Project Management has been successfully evolved and employed in such efforts as development of new products and services, process reengineering, etc. ( Pellegrinelli & Bowman, 1994 ). Furthermore, project management assists with continuous improvement by providing frameworks for quality control, resource management, and risk avoidance ( Bryde & Brown, 2020 ). For organizations that are stochastic in their innovation, project management ensures that strategic projects are executed efficiently and effectively, and in harmony with the company’s long-term goals ( Kwak & Stoddard, 2021 ).

Predictive project management methods like Waterfall, CPM and PERT have been widely used for managing projects in structured project execution. However, each of these approaches has its benefits that come with criticism for being too rigid, especially in fast-paced environments where change is frequent. Below is a summary of the main predictive methods and challenges associated with them, from real case projects, based on existing academic research.

According to ( Boehm & Turner, 2003 ), Waterfall is most suitable for projects with well-defined boundaries and low project changes throughout the life cycle, thus being most appropriate in construction or manufacturing industries which are characterized by the necessity of clear and specific processes. Bertschek et al. (2020) state that due to the simplicity and clarity of the Waterfall approach, it is rather easy to manage and control, particularly in projects with clear requirements and products.

However, if such consequences as unanticipated problems and new project specifications are likely to occur during the project, then the rigidity of Waterfall may become a problem. Furthermore, ( Kerzner, 2015 ) notes that large amount of documentation and signature collection at various stages of the project increases the time and effort which are usually not productive in dynamic environments that require quick actions.

NASA’s Mars Rover Mission project is an ideal example of the Waterfall approach that has faced the above-mentioned drawbacks, especially the time lag issues in communication between the rover and the mission control. Thus, the more Agile approach keeping the Waterfall approach for major decision making and using the iterative approach for the product testing product was implemented. Subsequently, it reduced bureaucracy in some parts, increased responsiveness to the unforeseen issues and accomplished the project goals ( Harris & Harper, 2018 ).

CPM is a very useful scheduling tool that determines the total number of dependencies, shows the sequence of tasks and identifies critical activities that directly affect the overall project timeline. Project managers can, therefore, improve on the allocation of resources, prevent delays and ensure that critical tasks are completed on time. Lock (2017) points out that this effective scheduling approach minimizes risks in a project since it ensures that timelines are met while at the same time ensuring that dependencies are well managed. Kloppenborg et al. (2021) stress that CPM is crucial in large and complex projects where scheduling, resources and coordination are crucial.

However, one of the main drawbacks of using CPM, is an assumption that the task durations are given, which is often not the case in dynamic and unpredictable environments. Changes in resource availability or the presence of obstacles can lead to the CPM schedule being inaccurate ( Shenhar & Dvir, 2007 ). Additionally, CPM can further complicate projects when many interdependent tasks take place, as scheduling becomes more daunting while identifying relationships between tasks and resources.

Boeing 787 Dreamliner project is a great illustration of the above-mentioned CPM limitations. The project experienced considerable delays and cost overruns, which were mainly attributed to overly bureaucratic processes with involvement of more than 300 subcontractors. To combat this, Boeing adopted a streamlined CPM approach that enabled the subcontractors to make decisions on the non-critical tasks without referring to their higher authorities. That move to optimize CPM enabled Boeing to improve the coordination and decrease the bureaucratic delays in the production process ( Thomas & Fernandez, 2015 ).

PERT is a more detailed tool for project management mainly used to identify and depict tasks with a high level of uncertainty and unwell defined durations. As stated by ( PMI, 2017 ), this approach is most useful in projects where time is the critical factor and can be used to identify and manage risks that are associated with time. Morris (2018) notes that the effectiveness of the PERT system cannot be questioned because it allows project managers to include provisions for the uncertain factors during a project, which is crucial in complex and dynamic projects.

However, there is a major drawback of the PERT and that is a time-consuming nature of defining the three-point estimates for each task. As pointed out by ( Boehm & Turner, 2003 ), this step usually involves a lot of work and data collection, especially in large projects with many tasks. The need for frequent revisions and changes to the estimates also increases the administrative burden. As argued by ( Bertschek et al., 2020 ), if there are projects where task durations are known and stable, using probabilistic approach may be unnecessary and could even be counterproductive.

Nevertheless, one of the most complex transport projects in Europe, the London Underground project, employed a traditional PERT approach and managed to tackle the estimation and scheduling issues. To streamline the process, the project managers introduced a customized PERT methodology, where they reduced the frequency of updates for tasks that were on the critical path and allowed for more autonomy at lower levels. This reduction in bureaucracy, in turn, cut decision-making time and led to faster project delivery ( Egan, 2016 ).

