtration housing product, and sensors reading power consumption off ...
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A Decision Framework for Early-Stage Construction Robotics Deployment: Evidence from 10 Commercial Envelope-Installation Projects

Every robot deployment forces the same choice: fix the product, or hand the crew a procedure. Kenrick Tjandra of Raise Robotics built a decision framework for that choice. With co-author Rishabh Aggarwal, Tjandra opened on a robot arm post too tall for the construction hoist: folded by hand on a loading dock, then lowered by default in the next release. Field evidence spans ten deployments, nine general contractors, and eight states: more than 5,000 robot hours, zero safety incidents, and roughl...
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From Visual Perception to Flow Observability in Anchored Shotcrete Retaining Walls: A Preliminary Computer Vision-Based PPM Framework

Construction sites photograph everything, yet images are documents, not production data. William Yalico Arango of COSAPI turned drone images into production events. A photo shows the site but not when work started or how long a wall waited; the preliminary framework closes that gap for anchored shotcrete retaining walls in deep urban excavation. The dataset is 13,760 drone images from daily flights over a healthcare project in Lima: two sectors, six levels, 46 wall panels per level. Field realit...
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Applying AI Within a Project Production Management Framework to Optimize Construction Operations

Asked to pick a wall system for a data center, AI answered a better question. Stephanie Ware, an independent consultant, tested AI inside the PPM framework. The structured sequence began with product selection and ended in production system design, contracting strategy, and variability, which Ware presented as the core insight of the work. The method is the contribution: a five-step prompt sequence, four large language models cross-checking one another as a built-in peer review, and foundational...
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AI-Driven 4D BIM: A Framework for Real-Time Construction Planning

Most 4D BIM models stop being used the day they are first shown. Juan Diego Delgado of the University of Houston asked whether AI can keep them alive. A scope change cannot reach the model until someone revises the schedule, re-exports, relinks, and reruns, so the model falls behind the project it depicts. The baseline is the Penn State BIM guide workflow, whose model-follows-schedule dependency Delgado called “a death sentence for the 4D models.” Four bottlenecks followed: a rework ...
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Production System Optimization in Turnarounds: Controlling Flow, Reducing Variability

Turnaround delays are rarely caused by the craft task itself. Keith Grimes of Turnaround EPC blamed the system around the task. More than 65 percent of turnarounds exceed planned cost or schedule, and the causes sit in permits, inspections, scaffold access, and decision delays, not in how fast crews work. The headline measurement comes from a recent shutdown where waiting accounted for 60 to 80 percent of total cycle time across welding workflows: a pipe weld needs limited touch time but queues ...
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Wrap-Up

One theme ran through every session: projects behave as production systems, and the field is moving from qualitative judgment to quantitative measurement. Gary Fischer of the Project Production Institute closed the Fourth Annual Technical Conference with the takeaways he recorded across the day. Research is advancing but far from finished, and artificial intelligence is changing the equation in ways the field is only starting to see. The shift applies wherever work is repeatable, from nuclear an...
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