IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. EM-19, NO. 4, NOVEMBER 1972

ment for automatic malfunction detection. It will further contribute to determination of the cost-effective number of spares and support equipment.

GENERAL REQUIREMENTS

The general requirements document could incorporate any requirements that either are system oriented or apply to more than one subsystem. In addition to containing the require-' iïients for reliability and quality assurance (R&QA), safety, testing, configuration management, materials, design reviews, etc., it could also include the results of studies defining the allowable budgets per subsystem of electrical power, space, weight, etc. Utilization of a general requirements document in any program will preclude duplication of system requirements in each specification, thereby eliminating the possibility of inadvertent differences. It further enhances the probability of having design and documentation uniformity.

SCHEDULING

Scheduling and all the related program control techniques serve the same function in this approach to system definition, oc r»n i m r n r A n r o m P Y ^ f l n t t h a t a l l tVlA QnalvClQ p f T n r t Ç ΡΓΡ **.w v " ~"-r r*~er » r - — J— -' included in the scheduling. This will provide management with the potential for a more comprehensive view of program

progress. It will also provide a means of identifying problems in all phases of program development, and facilitate manage­ment decisions based on a more complete compilation of related facts. This will be especially significant following the critical design review (CDR); when by joint utilization of the formal analysis, schedules, and cost, the total impact of proposed changes can be evaluated.

CONCLUSION

The SRA approach will provide a disciplined medium for effective program management, in addition to complete definition of all system hardware, software, and logistics. The Minuteman Program, with its inherent complexities, demon­strates the extreme effectiveness of this technique. The available procedures (ΜΙ^8ΤΙ>499, AFSCM 375-5, etc.) may, however, be more comprehensive than is necessary for less complex programs. The author therefore recommends that these procedures, as modified by this paper, be adapted to the individual needs of each program.

Because this analysis procedure is adaptable to any pro­gram, the potential benefits should not be overlooked by private industry. A cost savings for a governmental agency can readily be equated to an increased profit margin for a private concern.

Forecasting Costs and Completion Dates for Defense Research and

Development Contracts ARNOLD M. RUSKIN AND ROBERT LERNER

Abstract-It is hypothesized that the actual final cost and time required to develop a new technological system can be forecast from initially negotiated costs and times and other administrative details that are available when the contract is negotiated. This hypothesis was tested by examining multiple regressions of total cost and time as functions of data available at the end of negotiations for 73 contracts administered by one division of the Air Force.

Factors correlating with over 70 percent of the cost growth were identified: 1) the initial cost and period of performance, 2) whether or not the entire system is investigated, 3) whether or not a significant change in scope of ihc effort is anticipated, 4) whêîhcï öi Γιοΐ the effort is classified as a study (in contrast to hardware development), and 5) who is the procurement officer.

Manuscript received December 14, 1971; revised March 31,1972. A. M Rusk in is with Haxvey Mudd College, Claremont, Calif. R. Lerner was with the Claremont Graduate School, Claremont,

Calif. He is now a Financial Consultant in San Bernardino, Calif.

Factors correlating with one-third of the growth in the time required to perform the contract were identified: 1) what type of performance incentives and limitations are specified, if any, 2) whether or not the study is classified as "level of effort," 3) the initial cost and period of performance, 4) the project branch, and 5) whether or not a significant change of scope is anticipated.

Regression equations and coefficients are reported for the division studied.

INTRODUCTION

FORECASTING completion dates and costs in the defense industry is a complex art [1] - [6], Nevertheless, it is

hypothesized that the final cost and the time required to develop a system can be estimated with some accuracy on the basis of past experience, although the intrinsic reasons for changes may not be fully understood. This hypothesis is tested

RUSKIN AND LERNER: FORECASTING COSTS AND COMPLETION DATES 129

by examining multiple regressions of total cost and time as functions of certain administrative details, such as scope of the project and incentives and limitations placed on the project, for a population of 73 contracts administered by a division of the Air Force.

The division studied deals only in research and develop­ment of advanced ballistic reentry systems (ABRES) and is the only agency in the Air Force working in its field. A different agency is responsible for applying weapon system concepts, and the division studied has no weapon system commitments to meet. Once a program is approved, the division is fairly well isolated from direction by and commitments to other agencies, and the group manages its programs as it sees fit. The group's annual budget is firmly limited, but it can divert money from one contract to another within its total budget.

The sample of 73 contracts includes all available completed contracts managed by the division for its own mission objectives during an 8-year period.1 During this time, the mission of the division did not change.