Bureaucracy is a widespread issue in predictive project management, which is due to the necessity of a high level of formal documentation, approval, and decision making in most organizations. Lock (2017) states that the main disadvantage of these methodologies is the bureaucratic paradigm that hampers flexibility and ability to address unforeseen issues or challenges in a project. This focus on detailed reports, formal project plans, and frequent stakeholder reviews results in overregulation, which may be unnecessary for many smaller, more straightforward projects but is still often demanded because of the traditional rigidities of project management frameworks ( Kerzner, 2015 ).

Bertschek et al. (2020) point out that although documentation and structure are necessary for large and complex projects, overregulation can slow down the achievement of project goals. Project management tools like status meetings and reports, if they take over the workflow, can prevent the project teams from focusing on the real activities that create value. Jabnoun and Al-Khalifa (2020) also explain how these bureaucratic procedures impact project timelines, stating that the numerous approval delays and administrative procedures slow down the decision-making process and thus the project implementation. Therefore, the bureaucratic tendency in predictive project management can become a significant problem, especially in fast-growing industries that need flexibility.

To address the limitations of over-bureaucratization in predictive project management, scholars and practitioners have recommended traditional methodologies optimization to fit specific project context. Tailoring is defined as the customization of existing project management practices to make them more appropriate for a given project considering its size, complexity and stakeholder needs.

From Morris’s (2018) view, it is possible to cut down on the bureaucratic controls through adjusting the review frequency, changing the level of documentation or changing the approval procedures to fit the current situation and still be able to maintain acceptable levels of control for project success. This way, project managers can concentrate on the most critical processes for the success of the project as opposed to implementing generic processes that may not be important in every project.

PMI® (2017) in its guide, also advises to consider specific needs of each project. Thus, in response to the specificities of a particular project, some tasks can be avoided, for instance, the development of extensive documentation or the holding of unnecessary approval meetings. Thus, by recognizing the differences in the project contexts and applying a more fluid approach, organizations can enhance the efficiency and effectiveness of their project management practices and hence, develop projects on time, to the specified budget, and to the required scope.

An inductive coding approach was used to generate initial codes from recurring themes identified in the responses. This approach gave full perspective on the PMs’ experiences and perceptions of the challenges and inefficiencies of predictive methodologies. The systematic review of the interview transcriptions was performed to identify meaningful segments that provided insight into the research questions. The initial codes were attached to these segments, enabling the data to be categorized by issues and concepts reported by the participants ( Table 1 ).

Following the coding process, thematic analysis was applied to identify the overarching themes and patterns within the data. Thematic analysis allowed for a deeper understanding of how the issues of themes emerged from the interview responses. The three main themes that emerged from the data were:

Bureaucratic Inefficiencies: Participants equally emphasized the negative impact of bureaucratic processes in the Waterfall methodology, namely, excessive documentation, many approval layers, and delayed decision making. Most of the interviewees stressed how these bureaucratic barriers slowed down the completion of projects. A major problem expressed by several PMs was the significant time consumed in reporting and obtaining approval. As PM B noted, “Approval meetings delay critical decisions which negatively impacted the ability to make quick changes to the plan”. This finding is in line with literature by ( Bertschek et al., 2020 ; Jabnoun & Al-Khalifa, 2020 ) where bureaucratic inefficiencies were seen as a major drawback of traditional project management methodologies.

Methodology Adaptations: Some of the interviewees explained the importance of adapting the conventional Waterfall approach to suit the particular aspects of the company’s projects. They stated that although waterfall is a structured approach, it is too inflexible to fit the dynamics of large industrial projects that can experience sudden changes during execution due to such issues as equipment failure, weather conditions or supply chain problems. PM C also stressed the need to simplify documentation and to change the project phases as the project progresses. PM D added, “We reduced the documentation to keep the relevance” with the aim of making project management more agile without jeopardizing the critical management controls. This theme is in line with the work of ( Morris, 2018 ) and ( Shenhar & Dvir, 2007 ) who recommend customizing the methodologies to the project context, especially in complex settings such as industrial organizations.

Improvement Suggestions: A recurring suggestion across interviews was the use of hybrid methodologies to combine the best of both predictive and agile frameworks. Several managers pointed out that incorporating agile principles like short sprints and iterative feedback loops could help to reduce the bureaucratic overhead. PM E highlighted, “I would also introduce short sprints to address changing requirements,” indicating that agile techniques might provide a more adaptable, real-time way of handling project management without compromising the formality of predictive planning. Moreover, the participants stressed the need for real-time tracking systems and digital tools to enhance decision making and improve coordination. The desire to enhance the current project tracking systems was in line with literature ( Kwak & Anbari, 2009 ), in which the integration of predictive and adaptive approaches was proposed as a way to address the inflexibility of conventional project management techniques.