Bildry: A Gantt Chart-Based Construction Planning System Integrating Regional Risk Data
Corresponding author: iam@romanvlasov.com
Abstract
Construction project schedules often fail to account for regional risk factors such as weather variations, local holidays, state specific building codes, and labor market conditions. This paper presents Bildry, a Gantt chart based planning system that integrates these regional risk data across United States states to improve resource estimation and schedule reliability. The problem addressed is the high incidence of schedule delays and manual adjustments in construction projects caused by unanticipated local factors.
The methodology involves aggregating historical climate data, state calendars, code compliance requirements, and labor productivity metrics and embedding them into the scheduling algorithm. The system description illustrates how Bildry augments traditional Critical Path Method schedules with risk informed adjustments in task durations and sequencing.
A case study is presented using a hypothetical construction project with a Gantt chart, demonstrating how Bildry proactively inserts weather buffers, holiday workarounds, and code related inspection lead times. The results show that the Bildry generated schedule reduced unexpected timeline shifts and the need for reactive manual edits while improving completion date predictability and transparency for stakeholders.
The discussion compares this approach with conventional planning methods and outlines implications for industry practice. The findings suggest that integrating regional risk intelligence into Gantt based scheduling significantly enhances the robustness and credibility of construction project plans, thereby reducing delays and contractual disputes.
Keywords
construction scheduling, risk based planning, regional factors, Gantt chart, weather delays, building codes, labor productivity, project management
1. Introduction
Construction projects are inherently complex, involving numerous interdependent activities and uncertainties. Traditional project scheduling methods such as the Critical Path Method (CPM) and the Program Evaluation and Review Technique (PERT) typically assume fixed task durations and deterministic timelines [13,6]. In practice, however, unforeseen disruptions such as adverse weather conditions, workforce fluctuations, and regulatory delays are common. These uncertainties frequently lead to schedule overruns, cost escalation, and repeated manual revisions of project plans.
The problem addressed in this research is that conventional Gantt chart schedules generally lack integration of region specific risk factors. As a result, project plans remain brittle and poorly adapted to local conditions. Schedulers often add generalized contingency buffers, but without data driven insight into which risks are most probable in a particular region or season, these buffers are largely heuristic [13,12]. Consequently, many projects experience delays when real world conditions deviate from baseline assumptions, forcing project managers into reactive re planning and creating uncertainty for stakeholders [13].
Recent industry surveys highlight the scale of this issue. Approximately 45 percent of construction projects worldwide are affected by weather related delays, resulting in substantial financial losses [12]. Labor shortages constitute another major risk factor. A 2025 survey of U.S. construction firms reported that 92 percent of companies struggled to fill open positions, while 45 percent experienced direct project delays due to workforce shortages [9]. Regulatory variability further compounds scheduling challenges. A national survey of real estate developers conducted in 2024 found that 92 percent of respondents considered inconsistent building code requirements across jurisdictions to be a significant source of project risk [10]. In addition, local public holidays and working hour restrictions vary by state and municipality, yet these calendar constraints are often omitted from standard schedules.
These factors collectively result in low schedule predictability. Project teams frequently commit to optimistic timelines that fail to account for regional realities, leading to delays, scope changes, and disputes when planned assumptions prove inaccurate. This situation underscores the need for planning systems that incorporate regional risk intelligence directly into the scheduling process.
Prior research in construction management has repeatedly emphasized the limitations of traditional scheduling techniques under conditions of uncertainty. Zhasmukhambetova et al. (2025) note that while CPM and PERT remain dominant in practice, they are insufficient for capturing the dynamic nature of construction risks, and advanced methods such as Monte Carlo simulation and fuzzy logic offer improved reliability [6]. Aven [2] and Kaplan and Garrick [3] similarly argue for structured risk analysis frameworks that combine qualitative expertise with quantitative data to enhance decision making in project planning. Despite these insights, practical adoption of risk based scheduling remains limited due to the complexity of handling location specific data and integrating it into user friendly planning tools.
This paper addresses this gap by introducing Bildry, a Gantt chart based planning system that integrates regional risk data for construction projects across the United States. Developed by the author, Bildry incorporates historical weather data, state specific holiday calendars, local building code requirements, and labor productivity factors into the scheduling logic. The system adjusts task durations, sequencing, and resource allocations based on these inputs, producing schedules that are tailored to the geographic and regulatory context of each project.
The underlying hypothesis is that schedules enriched with regional risk intelligence will reduce unexpected delays, minimize the need for manual schedule revisions, improve completion date predictability, and enhance transparency for project stakeholders.
The remainder of this paper is structured as follows. Section 2 describes the research methodology, including data sources and risk integration mechanisms. Section 3 presents the architecture and functional features of the Bildry system. Section 4 provides a case study demonstrating the application of Bildry to a hypothetical construction project. Section 5 reports the results and compares them with outcomes from conventional scheduling approaches. Section 6 discusses the implications and limitations of the proposed method. Section 7 concludes the paper and outlines directions for future research.
2. Methodology
2.1 Integrating Regional Risk Data into Schedules
The methodology underlying the Bildry system is based on the systematic integration of region specific risk data into the Gantt chart scheduling process. Four primary categories of regional risk factors are considered: (i) weather conditions, (ii) public holidays and calendar events, (iii) local building codes and permitting requirements, and (iv) labor related factors. These categories reflect the most common sources of schedule uncertainty in construction projects and are consistently identified in industry surveys and academic literature as major contributors to delays and re planning [9,10,12,13].
Figure 1, presented later in the Case Study section, illustrates how these risk factors are translated into adjustments within a project timeline. The following subsections describe the data sources and integration mechanisms applied for each category.
Weather risk integration. Bildry incorporates historical climate datasets and regional forecasting statistics at the county or ZIP code level. These datasets include long term records of precipitation, temperature extremes, seasonal storm patterns, and other climate variables relevant to construction activities. Rather than applying a uniform contingency buffer, the system allocates weather related allowances dynamically, concentrating contingency during periods with a statistically higher probability of disruption [12,13].
For example, when scheduling a project in Florida during the summer months, Bildry identifies elevated risks associated with thunderstorms and hurricanes. Weather sensitive activities such as excavation, roofing, and exterior finishes are either shifted away from peak risk windows or assigned extended probabilistic durations. This approach reflects emerging practices in risk based scheduling, where weather impacts are modeled as probability distributions rather than deterministic delays [13]. By embedding multi year climate data, Bildry enables more realistic sequencing decisions, such as prioritizing interior work during high precipitation seasons and exterior tasks during more favorable periods [12].
Public holidays and calendar constraints. Construction schedules are highly sensitive to local non working days, including federal holidays, state specific observances, municipal events, and customary industry shutdown periods. Bildry automatically compiles a location specific project calendar by aggregating data from state government sources and municipal ordinances. All applicable holidays and typical construction breaks are marked as non working time within the Gantt chart.
This practice aligns with established project management guidelines, which emphasize the explicit definition of non working days in schedule models [1]. By embedding calendar constraints directly into the scheduling logic, Bildry prevents the inadvertent allocation of work during periods when labor, inspections, or material deliveries are unavailable. For example, if a critical activity is initially planned during late December, the system adjusts the timeline to account for holiday closures of inspection offices and suppliers. Field experience confirms that failure to anticipate such closures is a common source of avoidable delay [11].
Local building codes and permitting requirements. Regulatory processes represent a significant and often underestimated source of schedule risk. Building codes and permitting procedures vary widely across U.S. jurisdictions, and additional inspections or approvals can substantially extend project timelines if not planned for in advance [10]. Bildry addresses this risk through a structured regulatory knowledge base derived from the International Building Code and its state level amendments, supplemented by historical permitting data.
When a project location and scope are defined, the system automatically loads any mandatory regulatory tasks relevant to that jurisdiction. Examples include additional seismic inspections in California or hurricane resilience inspections in Florida. Expected durations for permitting and approval tasks are adjusted based on local historical data, recognizing that approval timelines in major metropolitan areas are often significantly longer than in smaller municipalities. This ensures that schedules do not assume construction start dates that are unrealistic given local permitting realities. Industry guidance consistently emphasizes early integration of regulatory constraints as a key strategy for reducing schedule risk [15].
Labor related factors. Labor availability and productivity are integrated to reflect regional workforce conditions. Construction productivity varies due to climate, union rules, work practices, and the availability of skilled trades. Bildry incorporates labor productivity indices and workforce availability data derived from public statistics and industry surveys, including reports on regional labor shortages [9].
Task durations are adjusted based on historical productivity trends. For instance, activities scheduled in regions with severe winter conditions may receive extended durations to reflect reduced productivity. Where data indicate shortages in specific trades, the system applies resource constrained scheduling logic, limiting task overlap when the same trade is required. This prevents unrealistic assumptions about parallel execution that cannot be supported by the local labor pool.
In addition, union calendars and region specific labor restrictions are treated as calendar constraints similar to public holidays. By embedding these labor considerations directly into the schedule, Bildry produces project plans that are both time feasible and resource feasible. Empirical evidence shows that failure to account for labor constraints is a major contributor to schedule delays in U.S. construction projects [9].
Collectively, the integration of weather, calendar, regulatory, and labor data transforms the Gantt chart from a static representation into a risk informed planning instrument. This methodology reduces reliance on subjective contingencies and supports proactive, data driven decision making during the planning phase.
2.2 Data Collection and Technical Implementation
To implement the proposed risk integrated scheduling approach, the Bildry system is built around a modular architecture consisting of multiple data modules and a central scheduling engine. Each module is responsible for collecting, processing, and normalizing a specific category of regional risk data before it is applied to the Gantt chart logic.
