Pinto, J. K. 1999. "Some constraints on the theory of constraints-Taking a critical look at the Critical Chain," PM Network 13(8):4951.

Pinto, J. K. and Mantel, S. J. 1990. "The causes of project failure," IEEE Transactions on Engineering Management 37(4):269275.

Pinto, J. K. and Presscott, D. P. 1988. "Project success: Definitions and measurement techniques," Project Management Journal 19(1):6771.

Pinto, J. K. and Slevin, D. P. 1987. "Critical factors in successful project implementation," IEEE Transactions in Engineering Management EM-34(1):2227.

Pinto, J. K. and Slevin, D. P. 1989. "The project champion: Key to implementation success," Project Management Journal XX:1520.

Pitagorsky, G. 2001. "A scientific approach to project management," Machine Design 73(14): 7882.

Pittman, P. H. 1994. Project management: A more effective methodology for the planning and control of projects. Unpublished doctoral diss., University of Georgia.

Platje, A. and Seidel, H. 1993. "Breakthrough in multiproject management: How to escape the vicious circle of planning and control," International Journal of Project Management 11(4):209213.

Pocock, J. W. 1962. "PERT as an analytical aid for program planning-Its payoff and problems," Operations Research 10(6):893903.

Rand, G. K. 2000. "Critical chain: The theory of constraints applied to project management," International Journal of Project Management 18(3):173177.

Raz, T., Divr, D., and Barnes, R. 2003. "A critical look at critical chain project management," Project Management Journal 34(4):2432.

Roseboom, J. H. 1961. "Comments on a paper by Thomas Healy," Operations Research 909910.

Rydell Group. 1995. TOC at Saturn and GM Dealers. Paper presented at the North American Jonah Upgrade Conference, September 2124, Philadelphia, PA.

Schonberger, R. J. 1981. "Why projects are 'always' late: A rationale based on manual simulation of a PERT/CPM network," Interfaces 11(5):6670.

Sonawane, R. 2004. Applying systems dynamics and critical chain methods to develop a modern construction project management system. Unpublished master thesis, Texas A&M UniversityKingsville.

Speranza, M. G. and Vercellis, C. 1993. "Hierarchial models for multi-project planning and scheduling," European Journal of Operations Research 64(2):312325.

Srinivasan, M. M., Best, W. D., and Chandrasekaren, S. 2007. "Warner Robins Air Logistics Center streamlines aircraft repair and overhaul." Interfaces 37(1):721.

Steyn, H. 2000. "An investigation into the fundamentals of critical chain project scheduling," International Journal of Project Management 19(6):363369.

Steyn, H. 2002. "Project management applications of the theory of constraints beyond critical chain scheduling," International Journal of Project Management 20(1):7580.

Trietsch, D. 2005. "Why a critical path by any other name would smell less sweet? Towards a holistic approach to PERT/CPM," Project Management Journal 36(1):2736.

Trypia, M. N. 1980. "Cost minimization of simultaneous projects that require the same scarce resource," European Journal of Operations Research 5(4):235238.

Tsai, D. M. and Chiu, H. N. 1996. "Two heuristics for scheduling multiple projects with resource constraints," Construction Management and Economics 14(4):325340.

Tsubakitani, S. and Deckro, R. F. 1990. "A heuristic for multi-project scheduling with limited resources in the housing industry," European Journal of Operational Research 49(1):8091.

Van Slyke, R. M. 1963, "Monte Carlo methods and the PERT problem", Operations Research 11(5):839860.

Vercellis, C. 1994. "Constrained multi-project planning problems: A Lagrangean decomposition approach," European Journal of Operational Research 78(2):267275.

Yang, K. and Sum, C. 1993. "A comparison of resource allocation and activity scheduling rules in a dynamic multi-project environment," Journal of Operations Management 11(2): 207218.

Yeo, K. T. and Ning, J. H. 2002. "Integrating supply chain and critical chain concepts in engineer-procure-construct (EPC) projects," International Journal of Project Management 20(4):253262.

Walker II, E. D. 1998. Planning and controlling multiple, simultaneous, independent projects in a resource constrained environment. Unpublished doctoral diss., University of Georgia.

