Improving Project Cost Estimates

At the end of his “Don’t Accept Dictation,” reprinted above, McCarthy wrote “Each per­son who has a task to do must own the design and execution of that task and must be held accountable for its timely achievement. Accountability is the twin of empowerment.” If empowerment is to be trusted, it must be accompanied by accountability. Fortunately, it is not difficult to do this. This section deals with a number of ways for improving the process of cost estimating. These improvements are not restricted to cost estimates, but can be applied to almost all of the areas in project management that call for estimating or forecasting any aspect of a project that is measured numerically; for example, task durations, the time for which specialized personnel will be required, or the losses associ­ated with specific types of risks should they occur. These improvements range from better formalization of the estimating/forecasting process, using forms and other simple proce- Risk dures, to straightforward quantitative techniques involving learning curves. We conclude with some miscellaneous topics, including behavioral issues that often lead to incorrect budget estimates.

1. Forms

The use of simple forms such as that in Figure 4-2 can be of considerable help to the PM in obtaining accurate estimates, not only of direct costs, but also when the resource is needed, how many are needed, who should be contacted, and if it will be available when needed. The information can be collected for each task on an individual form and then aggregated for the project as a whole.

2. Learning Curves

Suppose a firm wins a contract to supply 25 units of a complex electronic device to a customer. Although the firm is competent to produce this device, it has never produced one as complex as this. Based on the firm’s experience, it estimates that if it were to build many such devices it would take about 4 hours of direct labor per unit produced. With this estimate, and the wage and benefit rates the firm is paying, the PM can derive an estimate of the direct labor cost to complete the contract.

Unfortunately, the estimate will be in considerable error because the PM is underes­timating the labor costs to produce the initial units that will take much longer than 4 hours each. Likewise, if the firm built a prototype of the device and recorded the direct labor hours, which may run as high as 10 hours for this device, this estimate applied to the contract of 25 units would give a result that is much too high.

In both cases, the reason for the error is the learning exhibited by humans when they repeat a task. In general, it has been found that unit performance improves by a fixed percent each time the total production quantity doubles. More specifically, each time the output doubles, the worker hours per unit decrease by a fixed percentage of their previous value. This percentage is called the learning rate, and typical values run between 70 and 95 percent. The higher values are for more mechanical tasks, while the lower, faster- learning values are for more mental tasks such as solving problems. A common rate in manufacturing is 80 percent. For example, if the device described in the earlier example required 10 hours to produce the first unit and this firm generally followed a typical 80 percent learning curve, then the second unit would require .80 x 10 = 8 hours, the fourth unit would require 6.4 hours, the eighth unit 5.12 hours, and so on. Of course, after a certain number of repetitions, say 100 or 200, the time per unit levels out and little further improvement occurs.

Mathematically, this relationship we just described follows a negative exponential function. Using this function, the time required to produce the nth unit can be calculated as

where T_n is the time required to complete the nth unit, T₁ is time required to complete the first unit, and r is the exponent of the learning curve and is calculated as the log(learning rate)/log(2). Tables are widely available for calculating the completion time of unit n and the cumulative time to produce units one through n for various learning rates. The impact of learning can also be incorporated into spreadsheets developed to help prepare the budget for a project as is illustrated in the example at the end of this section.

The use of learning curves in project management has increased greatly in recent years. For instance, methods have been developed to approximate composite learning curves for entire projects (Amor and Teplitz, 1998), for approximating total costs from the unit learning curve (Camm, Evans, and Womer, 1987), and for including learning curve effects in critical resource diagramming (Badiru, 1995). The conclusion is that the effects of learning, even in “one-time” projects, should not be ignored. If costs are under­estimated, the result will be an unprofitable project and senior management will be unhappy. If costs are overestimated, the bid will be lost to a savvier firm and senior man­agement will be unhappy.

Media One Consultants

Media One Consultants is a small consulting firm that specializes in developing the electronic media that accompany major textbooks. A typical project requires the development of an electronic testbank, PowerPoint lecture slides, and a website to support the textbook.

A team consisting of three of the firm’s consultants just completed the content for the first of eighteen chapters of an operations management textbook for a major col­lege textbook publisher. In total, it took the team 21 hours to complete this content.

The consultants are each billed out at $65/hour plus 20 percent to cover overhead. Past experience indicates that projects of this type follow a 78 percent learning curve.

The publisher’s developmental editor recently sent an email message to the team leader inquiring into when the project will be complete and what the cost will be. Should the team leader attempt to account for the impact of learning in answering these questions? How big a difference does incorporating learning into the budget make? What are the managerial implications of not incorporating learning into the time and cost estimates?

The team leader developed the spreadsheet below to estimate the budget and completion time for this project. The top of the spreadsheet contains key param­eters such as the learning rate, the consultants’ hourly billout rate, the overhead rate, and the time required to complete the first chapter. The middle of the spreadsheet contains formulas to calculate both the unit cost and time of each chapter and the cumulative cost and time.

According to the spreadsheet, the cost of the project is $14,910 and will require in total 191.2 hours. Had the impact of learning not been considered, the team leader would have likely grossly overestimated the project’s cost to be $29,484 (1,638 x 18) and its duration to be 378 (21 x 18) hours. Clearly, several negative consequences could be incurred if these inflated time and cost estimates were used. For example, the publisher might decide to reevaluate its decision to award the contract to Media One. Furthermore, the time when the team members would be able to start on their next assignment would be incorrectly estimated. At a minimum, this would complicate the start of future projects. Perhaps more damaging, however, is that it could lead to lost business if potential clients were not willing to wait for their project to begin based on the inflated time estimates.