Weather data processing. The weather module retrieves historical climate data through external application programming interfaces, including public sources such as NOAA climate databases and selected commercial weather services [12]. For each project location and planned timeframe, the system extracts statistical indicators such as average precipitation days per month, seasonal storm probability, and temperature related work constraints.
These indicators are translated into expected downtime allowances for weather sensitive tasks. For example, if historical data show an average of four rain affected days in July for a given location, the scheduling engine inserts an expected weather delay allowance of approximately four days for outdoor activities scheduled during that month. This allowance may be represented either as a contiguous buffer period or distributed across the affected tasks, depending on task dependencies and user preferences.
Advanced analytical functionality includes Monte Carlo simulation. In this mode, the engine can execute hundreds or thousands of schedule iterations, randomly assigning weather on and off days according to historical probability distributions. The result is a probabilistic distribution of possible completion dates rather than a single point estimate. However, to maintain usability and interpretability for practitioners, the default output remains a deterministic schedule with explicitly labeled weather contingency buffers. This design choice reflects findings by Koulinas et al. [7], who demonstrated that simulation enhanced prediction accuracy while remaining acceptable to project managers when presented in a clear form.
Holiday and calendar integration. Holiday and calendar constraints are handled through a maintained library of non working day calendars. During system development, federal and state holiday lists were compiled and are updated annually. In addition, typical industry shutdown periods, such as the year end break between Christmas and New Year’s Day, are included by default for U.S. based projects, with the option for user override.
The scheduling engine implements these constraints as a project calendar with explicit non working exceptions [1]. Any task that would overlap with a designated non working day is automatically extended or split around the interruption. For instance, a task initially scheduled from December 20 to December 30 will be divided to reflect a pause during Christmas, with execution resuming after the holiday. This behavior corresponds to established calendar logic in professional project management software [1]. In the case study presented later, this mechanism is visible as a clear gap in the late December portion of the Gantt chart.
Building code and permitting logic. Regulatory data were incorporated using a hybrid rule based and data driven approach. The system’s regulatory database was assembled from public sources such as federal guidance documents, state and municipal building department publications, and structured input from experienced project managers [15]. For each state and major metropolitan area, templates of typical permitting steps, inspections, and associated lead times were defined.
When a project location and scope are specified, Bildry automatically activates the relevant regulatory template. Mandatory inspections and approvals are inserted into the schedule as explicit tasks with defined dependencies. For example, foundation inspections are linked to concrete placement activities, and certain permits are required before construction mobilization can proceed. Where jurisdictions are known for slower approval processes, the system increases task durations or inserts lag time before dependent activities begin.
The system also accounts for regionally mandated work stoppages, such as construction bans during major public events or temporary stop work orders during severe weather alerts in coastal regions. These intervals are treated as non working periods within the project calendar. By explicitly modeling regulatory constraints, Bildry transforms implicit delays into visible schedule elements, improving transparency and reducing the likelihood of downstream disputes. This approach is consistent with industry recommendations that emphasize making risk response actions visible within the project plan [14].
Labor data and resource feasibility. The labor module integrates statistical workforce data with user defined resource assumptions. At project setup, the user specifies expected crew sizes and trade allocations. Bildry then cross checks these assumptions against regional labor availability and productivity indicators derived from public labor statistics and industry surveys [9].
Task durations are adjusted using regional productivity coefficients. For example, if data indicate that extreme climate conditions or local work practices reduce productivity by twenty percent, a task originally estimated at ten days is scheduled for twelve days. Where regional labor shortages are identified for a particular trade, the system issues warnings and may prevent unrealistic parallel task execution by applying resource constrained scheduling logic.
This approach aligns with principles of Critical Chain project management, which explicitly accounts for resource constraints and introduces protective buffers to improve schedule reliability [1]. Bildry applies similar logic by sequencing tasks based on realistic crew availability and inserting buffers that absorb anticipated disruptions such as weather or labor shortages. While this results in schedules that may be longer than optimistic baseline plans, the resulting timelines are substantially more robust and executable.
In summary, Bildry treats schedule generation as a multi criteria optimization problem. The objective is not to minimize theoretical project duration, but to balance logical dependencies, contractual milestones, local constraints, and quantified risks. The resulting schedules represent a compromise between the shortest possible timeline and the highest probability of on time completion. This methodology embodies the risk based scheduling philosophy advocated in recent literature, favoring realistic and transparent plans over optimistic best case assumptions [13].
3. System Description
Bildry is implemented as a web based application consisting of a backend scheduling engine and a front end interface designed for interactive project planning and analysis. The system architecture follows a layered design that separates data management, scheduling logic, and user interaction. This structure improves scalability, transparency of calculations, and ease of future extension.
The architecture comprises three primary layers: (1) the Data Layer, which stores regional risk knowledge and reference datasets; (2) the Logic Layer, which contains scheduling algorithms and risk adjustment models; and (3) the Presentation Layer, which provides planners with visual Gantt charts and analytical feedback. In the following subsections, each layer is described, with particular emphasis on how an end user interacts with the system to develop a construction schedule.
3.1 Data Layer: Regional Risk Knowledge Base
The data layer serves as the foundation of the Bildry system. It stores and organizes the regional datasets required to embed local risk intelligence into the scheduling process. These datasets are structured as a unified knowledge base that can be queried dynamically based on project location, timeframe, and scope.
Climate database. This component contains historical and statistical weather data for all U.S. states and counties. Typical records include average monthly precipitation, frequency of extreme weather events, seasonal temperature ranges, and historical weather related delay indices for construction activities. Data sources include NOAA climate records and aggregated industry data documenting the impact of weather on project performance [12].
Calendar database. The calendar database stores federal and state holiday schedules, as well as customary construction industry non working periods. Examples include year end holiday shutdowns, state specific observances, and local events that traditionally interrupt construction activities. Calendar data are reviewed and updated on an annual basis or as regulatory changes occur.
Regulatory database. This module maps geographic locations to required inspections, permitting workflows, and code specific construction tasks. Entries include structured rules such as “Florida: hurricane strap inspection required after roofing, duration one day” or “California: mandatory seismic inspection following structural framing”. In addition, the database stores typical permit approval lead times for major cities and records known local restrictions, for example temporary construction bans during major public events.
Labor statistics database. This database captures regional labor productivity coefficients, workforce availability by trade, union rules, and working hour limitations. For instance, it may reflect reduced effective working hours during extreme summer heat in southern regions, or the prohibition of weekend work under certain union agreements. These parameters are used to adjust task durations and resource feasibility during schedule generation.
To maintain accuracy and relevance, the knowledge base is designed for periodic updates. Weather and calendar data are refreshed annually or in near real time during project execution. Regulatory information is updated following revisions to building codes or state legislation, with changes imported from authoritative publications such as ICC updates or official state bulletins. Labor statistics are updated using public APIs, industry surveys, and curated manual inputs as new data become available.
This continuous data maintenance strategy ensures that Bildry’s scheduling recommendations reflect current regional conditions rather than static assumptions. As a result, planners can rely on the system to generate schedules that remain aligned with evolving environmental, regulatory, and labor contexts.
3.2 Logic Layer: Scheduling Engine with Risk Adjustments
The core scheduling engine in Bildry extends a conventional Critical Path Method (CPM) algorithm by incorporating explicit rules for regional risk adjustment. At the initial stage, the system constructs a standard activity network based on user defined tasks and precedence relationships. This baseline network is equivalent to that produced by traditional scheduling software. Subsequently, the engine modifies task durations, start dates, and dependencies by applying risk informed rules derived from the data layer.
Application of calendar constraints. The engine assigns an appropriate project calendar to all activities, including weekends, federal and state holidays, and other non working days. Scheduling calculations automatically skip these periods. If an activity would otherwise start or finish on a non working day, its execution is shifted to the next available working day. This behavior follows standard calendar based scheduling logic as defined in PMI guidelines [1]. As a result, the initial schedule inherently respects known breaks and avoids unrealistic assumptions about continuous work.
Insertion of weather buffers. For activities classified as weather sensitive, either automatically by task type or manually by the planner, the engine evaluates the historical weather risk for the scheduled time window. Task durations are then adjusted or supplemented with buffer periods to reflect expected weather related downtime [12]. For example, a ten day roofing activity scheduled during a month with an average of four rain days may be planned over fourteen calendar days, consisting of ten working days and approximately four days of anticipated interruption. Alternatively, the engine may insert a separate weather contingency task following the activity. Both approaches achieve the same objective, namely allocating realistic time for weather disruptions.
When enabled by the user, the engine can also perform weather aware resequencing. Interior activities may be shifted into historically wetter periods, while exterior tasks are moved to drier windows, provided that logical dependencies allow such rearrangement. This automated adjustment improves schedule robustness and is consistent with research that models weather impacts probabilistically in construction schedules [13].
Enforcement of code and inspection requirements. The scheduling logic ensures that all mandatory inspections, approvals, and permit milestones required by local regulations are explicitly included in the activity network. If a task requires inspection sign off before subsequent work can proceed, the engine inserts a corresponding inspection activity with appropriate predecessor relationships. For instance, an electrical rough in task is followed by an electrical inspection task, and wall closure activities cannot begin until that inspection is completed.
Typical inspection booking delays are also modeled. If local data indicate that inspections are usually scheduled forty eight hours in advance, the engine reflects this either through task duration or lag time. Permit lead times are treated in a similar manner. When a permit approval is required prior to construction start, the engine schedules this activity at the beginning of the project and prevents construction activities from commencing until approval is obtained. By making regulatory processes explicit, Bildry produces schedules that are feasible under real world compliance constraints and aligns with recommended risk response practices that advocate embedding mitigation actions directly into project plans [14].
