by Jeffrey C Kadlowec, Architect
Abstract
Increasing complexing in construction requires greater collaboration between individuals and companies to achieve successful completion of projects at required quality standards within estimated budgets and timelines. This paper explores various techniques to manage risks, evaluate performance and address the multitude of factors affecting planning and scheduling. The first part reviews concepts from a study on integrated planning, highlighting the likelihood, impact and severity of risks, and listing events by relative importance index. Several methods, including Program Evaluation and Review Technique, critical path method, work breakdown structure, earned value analysis, and Last Planner System are discussed in relation to time, cost and quality. Additional information from a variety of sources is provided to detail current and future trends in support of these ideas. As emerging technologies are further implemented throughout the industry, staying aware of their benefits and effects on design and execution is essential for construction professionals and project managers.
Keywords: construction planning, project scheduling, lean principles, risk management, uncertainty analysis
Strategic Planning
1. Research Summary
Construction is a complex and multidisciplinary endeavor requiring the participation of various companies and individuals to complete many related tasks and activities. The intricate and disjointed work combined with short timelines and tight budgets creates a difficult environment that is often prone to failure. Proper management and effective processes that utilize risk assessment and cost analysis can mitigate these troubling factors [Zaneldin 2024]. Program Evaluation and Review Technique (PERT) and Earned Value Analysis (EVA) are common methods of estimating the time, cost and associated risk towards project completion.
The fragmented nature of construction involves substantial risks to stakeholders because of long durations, numerous details and technological advancements. Errors, discrepancies and mismatches further add to these risks, often resulting in delays, rework and cost overruns. Uncertain weather and site conditions, material and labor shortages, financial constraints, contract disputes, and communication issues can create even more problems [Zaneldin 2024]. Properly assessing risks and uncertainty based on reliable data throughout a project lifecycle can reduce or eliminate potential consequences that may otherwise be overlooked. Effective risk management through identification, analysis, control, and monitoring with greater stakeholder engagement and utilization of technology will yield better results. Though derived from subjective judgment, factoring in contingencies as an approximate percentage based on risk should be added to estimates of bid price.
A wide range of studies have been completed involving the principles and techniques for assessing and mitigating risks in construction project management [Zaneldin 2024]. Analytic hierarchy process (AHP) and decision trees employed during early stages present response strategies utilizing expected monetary value instead of merely quantifying the impact of risks. Bayesian belief network (BBN) models determine probability of risk through statistical methods and quantify delays using historical data. Fuzzy sets utilizing binomial regression can estimate accident frequency based on empirical databases. Tolerable levels of risk are calculated based on performance expectations and design criteria. Investigating a variety of factors on small projects revealed that a lack of time, inadequate budget, and low profit margins were barriers towards implementation of risk management, though a positive correlation exists in improving quality, cost and schedules.
Risks by project objectives have been ranked with those in public-private partnerships (PPPs) transferred from government to private sector. Weighted function results on risk assessment showed that threats are overestimated by 8% and opportunities underestimated by 7.5% [Zaneldin 2024]. Artificial neural networks (ANNs) predict cost and schedule overruns in tall commercial buildings and multi-family residential projects through learning models and machine algorithms. Qualitative information obtained from interviews and empirical data generated from building information modeling (BIM) can be analyzed with fuzzy synthetic evaluation. Opportunities exist for the applications of BIM in safety management throughout the construction industry, especially for hazard reduction with modular and prefabricated elements. Risk maturing models are validated by numeric analysis and focus group discussion guiding decision-making by commercial developers and general contractors.
Developments in artificial intelligence (AI) tools and technology have created new methods for structural equation modeling and analysis of critical factors in mega-housing projects. Greater amounts of data, including the consequences of time and cost, environmental factors, and force majeure events, can be analyzed in multi-criteria decision-making frameworks with fuzzy logic. Investigating contract claims through probability of occurrence and predictable impact aids in resolving disputes related to completion dates and quality standards. Manufacturing continues to benefit from the technological shifts in digitalization and automation brought on by the Fourth Industrial Revolution (Industry 4.0 or 4IR), though the construction industry lags behind in adopting these concepts and greater integration is still necessary to improve productivity [Zaneldin 2024]. Overcoming barriers towards implementation, including further education, more training and better managerial practices, are the primary challenges that must be addressed and resolved.
The Project Management Body of Knowledge (PMBOK), the International Organization of Standardization (ISO) and the US Department of Defense (DoD) define risk management as the process of identifying, analyzing and prioritizing risks, then responding through the application of resources to maximize opportunities and minimize adverse events based on probability and impact. Risk planning by data analysis, expert judgement and team meetings; qualitative analysis through interviews, hierarchical charts and impact matrices; quantitative analysis to represent uncertainty with influence diagrams, decision trees and simulations; response planning to develop strategies utilizing cost-benefit analysis and multi-criteria decision-making; response implementation through interpersonal skills and project management systems; and monitoring and controlling of technical performance and audits are some of the many tools and techniques available [Zaneldin 2024]. Lack of knowledge or understanding along with a low extent of usage raise concerns about the relevant and practical application of these principles.
