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Managing Resources in Project Scheduling

It has been estimated that of the million people who have bought Microsoft Project, only a few percent are using the re- source leveling feature. I would concur with that estimate. Of the thousand people who attend my classes every year, only a small number are doing much with resource allocation. They have tried as- signing people to tasks, even leveling them, but the situa- tion is so complex that many of them give up.

The problem is, this is the key to meeting a schedule. If you don’t have adequate resources, your schedules are not realistic, and in my opinion, a wrong schedule is worse than no schedule because it sets up an unrealistic expectation that causes frustration when the deadline is missed. As you may know, developing critical path diagrams involves a hidden assumption that you have unlimited resources. This is be- cause each task is estimated independently of the others.

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19C H A P T E R

If you can’t manage resources, your schedule is unrealistic.

Copyright © 2008 by James P. Lewis. Click here for terms of use.

C o p y r i g h t 2 0 0 8 . M c G r a w - H i l l P r o f e s s i o n a l .

A l l r i g h t s r e s e r v e d . M a y n o t b e r e p r o d u c e d i n a n y f o r m w i t h o u t p e r m i s s i o n f r o m t h e p u b l i s h e r , e x c e p t f a i r u s e s p e r m i t t e d u n d e r U . S . o r a p p l i c a b l e c o p y r i g h t l a w .

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Then when you do your diagram, you are supposed to show what is logically possible to do. At some point, you have two tasks that can logically be done in parallel. However, the same person is assigned to both of them. If that assignment is full time on each, then you have the individual double-sched- uled, which won’t work. The hidden assumption was that you had two persons available, when you really only had one. So, unless you manage the allocation of resources, you wind up with a schedule that can’t be met.

ASSIGNING RESOURCES TO TASKS

The first problem in scheduling is estimating task durations. This is discussed in detail in Chapter 28. We must begin with the number of working hours (or days or weeks) that will be required to complete a task with a certain person assigned to it. If you can’t assign a specific person to a task at the plan- ning stage because you don’t know who will actually do the task when the time comes, then you will have to assign a skill-category to the task. This means that you would assume

t h e p e r s o n a s- signed is going to be someone from the required disci-

pline having a certain level of skills. Members of that disci- pline would then have to estimate how long the task would take a person having those skills. It isn’t perfect, but it’s the best you can do sometimes.

Next you have to ask if the person will be assigned to the task on a full-time or part-time basis. Let’s begin by as- suming a full-time assignment. The person will work on the assigned task and nothing else until it is complete. I know this is very unrealistic in most organizations. Most people are working on many assignments at once. Still, we’ll start with this scenario just to show that even with the best of situa- tions, there are still problems to resolve.

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What does full time really mean?

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The first problem is, what do we mean by full time? If we assume we are scheduling people to work a standard 8-hour day, 40-hour week (which you really should do—keeping overtime in reserve as a way of handling unex- pected problems), then you could say that a task requiring 16 hours of working time would span two days. However, this assumption would get you into trouble. If the working time required is really 16 hours, it will take more than two days to accomplish it, because nobody works 8 productive hours each day.

Industrial engineers reduce the 8-hour day by 20 percent to account for what they call personal, fatigue, and delays (PF&D). People need to take occasional breaks (personal), they get tired (fatigue), and their work is held up by other people, unavailability of materials, information, or other re- sources (delays). Thus, a standard 8-hour day yields only 6.4 hours of productive work. This means that our 16-hour task will span 2.5 days. If the person starts first thing Monday morning, he will finish around noon on Wednesday.

At this point, we better evaluate the assumption that people are available even 80 percent of their time to do pro- d u c t i v e w o r k . I was told by a fel- low that his com- p a n y b e c a m e frustrated because t h e y m i s s e d s o many project dead- lines and couldn’t understand why. They seemed to have enough resources, based on the 80 percent assumption.

As a test of their assumption, they gave each project member a log sheet and told them to record, once an hour, what they had been doing during the previous hour. They did this for two weeks. To their surprise, they found that many of their team members were working on project assign- ments only 25 percent of the time! No wonder nothing was getting done on time! With people working on projects only

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Typical availability of knowledge workers for project work is 40 to 60 percent.

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25 percent of the time, rather than 80 percent, the calendar time required to finish a task is at least 3.2 times longer than planned.