Resource leveling and labor adaptation. After applying calendar, weather, and regulatory adjustments, the engine performs a resource leveling pass. It checks for overallocation of crews and key resources and resolves conflicts where possible by delaying non critical activities within their available float. If conflicts occur on the critical path and cannot be resolved without extending the project duration, the system alerts the planner and presents the trade off explicitly.
Task durations are further adapted to reflect regional labor conditions. If workforce availability or productivity is lower than assumed in the baseline plan, durations are increased accordingly. The engine can also simulate changes in crew size to evaluate their effect on the overall schedule. This logic is conceptually aligned with resource constrained scheduling and Critical Chain principles described in project management literature [1]. The resulting plan may be longer than an optimistic schedule, but it is achievable given realistic labor constraints.
Optional risk metrics calculation. As an advanced feature, Bildry can compute quantitative risk metrics for the finalized schedule. By running Monte Carlo simulations on the adjusted activity network, the engine estimates the probability of meeting the target completion date and identifies activities with the greatest contribution to schedule risk. For example, the system may report an eighty five percent probability of completion by a specified date. Although this paper focuses on deterministic schedule outputs for clarity, such probabilistic indicators provide valuable decision support and are consistent with prior research demonstrating improved stakeholder understanding when uncertainty is communicated explicitly [13,7].
3.3 Presentation Layer: Gantt Chart Interface
Bildry’s front end presents the project schedule in a familiar Gantt chart format, augmented with visual indicators that reflect embedded regional risk factors. Each activity is displayed as a horizontal bar along a time scaled calendar axis. Tasks affected by specific risks are visually distinguished using color coding. In the case study presented later, standard activities are shown in blue, weather impacted tasks in orange, and code or inspection related activities in green.
Non working periods, including weekends and public holidays, are clearly marked on the Gantt chart using shaded columns or vertical separators. This visual treatment allows planners to immediately verify that no activities are scheduled during prohibited working days. For example, a visible gap appears in late December on the project timeline, corresponding to the holiday shutdown period. The shaded background reinforces that these dates are intentionally excluded from productive work.
When task durations are extended or buffered due to identified risk factors, Bildry provides contextual annotations to ensure transparency. By hovering over an activity bar, the user can view explanatory tooltips such as “Duration extended by 2 days for rain contingency based on historical averages” or “Includes 3 days lead time for inspection scheduling.” These annotations make the underlying assumptions explicit and allow stakeholders to understand why certain tasks require additional time or are positioned at specific points in the schedule.
In addition to task level notes, the interface generates a consolidated list of planning assumptions in a side panel or summary report. Typical entries include weather data sources and reference periods, applicable state code inspection requirements, adjusted labor productivity factors, and predefined non work intervals such as year end holidays. By exposing these assumptions directly, Bildry addresses a common criticism of construction schedules as opaque or difficult to justify. The visibility of inputs and risk allowances improves accountability and strengthens stakeholder confidence in the resulting plan [14].
From the user perspective, schedule creation in Bildry follows a workflow similar to conventional project management software. The planner enters the activity list, precedence relationships, baseline durations, project start date, location, and initial resource assumptions. Upon triggering schedule generation, the system applies its regional risk intelligence and produces an adjusted Gantt chart. The user can then review the timeline, inspect inserted buffers or gaps, and examine the color coded indicators corresponding to weather, regulatory, or labor related risks.
The interface supports iterative refinement and scenario testing. Planners can modify parameters and regenerate the schedule to explore alternative strategies. For instance, adding an additional crew to a weather sensitive task may reduce its exposure period, while allowing limited weekend work may offset anticipated delays. These scenarios can be evaluated visually and quantitatively before a final plan is adopted.
A key feature of the presentation layer is scenario comparison. Bildry allows planners to generate a traditional baseline schedule without risk adjustments and compare it side by side with a risk informed schedule. This comparison highlights the trade off between optimistic planning and realistic execution. Although the risk informed schedule may show a later completion date, it is protected by explicit buffers and contingencies, whereas the baseline plan is more vulnerable to disruption [13].
Beyond the Gantt chart, Bildry provides a dashboard of summary metrics, including predicted completion date, total buffer time, critical path length, and resource utilization profiles. If probabilistic analysis is enabled, the dashboard may also display confidence levels for meeting key milestones. These metrics help project teams identify schedule sensitivities and focus management attention on high risk activities. For example, the system may indicate that a roofing task lies on the critical path and contains a significant weather contingency, signaling the need for proactive coordination.
Overall, the presentation layer is designed to remain intuitive for users familiar with standard Gantt charts while providing additional layers of risk related information. By combining visual cues, annotations, and summary metrics, Bildry keeps planners in control of the schedule while informing their decisions with regional and historical data. In the following section, the system is demonstrated through a case study that contrasts a conventional schedule with a risk integrated Bildry schedule.
4. Case Study: Risk Informed Schedule for a Construction Project
To demonstrate the capabilities of the Bildry system, a case study was developed based on a hypothetical construction project. The selected scenario represents the construction of a mid sized commercial building located in coastal Florida. The planned project duration is approximately six months, with a start date in early fall. This geographic and temporal context was chosen intentionally, as it combines several prominent regional risk factors, including exposure to hurricane season, end of year holiday disruptions, state specific building code requirements, and ongoing labor shortages driven by regional construction growth.
Project overview. The project scope includes typical construction phases: site preparation and foundation works, structural framing, roofing, interior build out including mechanical, electrical, and plumbing systems, finishing works, and final inspections leading to issuance of a certificate of occupancy. Under conventional planning assumptions, these activities are scheduled sequentially using a standard CPM approach, resulting in an optimistic completion target in late spring. The purpose of the case study is to compare this notional baseline schedule with a risk informed schedule generated by Bildry.
Using Bildry, the task sequence was entered along with the project location (southwest Florida) and a nominal project start date in September of Year 1. Baseline task durations under normal working conditions were defined as follows:
- Foundation: 30 days, including excavation, forming, concrete placement, and initial curing.
- Framing: 30 days for erection of the primary structural frame.
- Roofing: 15 days for installation of roof structure and waterproofing systems.
- Interior work: 45 days covering HVAC, electrical, plumbing, drywall, fixtures, and finishes.
- Final inspection and handover: 10 days for inspections, final walkthrough, and occupancy permit issuance.
For simplicity, all tasks were linked using finish to start dependencies, producing a single continuous critical path from foundation to final inspection. Under these assumptions, the baseline schedule results in a total duration of 130 days, corresponding to approximately 4.3 months. If starting in September, the naive completion date would fall in January of the following year.
Once regional risk intelligence was applied, Bildry introduced several adjustments to the baseline schedule.
Weather contingency for roofing. In the baseline plan, roofing was scheduled for October, a month that remains within the Atlantic hurricane season and is characterized by high rainfall in Florida. Based on historical climate data, Bildry identified a substantial probability of weather related disruption during this period. As a result, the system inserted an explicit weather buffer of approximately ten days around the roofing task and shifted its execution toward early November, when hurricane risk declines. Although the roofing work still requires only 15 productive days, it was scheduled to span nearly three calendar weeks to accommodate expected rain interruptions. This adjustment reduces the likelihood that severe weather will force unplanned delays later in the project.
Holiday break adjustment. The baseline interior work phase would have continued uninterrupted through December. Bildry automatically detected the presence of major public holidays in late December and marked this period as non working. The interior work task was therefore split, with a planned pause around December 24 and resumption in early January. This resulted in an inserted gap of approximately seven days, pushing completion of interior works into late January. Although this extends the overall timeline slightly, it reflects realistic conditions in which subcontractor availability and inspection capacity are reduced during the holiday season.
Florida specific code compliance tasks. Bildry introduced several inspection activities required by Florida building regulations that were not present in the naive schedule. After framing and roofing, hurricane related inspections such as roof strapping and secondary water barrier checks were added. During interior work, rough in inspections for electrical, plumbing, HVAC, and fire protection systems were scheduled prior to enclosure of walls and ceilings. As a result, the original single block allocated for final inspection was replaced by multiple distributed inspection tasks and a slightly extended final handover phase. A small buffer was also included after final inspections to account for potential re inspections or minor corrective actions.
Labor constraint flagging. Based on regional labor market data, Bildry identified elevated demand and limited availability for skilled mechanical and electrical trades in Florida. The interior work phase, which relies heavily on these trades, was flagged as risk critical. While the system did not automatically extend the 45 day duration, it highlighted the potential for labor related bottlenecks. This information enables project managers to take proactive measures, such as early subcontractor engagement, adjusted sequencing, or supplemental staffing, to mitigate this risk.
The resulting risk informed schedule is presented in Figure 1. Compared with the baseline CPM plan, the Bildry generated schedule has a longer nominal duration, but it explicitly accounts for known regional risks. Rather than promising an optimistic but fragile completion date, the schedule provides a realistic and defensible timeline that is more likely to be achieved without major reactive replanning.
The Roofing task, highlighted in orange in Figure 1, does not start immediately after framing as it would in a standard schedule. Instead, after framing is completed, an intentional gap of approximately ten days is visible before roofing begins. This gap represents the weather buffer introduced by Bildry. In effect, roofing is shifted from late October to early November, thereby avoiding a time period that is historically prone to storms and heavy rainfall in coastal Florida.
The roofing activity itself spans a longer calendar duration than its nominal workload of fifteen working days. In the Gantt chart, the orange bar extends over more than twenty calendar days, indicating that non-working rain days are anticipated within the roofing phase. This may be represented either as an internal interruption within the bar or as a single extended duration, with the understanding that not every calendar day is productive. Crucially, subsequent tasks, specifically Interior Work, do not begin until the entire roofing period is completed. This means the plan explicitly allows roofing to take up to this extended duration without affecting downstream activities.