Current research displays gaps in literature aimed at identifying issues related to cost risk and schedule management both in qualitative and quantitative methodology [Zaneldin 2024]. By reviewing various events through questionnaire surveys, analysis can be applied to determine the probability of occurrence and estimate the degree of impact and level of severity (see Tables 1 & 2).
Table 1. Likelihood of occurrence and level of impact of risk events [Zaneldin 2024]
Table 2. Level of severity of risk events [Zaneldin 2024]
Results calculated from a relative importance index indicated 15 of 46 risk events as the most prominent in monetary effect on progress of budget and timeline (sees Tables 4 & 5). The severity of these events is determined by relative importance index (RII), which is the product of likelihood and effect, Severity (S) = Frequency of Occurance (F) × Impact of Risk (I). Mean and standard deviation (SD) were calculated to determine maximums, with poorly defined scope, inadequate design detail and scope changes ranking highest (see Table 4, 5 & 6).
Table 4. Likelihood of occurrence of risk events [Zaneldin 2024]
Table 5. Level of impact of risk events [Zaneldin 2024]
Table 6. RII values for the likelihood and impact of risk events [Zaneldin 2024]
Data received from construction experts was analyzed through of framework of modules: risk severity, project scheduling, quantitative risk analysis, and risk response [Zaneldin 2024]. The likelihood of occurrence and level of impact ranges for 15 events were used to develop the risk severity matrix generation module (see Table 3). Three PERT criteria: optimistic (o), most likely (m) and pessimistic (p); along with several equations: the expected time ( te = ( o + 4m + p ) / 6 ), standard deviation ( σ = ( p – o ) / 6 ) and variance ( v= σ2 ); were utilized in the project scheduling module. The monetary effect of cost and schedule risks was used to calculate the probability of risk in the quantitative risk analysis module where EVA techniques applied as SRc equals EAC × PRc. Finally, an extensive list of suggested responses to risk events were compiled into the risk response module.
Table 3. Risk severity matrix [Zaneldin 2024]
Strategies should be developed to effectively mitigate risks on project costs and schedules including preventative measures, reduction of occurrence probability and minimizing impact of events. Clearly defining responsibilities, proper allocation of resources, and accommodations for changes in funding, scheduling and technical specifications aid in successful management. Preparing and implementing a detailed risk assessment plan, obtaining firm commitment by organizational staff; using standard, traditional and modular methods while avoiding complexity and overdesign; and minimizing health and safety issues are some of the recommended guidelines [Zaneldin 2024].
Further study is required to incorporate recent advancements in engineering and construction. Though PERT and EVA are effective tools, many advanced technologies and innovative methods of modern projects have not yet been addressed. BIM, AI and machine learning (ML) offer potential for more precise and efficient analysis but are still being explored and not yet fully integrated [Zaneldin 2024]. Although “errors in the estimation of task duration” is the most probably risk event, few construction practitioners are aware of risk management tools or avoid using them due to complexity in application.
2. Opposing Argument
The benefits of creating and maintaining a multi-dimensional (nD) model through BIM software has been promoted as the ‘holy grail’ of architecture, engineering and construction for over two decades. Full integration of these tools remains a challenge for professionals that are traditionally specialized in a specific discipline. Further problems arise from ownership of intellectual property and copyrights on digital media. Standardization of nD models can only be achieved by large architecture & engineering (A/E) firms or highly coordinated design-build (D/B) construction companies with greater control over the entire process.
The amount of knowledge and training necessary to apply new methods is typically not available for the majority of projects, nor is there room in the average budget or project timeline for additional planning. Countless research studies have been completed in the past decades on the advantages of BIM, though most have been focus on specific aspects of design and planning without addressing the extra time and costs required relative to the value added. This is a similar issue with the upfront investment for green technology which do not guarantee future savings or higher returns.
Various topics were selected to gain insight into current trends throughout the industry [Tamari 2023]. Visualization of project planning in 4D is useful for presenting construction activities and avoiding collisions. Construction scheduling through 4D modeling allows for more accurate quantity takeoffs, optimizing material procurement, minimizing logistic costs and improving planning of equipment operations. Temporary site facilities can be setup more efficiently to utilize limited space around site constraints to work around obstacles and maintaining safe distances. Mobile and tower cranes are analyzed for best location and reach radius to improve efficiency and limit potential risks of falling objects and fatalities. On-site material storage should prioritize shortest distances to reduce movement of manpower and equipment. Prefabricated components are identified for off-site manufacture, providing shorter lead times and better control of transportation to site. Cost assessment for material, equipment and productivity is accomplished and risk analysis of uncertainty, pollution and hazards can be performed through integrated modeling. Researchers and practitioners continue efforts to address these challenges towards integration of BIM and its further implementation in construction planning.
