Managing Tipping Point Dynamics in Complex Construction Projects

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1 Managing Tipping Point Dynamics in Complex Construction Projects Timothy R. B. Taylor, P.E., M.ASCE 1 ; and David N. Ford, P.E., M.ASCE 2 Abstract: Complex construction projects are vulnerable to tipping points. Tipping points are conditions that, when crossed, cause system behaviors to radically change performance. Previous research identified tipping point dynamics as capable of explaining the failure of some nuclear power plant construction projects. These dynamics can also threaten the success of other large, complex construction projects. The current work uses a dynamic project model to test policies for managing tipping point dynamics. The Limerick Unit 2 nuclear power plant project is used to test model usefulness. Sensitivity analysis reveals the rework fraction, strength of subsystem interdependence, and sensitivity of the project to schedule pressure as potential high-leverage points for policy design. The model is used to test policies for managing tipping points that were used to complete the Limerick Unit 2 nuclear power plant after a tipping point threatened project completion. Implications for construction project design and management and research opportunities are discussed. DOI: / ASCE :6 421 CE Database subject headings: Project management; Dynamic models; Simulation models; Change management; Errors; Nuclear powerplants. Introduction Although development projects are pursued to add value to their developers or users, many projects fail to do so as originally planned Flyvbjerg et al. 2003; Matta and Ashkenas 2003; Evans 2005; Nassar et al Examples of failed large, complex projects include the U.S. Navy s development of the Littoral Combat Ship that is currently $100 million over the original budget estimates Karp 2007, the Channel Tunnel connecting Great Britain and France that when completed was approximately $10 billion over its original budget and two years late Kharbanda and Pinto 1996, the Boston Central Artery project that is approximately $10 billion over its original budget and seven years late USS 2000; USHOR 2005, and the U.S. Department of Energy s National Ignition Facility that exceeded the original budget by approximately $1 billion and was six years late D. Ford and V. Parasnis, unpublished internal report, November The failure of large complex projects can lead to severe economic consequences for project stakeholders. Large, complex projects are very susceptible to failure. Project management research has identified many factors that can lead to project failure including overestimation of benefits Evans 2005, 1 Ph.D. Candidate, Construction Engineering and Management Program, Zachry Dept. of Civil Engineering, Texas A&M Univ., College Station, TX Associate Professor, Construction Engineering and Management Program, Zachry Dept. of Civil Engineering, Texas A&M Univ., College Station, TX corresponding author. tamu.edu Note. Discussion open until November 1, Separate discussions must be submitted for individual papers. To extend the closing date by one month, a written request must be filed with the ASCE Managing Editor. The manuscript for this paper was submitted for review and possible publication on October 23, 2006; approved on October 18, This paper is part of the Journal of Construction Engineering and Management, Vol. 134, No. 6, June 1, ASCE, ISSN /2008/ /$ errors Busby and Hughes 2004, lack of knowledge transfer between projects Cooper et al. 2002, rework Cooper 1993; Gidado 1996; Love et al. 1999, 2000b, 2002, concealing rework Ford and Sterman 2003a, schedule pressure Nepal et al. 2006, and project complexity Gidado 1996; Lyneis et al. 2001; Lee et al In large, complex projects, the impact of these and other factors can be magnified due to feedback dynamics within the project. In addition, these projects are composed of multiple interrelated systems where changes in one system can also require unforeseen changes in connected systems. Causal feedback between these systems cause projects to evolve over time in ways that greatly increase project complexity and make them difficult to manage Lyneis et al Consider some American commercial nuclear power plant projects as a specific example of a type of large, complex construction project that failed. Many nuclear power projects failed to meet their original cost and schedule targets. The average construction duration for American commercial nuclear power plants tripled from 1959 to 1988 NRC 1982 and unit costs grew at an exponential rate Fig. 1. Based on analysis of data from the Nuclear Regulatory Commission NRC 1982 and the Energy Information Administration EIA 1988, the mean project duration during this time was 239% of the planned duration and the mean cost was 338% of the original estimate. Although the construction of nuclear power plants in the United States ceased in the late 1980s, current public policy and industrial planning include nuclear power projects. The United States increasing energy demand has raised the need for additional generating capacity and initiated a possible resurgence in the construction of nuclear power plants. In both his 2005 and 2006 State of the Union Addresses, President George W. Bush promoted nuclear power as part of the nation s energy future. In a 2006 speech at the Limerick nuclear power plant, the President stated, For the sake of economic security and national security, the United States of America must aggressively move forward with the construction of nuclear power plants. Pulizzi The energy industry is also planning to build new nuclear power JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT ASCE / JUNE 2008 / 421

