Project Management Institute

Integrated project control

Control to Maximize Profits

Gerhard L. Hollander

Hollander Associates

Fullerton, California 92633


“Management” usually implies control of an operation’s critical parameters, such as cost, schedule, quality, reliability. Historical accounting and personal inspections by the manager are now supported or replaced by almost real-time information systems and sophisticated control techniques. However, all these techniques, such as PERT for schedule control, focus on individual parameters instead of the overall management objective: maximum long-term profit.

This paper presents a new approach for managing complex programs or projects. A new method for describing alternatives by incentives and cost functions permits trade-offs of cost, performance and schedule alternatives to provide the manager specific actions that will maximize profit or similar organizational objectives. The formal incentives are a good communication tool between vendor and customer or between various levels of management. While proven so far on major projects, the concepts also can be applied informally to smaller, less complex projects.


A program manager who completes a major project on time, within the original budget, and without compromising original performance specification, is credited with a major achievement. But requirements and other conditions change. Consequently, attempts to maintain the original cost, schedule, and performance specifications may not produce the best possible results. A new material or process can improve performance, schedule, or cost; and the right decision is just as important with favorable as with unfavorable events.

Editor’s Note:

This is the first of a two-part article. Part I, “Control to Maximize Profits,” presents Mr. Hollander’s views of program management and the application of a tops/ scheduling management system. Part II, to be published in the June issue, presents an example of TOPS as a control device for project management. We believe you will find the views expressed here of particular interest.


In a simple project, the project manager decides these questions, because he can personally acquire and evaluate all pertinent facts. When projects become complex and require interaction of many specialists or departments, more formal procedures must replace the project manager’s intuition and personal grasp.

In building a major power-plant, a new transportation system, or a space system, the project manager must delegate all but the most central decisions to hundreds of foremen or department managers. Only the overall or global decisions can demand his personal attention; and even their resolution will often be later than desirable, because many problems are obscured in the reporting hierarchy.

Such a complex environment, in which the program manager cannot marshall and quickly evaluate all the facts, calls for a formal control method. Such control methods should produce the most cost-effective end product and maximize the profit for the project manager’s company.

Recognizing that neither the customer’s needs nor the program manager’s requirements are absolutely fixed, but in reality represent a set of trade-offs in a changing environment, such a control system should:

  1. Provide a method for the customer to formally state acceptable alternative cost, schedule, and performance-characteristic combinations.
  2. Provide a format for a formal statement of the company’s trade-off objectives to maximize profit.
  3. Monitor the progress of each activity on a current or periodic basis.
  4. Provide guidance for the program manager and activity managers to optimize the results of their efforts.

The optimum is defined by the objective specifications (number 1 and 2 above) and the current status of the activities (number 3 above).

This paper describes such a system. It has been used successfully on an experimental basis for the design of a major space system and for the boiler-erection of a large power-plant (3). As a rule of thumb, such an integrated system should be considered for any project complex enough to require computer-processed PERT or CPM support. The techniques, however, also can be used informally for smaller projects.

Overview of Paper

Such a control system that produces optimum actions without human intervention requires a completely new approach. The next section examines the fundamental relationships of the buyer and various activities in the seller’s organization to find how the acceptability of alternative results can be communicated. This basic understanding leads to an approach to optimal trade-offs.

To illustrate the overall approach, Section III focuses specifically on the cost/schedule trade-offs. The 2-dimen-sional case can explain graphically the operation of the incentive and activity-cost functions. A simple network example illustrates the principles. Section IV discusses the practical implementation of such a system and its implications for the project manager and for general management.

ARTICLE II describes the actual system.


To obtain mechanically the greatest benefit from a program, all factors must be clearly stated and quantified. Often, this exercise alone uncovers overlooked alternatives or proves intended approaches impractical.

Major transactions involve four parties whose conflicting interests must be aligned to yield the greatest overall benefit:

  1. Customer
  2. Vendor’s corporate management
  3. Project manager
  4. Workers (and their supervisors)

These parties just communicate their needs in a quantitative form to each other and for systematic solution.