3. Other Factors

Studies consistently show that between 60 and 85 percent of projects fail to meet their time, cost, and/or performance objectives. The record for information technology (IT) projects is particularly poor, it seems. For example, there are at least 45 estimating models available for IT projects but few IT managers use any of them (Lawrence, 1994; Martin, 1994). While the variety of problems that can plague project cost estimates seem to be unlimited, there are some that occur with high frequency; we will discuss each of these in turn.

Changes in resource prices, for example, are a common problem. The most common managerial approach to this problem is to increase all cost estimates by some fixed per­centage. A better approach, however, is to identify each input that has a significant impact on the costs and to estimate the rate of price change for each one. The Bureau of Labor Statistics (BLS) in the U.S. Department of Commerce publishes price data and “inflators” (or “deflators”) for a wide range of commodities, machinery, equipment, and personnel specialities. (For more information on the BLS, visit http://stats.bls.gov or www.bls.gov.)

Another problem is overlooking the need to factor into the estimated costs an ade­quate allowance for waste and spoilage. Again, the best approach is to determine the individual rates of waste and spoilage for each task rather than to use some fixed percentage.

A similar problem is not adding an allowance for increased personnel costs due to loss and replacement of skilled project team members. Not only will new members go through a learning period, which increases the time and cost of the relevant tasks, but professional salaries usually increase faster than the general average. Thus, it may cost substantially more to replace a team member with a newcomer who has about the same level of experience.

Then there is also the Brooks’s “mythical man-month” effect[1] which was discovered in the IT field but applies just as well in projects. As workers are hired, either for addi­tional capacity or to replace those who leave, they require training in the project envi­ronment before they become productive. The training is, of course, informal on-the-job training conducted by their coworkers who must take time from their own project tasks, thus resulting in ever more reduced capacity as more workers are hired.

And there is the behavioral possibility that, in the excitement to get a project approved, or to win a bid, or perhaps even due to pressure from upper management, the project cost estimator gives a more “optimistic” picture than reality warrants. Inevitably, the estimate understates the cost. This is a clear violation of the PMI’s Code of Ethics. It is also stupid. When the project is finally executed, the actual costs result in a project that misses its profit goals, or worse, fails to make a profit at all—hardly a welcome entry on the estimator’s resume.

Even organizational climate factors influence cost estimates. If the penalty for over­estimating costs is much more severe than underestimating, almost all costs will be underestimated, and vice versa. A major manufacturer of airplane landing gear parts wondered why the firm was no longer successful, over several years, in winning competi­tive bids. An investigation was conducted and revealed that, 3 years earlier, the firm was late on a major delivery to an important customer and paid a huge penalty as well as being threatened with the loss of future business. The reason the firm was late was because an insufficient number of expensive, hard to obtain parts was purchased for the project and more could not be obtained without a long delay. The purchasing manager was demoted and replaced by his assistant. The assistant’s solution to this problem was to include a 10 percent allowance for additional, hard to obtain parts in every cost proposal. This resulted in every proposal from the firm being significantly higher than their com­petitors’ proposals in this narrow margin business.

There is also a probabilistic element in most projects. For example, projects such as writing software require that every element work 100 percent correctly for the final prod­uct to perform to specifications. In programming software, if there are 1000 lines of code and each line has a .999 probability of being accurate, the likelihood of the final program working is only about 37 percent!

Sometimes, there is plain bad luck. What is indestructible, breaks. What is impen­etrable, leaks. What is certified, guaranteed, and warranteed, fails. The wise PM includes allowances for “unexpected contingencies.”

There are many ways of estimating project costs; we suggest trying all of them and then using those that “work best” for your situation. The PM should take into considera­tion as many known influences as can be predicted, and those that cannot be predicted must then simply be “allowed for.”

Finally, a serious source of inaccurate estimates of time and cost is the all too common practice of some managers arbitrarily to cut carefully prepared time and cost estimates. Managers rationalize their actions by such statements as “I know they have built a lot of slop in those estimates,” or “I want to give them a more challenging target to shoot at.” This is not effective management. It is not even good common sense. (Reread the excerpt from McCarthy, 1995 appearing above.) Cost and time estimates should be made by the people who designed the work and are responsible for doing it. As we argued earlier in this chapter, the PM and the team members may negotiate different estimates of resources needs and task durations, but managerially dictated arbitrary cuts in budgets and schedules almost always lead to projects that are late and over budget. We will say more about this later.

Boston’s Big Dig highway/tunnel project is one of the largest, most complex, and technologically challenging highway projects in U.S. history. The “Big Dig,” originally expected to cost less than $3 billion, was declared complete after two decades and $14.6 billion for planning and construction, almost a 500 percent cost overrun (Abrams, 2003 and PMI, August 2004). With an estimated benefit of $500 million per year in reduced congestion, pollution, accidents, fuel costs, and lateness, the project has an undiscounted payback of almost 30 years. This project was clearly one that offered little value to the city if it wasn’t completed, and so it continued far past what planners thought was a worthwhile investment, primarily because the federal government was paying 85 percent of its cost. One clear lesson from the project has been that unless the state and local governments are required to pay at least half the cost of these megaprojects, there won’t be serious local deliberation of their pros and cons. The overrun is attributed to two major factors: (1) underestimates of the initial project scope, typical of government pro­jects; and (2) lack of control, particularly costs, including conflicts of interest between the public and private sectors.

Source: Meredith Jack R., Mantel Jr. Samuel J., Shafer Scott M., Sutton Margaret M. (2017), Project Management in Practice, John Wiley & Sons, Inc. 3th Edition.