2 Fig. 1. Unit costs of American nuclear power plant construction data from EIA 1988 plants. As of January of 2007, the NRC expects to receive construction permit applications for at least 30 new nuclear reactors Smith The possible resurgence of nuclear power plant construction in the United States could lead to more projects that fail to meet cost and schedule plans. What caused the failures of previous projects and how can future nuclear power plant projects be better designed and managed? In complex projects, interdependent project components such as rework, schedule pressure, and project complexity interact over time in ways that can lead to project failure. In the case of the U.S. nuclear power industry, several researchers identified ever increasing and ever changing governmental regulations imposed on nuclear power plants as a root cause of cost and duration increases in nuclear plant construction Friedrich et al. 1987; Feldman et al. 1988; Lillington These changes can initiate unforeseen project system interactions. Understanding the structure of these interactions and their impacts is critical to improving nuclear power plant projects and more generally large, complex construction projects. Recent research Repenning 2001; Taylor and Ford 2006 has identified structures of these interactions that create tipping points. This research demonstrates how tipping points can explain and quantify interdependent project components that influence project performance. Tipping Points A tipping point is a set of conditions that separate two very different, internally driven, behavior modes. In The Tipping Point, Gladwell 2000 defined one tipping point as that one dramatic moment in an epidemic when everything can change all at once. Sociologists have used tipping points to explain the increase of participants in a riot Granovetter 1978; Granovetter and Soong 1983, school desegregation Clotfelter 1976, societal segregation Schelling 1971, and social problems associated with ghettos Crane The term tipping point and the concept have grown into widespread use and have been used satirically to explain the popularity of names for newborn children Fry and Lewis The causal structures of these and other systems, including construction project processes and management, can create tipping points. From a feedback perspective, systems tend to remain stable as long as the feedback loops that control progress dominate the system Sterman However, when dominance shifts away from project feedback loops that complete work to feedback loops that add work, a tipping point is crossed and the addition of new work can overwhelm the project. When this occurs, projects can become temporarily unstable and fail. In construction projects, crossing a tipping point can change improving net progress to declining or no net progress. The effects of tipping points in multiple development project systems has been investigated. Repenning 2001 showed that a tipping point can help explain fire fighting, the phenomenon of projects being overwhelmed with work to the point of failure, and can threaten the success of a series of development projects. Black and Repenning 2001 investigated the effectiveness of several managerial policies to counteract fire fighting, including adding resources to the project, releasing lower quality work, and resource allocation policies. However, these and other policies for managing tipping points have not been tested for use in single development projects, such as construction projects. Taylor and Ford 2006 showed that tipping point dynamics can lead to run away project backlogs and explain some forms of single development project failure, but did not address operational solutions. The current work uses a project from the American nuclear power industry to demonstrate the impacts of tipping point dynamics on complex construction projects and investigates possible solutions. An example of a construction project that displayed tipping point behavior is Unit 2 of the Philadelphia Electric Company s PECO Limerick nuclear power plant. Construction of the 1,065 megawatt unit began in June of The schedule when the construction permit was issued called for completion in September However, Unit 2 was only 36% complete at the original planned construction completion date. In September 1980, PECO increased the estimated completion date by two years because of an increase of scope due to design changes and new regulatory requirements NRC Increases in scope and design changes reflect ripple effects and rework, two factors that will be shown to be capable of generating tipping point dynamics. Construction of Unit 2 was halted in July 1982 by order of the Pennsylvania Public Utility Commission due to escalating costs NRC Tipping points have been used to describe how projects can evolve to conditions in which processes are in control that lead to project failure. However, tipping points have not been used to investigate potentially effective designs for, and responses to, this threat to large, complex construction projects. Effective designs and policies are needed to mitigate tipping point threats and thereby improve future performance on complex projects. The current work extends the theory of tipping point dynamics in complex construction projects by mathematically describing a tipping point structure in a way that facilitates its use in understanding, designing, and managing projects. This description is used to investigate process designs and policies for managing tipping point conditions in complex construction projects. A project model of tipping point dynamics is described next, followed by model analysis to identify key inputs driving tipping point dynamics. Policies used during the construction of the Limerick Unit 2 nuclear power plant are presented and investigated with the model. Descriptions of implications for practice and future research opportunities conclude the work. A Simulation Model of Tipping Point Dynamics Based upon the experience of the Limerick Unit 2 nuclear power plant and failures of large, complex projects, a system dynamics model capable of producing tipping point dynamics in project 422 / JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT ASCE / JUNE 2008