From the project manager’s viewpoint, the needs of his customer and his management can be expressed by incentives, rewards or penalties for producing certain results. These are compared to the cost/performance/ schedule alternatives in the design and production implementation of the product. The project alternatives and the incentives reflect the product’s cost and value for different implementation plans. How can they be measured and communicated?

Stating the Requirements

Disappointing results in major projects are often caused by poorly specified requirements. The buyer makes incorrect assumptions concerning vendor alternatives. The vendor’s management fails to take into account all factors affecting profit. Often management, through oversight or on purpose, fails to specify important instructions. This situation can be remedied by proper quantitative communication.

Customer Requirements (Contractual Incentives). For simple purchases, the customer reviews in catalogs or displays the performance characteristics, cost and delivery of candidate items and then selects the one best suited to his needs. Whether it is a stock item or a “custom” option, the supplier fixed the specifications.

When standard items are unsatisfactory, the customer must negotiate with a vendor a special design to his specifications. The buyer can develop these specifications unilaterally or by discussion with one or several potential vendors.

The resulting contract specifications may not be or remain the best. Often, the selected vendor has better alternatives; or time and advancing technology change the original assumptions. If the vendor follows the original specification, the resulting product will be less than optimal. If he recommends changes that improve some characteristics but deteriorate another (for example, better performance at slightly delayed schedule), the resulting negotiations may prohibitively delay the project. Therefore, the vendor may not even inform the buyer of the alternatives.

In reality, the buyer usually does not consider his specifications as absolute. If the specified cruising range of an airplane is 3,000 miles, he would probably accept 2,950 miles if it significantly lowers operating cost. Similarly, “firm” delivery dates contain safety factors and contingencies. This ambiguity results in a guessing game between buyer’ and seller as to the real need.

During the late decade, major U.S. Government procurements contained contractual incentives, negotiated rewards for surpassing and penalties for falling below critical specification goals. These incentives were applied to delivery schedule, to cost, and to performance factors, such as range, weight, reliability, maintainability, etc. Thus, the contractor could continually trade off internally design and production alternatives that maximize Iris profit, — and hopefully produce the most cost-effective product for the customer.

For example, an airline company procures a hypothetical device that will lower the operating cost of each airplane that carries it approximately $100 per day. Thus, the contract for this new device might contain per unit a $100-per-day reward for early delivery and a $100-per-day penalty for late delivery. However, since the weight of the unit reduces the capacity for revenue cargo, the airline may offer a $50-per-pound weight incentive. Similar incentives can be placed on whether the device increases the operating efficiency more or less than the original $100-per-day target and on the reliability of the unit. In addition, the airline may place a large completion incentive on the prototype test of the device; because if this development performs below expectations, they can quickly shift to alternative devices. If the buyer shares or reimburses the research expenditure, the contract may provide profit incentives for staying under the target cost.

Major construction projects often use schedule and cost incentives, and many new contracts contain performance incentives. In fact, nearly any type of product made to a customer’s specification can and should be specified with incentives on schedule, performance, and possibly cost.*

Vendor Management Requirements (Internal Incentives). When the vendor management enters into an incentive contract with a customer, the contractual incentives guide part of its actions. But long-range profit involves additional factors. Late delivery means later receipt of payment which means paying or foregoing interest on the funds tied up in the development. An expected strike leading to higher wage rates would also suggest earlier completion. Better performance or lower production cost may create a broad commercial market for the item. Thus, the vendor management provides the project manager additional incentives to further the long-range company objectives.

Products developed strictly as a future catalog item without any prior customer support have no contractual incentives. Instead, management imposes internal incentives based on their estimate how selling price, market penetration, and profit are affected by product performance and features, date of introduction, and cost. Management can quantify these factors as incentives just as the customer stated his.

