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The proposed BM-based expediting procedure for project time and cost control.
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The rapidly changing marketplace together with the increasing complexity of contemporary projects makes it more likely that project activities will have uncertain durations, incurring a generally low probability of on-time delivery. Thus, project control that aims to track the project performance and to expedite relevant activities when necessary h...
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... In terms of future research, it is emphasized that there is room for enhancing buffer monitoring and implementing an early warning mechanism for project execution. Xuejun, Nanfang and Demeulemeester (2015) propresed new control procedure has been introduced, building upon the CC/BM framework, which assesses the likelihood of successfully completing a project in relation to the cost of expediting activities. This procedure determines the optimal timing for expediting specific activities in a cost-effective manner. ...
Construction and engineering projects often face delays and inefficiencies, which can be attributed to various factors. CCPM, a resource-focused project management technique, utilizes buffers strategically to mitigate delays. By leveraging blockchain's shared and secure ledger capabilities, this study proposes a framework for effectively measuring, monitoring, and controlling CCPM projects. The integration of blockchain technology aims to provide a more efficient approach to meeting project milestones and supporting project plan success. This paper explores the implementation of blockchain technology to enhance project buffer monitoring and progress sharing in Critical Chain Project Management (CCPM).
... Approximately 30% of the global economy, with an annual value of about $27 trillion (Hu et al. 2015), is managed as projects. Along with controlling scope and cost, the other critical issue in managing projects is meeting a tight deadline (Kerzner 2013). ...
Problem definition: Intraproject learning in project scheduling involves the use of learning among the similar tasks in a project to improve the overall performance of the project schedule. Under intraproject learning, knowledge gained from completing some tasks in a project is used to execute similar later tasks in the same project more efficiently. We provide the first model and solution algorithms to address this intraproject learning problem. Academic/practical relevance: Intraproject learning is possible when, for example, the difficulty of the tasks becomes better understood, or the efficiency of the resources used becomes better known. Hence, it is necessary to explore the potential of intraproject learning to further improve the performance of project scheduling. Because learning consumes time, firms may underinvest in intraproject learning if they do not recognize its value. Although the project scheduling literature discusses the potential value of using obtained information from learning within the same project, we formally model and optimize the use of intraproject learning in project scheduling. Methodology/results: We model the tradeoff between investing time in learning from completed tasks and achieving reduced durations for subsequent tasks to minimize the total project cost. We show that this problem is intractable. We develop a heuristic that finds near optimal solutions and a strong relaxation that allows some learning from partially completed tasks. Our computational study identifies project characteristics where intraproject learning is most worthwhile. In doing so, it motivates project managers to understand and apply intraproject learning to improve the performance of their projects. A real case is provided by a problem of the Consumer Business Group of Huawei Corporation, for which our model and algorithm provide a greater than 20% improvement in project duration. Managerial implications: We find consistent evidence that projects in general can benefit substantially from intraproject learning, and larger projects benefit more. Our computational studies provide the following insights. First, the benefit from learning varies with the features of the project network, and projects with more complex networks possess greater potential benefit from intraproject learning and deserve more attention to learning opportunities; second, noncritical tasks at an earlier project stage should be learned more extensively; and third, tasks that are more similar (or have more similar processes) to later tasks also deserve more investment in learning. Learning should also be invested more in tasks that have more successors, where knowledge gained can be used repetitively.
Funding: This work was supported by the National Natural Science Foundation of China [Grant 71732003 to N. G. Hall and Grants 72131010 and 72232001 to W. Zhao], the Shanghai Subject Chief Scientist Program [Grant 16XD1401700 to G. Wan], and the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning [Grant TP2022019 to W. Zhao].
Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0159 .
... cost spending patterns up to the current date) and applying this knowledge in new situations (e.g. to forecast the final project cost). Even though ML has not been commonly used in project forecasting, they are appropriate as it can consider various duration and cost performance patterns due to project uncertainty (Chen et al. 2019;Hu, Cui, and Demeulemeester 2015;Kim 2015). ...
Project managers need reliable predictive analytics tools to make effective project intervention decisions throughout the project life cycle. This study uses Machine learning (ML) to enhance the reliability in project cost forecasting. A XGBoost forecasting model is developed and computational experiments are conducted using real data of 110 projects representing 1268 cost data points. The developed model performs better than some Earned value management (EVM), ML (Random forest, Support vector regression, LightGBM, and CatBoost), and non-linear growth (Gompertz and Logistic) models. The model produces more accurate estimates at the early, middle, and late stages of the project execution, allowing for early warning signals for more effective cost control. In addition, it shows more accurate estimates in most projects tested, suggesting consistency when repeatedly used in practice. Project forecasting studies mainly used ML to estimate the project duration; a few ML studies estimated the project cost at the project's conceptual stage. This study uses real data and EVM metrics, proposing an effective XGBoost model for forecasting the cost throughout the project life cycle.