3 Fig. 2. Work flows through a project with a tipping point structure simulations was developed. System dynamics is a methodology for studying and managing complex systems Sterman The approach focuses on how performance evolves in response to the interactions of management decision making and development processes through feedback. System dynamics has been successfully applied to a variety of project management issues including the effect of rework on project performance Cooper 1993; Ford 1995; Love et al. 1999, 2000b, 2002; Cooper et al. 2002; Lee et al. 2006, construction firm performance Tang and Ogunlana 2003; Ogunlana et al. 2003, failures in fast track implementation Ford and Sterman 1998, 2003b, poor schedule performance Abdel-Hamid 1984, the management of project contingencies Ford 2002, the planning of fast-track construction projects Pena-Mora and Li 2001; Pena-Mora and Park 2001, construction innovation Park et al. 2004, change management Lee et al. 2005, 2006; Park and Pena-Mora 2003, and concealing rework requirements Ford and Sterman 2003a. A system dynamics simulation model is a series of difference equations based on feedback relationships that represent interactions between elements of a system Sterman The system dynamics methodology assumes that system changes occur in small increments over continuous time. System conditions are calculated at each time step based on the conditions in the previous time period and the difference equations. The tipping point model used here has time units of months with a time step of one-eighth of a month. Thus, project conditions for the Limerick Unit 2 simulation change twice weekly over the course of the 200 month simulation. The equations for the tipping point model are shown in Appendix I. The model is purposefully simple relative to actual practice to expose the relationships between tipping point structures, project behavior modes, and management. Therefore, although many development processes and features of project participants interact to determine project performance, only those features that describe a particular tipping point structure, project management policies, and the fundamental processes they impact are included. For example, resource productivities are assumed fixed, work packages are assumed to be available for development, and work packages are completed in accordance with schedule requirements i.e., work packages on the critical path are given priority over work packages not on the critical path. The literature cited above investigates the impacts of these and other factors on performance. References in parentheses e.g., 7 in the model description refer to equations in Appendix I. The complete model with documentation is also available from the authors or at ceprofs.tamu.edu/dford/. The model consists of three sectors: A workflow sector Fig. 2, a resource allocation sector, and a schedule pressure sector. The workflow sector is based on the Ford and Sterman 1998 structure of a development project value chain with a rework cycle. The same or similar structures have been used extensively to investigate project dynamics and management issues Cooper 1993, 1994; Ford and Sterman 2003a, b; Joglekar and Ford 2005; Lee et al. 2005; Lee et al In Fig. 2, boxes represent backlogs of work that must be completed to finish the project. As each work package is completed, it moves from one backlog to another, represented by arrows with valve symbols. Work is initially completed 9 and moves from the initial completion backlog 4 to the backlog of work requiring quality assurance 5. Work that passes quality assurance 9 is approved 11 and adds to the backlog of work that has been approved and released 7. Work discovered to require change either through errors, omissions, or regulation changes 10 moves into the backlog of work known to require rework 6. The IC backlog can also be increased by work created by ripple effects 12. The ripple effect strength describes the interdependence of project work packages. Completing rework returns work packages to the QA backlog 9 for checking again because rework can reveal JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT ASCE / JUNE 2008 / 423

4 previously hidden, or create new, rework requirements. The initial completion, quality assurance, and rework flows are constrained by either development processes 13 or available resources 14. In the resource allocation sector, resources are allocated to the three development activities 22 after an adjustment delay 21 in amounts proportional to demand. Each proportion is the size of the activity s backlog compared to the project backlog IC backlog + QA backlog + Rework backlog In addition to describing the accumulations and flows of work, Fig. 2 describes the feedback loops that drive work, rework, ripple effects, and schedule pressure. The direction of arrows indicates the direction of influence. Plus signs at arrowheads indicate that an increase in the component at the arrow s tail results in an increase of the component at the arrow s head and vice versa i.e., decreases result in decreases. For example, as the ripple effect strength increases, the ripple effect rate increases. Negative signs at arrowheads indicate that an increase of the component at the arrow s tail results in a decrease of the component at the arrow s head and vice versa i.e., decreases result in increases. For example, as the project deadline increases making more time available to complete the project, schedule pressure decreases. Closed feedback loops are either reinforcing R and tend to generate accelerating divergent behavior or balancing B and generate goal-seeking behavior. See Sterman 2000 for a more detailed description and analysis of reinforcing and balancing loops. Feedback loops can explain tipping point dynamics. Balancing feedback loop B1 Project Progress withdraws work from the rework cycle. The QA backlog increases due to initial completion and rework, causing the QA rate to increase as resources are shifted to quality assurance. This increases the work approval rate, reducing the QA backlog, and increasing the amount of work released. This balancing loop drives the project to completion as the backlogs that represent the remaining project work decline to zero. In the absence of ripple effects e.g., if no rework is discovered, B1 completes a project as quickly as processes and resources allow. Reinforcing loop R1 Ripple Effect adds work to the project through the discovery of rework and ripple effects. Increasing the QA backlog increases the QA rate, increasing the rate at which work is discovered to require rework. This increases ripple effects, adding work to the IC backlog. As the IC backlog increases, resources are shifted to initial completion, the initial