The organization has two parallel sections: a projects section and a procurement section. The projects section is

development contracts that ABRES places; the procurement section is responsible for legal, fiscal, and other administrative aspects of ABRES's contracts. All decision-making personnel in the projects section are military; they sometimes consult, however, with civilian personnel of the Aerospace Corpora­tion, with whom they maintain a continuing relationship. In the procurement section there are buyers and contract officers, both civil-service career positions. The buyers report to the contract officers, and the latter report eventually to a military officer in charge of the procurement section. The minimum assignment of the military members of ABRES, whose tenure is shortest, was three years.

BACKGROUND Several models have been proposed in recent years for

predicting costs and scheduling for research and development tasks. Doering, for example, has proposed a model for risks in scheduling [2]; Dean et al. have proposed a model of cost distributions for a collection of projects [1]; Souder has proposed a project control model [5 ] , [6] ; and Roberts has proposed a general theory of research and development [4].

Doering studied the regression of 11 variables or di­mensions with differences between planned and actual com­pletion dates [2]. His work is based on 16 historical research and development tasks. The results of the analysis are used to structure a predictive relation. The variables whose effect Doering examined are basically characteristics of the task, e.g., design complexity, flexibility of systems specifications, flexi­bility of quality assurance requirements, similarity to previous performance requirements, and so forth. Doering employed Delphi techniques to quantize each variable for each project. He found that four variables, i.e., similarity to previous performance requirements, the extent of precontractual effort,

1 Sixty-four other contracts were issued during this time that could not be included; 4 were prematurely terminated and 60 were lodged in a permanent storage area that was not accessible to the authors.

the extent of systems knowledge, and the extent of the group's experience, were significantly more important in their correlations and for predictive purposes than the other seven variables.

x̂ ean s niOuCi tocuses on îoïig-range uUuget forecasting [i j . Individual projects are classified by categories, program elements, projects, tasks, and work units. Actual research costs are studied for the various classifications and their probability distributions and related parameters are determined for the various classes. The distributions are then used to predict overall budgets for future periods in which projects will be undertaken. In the cases considered, the data fit the log-normal distribution with a type I risk of less than one percent.

Souder reported experience with a dynamic model for controlling cost and achievement in research and development projects [5], [6] . His approach was ΐο âsk project personnel periodically to evaluate their project progress in detail. He thereby obtained early warnings of impending project failures, a conceptual understanding of forces affecting these project failures, and an analysis of achievement per dollars spent. This evaluative information can be used, among other things, to nrwr&ni H r i f t frr\rn / \rir»ina1 r»1ar»o

Roberts' general theory is an analytic representation of project dynamics [4]. It directs attention to the sequential and iterative or cyclic aspects of projects, including their inter­actions with resource and output markets. The theory thus suggests critical points in project progress and control that may be examined in detail to learn the details of project activity.

Roberts' work and the work of Doering, Dean, and Souder tend to complement each other. Roberts' model is conceptual and general, while the work of the others might be character­ized as experiential and specific. At various times, Roberts points out the lack of data that are required to fully exploit his approach. Doering, Dean, and Souder, whose works were reported after Roberts published his general theory, are getting data, although they often seem not to be measuring the parameters that Roberts' model calls for. In time, no doubt, models that take advantage of the accumulated data will be formulated and new empirical investigations will be made to provide data specifically needed by the models.

Doering offers a description of how adequate models are developed in the introduction to his work [2]. In Doering's terms, Roberts' work represents the first phase: a macro examination. Detailed investigations of specific relations con­stitute a second phase: micro studies. Finally, when sufficient understanding exists at the micro level, attention is again focused at the macro level as attempts are made to fit all the separate parts into a comprehensive model. At the moment, it seems as if modeling research and development is in its second phase and will be there for some time.

METHODOLOGY

This study was made to determine if final costs and completion dates for research and development contracts correlate reliably with certain administrative details and other factors that are known when the contract is negotiated. There are two possible uses for such a study if a positive outcome is

IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, NOVEMBER 1972

TABLE I INDEPENDENT VARIABLES USED IN COST AND

PERFORMANCE PERIOD REGRESSIONS

Svmboi Description

Cn nth contractor: n = 1 refers to a misceUaneous group of con­tractors with only one contract each in the sample, n - 2, —, 8 each refer to a single contractor. Cn = 1 for the contractor of the particular project and Cn = 0 for all other contractors.

E contractor's prior experience in the field, in months. 5 study effort; S = 1 for mere study efforts; S = 0 for all other

projects. G ground test effort: G = 1 for projects involving ground tests;

G = 0 otherwise. F flight test effort: F=l for projects involving flight tests;

f = 0 otherwise. An nth project branch', n ranges from 1 to 4. An = 1 when the

project is administered by branch Ap\ An = 0 for ali other branches.

K system effort: K - 1 for projects involving entire reentry systems; K = 0 otherwise.

