The management of construction projects has, historically, been carried out based on available information about past projects and, once the management plan has been created, it has been left alone for the course of the project unless there is an emergency. While such an approach to project management has and does work reasonably well, it has the drawback of not using the most up-to-date information about ordering of tasks, material costs, life-cycle costs, contractor and subcontractor availability, and other factors that can significantly affect the final cost, performance, and completion time for a project. Incorporating such information requires using data and evidence of differing conditions in making management decisions, which in turn requires development of an entirely new paradigm of construction management, one using the concept of "evidence-based decision making." This research entails development of such a paradigm.
The management of manufacturing processes, in theory, should share many aspects with the management of construction processes. However, the reality is that they differ greatly. In manufacturing, presuming the product has a sufficiently long-lasting market, there is sufficient time for the process to be enhanced as more is learned about the product. This means adjustments can be made in material selection, processing steps, machinery, tooling, and other possible variables in the process. In construction, however, the time to produce the final product, i.e., the building, road, bridge, or other structure, is on the order of months or a few years, after which the construction is done. This means that lean production techniques such as those developed for automobile production have not been applied to construction. Shakeri, et. al., developed a lean construction process based on the Toyota Production System. The lean construction required daily meetings, development of plans for 6-weeks ahead that could be updated, involvement of all contractors in meetings and scheduling, with demonstrations of what effect even minor contractors have on the overall project, and use of visual cues to let workers know where the project stood with regard to completion. As is true of lean manufacturing, lean construction depends on managing the human element as well as technology, with the management taking place by continual communication. Other moves to improve construction have included use of Total Quality Management techniques (Arditi), Development of better organizational structures (Moore), and use of production management flow principles (Dos Santos).
All of the above were working mostly on the human element in attaining lean construction. There are technical aspects that also should be addressed as a construction project goes forward. Shahi, et. al. developed a method for sharing construction data both inside and outside of a project team. Lee, et.al., made it easier to share benchmarking data over the Internet. All of this serves to deliver the information needed to use the technique of "evidence-based decision-making" to make adjustments to a construction project's plans and schedule to make the project more cost-effective. Evidence-based decision-making has mostly been developed for the medical and public health fields. Lovelace, et. al., quote Brownson, et.al. in describing evidence-based decision-making as "making decisions using the best available scientific evidence, systematically using data and information systems, applying program-planning frameworks (that often have a foundation in behavioral science theory), engaging the community in assessment and decision making, conduction sound evaluation, and disseminating what is learned." In the medical and public health areas, the evidence typically centers on treatment techniques and health outcomes. In the case of construction projects, the parallel evidence would including construction techniques (including materials) and the outcomes, including not just cost, but also return on investment, which thus includes the profit the building owner realizes on his or her investment in new construction.
1. Identify variables that would be most important in affecting costs and revenues (e.g, material selection, building design, planned building use, climate, etc.
2. Per Misirli and Bener, as well as Ashby and Smith, introduce as needed stochastic modeling to account for variability in the data for completed projects. A Bayesian process seems to be applicable.
3. Collect the required data for construction projects. Depending on location and standard practices, it may be necessary to find projects that are just beginning construction and gather the data as they proceed, so that all the required data are gathered.
4. Create the decision-making model, with the data collected in step 3 used to construct the model
5. Find a case study project to use and validate the model. The case study project should be similar to the projects used to create the model so that they are comparable. For example, both the completed and the new project should be similarly-sized office buildings.
6. Prepare extensions of the decision-making model to demonstrate the general usefulness of this approach.
This research will move the whole area of evidence-based decision making from the medical field into new areas of scientific endeavor. In addition, this will provide new types of information to be gathered about construction projects which, when combined with other new construction management techniques such as lean construction, will even further enhance construction quality, safety, and costs.
Arditi, D. & Gunaydin, H. (1997). Total quality management in the construction process. Int. J. Proj. Manage., 15(4), 235-243.
Ashby, D. & Smith, A.F.M. (2000). Evidence-based medicine as Bayesian decision-making. Statist. Med., 19, 3291-3305.
Brownson, R.C., Fielding, J.E., & Maylahn, C.M. (2009). Evidence-based public health: A fundamental concept for public health practice. Ann. Rev. Public Health, 30, 175-201.
Dos Santos, A. (199). Application of flow principles in the production management of construction sites. Ph.D. Dissertation, Univ. of Salford (U.K.).
Lee, S., Thomas, S., & Tuker, R. (2005). Web-based benchmarking system for the construction industry. J. Constr. Eng. Manage., 131(7), 790-798.
Lovelace, K., Aronson, R., Rulison, K., et. al. (2015). Laying the groundwork for evidence-based public health: Why some local health departments use more evidence-based decision-making practices than others. Am J. Pub. Health, 105 (Supplement 2), S189-S197.
Misirli, A., & Bener, A. (2014). Bayesian networks for evidence-based decision-making in software engineering. IEEE Transactions on Software Engineering, 40(6), 533-554.
Moore, D. (2002). Project management: Designing effective organizational structures in construction. Oxford (UK). Blackwell Science.
Shahi, A., Haas, C., West, J., & Akinci, B. (2014). Workflow-based construction research data management and dissemination. J. Computing Civ. Eng., 28(2), 244-252.
Shakeri, I., Boroujeni, K. A., & Hassani, H. (2015). Lean construction: From theory to practice. International Journal of Academic Research, 7(1) 129-136.