3. Supporting Factors
High uncertainty and a myriad of challenges inherent in complex construction projects require innovative solutions not available through traditional scheduling methods. Enhanced Planning and Scheduling (EPS) combines the systematic Work Breakdown Structure (WBS) with computational strategies utilizing labor hours as a key metric to optimize schedules and allocate resources while addressing concerns over quality, time, cost, and safety [Amarkhil 2023]. Traditional project management emphasizes activity durations but lacks the flexibility necessary to address complexities inherent in construction; graphical techniques for linear scheduling include Gnatt charts, Linear Scheduling Method (LSM) and Line of Balance (LOB). Though simple and ease to interpret, these charts fail to illustrate the interdependent nature of tasks and are inefficient for collaborative planning. Network schedules provide comprehensive diagrams of activity relationships through Critical Path Method (CPM), Precedence Diagram Method (PDM) and PERT. The Last Planner System (LPS) focuses on enhancing productivity and reducing waste by providing an adaptable strategy for managing changes during the entire construction process, which are based on Agile manufacturing principles of iterative development and value creation, and the 5S (Sort, Set, Shine, Standardize & Sustain) methodology of workplace organization and standardization for profitability, safety and wellbeing.
Scheduling and planning are fundamental but challenging tasks in construction management that must achieve the owner objectives and quality requirements while delivering the project within a specific budget and timeline. Time, cost and quality are interrelated yet conflicting objectives that often lead to trade-offs and alternatives. Optimization models, such as fuzzy-multi-objective particle swarms, can be used to analyze and solve the time-cost-quality tradeoff (TCQT) problem [Wang 2021]. These models identify resource constraints to evaluate trade-off between duration and cost while utilizing resource leveling and smoothing techniques for greater efficiency. Decisions are determined through various equations using key variables including labor, quality, materials, equipment, and administration. Total project duration is calculated as:
where T is total duration, k the project activity, K the number of activities, ESTk the earliest start time of k, and Durk the duration of activity k. Total project cost is the summation of direct costs and indirect costs:
where LCk, MCk and ECk are labor cost, material cost and equipment cost for activity k, and Cwc, Cmo, Cfo, and Cfs are workers compensation, main office expenses, field office expenses, and field supervisions expenses, respectively. The project quality index is a weighted sum represented by:
ranging from 0 to 100 percent, where QWTk is the weight for LWTk the labor force, MWTk the weight of materials, EWTk for equipment, and AWTk for administration with LQk, MQk, EQk, and AQk the quality of labor, materials, equipment, and administration for activity k, respectively. Construction managers face the challenge of prioritizing and balancing these critical objectives throughout the planning and scheduling of projects.
Implementation of lean principles in construction is intended to improve productivity, increase quality and reduce waste in all forms. These concepts are particularly useful in complex large-scale projects, focusing on continuous improvements in efficiency and effectiveness by eliminating wasteful activities to avoid delays, prevent cost overruns and maintaining quality standards [Abdullahi 2023]. Though lean principles have led to better performance, greater cost efficiency and higher customer satisfaction; inadequate training and education, lack of understanding and resistance by the industry remain challenges towards implementation. Continuous improvement, pull planning, Value Stream Mapping (VSM) and LPS are the fundamental techniques of lean construction. Reducing time spent on ‘non-value adding’ activities such as rework and focusing on tasks that add value is a core concept. Just-in-time (JIT) delivery optimizes material and resource usage to minimize stockpiling and storage. Monitoring and analyzing key performance indicators (KPIs) provide a measure of efficiency and effectiveness allowing for further improvements.
Project planning is crucial in early phases to increase the probability of successful completion through the allocation of adequate time and resources, reducing risks encountered during execution. The planner seeks to achieve the proper balance between load and capacity through an understanding of their relationship to one another [Abou-Ibrahim 2019]. A hierarchical approach divides complex projects into simpler problems and individual tasks or different stages and levels of detail. Major milestone can be defined in long-term planning followed by look-ahead schedules and weekly work plans (WWP). Capacity and ‘Ready Ready’ Variance (CRRV) is the difference between resource allocation and the planned activity for each week, . Capacity to Load Ratio (CLR) is an indirect measure of assigned resources to executed work, . Capacity Planning Quality (CPQ) is the deviation from a value of 1 throughout the project schedule where the planned capacity is equal to the executed load.