Project Manager’s and Supervisor’s Options (Implicit Incentives). The contractual and internal incentives tell the project manager how his customers value various result alternatives. Any alternative without incentive leaves its values to the project manager’s judgment. Formally or informally, he then adds a set of implicit incentives, his impression of the customer’s or management’s values. By the same token, any open alternatives not specified through the project manager’s decisions leave implicit incentives for the activity supervisors.

Implicit incentives usually exist by default. For example, if the project manager gets merely a completion date without a specific incentive function, he can imply three meanings:

  1. Target date. Meet it if possible; but if it costs more, let it slip.
  2. Absolutely firm. Expend all available resources to meet it.
  3. Intermediate. Formulate effective reward function based on supplementary information, such as “A few days slip is okay, but more than a week gets us in real trouble.”

Alternatives 1 and 2 can be stated as formal incentive functions. All real cases lie somewhere between these two extremes.

Incentive Recap. Incentives formally specify permissible alternatives to lower levels in the customer/management/project/activity hierarchy. Incentives are effective communication tools. They force each level to think out the constraints under its control and to communicate them downward. This eliminates the many default decisions at lower levels which are agonizing because the decision-maker-by-default lacks the scope or visibility to make a rational decision. When all incentives are expressed, each level can select the options that produce the greatest benefit within the constraints.

Incentives. communicate downward the requirements without regard to which implementation (design and construction) will prove to be best. The possible performance, cost, and schedule of different design and fabrication alternatives are communicated up from the appropriate activity. At some levels, the incentives can be compared to possible implementation alternatives to select the best. Choices with wide impact are made by the project manager; other choices, at appropriate lower levels.

Implementation Alternatives

Before the project manager can select the best alternative, he must know the cost/performance/schedule alternatives that his design and production teams can achieve. If the project is complex, the design and fabrication alternatives must be stated as formally as the incentives for computer evaluation.

A complex project consists of many sub-systems, each containing many assemblies, which are produced by a multitude of operations. Since each operation allows many cost/performance/schedule alternatives, the project manager can end up with an almost astronomical number of alternatives.

To handle so many choices, the project can be broken into a formal hierarchy of sub-projects and sub-sub-projects. A systematic formulation is the Work Breakdown Structure (WBS). In this paper, and also often in practice, the lowest level in the Work Breakdown Structure is a set of activities in the PERT and CPM sense.

The description of the activity alternatives must allow mechanical aggregation into project alternatives. For example, if end-product weight is important, the weight alternatives from all design activities that specify component weights add up to final product weight. Similarly, engineering or production costs can be aggregated by a simple addition.

To aggregate other performance characteristics, the relationships between the characteristics of a component and the total system must be known. For example, the literature contains models for aggregating component failure rates into failure rates of assemblies, sub-systems, and systems. Other forms of aggregation exist specific to the performance of individual products.

For schedule aggregation, the network used in PERT and CPM is an excellent model. Although nonlinearities complicate it, the model applies to all projects and thus can use a single general-purpose program.

For example, each activity can finish on different schedules and cost, depending on whether it works normal or overtime, single or multiple shifts, uses inexpensive or costly tools, employs in-house or outside facilities. Each of these approaches alone produces a cost and time to complete an activity. For different combinations of activity alternatives, time and cost along different network branches can be combined to attain different project cost/schedule alternatives. Obviously, acceleration involving premiums need only be used along the critical path. Part 2 contains a semi-automatic method of estimating the cost/time alternatives at the activity level and for aggregating them automatically to the system level.


All projects have or should have incentives for early completion. Most projects can be accelerated by spending extra money for such items as premium pay, outside contracts, or training additional workers. The schedule incentive problem boils down to accelerating those activities where the resulting reward is greater than the additional costs. This simple statement covers many logical and computational difficulties. This section shows an approach for overcoming them and an example for solving the problem.

The General Formulation

A project can be divided into many activities whose start and completion time dependence can be expressed by a network. Similar to PERT or CPM, the manager of each activity (or an equivalent staff function) estimates locally his cost and progress. Unlike the conventional methods, each activity manager supplies a range of feasible time/cost alternatives.