Bn nth contracting officer: Bn = 1 when the project contracting officer is Bn\ Bn - 0 for all other contracting officers.

Tn *>'Ρβ of contract: e.g., cost plus fixed fee, cost plus incentive fee, ?tc. (see Table H) T~ - 1 for a proiect with contract type Tn; Tn = 0 for all other contract types,

/ performance incentives: / = 1 when a project contract includes performance incentives;/ = 0 otherwise.

P originally negotiated period of performance, in months/10. H letter contract: H = 1 for projects under a letter contract;

H = 0 otherwise, D function of the initially negotiated cost (in thousands of

dollars): Dx is the common logarithm of the cost; D2 is the square root of the cost.

L level of effort limitations: L = 1 for projects with level of effort limitations; L = 0 otherwise.

V change of scope anticipated: V - 1 for projects with a significant change of scope anticipated at the outset; V- 0 otherwise.

reached: l ) t o enable* the organization studied to forecast costs and completion dates for its own research and development contracts, and 2) to provide insight at the micro level that may be useful in a more general model of research and develop­ment.

The study was formulated as a hypothesis to be tested, namely that multiple regressions of costs and completion dates as functions of certain independent variables are statistically significant. The dependent variables, i.e., costs and completion dates, were cast in the following forms:

final cost to the Government Yi =

Υτ =

initially negotiated cost

actual performance period initially negotiated performance period

The independent variables were drawn from five categories: 1) the degree of project complexity, 2) the type of contract, 3) the characteristics of the contractor, 4) the key personnel in the procurement section of ABRES, and 5) the project branch. Each category is described below. Altogether, these 5 categories yielded 15 independent variables, which are shown

in Table !. Most of the independent variables were incor­porated into the regressions by the technique of dummy variables.

Five measures of project complexity were developed from data available to ABRES when the contracts were negotiated.

1) Initially negotiated period of performance. (Longer contract periods were believed necessary for more complex projects.)

2) A function of the initially negotiated cost; the cost, its square root, and its log were tried. (More complex projects were believed to cost more, if only to provide for more experiments before reaching success.)

3) Whether hardware was required for ground or flight test or only a study was involved. (Studies were believed to be inherently simpler than projects involving ground or flight testina of hardware.^

4) Whether the entire reentry system was investigated or only a subsystem. (The contracts were initially divided into ten areas being handled by the division. These areas included such specialities as fuzing devices, heat-shield materials, and decoys. This approach did not increase the statistical value of either equation, and this level of subdivision was abandoned.)

5) Whether or not a change of scope was anticipated at the outset. (Three of the 73 contracts had significant changes made during the contract. Although they were not negotiated at the start of the original effort, they were anticipated. Thus anticipated change of scope was used as a separate variable. Two of the three contracts started without flight testing, and the third began with a much smaller test program than the one that finally evolved during the contract.)

Four types of contracts and three qualifying provisions were identified, as shown in Tables Π and ili. The different contract types, Table II, refer to various arrangements for paying for the contractor's direct efforts and his profit or fee. The qualifying provisions, Table III, refer to rewards (or penalties) for extraordinary performance and to special arrangements for undertaking contracts.

For characteristics of the contractor, five items suggested by Roberts [4] were considered: 1) willingness to accept risk, 2) previous experience, 3) integrity, 4) resource limitations, and 5) quality of the organization. Data were available only for the second item, previous experience, and they were used as an independent variable in the regressions in the form of months of previous experience in the field. The other four items could not be handled separately. Instead, they were coalesced for each contractor, along with all other contractor characteristics, by a dummy variable procedure that gave each contractor its own regression coefficient.

Key personnel in the procurement section were treated in a manner similar to the way the contractors were treated. Individual characteristics of the personnel were not available, but the names of the buyers and contracting officers for each contract were. Accordingly, each person was assigned a dummy variable with its own regression coefficient.

The project section of ABRES is divided into four branches. Each branch was assigned a dummy variable with its corresponding regression coefficient.

RUSKIN AND LERNER: FORECASTING COSTS AND COMPLETION DATES 131

TABLE II TYPES OF CONTRACTS

TABLE IV COEFFICIENTS FOR THE COST REGRESSION

Contract Description

Firm fixed price (FFP)

Cost plus fixed fee (CPFF)

Cost plus incentive fee (CPIF)

Cost reimbursable (CR)

A fixed price, including profit, is paid to the contractor to assure fixed responsibility for costs while pro­viding a maximum incentive for effective cost control and contract performance. This type of contract is employed when a determination has been made that uncertainties have been identified and reasonableness of costs applicable thereto can be made.

The contractor is paid a certain fee in addition to cost expenditures. This type of contract is used for studies, research and development, and so forth.