If weather conditions are more favorable than expected, roofing may finish earlier and interior work can start ahead of schedule, thereby consuming part of the buffer. Conversely, if a hurricane or prolonged rainfall occurs, the schedule has sufficient flexibility to absorb the delay without shifting the official project completion date. This mechanism substantially increases confidence in meeting the planned deadline, as further discussed in the Results section.
The Interior Work task, shown in blue, begins in November and extends into January. However, it is intentionally interrupted by a clear break during the final week of December. This interruption corresponds to the holiday non-working period. Prior to this break, interior work progresses for approximately seven weeks, during which multiple trades operate within the building. From roughly December 24 to January 1, no work is scheduled. Activity then resumes in early January and concludes during the second week of the month.
In a conventional schedule, interior work might be shown as continuing uninterrupted through late December. In practice, however, this period is rarely fully productive due to workforce unavailability and limited inspection services. Bildry explicitly models this reality by inserting a pause, thereby avoiding unrealistic expectations. This structure allows the project team to complete defined sub-tasks before the break, such as wall rough-ins, and to allocate the first week of January to finishing works and preparatory inspections after crews return.
Immediately following interior work, the Final Inspections and Handover phase, shown in green, begins in mid-January. Although this phase represents approximately ten working days of effort, it spans nearly two calendar weeks. This reflects the practical need to coordinate multiple inspections, including building, electrical, plumbing, and fire inspections, which rarely occur on the same day. Additional time is also reserved to accommodate potential re-inspections or administrative processing. As scheduled, the project achieves occupancy approval and handover by late January.
For comparison, the baseline unadjusted schedule, overlaid in gray, would have shown roofing commencing immediately after framing in late October and finishing by early November without any weather allowance. Interior work would have extended through December without interruption, and final inspections would have concluded in early January, yielding a projected completion around day 130. The Bildry-generated schedule, by contrast, completes around day 152, corresponding to late January. While this represents an extension of approximately three weeks, it reflects realistic operating conditions. In practice, the baseline schedule would almost certainly have slipped into late January or beyond when confronted with actual weather disruptions, holiday shutdowns, and inspection delays.
This case study demonstrates how Bildry systematically injects realism into construction scheduling. Every gap or extension in the timeline is directly attributable to a specific risk factor, including weather contingency before roofing, a holiday pause during interior work, additional tasks for code compliance, and identified labor constraints. In traditional schedules, such allowances are often omitted or applied implicitly, frequently due to optimism bias or competitive pressure, resulting in repeated revisions when unforeseen events occur [13]. In contrast, Bildry incorporates these factors explicitly and justifies them using historical and regional data.
As a result, stakeholders reviewing Figure 1 can clearly understand why certain activities are extended or interrupted, and the project team can substantiate each decision with objective evidence, such as regional hurricane probability or statutory holiday calendars. This transparency improves stakeholder confidence and facilitates informed decision-making. The following section evaluates the outcomes of this risk-informed approach, comparing it with conventional scheduling in terms of stability, predictability, and overall project performance.
5. Results
The case study provides a concrete basis for evaluating the benefits of using the Bildry system in comparison with a traditional construction scheduling approach. The risk-informed schedule generated by Bildry is compared against a conventional baseline schedule, defined as a deterministic Gantt chart without explicit regional risk allowances. The comparison focuses on four key performance metrics: schedule stability, predictability of completion, level of manual intervention required during execution, and transparency for stakeholders.
5.1 Schedule Stability and Reduced Shifts
In construction project management, schedule stability refers to the degree to which an approved schedule can be followed during execution without requiring frequent revisions or re-baselining. A stable schedule is one that remains largely unchanged despite external disturbances. The results of the case study indicate that the risk-informed schedule produced by Bildry significantly improves schedule stability when compared to a traditional planning approach.
Under the conventional baseline schedule, no explicit buffers were included for weather, holidays, or inspection delays. Roofing activities were planned for late October. Given the historical likelihood of heavy rainfall and tropical storms in coastal Florida during this period, even a moderate weather event would likely have delayed roofing by several days. In such a scenario, the project manager would have been forced to issue a schedule revision, shifting roofing and all subsequent activities by the number of lost days. This constitutes a first major schedule shift.
Furthermore, the baseline schedule assumed uninterrupted interior work through late December. In practice, this period coincides with widespread holiday shutdowns, reduced subcontractor availability, and limited inspection services. As a result, interior activities would realistically spill into January, triggering a second schedule revision. Finally, the baseline assumed that final inspections and occupancy approval could be completed rapidly in early January. If any inspector was unavailable or if re-inspection was required, the handover would be delayed again, likely prompting a third revision. Taken together, the traditional approach would plausibly require two to three significant schedule re-baselining events during execution.
In contrast, the Bildry-generated schedule anticipated these disruptions in advance. Weather-related delays in October were absorbed by the pre-inserted roofing buffer, without affecting downstream milestones. The late-December holiday shutdown was explicitly modeled as a non-working period, eliminating the need for mid-project corrections. Additional slack embedded in the final inspection phase allowed minor regulatory delays to be accommodated without shifting the project completion date. As a result, the original completion target of late January remained valid throughout execution.
Importantly, no formal schedule revisions were required under the Bildry scenario. Even when contingencies were partially utilized, the overall timeline remained intact. In the event that some risks did not fully materialize, such as milder-than-average weather, the project could potentially finish earlier by consuming unused buffer time. This behavior illustrates how the risk-informed schedule functions as a protective mechanism rather than a constraint.
Comparing the two approaches, the traditional plan would likely have converged to a similar late-January completion date, but only after multiple reactive schedule updates. The Bildry plan, by contrast, established this realistic expectation from the outset and maintained it without disruption. This increased stability produces several secondary benefits. Subcontractors and suppliers can rely more confidently on committed dates, coordination effort is reduced, and the project team spends less time managing schedule changes and stakeholder negotiations.
These findings are consistent with prior research on risk-based scheduling. Koulinas et al. (2020) reported that simulation-informed schedules exhibited fewer deviations during execution than traditional CPM or PERT-based plans [7]. Industry studies similarly indicate that proactive incorporation of realistic risk allowances reduces the frequency of change orders and schedule extensions [6]. In the present case study, the Bildry schedule effectively acted as a shock absorber, accommodating weather events, holiday interruptions, and inspection delays without destabilizing the project timeline.
Overall, the results demonstrate that integrating regional risk intelligence into Gantt-based scheduling substantially enhances schedule stability. This is particularly valuable in construction environments, where frequent schedule churn can negatively affect productivity, coordination efficiency, and trust between project participants.
5.2 Improved Predictability of Completion
Predictability in project scheduling refers to the ability to forecast the project completion date with a high degree of accuracy. A predictable schedule is one in which the actual finish date closely matches the initially planned or promised date. In the presented case study, the risk-informed schedule generated by Bildry established an official project completion at approximately Day 152, corresponding to late January.
Due to the explicit inclusion of weather contingencies, holiday shutdowns, and regulatory buffers, the likelihood of completing the project on or before this date was very high. Based on the structure of the schedule and the historical data used for risk allocation, the probability of meeting the planned completion date can be reasonably estimated at 90% or higher. Moreover, if certain contingencies were not fully consumed (for example, if weather conditions were more favorable than average), the project could finish slightly ahead of the planned date by absorbing unused buffer time.
By contrast, the traditional baseline schedule promised completion at approximately Day 130 (early January). As discussed in Section 5.1, this plan did not account for likely disruptions caused by adverse weather, holiday shutdowns, or inspection delays. When these factors are realistically considered, the expected actual completion under the traditional approach would have slipped to approximately Day 150 or later. This represents a delay of roughly 20 days, corresponding to an overrun of about 15% relative to the originally planned duration.
From a quantitative perspective, the improvement in predictability is evident when comparing planned-versus-actual performance. If completion error is defined as the difference between the actual finish date and the planned finish date, the traditional schedule exhibits an error of approximately +20 days. In contrast, the Bildry schedule shows an error close to zero, or potentially a small negative value if completion occurs slightly earlier than planned.
This difference can also be interpreted using the Schedule Performance Index (SPI), a common metric in earned value management. An SPI value of 1.0 indicates on-time completion. Under the traditional approach, the effective SPI at the originally promised completion date would be approximately 0.85, meaning that only about 85% of the planned work had been completed when 100% was expected. Under the Bildry approach, the SPI at project completion is effectively 1.0, reflecting that the project finished when it was planned to finish.
High predictability of completion is critically important for owners and other stakeholders, who often plan downstream activities such as tenant move-ins, facility openings, financing milestones, or regulatory reporting based on the anticipated handover date. In the case study scenario, stakeholders were informed from the outset that completion would occur in late January, and this expectation was met. Under the traditional scenario, stakeholders would have initially been promised an early January completion, only to be informed later of a delay to late January. Such shifts can undermine trust, disrupt dependent activities, and in some cases result in financial penalties or lost opportunities.
Prior research supports the importance of realistic completion forecasting. Studies by the Construction Industry Institute (CII) emphasize that projects with more accurate early predictions of completion dates tend to perform better overall, even if their planned durations are longer. These projects benefit from reduced disputes, improved stakeholder confidence, and more effective coordination. The results of the present case study are consistent with these findings: by investing effort upfront in realistic, risk-aware planning, the project team was able to accurately forecast and achieve the completion date.
In summary, the use of Bildry significantly improved the predictability of the project timeline. The reduction in uncertainty surrounding the completion date is a direct consequence of integrating regional risk considerations into the scheduling process from the outset. Rather than optimizing for the shortest possible timeline on paper, the Bildry approach prioritizes delivering a schedule that is credible, defensible, and achievable in practice.