Furthermore, certain key events carry a completion incentive. These incentives are global; usually imposed from the outside, they are affected by all activities that precede these events. The problem is to maximize available rewards, or minimize the penalties, at the least cost.

Incentive Functions. Figure 1 shows a typical incentive function. Completion of the incentive event in an elapsed time (T) less than guarantees maximum reward; in time greater than T1, minimum reward or maximum penalty.

The reward decreases — or the penalty increases — between T° and T1 by some functional relationship. The nature of many incentives makes this function linear, although step functions and other relations are common.

Figure 1 could represent a contractual completion incentive for something like a school building. Before time T°, the buyer has no need for the building; after T1, it would be too late for use in a given semester, so that other arrangements must be made. During the time between T° and T1, the buyer can benefit from the building each day. If the builder might overrun more than a full semester, the school board would increase the penalty again six months later at a time T2.

Typical Incentive Function

Figure 1         Typical Incentive Function

Despite its simplicity, the incentive function in Figure 1 can represent many practical situations by proper parameter assignment. Table I summarizes a few typical examples often encountered in practice.

The delivery incentive can be linear, if the customer gets about equal use from the item every day; or it can be a step function when after a critical day he must commit himself to added equipment, must pay for rescheduling subsequent activities, or must miss a favorable flight or departure date. The letters in the reward columns could be any two values, although B would be smaller (more negative) than A.

Three typical incentives represent only penalties; i.e., R0 is zero. Labor escalation affects all activities whose workers will get a general raise during the project. Since the activity cost reflects current wage scales, all work after the increase date reduces profit. Overhead is also a cost. If the overhead rate changes after certain milestones, separate overhead functions must be applied to each. (The incentive at the subsequent milestone can only represent the difference in rates.) The ∞ sign denotes that this slope continues well beyond the last expected date of project completion.

Figure 1 and Table I imply at most a 3-section incentive of which two are horizontal. In practice, the product use value between T° and T1 need not be constant, and the slopes can have several values. Some schedule incentives have positive and negative slopes; for example, an incentive accounting for loss of productivity during a period of inclement weather.

Typical Schedule Incentive Examples.

Table 1. Typical Schedule Incentive Examples.

Most factors and contingencies of a major project can be expressed as incentive functions. The first projects have yielded a catalog that can accommodate most needs; but so far, each project required some new formulations. Proper internal incentives (I) will always represent actual values. Contractual incentives (C) often represent additional factors, such as the relative negotiating positions of and risk division between the parties.

Activity Cost Functions. The activity cost function represents the activity manager’s estimate of the cost for various completion times. For example, Point A in Figure 2 shows the time and cost which yields the lowest completion cost with his available resources. Earlier completion requires premium pay or other expenditures. Later completion involves inefficiencies or cost of storing the intermediate product. The time and cost estimate represented by Point A is typically the first estimate contributed to a large project.

Typical Activity Cost Function

Figure 2         Typical Activity Cost Function

The most common form of acceleration is overtime; for example, a 6-day, 10-hour-day (6/10) week instead of a 5-day, 8-hour-day week (5/8). Considering overtime premium and fatigue inefficiencies, Point B represents the cost and completion-time estimate if the activity works only on a 6/10 basis. A mixed 5/8 and 6/10 schedule falls on a point on the line segment connecting A and B. Greater inefficiency and possibly higher overtime premium for the seventh day leads to Point C as estimate for a 7-day, 10-hour-day (7/10) schedule. The line connecting Points B and C represents a mixture of 6/10 and 7/10 schedules. So far, the incentive function represents a continuum of ever-increasing costs to accelerate the effort.

Further acceleration demands multiple shifts. Double-shifting lowers labor cost and raises efficiency compared to working the single shift 70 hours per week. Premium pay is saved, and two fresh crews are more efficient than one driven to the limit. Thus, where the curve reverses between Points C and D, the intermediate points are meaningless; because if any double shifts are planned, it is seldom practical to mix them on the same activity with single shifts on overtime.