completion work rate increases, and the QA backlog increases further. In the absence of loop B1 e.g., if all work required rework, loop R1 increases the rework and project backlog infinitely, thereby degrading project performance to eventual failure. Feedback loops B1 and R1 create a tipping point structure based on the approval of work and the addition of work to the project backlog. As long as loop B1 dominates, the project progresses albeit perhaps very slowly, the project backlog decreases, and the percent complete increases. However, if loop R1 dominates, the project backlog increases and the percent complete decreases. At the tipping point, the rate of work being added to and the rate of work being removed from the project backlog are equal. When work is being completed faster than new work is being added ripple effect rate approve work rate, the percent complete increases. When work is being added faster than it is being completed ripple effect rate approve work rate, the percent complete decreases. A third feedback loop is needed to endogenously shift feedback loop dominance between loops B1 and R1. Schedule pressure can create this third feedback loop. Schedule pressure is common in development projects and can lower development Fig. 3. Typical model behavior quality Park et al. 2004; Nepal et al. 2006, increase rework Cooper 1994; Graham 2000; Ford and Sterman 2003b; Nepal et al. 2006, and thereby have important impacts on performance. Schedule pressure can also have beneficial impacts that can be modeled with additional feedback loops see Ford 1995 for examples, but the current work models only the net effects of schedule pressure, which are assumed to be negative. This assumption is supported by the findings of Nepal et al Schedule pressure 30 increases with the time required to complete the project backlog 28 and decreases with the time available to complete the project backlog 26. As a project approaches a deadline, schedule pressure increases ceteris paribus and developers work faster to meet the deadline. This increases the risk of work being completed incorrectly i.e., increases the fraction of work requiring rework. Therefore, reinforcing Loop R2 Schedule Pressure can increase the strength of the ripple effect loop R1 by increasing the rework fraction 31. The resulting increase in ripple effects, the IC backlog, and thereby the project s backlog, increases the time required to complete the project, increasing schedule pressure further. Through loop R2, schedule pressure can push a project initially dominated by the Project Progress loop B1 across its tipping point into dominance by the Ripple Effect loop R1 and toward failure. Typical Model Behavior and Testing To illustrate typical model behavior, the simulated percents complete for two projects are shown in Fig. 3. The project structures are identical except that in Project A, schedule pressure has no impact on rework i.e., loop R2 is not active, while Project B experiences an increase in rework due to schedule pressure through feedback loop R2. Both projects begin with slow progress. Progress accelerates as work flows from the IC backlog to the QA and Rework backlogs and ultimately into the stock of work released. When the QA and Rework backlogs stabilize, the slope of Project A s percent complete becomes almost constant months 3 15, indicating steady progress. Near month 15, the slope of Project A begins to decrease significantly as the stocks of initial completion, quality assurance, and rework are emptied, until the project is completed. Though the rate of increase of project percent complete changes during the project slow initial growth, relatively steady growth during the middle of the project, and slow growth at the end of the project, the percent complete 424 / JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT ASCE / JUNE 2008

5 Fig. 4. Tipping point behavior mode test data from reports to NRC increases monotonically. This indicates that Project A is dominated by loop B1 throughout its duration. Project B initially displays similar, albeit slower, initial progress. As in Project A, its behavior is also dominated by loop B1 during this time. However, as Project B progresses, the schedule pressure loop R2 strengthens the ripple effect Loop R1, weakening project progress Loop B1, and increasing schedule pressure. Between months 14 and 15, the schedule pressure on Project B is twice that experienced by Project A, Project B crosses the tipping point, and Loop R1 dominates the project. Loop R1 dominates throughout the remainder of the project, causing the percent complete to decrease months During this time, schedule pressure increases until the rework fraction in Project B is four times greater than the rework fraction experienced by Project A at the same time. Rather than continue to decline for an indefinite period, Project B might be terminated or changes made to shift dominance back to loop B1. The model was tested using standard test methods for system dynamics models Sterman Basing the model on previously tested project models and the literature improves the model s structural similarity to development processes and practices, as do unit consistency tests. Extreme condition tests were performed by setting model inputs, such as initial scope or total project staff, to extreme values and simulating project behavior. Model behavior remained reasonable. The model s behavior for typical conditions is consistent with previous project models and practice e.g., the common S shaped increase in percent complete over time shown for Project A, Fig. 3. Model behavior was also compared to actual project behavior as described by Ford and Sterman 1998, 2003b, Lyneis et al. 2001, and others and found to closely match the behavior modes of actual projects. To test the ability of the model to replicate tipping point behavior modes, the model was calibrated to the Limerick Unit 2 nuclear power plant project and the simulated behavior compared to the actual behavior of the Limerick Unit 2 project Fig. 4. The similarity between the actual project and simulated behavior modes in Fig. 4 supports the model s ability to reflect the behavior of Limerick Unit 2, including tipping point dynamics. Later, the same model