While many obvious warning signs exist on projects that point to an unhealthy state of being – in jeopardy of not meeting business, cost, schedule, quality and safety objectives, there exist other red flags that are not as obvious and often overlooked by project teams. Among these, five standout: 1) Re-baselining, 2) Asking Superficial Questions to Determine Project Health, 3) More Will Fix It Approach, 4) We Have Plenty of Time, and 5) We Keep Doing the Same Thing (And Expecting Different Results). Regardless of their subtlety, the impacts to projects from these red flags can be (and usually are) quite detrimental. The purpose of this paper is to explore these five not-so-obvious signs including real project experiences on how they manifest, and in so doing, establish common themes for project professionals to be cautious about.

Good hand-offs from one performer to the next have always been an important part of the Last Planner System® (LPS). LPS features a rigorous process for making reliable commitments and the Percent Plan Complete (PPC) metric to measure how many were done as promised. LPS and PPC have helped reduce waste and frustration in countless weekly planning meetings and represent a major step forward in how we think about work on projects. Reliable commitments and PPC, however, focus primarily on the upstream side of the hand-off: What are we committing to do? What about commitments on the other side of the handoff: Who is committing use what was produced and how quickly will they do so?

Effective communication and coordination during daily planning meetings are crucial for successful project execution and fostering a robust safety culture in the construction industry. However, conventional project management approaches, rooted in what the Project Production Institute (PPI) identifies as Era 1 and Era 2 thinking, have significant gaps that prevent the satisfactory management and execution of today’s complex and dynamic capital projects. This paper presents a novel approach that aligns with PPI’s Era 3 thinking, viewing projects as production systems and applying operations science to optimize delivery.

As a project progresses throughout its lifecycle, it is important for the project team to learn from prior completed activities in the system. This can be used to adjust the remaining contingency for the project. In this paper, this situation is modelled using Erlang Distribution. Using Bayes’ Law, the associated cost for the remaining work packages is adjusted and fit to the required confidence based on the updated arrival rates of the bottleneck resource.

To meet the demand for infrastructure created by digital transformation, energy transition, and the need for commercial and residential space, owners and their contractors continue to move work offsite. From hospital modules to small modular reactors (SMR’s), the thinking is that construction can be made like manufacturing where safer, more productive work environments reduce the cost and time of project delivery. However, for many, the benefit of an offsite strategy remains elusive. If the promise of projects being delivered safer, faster, at lower cost and more predictably, by moving work offsite is to be realized, project professionals must understand how to design and control production systems and the facilities they require to efficiently operate. The purpose of this paper is to set forth a methodology for designing offsite production facilities including how to lay them out, select resources and how to effectively control the production system and associated supply chain.

To deliver projects predictably, organizations rely on the use of schedules and various techniques to create and visualize them, including Critical Path Method (CPM), Gantt charts, and physical/virtual “stickie note” plans to name a few. Schedules may be created either by highly skilled schedulers or through collaboration with input from various project participants. To make schedules more robust including better predicting the implications of variability and determining how much contingency to carry, many organizations have been utilizing Monte-Carlo schedule simulations. Under this perspective, and regardless of whether schedules are deterministic or stochastics, they all utilize activity durations (including leads and lags) as the means to absorb variability. However, and despite the fact all these techniques have been around for more than 50 years, many global project performance reports continue to expose the fact that achieving reliable project outcomes remains elusive. This gap is due to the lack of focus on understanding the underlying production systems and supply network that enable the delivery of capital projects (Zabelle 2024).

The adoption of offsite construction methods presents a forward-thinking solution to critical challenges such as stagnant productivity rates, widespread housing shortages, skilled labor shortages, and increasing carbon emissions. However, moving the production location from onsite to offsite makes management a more complex task, resulting in offsite construction projects not achieving the time and cost targets. For instance, we observed that an offsite production factory incurred unnecessary high inventory costs due to holding too many products in stock before the onsite installation started.

Capital project owners and service providers may be blind to the fact that working with excessive amounts of inventory contributes to longer schedules, cost overruns and impact to cash flow, which negatively affects return on investment. On the other hand, not enough inventory and the project will not achieve the required throughput while also compromising cost and schedule targets. So, in the spectrum of too much and too little, is there an optimal point? What are the drivers of this optimal point? Can we determine it, and if so, how?