Since he told me this, I have surveyed several hundred people informally, and they tell me that they experience simi- lar situations. At best, they tell me that the availability of peo- ple to do project work is about 50 to 60 percent.

What robs us of their time? Many factors are involved. Just to list a few, meetings that have nothing to do with the project, nonproject assignments, training classes, interrup- tions from people, helping people work on other assign- ments, working on proposals for future projects, working on next year’s budget, and solving problems in old projects that you thought were complete, but you’re the only person left who knows anything about it. One factor that stands out as a major issue is sharing a person with other projects. We will discuss this one in more detail later on.

The lesson, of course, is that you must be realistic about how much time a person has available to work on your pro- ject or your schedule will be grossly inaccurate. The only way to assure anyone is available to work 80 percent on a project is to tie them to a desk and keep them there. In factory envi- ronments, though, 80 percent availability is often the norm, since the worker’s situation is essentially that he or she is not free to wander around or be assigned other to other tasks. For knowledge workers, however, 80 percent availability is unrealistic.

THE EFFECTS OF MULTIPLE PROJECTS ON PRODUCTIVITY

Another reason why resources are not available more than 80 percent of the time is that they are shared with other projects. Informal surveys of my seminar participants indicates that most of them have team members working on two to six pro- jects at the same time, with the norm being about four projects.

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This means that team members are available to work on a given project only 20 percent of the time.

If this were a linear effect, it would be bad enough, but it is not linear. To illustrate, suppose I assigned you a task that you could complete in six working hours if you could work straight through until you finished it. Of course, this is a pretty long stretch, so you would probably work three or four hours in the morning and finish it in the afternoon. Would it still take six hours? No. The reason is that you would have to spend a few minutes getting reoriented to the task after taking a break. It may be only a few minutes, but it does reduce your productivity.

Now suppose, because of working c o n d i t i o n s , t h a t you have to work o n t h e t a s k t w o hours today, another two tomorrow, and two more the third day. Now the task will take even longer than it would by splitting it into two chunks. The reorientation time is called setup time in manufacturing, and we learned years ago that setup time is a total waste. Every minute of setup time elimi- nated is a minute gained for productive work.

This shows why we have problems with multiple pro- jects. People are constantly shifting back and forth from one project to another. Time management studies have found that if you get interrupted by a person (face-to-face or by phone) while you are working on a task, you may need 15 to 20 minutes to get reoriented to it. If you get interrupted three times in one hour, you may lose most of that hour, even though each interruption lasts only five minutes.

Not only do you lose productivity because of setup time, but consider the time people spend in meetings—usu- ally a minimum of one hour per week for each project. And, as we know, many meetings are virtually nonproductive, so

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Setup time adds no value to a process. Eliminate setup time and improve productivity.

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having people work on several projects at once also increases time wasted in meetings.

Experience has shown that there are big gains to be made by reducing the number of projects that people are working on. One company found that by having people work on one project as a primary project, with a secondary project that they could work on if they had some dead time, their productivity nearly doubled.

It can be very hard to get senior management to buy into this notion. They get trapped into thinking that every- thing must be done at once, not realizing that if they would prioritize projects and do them in priority order, they would get everything done in the same calendar time and at higher efficiency and higher quality.

QUEUING THEORY AND RESOURCE MANAGEMENT

Another factor that adversely affects projects is the lean-mean paradigm that is so prevalent in organizations today. As a brief historical perspective, you might think back to the 1980s and remember that many companies had layer upon layer of management. This was probably the ultimate consequence of the belief that a manager could only directly supervise about six people. Over time, organizations became pyramidal—one manager had six people reporting to her, and each of them in turn had six people reporting to them, and so on.

This might have been necessary in the early days of the industrial revolution, when many workers had little educa- tion and required a lot of supervision, but it had ceased to be true by the 1980s. Nevertheless, we clung to it, because the paradigm was that span of control should be limited to six direct reports, and that was that.

Then people began realizing that this was no longer true, and a wave of reductions in middle management fol- lowed. The metaphor was that we were getting rid of excess

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fat, and we were. The result was highly positive for business: The bottom line showed immediate improvement, and right- fully so. If you eliminate costs without affecting sales reve- nues, you have automatically increased profits.