Supervision or training costs for a second shift increase either the overall program overhead or the burden of a single activity. If the individual activity is charged, the cost at Point D may be higher than at Point C. If double-shifting produces a global charge, such as installation of flood lights or a night superintendent regardless of how many activities double-shift, such added charge must be represented through incentives.

Cost functions may appear too complicated for a first-line shop or construction supervisor. This appearance is false! The usual manufacturing or construction operations accelerate with only a few fixed patterns. The overtime rates and inefficiencies for a given shop or a given craft are constant. Labor shortages, union agreements or other considerations may prevent multiple shifts for some operations. Altogether, a company may have five to ten distinct acceleration patterns. Thus, as shown in Article II, a supervisor can estimate the normal (5/8) and time cost; and the computer generates the alternatives from the applicable acceleration pattern for that activity. For exceptional conditions, the program fits specific data points supplied by the supervisor.

After the initial estimate, the activity manager can either designate subsequent reports as “no change” or make essentially a new schedule and/or cost estimate. In any case, the information is no harder to obtain than the usual inputs for PERT.

Profit Maximization. In addition to a fixed profit or loss, project profit (P) is the total reward (R) for all incentive events less the total cost (C) for all activities, where the total reward and cost are the sums of all incentives and of the activity costs respectively.


where the total reward and cost are the sums of all incentives and of the activity costs respectively.


The rewards or incentives are referenced to real time, either days after project start or calendar days; the cost functions are referenced to time for activity start. Thus the incentive functions have a global characteristic, while the cost functions are local. The two times are related, in the normal PERT sense, in that the event time (T) equals the sum of the activity times (t) from project start along the critical path to the event.

Cost Impact on Profit. Up to this point, it has been assumed that activity cost is the cost in Equation 1. This condition exists when a manufacturer or builder receives a fixed price for his product.

In major projects, particularly those funded by government agencies or entailing large risks, the contract price could be cost reimbursement plus a profit. If the profit is independent of the cost, the actual cost to the vendor is zero; and he has no incentive to economize. His only objective is to maximize his rewards. Even worse, contracts with profit as a percentage of cost give the vendor an incentive to be inefficient!

To encourage vendor efficiency, cost-reimbursement contracts contain incentives for economy. For example, buyer and seller may agree on a target price; and the vendor gets a percentage of any savings, but must contribute from profit a percentage of the overrun. The cost value in the vendor’s cost function is the percentage he has to pay Actual cost in a fixed-price contract; nothing in cost-reimbursement; and a percentage with cost-sharing. Thus, the values in the cost functions and Equation 3 are modified costs. To avoid burdening each supervisor with this calculation, a simple conversion routine handles it during computer input if the ratio is fixed.

The vendor’s cost-share may vary with the amount of the overrun. A typical contract may provide for an 80/20 share of the cost between buyer and seller respectively, but with a contract ceiling of 130 percent of target cost. That means if the contract overruns more than 30 percent, all further costs must be fully absorbed by the seller. Since the ceiling makes cost a global factor, special provisions must be made for its aggregation. Though simple in concept, the details are beyond the scope of this paper.

A Simple Example

The schedule incentive trade-off and profit optimization is best illustrated with a trivial example. The simple project in Figure 3 has eight activities and two incentive events, one intermediate and one final.

The activity cost functions in Table II are assumed to have single slopes. Column 2 gives the crash time (t°), the shortest time in which the activity can be completed at any cost (c°) in column 3. Columns 4 and 5 give the duration for minimum cost (t1) and the minimum cost value (c1). These data specify the slope (m) of the segment joining the maximum and minimum cost points.

Table 111 shows the incentive functions at Event D and E in the form of Figure 1. The latest elapsed time for maximum reward (T°) and its value (R°) appears in Columns 2 and 3. Columns 4 and 5 give the elapsed time for minimum reward (t1) and the minimum reward value (R1) The slope(s) determined by these points appears in Column 6. As in Figure 1, before the maximum reward point and after the minimum reward point the slope is zero.