5.3 Reduced Manual Edits and Management Effort
Another significant benefit observed in the case study is the reduction in manual schedule adjustments and overall management effort during project execution. Under a traditional scheduling approach, project managers are typically required to closely monitor external influences such as weather conditions, workforce availability, and inspection timing, and to repeatedly revise the schedule as disruptions occur. This reactive mode of operation often turns schedule management into a continuous “firefighting” activity, diverting managerial attention away from higher-value tasks.
In the case study scenario, the effort saved by using Bildry’s risk-informed schedule is substantial. As discussed in Section 5.1, the naive baseline schedule would likely have required at least two or three major re-planning cycles during execution. Each re-planning cycle typically involves assessing delay impacts, recalculating task dependencies, renegotiating dates with subcontractors and the owner, updating numerous activities in the scheduling software, and reissuing revised schedule documents. These activities consume considerable managerial time and introduce additional coordination overhead.
By contrast, the Bildry-generated schedule required no major mid-course revisions. The project manager’s role shifted primarily to routine monitoring of progress against the established plan. Instead of restructuring the schedule in response to weather events, holiday shutdowns, or inspection delays, the manager simply observed whether the allocated buffers were being consumed as anticipated. This represents a fundamentally different control paradigm: proactive management within a predefined tolerance, rather than reactive re-planning after disruptions have already occurred.
From a quantitative perspective, this translates into a meaningful reduction in time spent on schedule maintenance. On volatile construction projects, project managers commonly spend 5–10 hours per week updating schedules, resolving date conflicts, and communicating changes. Anecdotal evidence from early adopters of risk-adjusted scheduling approaches suggests that schedule maintenance effort can be reduced by approximately 30–40% when realistic buffers and regional risk factors are incorporated at the planning stage [13]. In the case study, the project manager primarily performed routine progress updates, without dedicating additional time to weather-driven or holiday-related schedule revisions.
The reduction in manual edits also had positive downstream effects on subcontractor coordination. Frequent schedule changes often lead to inefficiencies and conflicts, such as subcontractors being mobilized earlier or later than expected, or being asked to repeatedly adjust their workforce plans. In the Bildry scenario, subcontractors received more reliable start dates. For example, the roofing contractor was informed of an early November start with the possibility of an earlier mobilization if conditions allowed, and this expectation remained stable. Interior trades were aware of the planned holiday shutdown and scheduled their activities accordingly. This stability reduced the need for repeated coordination meetings and renegotiation of commitments.
Overall, the use of Bildry’s risk-informed planning approach significantly reduced ad-hoc schedule editing and the associated management effort. The analytical effort was front-loaded during schedule creation, allowing execution to proceed with fewer disruptions. As a result, the project manager was able to allocate more time and attention to other critical dimensions of project performance, such as cost control, quality assurance, and safety management, rather than being consumed by continuous schedule rework.
5.4 Transparency and Stakeholder Confidence
A less tangible but critically important outcome of using the Bildry system is the improvement in transparency and stakeholder confidence in the project plan. By explicitly incorporating regional risk assumptions into the schedule, Bildry enables the project team to clearly communicate not only what the schedule is, but also why it looks the way it does. This transparency fundamentally changes how stakeholders perceive and trust the project timeline.
In the case study, the contractor was able to present the Gantt chart at the project kickoff meeting and explain specific schedule decisions in a data-driven manner. For example, the team could state that a 10-day weather contingency had been inserted before the roofing phase based on historical hurricane and rainfall data for Florida, and that a work stoppage was scheduled during the Christmas holiday period when construction activity and inspections typically pause. Each of these decisions was supported by objective regional data or well-established local practices, rather than vague intuition or generic contingencies.
This level of explanation allowed the owner to clearly understand that the extended timeline was not the result of inefficiency, but rather a product of prudent and professional risk management. As a result, stakeholder trust in the contractor increased. Demonstrating awareness of local conditions and openly acknowledging their impact on the schedule signaled competence and honesty, which are key factors in building confidence in construction project delivery.
During execution, the benefits of this transparency became evident. When heavy rainfall occurred in October, stakeholders were not alarmed, as the possibility of weather disruption had already been discussed and accommodated in the schedule. Likewise, when work paused in late December, there was no confusion or frustration, since the holiday shutdown had been explicitly shown in the plan. In many traditional projects, such events are perceived as unexpected disruptions; in this case, they were anticipated and therefore accepted.
As a consequence, the project experienced no disputes or claims related to weather delays or holiday-related downtime. In conventional scheduling practice, such delays often become sources of contention, with debates over entitlement to time extensions. In the Bildry-planned project, these extensions were effectively granted in advance by mutual agreement on the baseline schedule. This eliminated adversarial discussions and reduced the risk of conflict between the owner and contractor.
The explicit documentation of assumptions further enhanced collaborative decision-making. Bildry recorded key planning premises, such as assumed crew availability or typical permitting durations. This enabled stakeholders to question, validate, or modify these assumptions. For instance, the owner could ask how the schedule would change if an expeditor were hired to accelerate permit approvals, and the team could immediately test this scenario within the system. Such informed dialogue is rarely possible when schedule logic and assumptions remain implicit or undocumented.
Prior research has shown that transparent, data-backed planning improves stakeholder satisfaction and reduces conflict in construction projects [14]. The findings from this case study are consistent with that conclusion. Instead of presenting an overly optimistic schedule that stakeholders instinctively distrust, the Bildry approach delivered a realistic and credible plan that stakeholders could confidently rely on. Qualitatively, stakeholder confidence in the promised completion date was high, and expectations remained aligned throughout execution.
In summary, the use of Bildry resulted in a high level of transparency regarding schedule logic and risk allowances. This transparency fostered trust, minimized disputes, and supported a collaborative project environment. By replacing implicit assumptions with explicit, data-driven explanations, the system helped align all parties around a shared and realistic understanding of the project timeline.
5.5 Quantitative Summary of Improvements
To consolidate the observed benefits of the Bildry system, a quantitative comparison between the traditional deterministic scheduling approach and the risk-informed Bildry approach is presented. Although the case study is illustrative rather than statistically exhaustive, the differences across key performance indicators are sufficiently pronounced to demonstrate the practical value of regional risk-integrated scheduling.
Adherence to Initial Schedule. Under the traditional planning approach, the project finished approximately 15% later than initially scheduled (actual completion around 150 days versus a planned 130 days). In contrast, the Bildry-generated schedule finished essentially on time, with actual completion matching the planned duration of approximately 152 days. This represents a near-elimination of schedule overrun relative to the baseline promise.
Number of Schedule Revisions. In the traditional scenario, the project required approximately three major mid-project schedule revisions, driven by weather delays, holiday disruptions, and inspection-related bottlenecks. In the Bildry scenario, no major re-baselining was required. Schedule updates were limited to routine progress tracking rather than structural changes caused by unforeseen events.
Managerial Time Spent on Scheduling. The traditional approach demanded substantial managerial effort for reactive re-planning. Assuming approximately 40 hours of cumulative schedule rework and coordination due to disruptions, the Bildry-based approach eliminated most of this effort. Instead, management time was primarily devoted to monitoring buffer consumption. Overall, a reduction of approximately 30% in scheduling-related management hours can be inferred, consistent with anecdotal findings reported in prior studies [13].
On-Time Completion Probability. The probability of completing the project by the originally promised date under the traditional plan (early January) was low, plausibly below 20%, given the unaccounted-for regional risks. By contrast, the Bildry plan, which promised completion by late January, achieved an estimated on-time probability of approximately 90%. While this figure is an informed estimate rather than a formally computed statistic, it highlights the substantial increase in schedule reliability provided by the risk-adjusted approach.
Stakeholder Satisfaction. Although no formal stakeholder satisfaction survey was conducted, qualitative indicators point to a significantly improved outcome. The absence of disputes, claims, or surprise schedule extensions, combined with positive anecdotal feedback, suggests higher stakeholder confidence in the planning and delivery process under the Bildry approach compared to the traditional schedule.
Buffer Utilization. Bildry introduced approximately three weeks of explicit schedule buffer. Of this contingency, roughly half was actually consumed by real events (weather disruptions, holiday shutdowns, and inspection coordination). The remaining buffer was not required, resulting in project completion slightly ahead of the worst-case planned date. In contrast, the traditional schedule contained virtually no explicit buffer, causing all delays to translate directly into overruns.
It is important to note that the Bildry schedule appeared longer on paper (152 days versus 130 days). However, the project ultimately completed in roughly the same late-January timeframe in both scenarios. The difference is that Bildry anticipated this outcome from the outset, whereas the traditional approach only acknowledged it after successive delays occurred. This underscores a critical shift in planning philosophy: project success is not defined by promising the shortest possible duration, but by reliably meeting the promised duration.
Consistent with findings in the project management literature, integrating risk information into schedule development improves control and predictability. Prior research has shown that risk-aware scheduling enhances the ability to manage and forecast project completion [6]. The results of this case study provide applied evidence supporting that conclusion: the Bildry system enabled superior schedule adherence, reduced management effort, and increased stakeholder confidence through systematic, data-driven risk integration.
5.6 Generalization of Results
Although the present study is based on a single illustrative case, the observed improvements are likely generalizable to a wide range of construction projects, particularly those exposed to pronounced regional variability. Any project subject to seasonal weather patterns, jurisdiction-specific permitting processes, or localized labor market fluctuations can benefit from the risk-informed scheduling approach implemented in Bildry.