... Standard features of such models are comprehensive risk representation and ranking, risk probability calculation, risk impact calculation, and iteration capabilities (Project Management Institute, 2019). Most such probabilistic models are based on Decision Tree analysis, probability-impact analysis, Expected Monetary Value (EMV), Program Evaluation and Review Technique (PERT), Buffer management, Bayesian Belief Network, and Monte Carlo simulation approaches (Cagliano et al., 2015;Hu et al., 2015;Nunez et al., 2016;Qazi et al., 2022). In projects, such models quantify risk probabilities and impacts using a risk register or risk breakdown matrix (Project Management Institute, 2019), which detailed explanation is given in Hillson et al. (2006). ...
To manage risks against unexpected cost overruns, project teams use Contingency Budget (CB). Its accurate estimation has been a subject of multiple studies proposing either deterministic or probabilistic models. In this study, we propose a deterministic Machine Learning-based approach to estimate CB. Based on the k-means clustering, our model integrates the Expected Monetary Value (EMV) method and binomial distribution concepts. We test our methodology using 20 risk registers containing 25 risks with associated probabilities and impacts. Using Monte Carlo simulation, we compare our model's estimates with the ones by the traditional EMV. The model provided more accurate CB estimates and is more straightforward in use than the Monte Carlo simulation.
... A project is a "temporary endeavor undertaken to create a unique product or service" (Project Management Institute 2013). The global impact of project management within the world's economic activity is estimated at 30% (Hu et al. 2015) with an annual value of about $27 trillion. Since 1996, membership in the Project Management Institute has increased from about 50,000 to more than 500,000. ...
Explaining and Resolving Delays in Projects
Project management is responsible for 30% of the world’s economic activity, with an annual value of $27 trillion. Yet, despite half a century of research and the training of millions of project managers, many projects are delivered late. This is typically attributed to Parkinson’s Law, meaning the expansion of work to fill available time. However, in “Work More Tomorrow: Resolving Present Bias in Project Management,” Shi, Hall, and Cui identify and demonstrate the alternative explanation of time-inconsistent behavior, that is, present bias. Under present bias, a decision maker values immediate costs and rewards more than future ones. The authors show that this behavioral issue is responsible for procrastination by project workers and overall project delay. Borrowing concepts from popular employee savings schemes, they develop an incentive scheme that mitigates present bias and significantly enhances project performance, as measured by on-time frequency and expected project tardiness.
... proposed a method to compute the rework safety time using the information output and input time factors. Hu et al. (2015) introduced a new control procedure based on critical chain scheduling and buffer management that evaluates the probability of successful project completion. Zhang et al. (2018) suggested the use of a dynamic buffer monitoring model based on the phase attributes of the project. ...
One of the major problems with projects is that they are not completed according to schedule. Uncertainty always exists at the heart of real-world project scheduling problems. This paper introduces a fuzzy project buffer management (FPBM) algorithm which is a combination of the adaptive procedure with resource tightness (APRT) and fuzzy failure mode and effects analysis (FFMEA) methods. This paper aims to present an efficient model for project buffer sizing by taking FFMEA into account to reach a more realistic schedule. In this research, for increasing the efficiency of the APRT method, the FFMEA technique is simultaneously applied with them. This research was carried out as a case study in a renewable energy (RE) project. The methodology of this research consists of two phases. The first phase is the implementation of the APRT buffer sizing method. In the second phase of the research methodology, the fuzzy FMEA method is implemented. To validate the proposed model, the results are compared to several buffer management models proposed recently. Also, the results were compared with the results of similar projects. The findings show that considering the fuzzy FMEA technique in the APRT method, a more realistic schedule was obtained in this project.
... They added a time buffer to the project activities as an additional time to compensate for uncertainty, and to protect the project against tensions. Hu et al. (2015) introduced a new control procedure based on Critical Chain Scheduling and Buffer Management (CC/BM) that evaluates the probability of successful project completion relative to the cost of crashing and that determines when to expedite which activities in a cost-effective manner. Results of an experimental application of the proposed method presented its relative dominance over the currently widely adopted buffer management approach with respect to project time and cost performance. ...
In the project management, buffers are considered to handle uncertainties that lead to changes in project scheduling which in turn causes project delivery delay. The purpose of this survey is to discuss the state of the art on models and methods for project buffer management and time optimization of construction projects and manufacturing industries. There are not literally any surveys which review the literature of project buffer management and time optimization. This research adds to the previous literature surveys and focuses mainly on papers after 2014 but with a quick review on previous works. This research investigates the literature from project buffer sizing, project buffer consumption monitoring and project time/resource optimization perspectives.