is calibrated to the entire Limerick Unit 2 project through completion, further supporting the use of the model. Based on these tests, the model was assessed to be useful for investigating tipping point dynamics in complex construction projects. Model Analysis Fig. 5. Project tipping point conditions The model equations in the work flow sector can mathematically describe the tipping point conditions and conditions for project improvement or degradation. The tipping point conditions occur when the withdrawal of work from the project backlog is equal to the addition of work to the project backlog and the net change in the project backlog is zero. The withdrawal is the rate at which work is approved and released, which is the compliment of the QA rate that is discovered to require rework 11. The addition of work is the ripple effect 12, which is the product of the discover rework rate and the ripple effect strength. The rate at which rework is discovered 10, is the product of the QA rate and the rework fraction. Therefore, at the tipping point: a q a q f r = r a q f r 1 where a q quality assurance rate work packages/week ; r ripple effect strength dimensionless ; and f r fraction discovered to require rework %. Simplification yields a description of the tipping point f r r +1 =1 2 When the left hand side of Eq. 2 exceeds 1, the project is degrading; when less than 1, the project is improving; and when equal to 1, the project is stagnant. The tipping point conditions are shown graphically in Fig. 5. Fig. 5 reveals intuitive insights about project conditions that generate tipping point dynamics. The negative slope of the tipping point line indicates that projects that have relatively independent project parts low ripple effect strength can tolerate a higher rework fraction before degrading than highly interdependent projects ceteris paribus and that simple projects, as reflected by low rework fractions, can tolerate higher ripple effect strengths than complex projects. However, the tipping point relationship between ripple effect strength and rework fraction is not linear. The convex shape of the tipping point line indicates that increases in ripple effect strength greatly reduce the tolerable rework fraction when ripple effects are small, but as ripple effect strength increases, the tolerable rework fraction decreases more slowly. Sensitivity analysis was performed to improve understanding of the drivers of tipping point dynamics. The analysis was performed on model variables that managers can influence through project designs and management policies. A project s buffer against tipping point failure b tp was used to quantify the impact of changes in model inputs. This tipping point buffer is the JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT ASCE / JUNE 2008 / 425

6 Fig. 6. Sensitivity analysis results distance a project s conditions are from the tipping point, as follows. By inspection of Eq. 2, if a project starts far enough away from its tipping point f r r+1 1 and increases in the rework fraction and ripple effect strength are small, the project will not cross the tipping point and will monotonically improve. In Fig. 5, this initial project condition is well below and left of the tipping point line. However, if the project starts near the tipping point or the magnitudes of the changes are large enough, the project can be pushed over the tipping point. Eq. 2 can be rearranged to define the tipping point buffer f r + f r r + b tp =1 3 where b tp buffer to tipping point-induced failure dimensionless. The right side of Eq. 3 represents 100% of the project s capacity to tolerate ripple effects and rework. This capacity has been disaggregated into the three parts on the left side of Eq. 3 : 1 Capacity fraction absorbed by rework f r, 2 capacity fraction absorbed by ripple effects f r r, and 3 the unutilized capacity fraction that provides the tipping point buffer b tp. When the tipping point buffer is positive, the project is below the tipping point improving ; when it is zero, the project is at the tipping point; and when it is negative, the project is above the tipping point degrading. For example, suppose a project has a fixed 20% rework fraction f r =0.2 and a fixed ripple effect strength r of 1 work package added per work package discovered to require rework. Applying Eq. 3, this project begins 0.6 from the tipping point initial buffer=60%. This project could tolerate schedule pressure-driven increases in the rework fraction of up to 30% making f r =50% without crossing the tipping point. Eq. 3 provides a means of analyzing the sensitivity of tipping point structures to different variables. However, tipping point buffers can vary significantly from their initial values during a project. For example, schedule pressure can increase the fraction of work requiring change f r and thereby reduce the buffer. The minimum distance that project conditions come to the tipping point during the project represents a project s most vulnerable conditions. Therefore, a project s minimum tipping point buffer is a better measure of project sensitivity than the initial buffer. Fig. 6 shows the results of sensitivity analysis of a project s minimum tipping point buffer to four variables that impact tipping point dynamics in the model. The horizontal axis of Fig. 6 represents the percent change from base case values of the reference rework fraction, ripple effect strength, rework sensitivity to schedule pressure, and project deadline. The vertical axis represents the minimum project tipping point buffer. As an example of the need to use the project s minimum tipping point buffer, for the base case the buffer at the beginning of the project 60% is reduced by schedule pressure during the project to a minimum of 51% center point 0%, 51% of Fig. 6. Values that fall off the bottom of the chart reflect negative buffers, when the project has crossed the tipping point and failed. Inspection of the slopes of the lines in Fig. 6 reveal the project s relative robustness to tipping point failure for each model input. Taguchi et al defines robustness as the state where the product/process is minimally sensitive to factors causing variability. A steeper slope in Fig. 6 indicates the project is more