Westney, R. E. 1991. "Resource scheduling-Is AI the answer?" 1991 American Association of Cost Engineers Transactions K.6.1K.6.9.

Wiest, J. D. and Levy, F. K. 1977. A Management Guide to PERT/CPM with GERT/PDM/DCPM and Other Networks. 2nd ed. Englewood Cliffs, NJ: Prentice Hall.

Williams, D. 2001. "Right on time." CA Magazine 134(7):3031.

Zhan, J. 1994. "Heuristics for scheduling resource-constrained projects in MPM networks," European Journal of Operational Research 76(1):192205.

About the Author.

Ed D. Walker II, Associate Professor of Management at Valdosta State University, is from Milledgeville, Georgia He is recognized as a CPIM by APICS and as a Jonah by the Avraham Y. Goldratt Institute and is certified in TOC project management, the TOC thinking processes, and TOC operations management by the Theory of Constraints International Certification Organization. He has a BS in Business Administration and Math/Physics from Presbyterian College and an MBA in Finance from Auburn University. Prior to receiving his PhD in Operations Management at the University of Georgia, Dr. Walker worked in production planning and control, distribution, and plant management in both the food processing and textile industries. He has published over 20 journal and conference articles in the areas of Theory of Constraints, project management, manufacturing planning and control systems, performance measurement, and classroom pedagogy. Two young children keep Dr. Walker and his wife quite busy. He enjoys volunteering at his church, working outdoors, officiating high school football, as well as hunting and fishing.

CHAPTER 3.

A Critical Chain Project Management Primer

Charlene Spoede Budd and Janice Cerveny

Introduction.

As evidenced by their support of professional certification from the Project Management Institute,1 organizations want to improve their project management skills. Even though the profession has recognized the need to improve and companies seriously try to improve their project management maturity, most are still on the lower levels of a typical five-level project management maturity model, and few have reached the top levels involving continuous improvement.

The previous chapter, by Ed Walker, is an excellent review of the entire history of project management. The next three chapters, one by Realization, one by Rob Newbold, and one by AGI, cover the latest Critical Chain (CC) advances. Compared to the other chapters in this section, this chapter contains tutorial material on how CC works, along with some implementation suggestions. Our basic assumption is that the reader knows little or nothing about Critical Chain Project Management (CCPM).

Why These Widespread Project-Related Problems Persist

Chapter 2 clearly outlines a host of very familiar problems with which project managers (PMs) continue to struggle. History suggests that a definitive solution is elusive.

Throughout his professional life, Eli Goldratt has stressed how complex and chaotic situations can be handled with a simple five-step approach (first detailed in Goldratt and Cox, 1984; Goldratt, 1990, 5962). This same approach applies to project management (Leach, 2005, 5254). The first of the five steps involves identifying the constraint. For projects, the constraint that prevents an organization from earning more, both now and in the future, is the time required to complete a project with available resources. In product development projects, for example, projects delivered late may lose a significant share of their potential market to competitors.

Copyright 2010 by Charlene Spoede Budd and Janice Cerveny.

For traditionally-managed projects, two assumptions guarantee project completion delays: (1) project task times can be accurately predicted, and (2) the traditional project management planning and control system is effective (Leach, 2005, 1011). Resources are asked to provide an estimate of the time required to complete a particular task. Once all project resources have reported their estimated (and safe) times, management frequently requires lower estimates. If those estimates are accepted by all resources (and resources usually have little choice), the estimate becomes a commitment upon which the resource will be evaluated.

Task Duration Uncertainty

We know that task times follow a distribution pattern that is skewed to the right. No task can be completed in zero time, but the maximum possible time can be extremely long. Look at a simple example such as the time required to drive to an important client's office. Let's say if you pressed the speed limit (exceeded it by 5 or 6 miles-9 or 10 is more common in Atlanta) and encountered no problems, you might make the trip in 20 minutes (the minimum task time). Normally, however, the trip takes about 30 minutes. If there was an accident on the freeway that you couldn't avoid, it may take several hours. If you had to promise your client that you would be there at a certain time or lose your account, how much time would you estimate? It would certainly not be 20 or 30 minutes. The same is true for a project resource who must promise to complete a task in a certain amount of time. The estimate typically will be in a range such that the resource has a 90 to 95 percent probability of successful on-time completion.