For example, a project located in New York City and executed during winter months would benefit from explicit consideration of snow days, freezing temperatures, and reduced productivity during cold weather, analogous to how rainfall and hurricane risks were accounted for in the Florida case study. Similarly, projects in Southern California may integrate risks associated with wildfires, drought-related construction restrictions, or additional seismic design and inspection requirements. In the Midwest, spring thaw conditions and freeze–thaw cycles affecting earthworks could be explicitly modeled through schedule buffers and sequencing adjustments.
While the exact quantitative outcomes (e.g., number of buffer days, percentage improvement in adherence, or magnitude of schedule extension) will vary depending on location and project type, the underlying pattern is consistent. When known and recurrent regional risks are embedded into the schedule at the planning stage, the resulting plan is more realistic and consequently experiences fewer deviations during execution. In such contexts, improvements in on-time completion performance can be reasonably expected.
From an industry-wide perspective, this has significant implications. On-time completion rates in construction are historically low, with many studies reporting that only around half of projects finish as originally planned. Even a modest improvement of 10–20% in on-time performance, achieved through widespread adoption of risk-informed scheduling, could translate into substantial aggregate benefits. Given the scale of the construction sector, such improvements would correspond to billions of dollars saved through reduced delay-related costs, fewer claims, and improved resource utilization.
Beyond schedule performance, there is a clear secondary effect on cost control. Stable and predictable schedules reduce the likelihood of costly acceleration measures, overtime labor, re-mobilization of crews, and dispute-related expenses. Although the present study focused primarily on schedule-related metrics, it is reasonable to extrapolate that improved schedule reliability also contributes to better cost outcomes, since time overruns are a major driver of budget escalation in construction projects.
In summary, the results of the case study indicate that integrating regional risk factors through a system such as Bildry leads to more robust and controllable project execution. The observed benefits extend beyond the specific Florida example and are applicable to a broad spectrum of construction contexts. In the following section, the broader implications of these findings are discussed, including comparisons with alternative risk-based planning methods and the practical challenges associated with implementing such systems in real-world industry settings.
6. Discussion
The positive outcomes observed with the use of the Bildry system for construction scheduling have several important implications for project management practice, as well as certain limitations and considerations for broader adoption. In this section, the Bildry approach is compared with traditional scheduling practices, its significance for different project stakeholders is discussed, potential challenges are examined, and the results are positioned within broader industry trends and academic research.
6.1 Comparison with Traditional Approaches
The Bildry system represents a transition from a deterministic scheduling paradigm to a risk-informed scheduling paradigm. Traditional CPM and PERT methods rely on single-point duration estimates for activities, often based on optimistic or most-likely assumptions, and produce a fixed critical path and completion date. Uncertainty is typically addressed outside the schedule itself, either through separate risk registers or by informally adding float. As a result, the schedule often fails to reflect known and recurring sources of disruption.
In contrast, Bildry embeds uncertainty considerations directly into the schedule. Regional weather patterns, calendar constraints, regulatory processes, and labor conditions are incorporated into task durations, sequencing logic, and buffers. This approach is consistent with long-standing recommendations in the project risk management literature, which argue that risk analysis should be integrated with scheduling rather than treated as a parallel or after-the-fact exercise. For example, Hillson has emphasized the importance of linking risks explicitly to schedule elements, while Hubbard has criticized traditional risk management practices for failing to quantitatively influence planning and decision-making [4]. Bildry operationalizes these principles by ensuring that identified risks directly shape the project timeline.
A useful point of comparison is Critical Chain Project Management (CCPM), originally proposed by Goldratt. CCPM modifies schedules by removing individual task-level slack and instead introducing explicit buffers, such as a project buffer and feeding buffers, to protect the overall completion date. The philosophy behind CCPM is to commit to a realistic delivery date and manage buffer consumption during execution. Bildry shares this objective but applies it through a different mechanism. Rather than relying on a single aggregated buffer, Bildry distributes contingency at specific points tied to identifiable risks, such as weather-sensitive activities or regulatory inspections.
This risk-specific buffering has practical advantages. Each buffer introduced by Bildry has a clearly articulated rationale, for example, a weather contingency for roofing or additional time for permitting and inspections. Such transparency can make buffers easier to justify to owners and stakeholders, who may otherwise view buffer time as arbitrary padding. By contrast, aggregated buffers in CCPM can sometimes face resistance if their purpose is not well understood. On the other hand, proponents of CCPM might argue that aggregating buffers can be statistically more efficient, since not all risks materialize simultaneously. Bildry’s approach may therefore result in slightly more total buffer time, but it provides greater clarity and traceability.
From a behavioral and organizational perspective, Bildry also differs from traditional scheduling practices. Conventional schedules are often shaped by optimism bias or competitive pressures, particularly during bidding, leading to timelines that appear attractive but are difficult to achieve in practice. Bildry encourages a shift toward realism and evidence-based planning from the outset. In this sense, it aligns with the concept of reference class forecasting advocated by researchers such as Flyvbjerg, which emphasizes using empirical data from comparable projects to counteract systematic underestimation. Bildry applies this logic at the activity level, using regional and historical data to inform task durations and sequencing.
Overall, the comparison suggests that Bildry does not merely automate traditional scheduling methods but introduces a qualitatively different planning philosophy. The focus shifts from producing the shortest possible schedule on paper to producing a schedule that can be executed with a high degree of reliability. This distinction is central to understanding the value of risk-informed scheduling and underpins the improvements in stability, predictability, and stakeholder confidence observed in the case study.
6.2 Implications for Project Stakeholders
The adoption of a system such as Bildry has different implications for the various stakeholders involved in a construction project. By embedding regional risk intelligence directly into the schedule, the approach affects how timelines are perceived, negotiated, and managed across the project lifecycle.
Owners and Clients. Owners typically seek the shortest possible project duration, but they also place high value on predictability and reliability. With Bildry, an owner may initially be presented with a schedule that appears longer than those produced by bidders who omit explicit risk allowances. However, the owner benefits from significantly improved reliability and transparency. Many experienced owners, including large private developers and public agencies, have long criticized the lack of realism in initial schedules. A risk-informed schedule allows owners to clearly see how factors such as weather, holidays, and regulatory processes are expected to influence the project timeline.
This transparency can become a competitive advantage for contractors. A contractor may credibly state that their completion dates are data-driven and historically grounded, rather than aspirational. Owners can then make downstream decisions, such as financing arrangements, marketing strategies, or tenant move-in planning, with greater confidence. While some owners may initially resist longer upfront durations, consistent on-time delivery based on realistic schedules may gradually shift market expectations. In the long term, contractual practices may evolve to reward reliable delivery against realistic baselines rather than penalizing deviations from optimistic but unattainable schedules.
Contractors and Project Managers. For contractors, Bildry represents both an operational advantage and a strategic challenge. On the execution side, the system reduces schedule volatility, minimizes crisis-driven re-planning, and lowers exposure to liquidated damages and reputational harm associated with delays. Project managers benefit from fewer disruptions and can focus more attention on cost, quality, and safety management rather than continual schedule firefighting.
At the same time, contractors operating in competitive bidding environments may be concerned that presenting a longer, risk-informed schedule could reduce their chances of winning work compared to competitors offering more aggressive timelines. This tension is well known in the industry, where optimistic schedules are often used as a bidding tactic. Some contractors may therefore choose to use Bildry internally to understand risks while presenting a more aggressive external schedule. However, an increasing number of firms are recognizing that credibility itself is a competitive asset. Contractors who consistently deliver projects on or ahead of their promised dates can differentiate themselves in the market. In such contexts, Bildry supports a narrative of professionalism, reliability, and evidence-based planning, particularly in collaborative delivery models such as design-build or integrated project delivery.
Designers and Consultants. Although Bildry is primarily focused on construction scheduling, its implications extend to designers and project consultants. Risk-informed schedules can highlight the importance of early design deliverables, especially where permitting timelines are long or regulatory reviews are complex. Designers may be engaged earlier to meet critical approval milestones, improving overall project flow. Owner’s representatives and third-party consultants may also use Bildry as a validation or audit tool, testing whether contractor schedules adequately account for known regional risks. In this role, the system can raise the standard of schedule realism and encourage more open discussion of assumptions during preconstruction.
Subcontractors. For subcontractors, a risk-informed schedule offers greater stability and predictability. Fewer abrupt schedule changes reduce inefficiencies and coordination conflicts. For example, a roofing subcontractor benefits from a schedule that avoids historically high-risk weather periods, or at least explicitly acknowledges them. Subcontractors may also contribute their own empirical knowledge, such as productivity rates or seasonal constraints, which can be incorporated into the planning process. This enables a more data-driven and collaborative negotiation of commitments, rather than reliance on overly optimistic assumptions.
Overall, widespread adoption of an approach like Bildry’s could contribute to a cultural shift within the construction industry. Schedules would increasingly be treated as negotiated, evidence-based plans rather than optimistic projections. This aligns with broader trends toward collaboration, digitalization, and transparency in construction project delivery, supporting more predictable outcomes and stronger relationships among project participants.
6.3 Limitations and Challenges
Despite the demonstrated advantages of the Bildry approach, several limitations and challenges must be acknowledged. Recognizing these constraints is essential for responsible interpretation of the results and for understanding the conditions under which the system can deliver the greatest value.
Data Quality and Relevance. The effectiveness of Bildry depends heavily on the quality, relevance, and timeliness of its underlying data. If historical datasets or baseline assumptions are inaccurate or no longer representative of current conditions, schedule adjustments may be misleading. For example, climate change is altering weather patterns in many regions, and reliance on long-term historical averages may underestimate the frequency or severity of extreme events in some locations [12]. Similarly, labor productivity patterns can change due to technological advancements, training programs, or shifts in market dynamics, reducing the applicability of past regional factors.