sensitive less robust to changes in the model input value the factor causing variability. In general, the project s robustness to tipping point-induced failure is lowest for the reference rework fraction reflecting project complexity, then ripple effect strength reflecting project interdependence, then rework sensitivity to schedule pressure, and is most robust to the project deadline. The results of this analysis are next used to explain and design projects to avoid tipping point failure. Project Management near Tipping Points Model analysis revealed high leverage model inputs for managing the minimum project tipping point buffer. Project design and management policies that impact these high leverage model inputs can reduce the potential of project failure due to tipping point dynamics. The literature suggests many construction management policies to improve the likelihood of project success that can be modeled using the four model parameters tested. For example, the rework fraction can be reduced through improved project learning Carrillo 2005; Cooper et al. 2002; Love et al. 2000a, the ripple effect strength can be reduced through project planning efforts focused on decoupling ripple effects Nepal et al. 2006; Love et al. 2002, and schedule pressure can be reduced by setting realistic project deadlines Nepal et al As described next, these policies, and others, used by Bechtel Western Power Corp. to complete construction of Limerick Unit 2 provide examples of policies that can be used to manage tipping point dynamics in large, complex construction projects. Completing the Limerick Unit 2 Nuclear Power Plant To test the effectiveness of policies for managing tipping point dynamics in large, complex construction projects, some of the policies used to complete Limerick Unit 2 were modeled to simulate the completion of construction of Limerick Unit 2. Bechtel Western Power Corp resumed work on Limerick Unit 2 in February 1986 Clarey 1987; Gotzis 1991 and the unit was finally completed in August of When construction resumed, the contractor began implementing steps to minimize rework on the project Clarey 1987; Gotzis Nearly 300 engineers and managers were retained from the construction of Limerick Unit 1, completed in late This allowed the construction of Unit 2 to proceed with lessons learned from the construction of Unit 1, which, according to Carrillo 2005, Cooper et al. 2002, and Love et al. 2000a, should have reduced the amount of project rework. Before the manual workforce was increased, the contractor also added approximately 600 project managers and engineers to the project to further reduce rework by increasing the level of 426 / JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT ASCE / JUNE 2008

7 Fig. 7. Comparison of Limerick Unit 2 simulation to available data data from NRC 1982 and Clarey 1987 planning detail prior to construction. According to Nepal et al and Love et al. 2002, this planning would also reduce ripple effect strength through the identification and decoupling of work dependencies. An example of this planning was the field review of complex installations by design engineers to identify potential problems prior to initiating work orders. This review identified potential problems before construction commenced and reduced the number of errors made during installation. In addition, design engineers were placed on the second shift to resolve issues that arose during second shift construction. The availability of these engineers not only reduced the rework fraction, it reduced ripple effect strength by making available their ability to decouple required changes and other construction work through their knowledge of the system design. A portion of the professional manpower buildup included increasing the number of quality control personnel available to check work. These additional personnel allowed more work to be checked in a given amount of time, which identified many errors more quickly and prevented their propagation. To manage schedule pressure on Limerick Unit 2, an aggressive, but realistic, schedule based upon the work remaining to be completed was set Clarey This is indicative of setting a realistic deadline as described by Nepal et al This reduced the potential impact of schedule pressure on the project Graham To prevent the buildup of the work backlog from increasing schedule pressure, the experienced manual labor force was rapidly placed on site Gotzis This fast buildup of an experienced labor force also allowed the project to make quick progress in reducing project backlog. This allowed the project to remain on schedule and reduced the pressure from managers to increase the work pace. Schedule pressure was further reduced through significant reductions in the number of work packages required to complete the project. Work packages such as pipes and pipe hangers, cable trays, wiring, and conduits were eliminated through system redesign, thereby reducing the project backlog. These and other project management policies implemented by Philadelphia Electric and Bechtel Western Power Corp to complete Limerick Unit 2 allowed the second phase of construction to be completed in accordance with the revised after the first phase budget and schedule requirements and helped the project earn recognition as the Project Management Institute s 1990 Project of the Year Gotzis Many of the policies used to complete the Limerick Unit 2 project affect project features that the model analysis indicates affect tipping point dynamics. The policies used to complete Limerick Unit 2 were implemented in the model. When available, actual project data were used e.g., project staffing levels, project deadline evolution, and work package reductions. In the absence of data, the authors assumed reasonable values and tested the robustness of results to the assumptions. Appendix II specifies the model calibration used to simulate Limerick Unit 2 construction completion and value ranges over which the behavior mode is robust. The entire Limerick Unit 2 project was modeled and simulated July, 1974 through August, 1989, 181 months based upon this project description. Fig. 7 compares the simulated and actual project percent complete. The similarity between the simulated and actual