Since task times follow a skewed distribution, as illustrated in Fig. 3-1, and have unique properties, completion times cannot be estimated with precision. Nevertheless, an estimated time must be provided. Resources operating in traditional project management environments, therefore, are forced to protect their careers by providing times with appropriate safety that will permit them to survive management "adjustments" and to deliver on their promises.

The area under the curve shows the probability of completing the task in a given time estimate. Estimated completion time if resources could dedicate their time to the task, without interruption, most likely would occur somewhere to the left of the longer vertical dotted line in Fig. 3-1 (between the two arrows pointing in opposite directions). A minimum time, the far left point in the distribution curve, can occur, but with very low probability. To provide for interruptions and urgent but unplanned assignments, resources typically elect to provide a time that they are 90 to 95 percent confident they can achieve. In general, if resources deliver on the accepted due date, they receive a good evaluation. If a task is delivered late, their evaluation is diminished, depending on how late a task is delivered. Typically, resources are evaluated based on how well they perform their assignments, independent of other resources working on the same projects.

FIGURE 3-1 Probabilities for a task with a skewed distribution.

In project-based environments, where multiple projects are performed using shared resources, "accurate" task estimates are even more critical for planning achievable schedules. Because making sure that an individual resource is not assigned to work on two tasks at the same time is logistically next to impossible in a multi-project environment (due to task completion uncertainty, where no single point estimate can be correct), only the most sophisticated (project mature) organizations attempt to solve this mathematically NP-hard problem.2 The mechanisms used to plan and schedule projects must minimize the risk of nonproductive, abortive, or misdirected effort. The methodology also must provide relevant, timely information for management control, such that appropriate intervention occurs when needed during project execution. In addition, the system must capture the correct information for improvement.

In traditional multi-project environments, a basic problem is the inability to ensure adequate progress on the projects already underway while simultaneously having the flexibility to take advantage of new business opportunities as they arise. Typically, new projects are entered into a system as soon as they are funded and few organizations appear to be able to successfully establish stable global project priorities.

Traditional Survivor Behaviors

Human resources may be assigned to three to five major projects, sometimes in addition to their functional duties. To deal with skewed task times, official resource estimates, those that are turned in to management, generally are two or more times their estimated dedicated durations. Dedicated duration estimates are those that could be met if resources were allowed to work without interruption. However, most project employees do not work without interruption.

Forced multitasking induces additional stress on already heavily loaded resources. In spite of the widespread praise for multitasking ability, most people realize that they are more productive when they concentrate their effort on one task (Rubenstein et al., 2001; Shellenbarger, 2003, 169).

Three behaviors typically are used by resources to deal with chaotic project situations: (1) student syndrome, (2) sandbagging, and (3) engaging in Parkinson's Law. They are discussed in the following sections.

Student Syndrome

The name student syndrome (Goldratt, 1997) developed from a common student behavior of lobbying for an extension to an exam date that is two weeks away (typically some time after an upcoming school event) so they can study. However, most students only begin studying for the exam a few hours or, at best, a couple of days before it is scheduled-whether or not they receive the requested delay. While this behavior is typical for students, it also is typical for the rest of us.

Negotiating additional time would appear to enable us to ensure on-time completion of current assignments. Of course, when we wait until the last possible minute to begin a new assignment, we should expect that we would run into problems we had not anticipated. Therefore, meeting the promised due date may be extremely difficult and stressful.

Sandbagging Completed Work

Sandbagging refers to holding completed work until a more beneficial time arrives to officially acknowledge its completion. A resource may have fought long and hard for the time allotted to its task. Therefore, if a task is completed early, there may be a very real reluctance to pass it on to the next activity, since their next task duration estimate may be discounted accordingly. Also, acknowledging an early completion frequently results in additional assigned work, increasing a resource's workload even more. In order to protect one's reputation and believing that the next resource will not be prepared to take advantage of an early start if one discloses early completion, most experienced resources will not pass on their work until just prior to, or on, the due date.

Sales people (including those who sell projects) who have met their quotas regularly engage in sandbagging. A similar delay in passing on work, but due to a different motivation, is work completion delays due to Parkinson's Law, discussed next.

"Improving" a Completed Task