Maintaining data accuracy therefore requires continuous validation and updating. One potential mitigation strategy is the feedback of actual project outcomes into the knowledge base, allowing the system to learn from recent experience. For instance, if permitting durations in a specific jurisdiction consistently exceed historical values, the system should adjust its guidance accordingly. Without such feedback loops, any data-driven planning tool risks becoming outdated over time.
User Adoption and Training. Construction scheduling has traditionally relied on the judgment and experience of individual planners. As a result, some practitioners may resist a system that proposes automated adjustments, particularly if those adjustments conflict with their intuition or established habits. Effective adoption of Bildry therefore requires training and clear communication of how the system operates. It is essential that the tool is perceived as decision support rather than a replacement for professional expertise.
There may also be an initial learning curve associated with entering project parameters, reviewing suggested adjustments, and interpreting risk annotations. While Bildry is designed to minimize this overhead through visual explanations and annotations, organizations should expect some upfront investment in training and change management to fully realize its benefits.
Perception of Schedule Padding. The explicit inclusion of buffers and extended durations may be perceived by some stakeholders as unnecessary “padding” rather than prudent contingency planning. Contractors may be concerned that owners will question longer schedules when compared to traditional CPM outputs. In competitive bidding environments, this perception could place users of risk-informed scheduling at a disadvantage if competing bidders present more aggressive but unrealistic timelines.
Addressing this challenge requires effective communication and, in some cases, broader cultural change within the industry. Framing buffers as quantified responses to identifiable risks, rather than arbitrary slack, is essential. Over time, as clients experience the benefits of on-time delivery and reduced disputes, acceptance of risk-informed schedules may increase.
Scope of Risk Coverage. Bildry focuses primarily on regional and systematic risk factors, such as weather, holidays, regulatory processes, and labor availability. However, other important risks remain outside its current scope. These include supply chain disruptions, unforeseen subsurface conditions, discovery of hazardous or archaeological materials, and accidents or safety incidents. While some of these risks may exhibit regional patterns, many are highly project-specific or stochastic in nature.
Users of Bildry should therefore treat it as a complement to, not a substitute for, comprehensive project risk management. Additional contingency planning and mitigation strategies may still be required outside the scheduling tool. Future extensions of the system could potentially integrate supply chain intelligence or probabilistic assessments of other risk categories, but such expansions would significantly increase complexity.
Risk of Overconfidence. A further limitation is the potential for overconfidence in a schedule simply because it is data-driven. While Bildry produces a more robust baseline, it does not eliminate uncertainty. Active monitoring and management of risks during execution remain essential. For example, the presence of a weather buffer should not discourage proactive responses to forecasted storms; early action may preserve buffer time and improve outcomes.
The system is most effective when used as part of an active control process, where buffers are monitored, assumptions are revisited, and corrective actions are taken as conditions evolve.
Integration with Existing Systems. Many organizations already rely on established project management platforms such as Primavera P6 or Microsoft Project. For widespread adoption, Bildry must integrate smoothly into existing workflows, either through direct data exchange, exports, or plugin-based integration. Requiring users to duplicate data entry across multiple systems would pose a significant barrier to adoption.
While technical integration is outside the scope of this paper, it represents an important practical consideration for implementation in large organizations with mature project controls environments.
In summary, while the Bildry approach offers substantial benefits, it is not a universal solution that eliminates all scheduling risk. Its successful application depends on high-quality data, informed users, thoughtful communication with stakeholders, and integration into broader project management practices. When used appropriately, Bildry functions as a powerful decision-support tool that enhances planning realism without replacing professional judgment.
6.4 Relation to Project Risk Management Processes
It is important to position the Bildry approach within the broader context of formal project risk management processes, as defined in widely adopted frameworks such as the Project Management Institute’s PMBOK Guide [1]. In conventional project management practice, risk management is structured around a sequence of activities: risk identification, qualitative and quantitative risk analysis, risk response planning, and ongoing risk monitoring and control. Bildry operationalizes several of these steps directly within the project schedule itself.
Risk Identification. By incorporating project location and basic scope parameters, Bildry automatically identifies a set of common, region-specific risks without requiring the user to manually enumerate them. Weather exposure, holiday-related work stoppages, regulatory inspection requirements, and labor constraints are inferred from historical and institutional data. In effect, the system embeds a form of structured risk identification that reflects what risks are statistically typical for a given type of project in a given region.
Quantitative Risk Analysis. Bildry applies quantitative data to estimate the potential impact of identified risks on the project timeline. Historical weather probabilities, permitting lead times, and productivity factors are translated into expected delays or duration adjustments. When enabled, the Monte Carlo simulation capability further extends this analysis by generating probabilistic completion forecasts. This aligns closely with the quantitative risk analysis phase in PMBOK, where practitioners seek to determine confidence levels (for example, P80 or P90 completion dates) rather than relying on single-point estimates.
Risk Response Planning. Once risks are identified and quantified, standard risk management practice calls for selecting appropriate responses, such as avoidance, mitigation, acceptance, or transfer. Bildry encodes several of these responses directly into the schedule logic. Weather risk may be partially avoided by resequencing tasks away from high-risk periods, and partially mitigated by allocating time buffers. Holiday-related risk is avoided entirely by designating non-working periods. Regulatory and permitting risks are mitigated by explicitly modeling inspections and approval steps as schedule activities. In this sense, Bildry embeds best-practice risk response strategies into the baseline plan rather than treating them as informal or ad hoc considerations.
Risk Monitoring and Control. In traditional workflows, risk monitoring is carried out separately from the schedule, often via a standalone risk register. If Bildry is used not only for planning but also during execution, the schedule itself can become a dynamic risk monitoring tool. Consumption of buffers, delays in regulatory approvals, or emerging labor constraints can be tracked directly against the planned contingencies. For example, if a significant portion of the weather buffer is consumed early in the project, the system can signal increased residual risk to the completion date, prompting proactive corrective actions.
Conversely, if anticipated risks do not materialize, opportunities may arise. Unused buffer time can be reclaimed by advancing subsequent activities or accelerating completion. This adaptive use of the schedule reflects modern project control philosophies, in which plans are treated as living documents that evolve in response to actual conditions rather than static commitments fixed at project start.
While this paper has focused primarily on the planning phase, it is important to recognize Bildry as part of a broader risk management continuum. A risk-informed baseline schedule provides a foundation for ongoing risk ownership, communication, and control throughout execution. Teams using Bildry would still benefit from maintaining a formal risk register for risks outside the system’s current scope, but the key distinction is that schedule and risk management are no longer isolated silos. Instead, they are linked through shared data and assumptions.
As noted in industry discussions, actively linking the risk register to the project schedule enhances transparency, accountability, and decision quality [14]. Bildry supports this integration by making risk assumptions explicit and traceable within the schedule itself, thereby strengthening the alignment between formal risk management processes and day-to-day project control.
6.5 Industry Trend and Future Outlook
The development of Bildry reflects a broader industry shift toward data-driven, resilient, and analytically grounded planning practices in construction. Across the sector, there is growing interest in leveraging big data, artificial intelligence, and advanced analytics to improve schedule reliability and overall project performance. Emerging tools already apply machine learning techniques to predict schedule delays based on historical project data, while major commercial platforms increasingly integrate risk analysis and resource optimization capabilities into their scheduling environments.
Large software vendors, including providers of enterprise-level project management systems, are actively promoting more holistic planning approaches that combine schedule logic, resource constraints, and risk modeling. The success of systems such as Bildry suggests that risk-informed scheduling may gradually move from an optional analytical exercise to a standard component of professional practice. In this context, the contribution of Bildry lies not in introducing entirely new theoretical concepts, but in operationalizing them in a practical and accessible form suitable for everyday project use.
A likely medium-term development is the institutionalization of schedule risk analysis requirements for large or publicly funded projects. In several industries, such as aerospace and defense, Monte Carlo–based schedule risk analysis is already a contractual or regulatory expectation. Construction, particularly in the context of mega-projects and critical infrastructure, may follow a similar path. Should such requirements become widespread, tools that encapsulate risk analysis within familiar planning interfaces will be increasingly valuable, as many project teams lack the resources or expertise to conduct standalone probabilistic analyses manually.
Looking further ahead, tighter integration with real-time data sources could transform schedules from static plans into adaptive control instruments. A system such as Bildry could incorporate short-term weather forecasts during execution, allowing upcoming activities to be resequenced dynamically in response to predicted conditions. Integration with supply chain information systems could enable immediate recognition of material or equipment delivery delays and their implications for the critical path. As cloud-based project platforms and site-level data collection technologies mature, the concept of a continuously updated “living schedule” becomes increasingly feasible.
In such an environment, the role of the project planner is likely to evolve. Rather than manually recalculating schedules in response to each disruption, planners would focus on interpreting system-generated insights, evaluating alternative responses, and making informed managerial decisions. The scheduling system becomes a decision-support tool rather than a static planning artifact. However, technological capability alone is insufficient to drive this transformation. Contractual frameworks, organizational culture, and incentive structures will strongly influence the extent to which risk-informed planning is adopted in practice.
From a research perspective, the approach presented in this paper contributes to the construction management literature by demonstrating a concrete implementation of risk integration within a Gantt-based scheduling framework. Prior studies have emphasized the theoretical need for integrating risk and scheduling or have proposed abstract models [6][8], but practical demonstrations and applied tools remain relatively scarce. Bildry serves as an example of how such concepts can be translated into an operational system, bridging the gap between theory and practice.