project behaviors supports the ability of the suggested policies to manage tipping point dynamics in nuclear power plant construction. In addition, Fig. 7 helps explain the impact of tipping point dynamics on the construction of Limerick Unit 2. The project begins with a gradual buildup of completed work months 0 5, followed by steady progress months 5 20, when its behavior is dominated by Project Progress Loop B1. During this time, the total project work backlog is being reduced at a faster rate than new work is being added to the project. Near month 26, the project crosses the tipping point and Ripple Effects Loop R1 dominate the system. The sudden change in project conditions suggests an abrupt shift in loop dominance, such as might be initiated by a new set of regulations. Progress declines as work is added to the project through ripple effects at a faster rate than work is approved and released months In the 32nd month, the project crosses the tipping point again, loop B1 dominates the system, and the project makes positive net progress months Above month 50, Schedule Pressure Loop R2 begins to build on the project, as reflected in the gradual slowing of project progress between months 50 and 66 that is similar to the behavior pattern shown in Project B in Fig. 3. During this time, the schedule pressure loop R2 strengthens the ripple effect loop R1 as schedule pressure increases project rework, which increases the ripple effect rate. Simultaneously the increase in the project rework rate decreases the rate of work being approved and released, thus weakening loop B1. In the 66th month, the project crosses the tipping point a third time, loop R1 dominates the system, and the project degrades. The project is halted in month 96 and project progress is stagnant until the project is restarted in month 139. Once the project is restarted, the policies implemented allow loop B1 to dominate the system and the project is completed in month 181. The model and the Limerick Unit 2 project data support tipping point dynamics as an explanation of this complex construction project performance. Conclusions and Implications for Practice The current work builds on existing tipping point dynamics research by testing and analyzing a project model with a tipping point structure to identify factors with high leverage impacts on project performance. Analysis reveals that, among the parameters tested, projects are least robust to rework, followed by ripple effects, sensitivity to schedule pressure, and are most robust to the project deadline. Existing project management research offers managers policies that can influence these variables. A nuclear power plant project was used to illustrate the successful application of policies for managing tipping point dynamics. The generic nature of tipping points and common characteristics of large con- JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT ASCE / JUNE 2008 / 427

8 struction projects suggest that policies for managing tipping point dynamics in the construction of Limerick Unit 2 may be applicable to other types of projects. Therefore, results from the current work can potentially be applied to other types of development projects. Project managers can benefit from developing an understanding of how tipping point dynamics can affect project performance. For example, knowing the relative sensitivity of project specific features due to tipping point structures can help managers design policies. This improvement is possible even though no known tools are currently available for managers to directly measure the rework fraction or the ripple effect strength. Project teams can ask questions such as, What systems in this project are likely to require the most iteration rework? How can this iteration be eliminated or minimized? Could this iteration lead to work that has not been anticipated ripple effects? and How can we decouple these parts of the project reduce ripple effects? Project managers can use policies such as those modeled here to manage tipping point dynamics in planned U.S. commercial nuclear power plant construction projects and other large, complex projects. Future research can improve the applicability of tipping points to project management in a number of ways. To successfully manage tipping point dynamics, project managers must identify tipping point structures and factors that can move projects to a tipping point prior to project initiation, or relatively early in the project. In the Limerick Unit 2 case, the tipping point structures were identified well after the project was completed. This a posteriori identification would not have aided Limerick Unit 2 project managers in the management of the project. The current work reveals that projects that are complex potential for high levels of rework and have highly interdependent components potential for high ripple effect strength can experience tipping point dynamics. Existing work has identified other feedback structures that can drive projects to failure e.g., Cooper 1980; Cooper et al. 2002; Ford and Sterman 2003a; Tang and Ogunlana These, and other structures, should be integrated into tipping point theory. Additional research is also needed to identify other project structures process designs and policies that can cause tipping point dynamics and how project managers can identify them. The model structure used in this work has limitations that can be addressed in future research to improve the understanding of tipping point dynamics. Expanded models can improve model structure consistency with actual projects, calibrate the model to additional projects, and continue the development of robustness as a practical measure of a project s protection against failure. In addition, the ability of policies to manage tipping point structures not tested in the current work e.g., with real options, could be modeled and tested. Finally, the current work and its implications have larger societal impacts. The failure of large projects, as exemplified by many projects in the American commercial nuclear power construction industry of the 1970s and 1980s, can have dramatic impacts beyond the project s direct stakeholders. For example, the Shoreham nuclear power plant in Long Island, NY, was