Stable workflow achieved through capacity (CAP) planning, pull planning, look-ahead schedules, and LPS improves productivity, resulting in better overall performance.
Computer supported risk management identifies, analyzes and quantifies risk factors, probability of occurrence and impact on project duration. Estimating marginal cost of time disruptions generates a proactive schedule to protect against delays that might occur during execution [Schatteman 2008]. Integrated risk management framework utilizes the various historic databases of relevant risk factors to aid in the detection, analysis and response for potential risk events, while quantitative and qualitative assessment of risk factors predict likelihood and severity. Results of these studies help in developing strategies to prevent risks and guide the response to an event. Proper allocation of resources limits delays and disruptions, reducing associated costs and increases timely project completion probability (TPCP).
4. Related Elements
Schedule optimization through linear scheduling method (LSM) improves performance in repetitive activities related to multi-story and multi-family projects, though work on most complex structures is non-repetitive in nature. Disorganized allocation of resources reduces productivity; increasing costs, extending timelines and lowering morale. Enhancing the utilization of resources improves learning curves and minimizes idle time while increasing productivity and cost efficiency [Yu 2023]. Reverse construction activity scenarios and grey wolf optimization (GWO) provide means of optimizing schedules through the LSM framework and multi-objective algorithms. The effectiveness of these models has been verified by the benefits experienced in large-scale problems in the generations of construction schedules.
Resource fluctuations and peak demands create inefficiencies that have costly impacts, requiring allocation of added resources or the temporary hiring and release of workers; making future recruiting and retention difficult, disrupting learning curves and maintaining productivity. Multi-objective resource leveling models simulate project startup, practical schedules and optimal performance through genetic algorithm modules [Jun 2011]. Simultaneously optimizing resource allocation and leveling provides a method for construction planners to maximize utilization efficiency, minimize project duration, and generate trade-offs between the two. Analysis results prove the unique capabilities of these models towards significantly improving productivity that lead to timeline reduction and cost savings.
5. Conclusion
Quality schedules are essential to the successful completion of construction projects with technical data, and material, labor and equipment costs influencing actions and decisions. Safety, space and structure (3S) rules provide an adequate work environment, sufficient room to perform activities, and proper sequencing of operations [Bragadin 2015]. Performance requirements dictated by the International Organization of Standardization (ISO) and guidelines developed by the Government Accountability Office (GAO) ensure construction practices achieve adequate life cycle performance. The Association for the Advancement of Cost Engineering International (AACE) recommends schedule quality analysis and constructability review processes. Practice standards from the Project Management Institute (PMI) describe the development procedures and evaluation of quality. Various metrics and control steps are defined by the Defense Contract Management Agency (DCMA) and National Defense Industrial Association (NDIA) to analyze, assess and validate integrated schedules. Schedule health indicators include five classifications of performance requirements that are a valuable tool in auditing project execution. Flow-line view of the network schedules is a key factor for checking quality and efficiency.
Performance during uncertain and variable conditions affects construction progress, therefore the effects must be carefully considered and analyzed when making critical decisions. Pre-construction planning incorporates technology and resources to derive schedules that must be flexible enough to allow for changes during execution (See Fig 1).
Figure 1: Schematic illustration of uncertainty in project outcome [Feng 2022]
Accurate evaluation of performance and informed decision-making is difficult due to various factors. Probability-based methods (PBMs) represent possible outcomes and distributions [Feng 2022]. Cumulative distribution function (CDF) for the variation range of uncertainty scaled to the coefficient α represents factors influencing project planning (See Fig 2). Solving deep uncertainty problems utilizing data-mining and integrated process simulation provides an analytical method for large-scale construction and mega-infrastructure projects, leading to increased efficiency, resulting in better performance and greater success.
Figure 2: Shifts in the CDF upon the introduction of scaling coefficient [Feng 2022]
6. Future Directions
Utilization of 5D BIM in planning, simulation and optimization offers potential for improvement in productivity, performance and scheduling by integrating design with time and cost data. Multi-crossed hybrid systems of selected principles operating throughout project phases provides more understanding of individual requirements and key aspects to enhance design and improve execution over conventional methods [Leicht 2020]. Implementation of technical analysis and feasibility studies are reliable methods of evaluating various effects on entire construction projects.
Application of AI in planning and scheduling is becoming increasingly advantageous in optimizing processes, increasing efficiency and improving safety. Project management has stood ready to be revolutionized by AI throughout the construction industry and related manufacturing sectors since the 2000’s in data analysis and task automation. Deep-learning and ANNs can further improve accuracy while augmented reality and virtual reality (VR) provide visualization tools not previously available. As the field of AI continues to grow, easier usability and greater integration will reduce errors and related costs, decreasing disputes through better resource allocation, resulting in greater success in project delivery and overall performance.
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