... The swift market change together with the growingly complex construction projects increases the inherent uncertainties of project activities yielding low probabilities of on-time project completion. On the other hand, project management increases the chance of on-time project completion and success (Hu et al., 2015). Several researchers have dealth with uncertainty in the field of project management under topics such as delay management (Koushki et al., 2005;Sambasivan and Soon, 2007;Kim, 2009;Shivambu and Thwala, 2014), disruption management (Zhu et al., 2004;Zhu et al., 2005;Kuster et al., 2009), project strategic and alignment management (Kerzner, 2011;Ansari et al., 2014) and critical chain management (Rand, 2000;Herroelen and Leus, 2001;Tukel et al., 2006;Fallah et al., 2010;Ma et al., 2012;Ma et al., 2014;Peng and Huang, 2014;Ma et al., 2015;Zhang et al., 2016). ...
In today's changing conditions, the random disturbances have caused complexity into the
construction project management. In this study, a multi-objective approach is proposed to
determine the size of the time buffers in engineering and construction projects. The problem
is formulated as a two-stage stochastic programming model. In order to examine the efficiency
of the model, the proposed method was compared to the classic and extended critical chain
management approaches. The validation is performed using simulation experiments carried
out in the benchmark data test and a real case of an engineering project. The numerical
examples and case studies were presented to validate the proposed methodology. The results
demonstrate the efficiency of the proposed multi-objective time buffering method in the
actual situation. The outcomes indicate that the proposed robust buffer sizing method results
in a more stable plan, as against the traditional methods.
... Project management organizes about 30% of the world's economy (Hu et al., 2015). Many recent projects apply critical chain project management (CCPM) methodology, which requires the design of project and feeding buffers. ...
... Approximately 30% of global economic activity is organized using project management, implying an annual value of about $27 trillion (Hu et al., 2015;Zhao et al., 2020). Applications have expanded from construction and engineering, to information technology, R&D, software development, and new product and service development (Hall, 2016). ...
Project management organizes about 30% of the world's economy. Many recent projects apply critical chain project management (CCPM) methodology, which requires the design of project and feeding buffers. Accurate sizing of these buffers is essential, because too small buffers result in emergency procedures to prevent late delivery, whereas too large buffers result in uncompetitive bids and lost contracts. Previous buffer sizing research, focused predominantly on the critical chain, typically results in excessive buffers, and in critical chains being challenged by feeding buffers during planning. This work also performs inconsistently, for example in makespan estimation, at execution. We propose a new procedure for buffer sizing based on network decomposition, which offers logical advantages over previous ones. First, the size of a feeding buffer is determined from all associated noncritical chains. Second, the project buffer incorporates safety margins outside the critical chain by comparing feeding chains with their parallel critical counterparts. Computational testing on a case study of a real project and extensive simulated data shows that our procedure delivers much greater accuracy in estimating project makespan, and smaller feeding buffers. Furthermore, the resulting critical chain is never challenged. Additional benefits include delayed expenditure, and reductions in work-in-process, rework, and multitasking.
... A project is a "temporary endeavor undertaken to create a unique product or service" (Project Management Institute 2013). As much as 30% of the world's economic activity is organized as projects ( Hu, Cui, & Demeulemeester, 2015 ), implying an annual value of around $27 trillion. Much of this activity is due to modern applications of project management, for example, information technology systems implementations, software development, and research and development. ...
Project management is a business process that supports about 30% of the world’s economic activity. Yet projects routinely suffer from the influence of Parkinson’s Law. This behavioural phenomenon routinely results in failure to deliver work that is completed early before its assigned deadline. As a consequence, the late completion of other work is not offset, and overall project performance suffers. Hence, project success rates below 40% are widely reported.
Our work uses mechanism design within non-cooperative game theory. A particular issue in the design process is to eliminate the possibility that a project worker with multiple dependent tasks can improve their incentive payment by falsely reporting some of their task completion times. From our review of the academic and business literature of project management, no incentive scheme used in practice accomplishes this.
Our results include the design of incentive schemes that eliminate or mitigate Parkinson’s Law. These schemes apply to projects designed under either traditional Critical Path Method (CPM) planning or modern Critical Chain Project Management (CCPM) planning, and are also invulnerable to group strategy. A large-scale computational study validates the resulting benefit to project performance as substantial and also robust across different project characteristics. We also provide what is apparently the first analytical comparison between traditional CPM and modern CCPM planning systems.
The incentive schemes we propose are simple and easily implementable. We recognize that performance incentives are structured differently by each organization, but our work provides a flexible basis from which various practical schemes can be designed.