completed but never operated. Its closure not only impacted the Long Island Lighting Company and its rate base, but also the taxpayers of New York, and to a lesser extent the United States, who ultimately paid for the lengthy legal process that accompanied the construction and decommissioning of the plant Aron Unfortunately, this important problem is not new or unique to nuclear power plant construction. In The Wealth of Nations, Adam Smith 1776/2004 describes society s losses due to project failure, noting that Every injudicious and unsuccessful project in agriculture, mines, fisheries, trade, or manufactures tends to diminish the funds destined for the maintenance of productive labor. In every such project...there must always be some diminution in what would otherwise have been the productive funds of the society. Failing to learn from past failures could lead the American nuclear power industry to repeat the troubles encountered during the construction of Limerick Unit 2 in planned future nuclear power projects. More generally, understanding and addressing tipping point dynamics can improve the management of large, complex construction projects and, thereby, society. Acknowledgments The writers thank Marsha Ward, NRC Research Librarian, for assisting in the acquisition of NRC nuclear power plant construction data, and the reviewers of this work. Appendix I. Model Equations j initial completion i, quality assurance q, rework r unless indicated otherwise. Workflow Sector d/dt A i = a i a s + a e d/dt A q =+a i a d + a r a w d/dt A r =+a q a r d/dt A w =+a w a s =IF t = 139 THEN A i c ELSE 0 a j = MIN p j,r j a d = f r a q a w = 1 f r a q a e = a d r p j = A j /m j r j = s j d j T = A i + A q + A r T w = T + A w C = A w /T w 17 where A j work backlog work packages ; A w approved work work packages ; a j work flow work packages/month ; a d discover rework rate work packages/month ; a w approve work rate work packages/month ; a e ripple effects due to rework work packages/month ; a s scope eliminated through redesign work packages/month ; c scope cancellation rate % ; t time month ; r ripple effect strength work packages added/ work packages requiring rework ; r j resource dependent work 428 / JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT ASCE / JUNE 2008

9 flow rate work packages/month ; p j process dependent work flow rate work packages/month ; f r fraction discovered to require rework % ; m j minimum work process time month ; s j staff assigned to work backlog people ; d j productivity of staff assigned to work backlog work packages/person-month ; T total project backlog work packages ; T w total project work work packages ; and C percent of work that has been completed %. Z t = D D f D a = MAX 1,D t Z f = MAX 0,Z t t /Z t Z p + MIN 1,t/Z t Z a D r = T/Z f Resource Allocation Sector D f = L j = A j /d j 18 R = MAX 1,MIN R m,d r /D a 30 L t = L i + L q + L r X j = L j /L t d/dt F j = X j F j /u s j = F j S 22 where L j labor required to complete work backlog people month ; L t total labor required to complete project people month ; X j indicated fraction of resource demand to each backlog % ; F j applied fraction of resource demand fraction ; u time required to adjust staff month ; and S total project staff people. f r = MIN 1,f m + R s R 1 31 where Z a actual release productivity work packages /month ; Z p planned release productivity work packages /month ; Z t time to transition from planned to actual release productivity month ; Z f release productivity for forecasting work packages /month ; I initial project scope work packages ; D project deadline month ; D f planned project duration to transition to actual release productivity % ; D a time available to complete work month ; D r time required to complete work month ; R schedule pressure ratio fraction ; R m maximum effective schedule pressure dimensionless ; f m minimum fraction discovered to require rework % ; and R s sensitivity of rework fraction to schedule pressure dimensionless. Schedule Pressure Sector Z a = A w /t Z p = I/D Appendix II. Model Calibration for Limerick Unit 2 Construction Completion Table 1 specifies model calibration values used to simulate the Limerick Unit 2 construction completion. Table 1. Limerick Unit 2 Construction Completion Model Policy Implementation Policy Clarey 1987; Gotzis 1991 Model variable affected base case value Change in model variable to reflect policy changes Increase CEM personnel Review of complex field installations Lessons learned from completing Unit 1 Increase quality control personnel Design engineers placed on second shift Rework fraction 30% Assume each policy reduces the base rework fraction 3% without overlapping reactions. This results in a cumulative rework fraction reduction of 5 policies 3%/policy 15%. Rework fraction becomes 30% 15% =15%. Design engineers on second shift to correct design errors. Increase CEM personnel. Rapidly hire and deploy qualified workforce Cancel scope through redesign Set realistic deadline Ripple effect strength 1WPA/WPRR a Sensitivity to schedule pressure 0.4, dimensionless Initial completion backlog 17,525 tasks Project deadline varies, month Assume each policy reduces the ripple effect strength by 0.35 WPA/WPRR. Results in a cumulative ripple effect strength reduction of 2 policies 0.35 WPA/WPRR/Policy =0.7 WPA/WPRR. Ripple effect strength becomes 1 0.7=0.3 WPA/WPRR. Assume sensitivity to schedule pressure is reduced by Sensitivity to schedule pressure becomes =0.02. Reduce IC backlog at restart by 8% based on work quantity reductions presented in Clarey 1987, Tables 1 and 2. Project deadline evolution based on data from NRC 1982 and Clarey 1987 Increased staffing levels Total project staff Increase staff see Clarey 1987, Fig. 3 for details 2,100 people Note: The project completion date shown in Fig. 7 is robust to within 10% of the actual completion date month 181 for minimum rework fraction values of 26% 0%, ripple effect strength values of 0.9 WPA/WPRR 0.0 WPA/WPRR, and sensitivity to schedule pressure values of a WPA work packages added; WPRR work packages discovered to requiring rework. JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT ASCE / JUNE 2008 / 429

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