Because that first dose of cost-cutting felt so good, man- agers were quick to apply more of it. W h e n y o u f i n d s o m e t h i n g t h a t works, it is natural to try it again, so they did. Now we are in the midst of the lean-mean paradigm. Companies have cut every expendable person they can identify.

Trouble is, some of them long ago got rid of all the fat, so now they’re cutting muscle.

In addition, since this is a biological metaphor, we know that you don’t want to remove all of the fat from any organ- ism. The fat provides reserve energy for hard times. But we have failed to realize this.

So, what does queuing theory have to do with the situ- ation? Queuing theory deals with things like highway ca- pacity, production line capacity, and so on. If you have ever tried to get onto a major high- way during rush hour, when every- o n e e l s e i n t h e whole world was also trying to get onto it, you under- s t a n d w h a t h a p- pens when any system is at its limits. The amount of time you must wait to get onto the highway gets very high at

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Insanity is defined as continuing to do what you’ve always done and expecting to get a different result. I think mindlessness is continuing to do something that has worked in the past and expecting it to get the same results as it always has.

A lot of organizations have focused on “trimming fat” to the point that they may die of anorexia.

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rush hour. At other times of the day, it is insignificant. Fig- ure 19–1 shows how this works. As the capacity of a system approaches 100 percent, the waiting time to use that system approaches infinity!

This same idea can be applied to a resource pool. As we load people up to their limit, the time new projects must wait to be started approaches infinity. Yet many managers think that the only way to be productive is to keep people working at 100 percent (or even greater) capacity. And when people try to say they’re overloaded, some of the very macho managers tell them to quit complaining, they’re lucky to have jobs.

THE WONDERFUL BENEFITS OF OVERTIME

When faced with the fact that there are only so many hours in the workday to get things done, we respond by having people work overtime. In many organizations, professionals

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F I G U R E 19–1

Waiting Time as a Function of System Utilization

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are expected to work 50- and 60-hour weeks routinely. After all, they’re paid the big bucks, and we want to get our money’s worth from them.

Here, too, we are being penny-wise and pound-foolish. Studies show that after people have worked 10 to 15 hours of o v e r t i m e e a c h week for several w e e k s i n a r o w , their productivity drops back to what t h e y w o u l d n o r- m a l l y d o i n 4 0 hours, and their er- r o r r a t e s g o u p . This is true for both factory workers and knowledge workers. It is possible to work overtime for one week and gain pro- ductive output. But people get tired after so many weeks of this, and fatigue takes its toll on performance.

For this reason, it is very bad practice to plan a project so that long stretches of overtime are required to meet the original end date. Not only will productivity drop rapidly, but if unforeseen problems occur, you can’t use overtime to solve them, as people are already working all they can stand.

To see the effect of overtime, consider Figure 19–2. While this represents construction work, you can be sure that similar effects exist for other kinds of work. For recent re- search on the overtime-productivity relationship, see also Brunies, 2001.

Figure 19–3 shows the impact of having too many work- ers on a construction site. While this might seem to be re- stricted only to construction, how about problems that people encounter with having too many people in a department, too few desks, too little office equipment, and so on? I believe it is a similar effect. These are all factors that we should con- sider in trying to accelerate projects.

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After working several weeks of 50 to 60 hours, productivity falls to the normal 40-hour level and errors go up.

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F I G U R E 19–2

Decline in Productivity with Overtime

F I G U R E 19–3

Decline in Productivity with Site Loading

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THE NEED TO SHARPEN THE SAW

Stephen Covey, in his book The 7 Habits of Highly Effective People, says that effective people spend some time sharpening the saw. They do not work all the time. His metaphor comes from the fact that if you saw wood for an extended period without stopping to sharpen the saw blade, it gets dull and won’t cut very effectively any more. Over a period of time, the woodcutter who keeps his saw sharpened will generally cut more wood than the one who does not.

It has always struck me as odd that many companies treat their capital resources better than their human re- s o u r c e s . W h e n I was in college, I worked summers in a shipping de- partment that was housed in an old cotton mill. Those old mills were not air-conditioned, and in July the tempera- ture in North Carolina can easily be 95 outside, which results in an inside temperature of 100 to 105 degrees. Needless to say, you can’t move at extreme speed in that temperature, or you’ll have a heat stroke.