Solving the Problem. This simple network can be optimized by inspection. Table IV shows three of the many combinations that must be examined in a realistic problem. If all activities operate at minimum cost, events D and E occur at times 66 and 146 respectively. Both D and E are at minimum reward, and the project sustains a loss of 249 units. If all activities are at their crash points, event D occurs at time 16; and E at 36. Although Events D and E earn substantial rewards, the higher acitivity costs produce an even larger project loss.

A quick analysis shows that when all activities are crashed, the critical path consists of activities 4, 5 and 7. For every day activity 7 alone is expanded (proceeds more slowly), the project saves 40 units of cost, but loses only 30 units of reward from incentive E, for a net daily profit change +10. Indeed, profit increases if activities 6, 7, and 8 are at minimum cost. Event E then occurs at time 96, which again corresponds to its minimum reward region.

Thus, incentive E has no impact on the project under current cost conditions. However, if later on, acceleration costs for the activities leading to E become smaller, it may be more profitable to complete the project at a time of positive reward.

For a simple example, this cut-and-try expansion can continue until maximum profit, the global optimum, is obtained. The last columns in Tables II and 111 show the activity durations and event times at global optimum that yield a project profit of 177 units.

Approach for Realistic Projects

In theory, the type of juggling described to find the optimum for a simple example could be extended to real projects; but the operations grow about exponentially. Any reader who verifies the global-optimum values in Tables II, and III, and IV, will recognize that random procedures for a 5,000 or 10,000-activity network would require a tremendous amount of computer time. Furthermore, unless the computer conducts an exhaustive examination of all possibilities the procedure described above will usually lead to a local, not the global, optimum. If all possibilities are exhaustively examined, each run for such a large project could take thousands of hours on a high-speed computer! Using a different approach, the processing time for the TOPS/Schedule algorithm to establish an optimum project schedule is competitive with an ordinary PERT network.


Integrated project control can identify the most profitable execution of major projects. The key concepts are simple and rational. The necessary computer programs can be obtained commercially. Experimental implementations have produced profit gains from 20 percent to 700 percent.

Project Activity Interrelationships

Figure 3         Project Activity Interrelationships

Activity Labels Max. Cost Min. Cost Slope m Global Opt t
Time to Value c0 Time t1 Value c1
1 10 206 30 6 −10 30
2 2 165 10 5 −20 10
3 5 290 25 10 −14 25
4 12 195 36 3 −8 12
5 4 421 30 5 −16 28
6 20 1220 60 20 −30 60
7 20 2460 80 60 −40 80
8 15 1440 50 40 −40 50

Table II Activity Cost Functions

Event Max. Rew Min. Rew Slope s Global Opt T
Time T0 Value R0 Time T0 Value R1
D 16 1150 66 -100 -25 40
E 30 1800 90 0 -30 120

Table III Incentive Functions

Condition Time at Reward of Total Reward Total Cost Profit
Min Cost 66 146 -100 0 -100 149 -249
Min Time 16 36 1150 1620 2770 6397 -3627
Optimum 40 120 155 0 550 373 177

Table IV Profit at Various Event Times

The new approach emphasizes management’s role as strategic decision makers. Project and activity managers receive the tools to make the tactical decisions that maximize the long-range organizational objectives.

Summary of Concepts

Integrated project control is based on four steps:

  1. State the value of alternative results (incentive functions)
    1. For all organizational interfaces.
      Customer—Vendor management
      Vendor management—Project manager
      Project manager—Activity supervisor
    2. For all significant alternatives: performance, schedule, cost
  2. Determine costs for alternative implementations (cost functions)
    1. Originate at activity level.
    2. Aggregate to project level.
  3. Determine global optimum at project level.
  4. Communicate action guidance to activity level.

Unlike any conventional approaches, the utility value of different results are stated in advance. Thus, the computer or lower-level supervision can select the appropriate alternative with fewer calls to the project manager.