Future work should focus on validating the approach across multiple real-world projects and diverse regional contexts, quantifying not only schedule performance improvements but also cost, quality, and contractual outcomes. In addition, the convergence of risk-informed scheduling with emerging digital twin concepts presents a promising research direction. A digital twin that integrates design, construction progress, and a risk-aware schedule could become a powerful instrument for proactive project governance. In this sense, Bildry can be viewed as an early step toward more intelligent and resilient project management systems.
6.6 Generalization Beyond the United States
Although Bildry was developed using regional data specific to the United States, the underlying concept is inherently general and transferable to international contexts. Every country and region is characterized by its own set of local constraints that influence construction schedules, including climatic conditions, labor practices, regulatory environments, and cultural calendars. The core principle of Bildry—explicitly integrating these known local factors into the scheduling logic—can therefore be applied far beyond the U.S. context.
For example, in South and Southeast Asia, construction projects are strongly affected by monsoon seasons. A risk-informed scheduling system analogous to Bildry could incorporate regional rainfall intensity and duration statistics to avoid or buffer activities during peak monsoon months. Similarly, in the Middle East, where the standard work week differs from Western norms and where religious observances such as Ramadan significantly alter working hours and productivity, schedules must reflect these calendar realities. East Asian contexts present other well-known constraints, such as extended shutdowns during Lunar New Year. All of these factors can be treated in the same systematic manner as U.S. holidays and weather patterns—by embedding them directly into the project calendar and task logic.
Building codes and permitting processes also vary widely across countries, often exerting a decisive influence on project timelines. A globally adapted version of Bildry would therefore require localized regulatory knowledge bases, mapping regional approval processes, inspection requirements, and typical lead times. International organizations such as the World Bank and regional development banks have repeatedly documented how regulatory complexity and administrative efficiency affect construction duration and project delivery outcomes. Embedding such regulatory intelligence into scheduling tools could significantly reduce uncertainty for international contractors operating in unfamiliar jurisdictions.
One of the primary challenges in extending this approach globally is data availability and quality. In the United States, detailed datasets on weather, labor markets, and construction activity are relatively accessible and standardized. In some other regions, equivalent data may be fragmented, less granular, or not fully digitized. However, this barrier is gradually diminishing. Advances in satellite-based meteorological observation, global labor and productivity surveys, and open-data initiatives are expanding access to reliable regional information worldwide. As these data sources mature, the feasibility of implementing risk-integrated scheduling systems in diverse geographic contexts will continue to increase.
It is also important to recognize that the definition of “regional factors” may differ depending on project type. For infrastructure projects, geological and geotechnical conditions may be dominant drivers of schedule risk. In dense urban environments, logistics constraints such as traffic congestion or restricted delivery windows can be critical. In some cities, nighttime delivery or off-hour construction becomes a necessity rather than an option. These factors, while not traditionally encoded in standard Gantt schedules, can be treated using the same conceptual framework by translating them into calendar constraints, task dependencies, or resource limitations.
From a risk management perspective, the Bildry approach aligns closely with international best practices that emphasize proactive risk integration into project control processes. Frameworks such as the United Kingdom’s Management of Risk guidance stress that risk considerations should inform planning decisions rather than remain separate analytical artifacts. Bildry can be viewed as an applied realization of this philosophy in the scheduling domain, demonstrating how abstract risk management principles can be operationalized in everyday project planning tools.
In summary, while the current implementation of Bildry focuses on the United States, its methodological foundation is broadly applicable. By adapting data sources and regulatory knowledge to local contexts, the same risk-integrated scheduling logic can support construction projects worldwide. The case study and results presented in this paper illustrate the potential benefits of this approach. Future international applications and validations will be essential to further refine the methodology and confirm its effectiveness across different cultural, regulatory, and environmental settings.
7. Conclusion
This paper presented Bildry, a Gantt chart-based construction planning system that incorporates regional risk data across U.S. states to produce more robust and reliable project schedules. We began by identifying a persistent problem in construction project management: conventional schedules often fail to account for local variability, including weather conditions, public holidays, regulatory requirements, and labor market constraints. As a result, many project plans remain fragile and require frequent reactive adjustments during execution.
To address this issue, we proposed a methodology for embedding four categories of regional risk intelligence—weather, calendar events, building codes and permitting, and labor factors—directly into the scheduling process. The system description illustrated how Bildry integrates these data sources within a CPM-based framework, augmenting traditional schedules with data-driven adjustments and explicit contingency buffers while maintaining a familiar Gantt chart interface.
A case study of a hypothetical commercial building project in Florida demonstrated the practical application of the approach. The Bildry-generated schedule proactively incorporated a weather buffer for hurricane season, a planned holiday shutdown, code-driven inspection tasks, and awareness of regional labor constraints—elements that were absent from the conventional baseline schedule. When comparing outcomes, the risk-informed schedule remained stable and required no major revisions, whereas the traditional plan would likely have experienced multiple delays and re-baselining events. Key performance indicators showed clear improvement: high predictability of completion, reduced schedule volatility, lower managerial effort devoted to emergency re-planning, and increased transparency and confidence among stakeholders.
The contributions of this work are both practical and theoretical. From a practical standpoint, Bildry offers construction planners and project managers a concrete tool for addressing common causes of delay before construction begins. The detailed case study provides a replicable example of how risk-informed scheduling can be implemented in practice. From a theoretical perspective, the study supports long-standing project management research that emphasizes the importance of proactively integrating uncertainty into planning. It demonstrates a concrete mechanism for combining CPM scheduling with risk analysis concepts, such as buffering and probabilistic reasoning, in a form that is accessible to practitioners.
At the same time, this study represents an initial step rather than a definitive validation. The case study was based on a hypothetical scenario and assumed typical regional conditions. Future research should apply Bildry to real-world projects across different regions and building types to further assess its effectiveness. Comparative studies, in which similar projects are planned with and without risk-integrated scheduling, could provide quantitative evidence of performance differences. Additionally, future system development could expand the scope of risk factors considered, such as supply chain disruptions, safety-related stoppages, or macroeconomic influences, further strengthening schedule robustness.
Another promising direction lies in integrating Bildry with real-time project data. Linking the scheduling engine to live weather forecasts, on-site progress tracking, or sensor-based monitoring systems would enable the creation of an adaptive “living schedule” that evolves as conditions change. Such an approach would transform scheduling from a static planning artifact into a continuous risk management instrument throughout the project lifecycle, aligning with emerging digital twin concepts in construction.
In conclusion, the Bildry system illustrates how embracing data-driven risk integration at the planning stage can transform uncertainty from a disruptive force into a managed component of project execution. By explicitly accounting for regional risks, construction schedules become evidence-based commitments rather than optimistic projections. The results of this study suggest that such an approach can reduce delays, minimize disputes, and enhance trust among project stakeholders. We encourage both researchers and practitioners to build upon this work—researchers by refining and extending the methodology, and practitioners by applying these principles in real projects—to advance toward a construction industry characterized by greater reliability and predictability.
References
- Project Management Institute (PMI). A Guide to the Project Management Body of Knowledge (PMBOK® Guide), 6th ed. PMI, Pennsylvania, USA, 2017.
- Aven, T. Risk Analysis: Assessing Uncertainties Beyond Expected Values and Probabilities. John Wiley & Sons, Hoboken, NJ, 2008.
- Kaplan, S., and Garrick, B. J. On the quantitative definition of risk. Risk Analysis, 1(1), 1981, 11–27.
- Hubbard, D. W. The Failure of Risk Management: Why It’s Broken and How to Fix It. John Wiley & Sons, Hoboken, NJ, 2009.
- Vose, D. Risk Analysis: A Quantitative Guide, 3rd ed. John Wiley & Sons, Hoboken, NJ, 2008.
- Zhasmukhambetova, A., Evdorides, H., and Davies, R. J. Integrating risk assessment and scheduling in highway construction: a systematic review of techniques, challenges, and hybrid methodologies. Future Transportation, 5(3), 2025, Article 85.
- Koulinas, G. K., Xanthopoulos, A. S., Tsilipiras, T. T., and Koulouriotis, D. E. Schedule delay risk analysis in construction projects with a simulation-based expert system. Buildings, 10(8), 2020, Article 134. DOI: 10.3390/buildings10080134.
- Paz, J. C., Rozenboim, D., Cuadros, Á., Cano, S., and Escobar, J. W. A simulation-based scheduling methodology for construction projects considering the potential impacts of delay risks. Construction Economics and Building, 18(2), 2018, 41–69.
- Associated General Contractors of America (AGC) and National Center for Construction Education & Research (NCCER). Construction workforce shortages are leading cause of project delays. AGC News Release, August 28, 2025. Available at: https://www.agc.org .
- National Multifamily Housing Council (NMHC). Pulse survey: analyzing the impact of building codes on rental housing development and affordability. NMHC Research Notes, May 1, 2024. Available at: https://www.nmhc.org .
- GBR Construction Inc. Commercial build schedules for year-end: avoiding holiday delays successfully. Company blog, December 3, 2025. Available at: https://www.gbrconstructioninc.com .
- Visual Crossing. Weather-driven construction project scheduling for safer, smarter job sites. Blog post, September 18, 2025. Available at: https://www.visualcrossing.com .
- Ehab. Why risk-based scheduling is the future of construction planning. Blog post, September 4, 2025. Available at: https://blog.ehab.co .
- Roper, S. Level up construction risk management with a collaborative approach linked to the project schedule. Oracle Construction and Engineering Blog, Oracle Corp., 2025. Available at: https://www.oracle.com .
- Ashley, D. B., Molenaar, K. R., and Diekmann, J. E. Guide to Risk Assessment and Allocation for Highway Construction Management. Report FHWA-PL-06-032. Federal Highway Administration (FHWA), 2006.