In many old factories, air-conditioning was out of the question. Costs too much, was the belief. Yet people were moving at a snail’s pace. I always thought the increased pro- ductivity that would have been achieved in an air-condi- tioned building would far offset the cost. But what do I know?

Then along came computers and computer-controlled machinery. These may fail if they get too hot, so guess what—they have to be housed in air-conditioned offices. On top of that, in the 1960s, when the computers were being in- stalled, smoking around them was banned—yet you could puff away around your coworkers.

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We treat our capital resources better than we do our human resources.

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The message here is simple: If you want to get maxi- mum value from your most valuable resource of all—a hu- man being—you must make it possible for him or her to sharpen the saw once in awhile. This is not just a soft-hearted humanitarian plea; it just makes good business sense!

APPROACHES TO RESOURCE LEVELING

Consider the bar chart schedule shown in Figure 19–4. Activ- ity A is a critical path task. Activity B has a duration of three weeks and has one week of float. Activity C has a duration of

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F I G U R E 19–4

Bar Chart Schedule with Resources Overloaded

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two weeks and has three weeks of float. These durations are based on having two people available to work on A, one on B, and one on C, full time for each task.

However, we only have three workers available. This clearly means that the job cannot be completed as scheduled.

Note that I am assuming what would be called generic or pooled resources in this example. Generic resources are people who can all do the same work. This is possible in some crafts, such as plumbing, carpentry, or electrical wiring, but would often not be true for specialists, such as engineers, certain machinists, and other professionals. In the case of specialists, you must assign specific individuals to tasks. We will con- sider that case further on.

Now suppose you were going to manually allocate re- sources so that no one is overloaded. How would you go about it? Well, you might begin by assigning two people to Activity A, since it is on the critical path. We know that if this task is not completed on time, the project will slip, so we must begin here.

This leaves one person to do B and C. It occurs to you that you might assign that person to work on each task half-time. If you do that, you will have to double the dura- tions of each task, which would work for C, since it has enough float, but would not work for B. Doubling durations, of course, assumes that time is a linear function of resources assigned, which is itself a faulty assumption in many cases.

Since changing durations appears unfeasible, you might next ask, “Since I need another person, maybe I can just get one and be done with it.” So you ask the powers that be in your company, and they tell you that three people is all you can have. You still are in a bind.

Next you might try assigning that one person to either B or C, but which one? You might think that you should assign the person to B, because it is longer than C. It turns out that B is the correct choice, but you have made it for the wrong rea- son. It is best to consider the float available to each task. You

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did this when you assigned two people to task A, which has no float. The most common rule for assigning resources is called the minimum-float rule. Assign resources to those tasks that have the least float, then the next least, and so on, until re- sources are exhausted. Then those tasks that have float and no resources can slip without (hopefully) impacting the deadline.

It is easy to see in Figure 19–4 that this works very nicely. Activity C has enough float that you can slide it over to the point at which task B is complete, and then resources will be available to do task C. Fortunately, this happens just at the point where C runs out of float. This is shown in Figure 19–5. Note also that because activity C has run out of float, it is now on the critical path. This is shown by making its bar a solid color.

You will realize that this is a very clean example. It never happens this way in the real world! What will happen is that task C will run out of float before B is complete, and now you have a dilemma. If you slip C any more, it will cause the pro- ject deadline to slip. What do you do in that case?

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F I G U R E 19–5

Bar Chart with Resource Overload Resolved

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TIME-CRITICAL VERSUS RESOURCE-CRITICAL ALLOCATION

In the above example, we assumed that you were allocating resources manually, that is, without benefit of a computer. Ultimately, however, this is impractical. As the number of tasks and resources increases, the solution to overloads be- comes very difficult, and a computer programmed to level-load resources will be required. The most common allo- cation rule (or heuristic, as it is sometimes called) is the mini- mum-float rule that we applied in the previous example.

In addition, the computer must be programmed to do ei- ther time-critical or resource-critical leveling. Under time- critical conditions, once a task runs out of float, the program will stop moving it, since to do so would slip the end date for the project. When this condition exists, you will have to find a way to deal with the remaining overloads. It may be that you can work people overtime for a brief spurt to resolve the problem, since working someone overtime is equivalent to adding resources to the project.

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