In any complex project, collecting the data for all points in the cost and incentive functions (Figures 1 and 2) would cost too much. Fortunately, automatic procedures can generate the cost curves from the usual cost and time estimates, (see Part II). Similarly, the incentive functions at the project/activity interfaces are derived from the few incentive functions at the project level. Thus, the bulk of the cost and incentive functions can be generated by a computer program.

Incentive functions should be generated from the top down. The customer communicates his utility value for alternative results of cost, schedule, and performance. Vendor management adds its internal incentives for plant loading, overhead distribution, interest rate, and similar internal business considerations. The rest is determined by the current project status and by the remaining cost functions. Thus, the third incentive level between the project manager and his activity supervisors can be generated (within limits) by a program.

Through rational definition of the source data, the optimization steps can be expressed algorithmically for program execution. Although the computations to optimize a complex project may be extensive, the process requires no outside decision. The project manager merely endorses the “best” action or selects one of the proffered alternatives.

Implication for Management

Integrated project control as described in this paper clearly separates the strategic from the tactical decisions. At the project level, the incentive functions represent the strategic constraints imposed on the project manager by management and the customer. The activity cost functions represent possible alternatives communicated from below. The aggregation and optimization algorithms produce the tactical decision data at the project level; and the activity managers make tactical decisions below.

Management moves out of the tactical decision-making loop, but must give better strategic guidance. Since policy choices are broader and made in advance rather than in response to an immediate crisis, the consequences of alternative results can and must be clearly thought out and expressed.

On the other hand, with the value of alternatives specified in advance, few changes or mishaps will require management’s attention. Instead, the incentive functions specify the new optimal action.

The integrated control system is managed at the same line or staff levels currently responsible for similar functions. Organizations with distinct functional systems, such as separate accounting and project control, must at least make their data compatible. This requires a thorough analysis of the different control functions, but the results are worth the effort. Use of different source information for project control and accounting is the most common cause for unexpected overruns.

Integrated project control can assure management of consistent reports, because they are generated from compatible data. Overruns due to inaccurate or inconsistent reporting that threatened even large conglomerates with bankruptcy have made the headlines. The Government now demands integrated project control on its major procurements. Management can adopt the best parts of such control systems to increase profit.


Integrated project control produces important benefits for the profitable execution of major projects.

  1. Greater profit through identification of best decisions and actions by computer program.
  2. Fewer crises by pre-specification of the more constant strategic constraints.
  3. Better utilization of management talent by separation of strategic and tactical decision processes.
  4. More reliable reports by use of compatible source data.

The formal expression of alternatives by incentives and cost function should have impact far beyond management control systems. Incentives can be used as a formal communication tool between customer and vendor and between various levels of management.

* Failure of early multiple-incentive contracts can be traced to incentives that did not equate the best product for the customer with maximum profit for the vendor. A future paper will show the criteria for proper incentives.


(1) Hollander, Gerhard L., “Sequential Decision Making for Complex Projects,” Proceedings, 1970 System Science And Cybernetics Conference (Oct. 1970), No. 70C43, New York: IEEE, Inc. 1970, pp. 243-246.

(2) Morse, Robert V., “Control Systems for Better Project Management,” Computer Decisions, July 1971, pp. 28-31

(3) Schrier, Robert J. and Tilley, Elizabeth A., “TOPS/ Schedule: A Construction Industry Example,” Proceedings, Project Management Institute, 3rd Annual Seminar/Symposium, Houston, Texas, October 14-16, 1971.

G.L. Hollander,

President and Technical Director of HOLLANDER ASSOCIATES, has over 25 years experience in teaching, research and system development. He has headed systems-management and computer operations at Philco Corporation and Hughes Aircraft Company. He is the author of over 40 papers and publications on topics ranging from managemtnt systems to computer and control systems. He is past Director of the American Federation of Information Processing Societies and is General Chairman of the ORSA 1971 National Meeting. His advice has been sought by DOD, AEC and other Government Agencies. He was on the faculty of St. Louis University and MIT. Mr. Hollander holds degrees from Illinois Institute of Technology, Washington University, and MIT.

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