This is a cost-reimbursable contract that provides an adjustment in the fee based on a relationship between total allowable cost and target cost.

and test.

This type of contract provides for payment of allowable costs incurred in performance of the contract. It is used only when performance cannot be estimated to allow using other types of contracts.

•TABLE III QUALIFYING PROVISIONS FOR CONTRACTS

Performance incen- Incentive contracts incorporate rewards or ti ve s penalties if the contractor exceeds or fails to

meet performance criteria.

Letter contract A letter contract is a less formal version of one of the contracts in Table II and it authorizes immediate performance on the part of the contractor. It is used when a definitive contract cannot be issued in sufficient time to meet procurement needs.

Level of effort This type of contract obligates the contractor to deliver a specified level of effort over a stated period of time for a specified amount of money.

RESULTS AND DISCUSSION

The following equation was obtained for contract cost :

Y1 = 1.008-0.236S + 0 . 6 2 5 ^ +0.285P- 0.0045\D2

+ 0.356Λ:+ 2.860 V

where

Yi' final cost to the Government

initially negotiated cost

Coefficient3 Value of the Coefficient Standard Error

of the Coefficient

s

P

k v

-0.236 0.625 0.285

-0.00451 0.356 2.860

0.173 0.136 0.108 0.0020 0.137 0.328

aThese coefficients correspond to the variables in Table I, letter by letter. Thus s is the coefficient for variable S.

TABLE V STATISTICAL CHARACTERISTICS OF THE COST REGRESSION

Multiple correlation coefficient Standard error of the estimate Average value of the dependent variable F-value of the equation Required minimum F-value for 0.01 significance

0.8402 0.522 1.697

26.41 3.09

and S, By, P, D2, K, and V are as defined in Table I. The standard errors of the coefficients are given in Table IV and the statistical characteristics of the regression are given in Table V.

The multiple correlation coefficient for predicting the final cost of a contract is 0.840, which is significant at the 0.99 confidence level. The square of the correlation coefficient for the regression indicates that, on the average, factors correlating with over 70 percent of a contract's cost growth have been identified. These factors are as follows.

l)S: whether the contract is merely a study or involves ground or flight hardware. Studies are less complex efforts than flight or model test programs. With fewer hardware problems and greater ease in revising study efforts, costs for studies do not usually rise as much as they do on the other types.

2)Βγ : the name of the contracting officer. The cost growth for one contracting officer is consistently higher than for all others, which were statistically indistinguishable. It is not known if this is â personal characteristic or some peculiarity of the contracts he managed. The computer runs and the raw data were originally based on the nine buyers. Nearly identical cost growth factors were shown by buyers 1,2, and 3. A check showed that during the period in which their contracts were active, they worked for the same contracting officer. The data were rerun using the contracting officer as the variable (instead of the buyers).

3)P: the initially negotiated period of performance (in months/10). This is apparently a measure of the complexity of the program, as had been hypothesized.

4 ) D 2 : the square root of the initially negotiated cost (in thousands of dollars). The project managers seem not to allow their large contracts to grow as much, percentagewise, as their small ones. Budget limitations and resistance to large absolute cost changes are among the restraining factors. Contrary to our

IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, NOVEMBER 1972

TABLE VI COEFFICIENTS FOR THE PERFORMANCE PERIOD REGRESSION

Standard Error Coefficient3 Value of the Coefficient of the Coefficient

/ 0.457 0.252 / -0.507 0.258 p -0.522 0.115 Jl 0.370 0.128 j 4 0.295 0.184 r 0.402 0.339

aThese coefficients correspond to the variables shown in Table I, letter by letter. Thus / is the coefficient for variable L.

TABLE VII STATISTICAL CHARACTERISTICS OF THE PERFORMANCE

PERIOD REGRESSION

Multiple correlation coefficient 0.576 Standard error of the estimate 0.546 Average value of the dependent variable 1.644 F-value of the equation 5.467 Required minimum F-value for 0.01 significance 3.09

original belief, an increase in initial cost does not lead to an increase in cost growth even though it may reflect project complexity.

5) A': whether or not an entire reentry system is being considered. The cost growth is greater when the project involves an entire reentry system.

6) V: whether or not a significant change in the scope of the effort is anticipated. The anticipated changes are over and above unplanned changes; accordingly, their presence greatly affects the cost growth.

The equation obtained for growth in completion time or performance period is

Y2 = 1.280 + 0.457/ - 0.507L - 0.522P + 0.370£>! + 0.295Λ 4 +0.402 V

where

_ final performance period 2 initially negotiated performance period ^

and /, L, P9 Dl9 A4, and F are as defined in Table I. The standard errors of the coefficients are given in Table VI and the statistical characteristics of the performance period re­gression are given in Table VIL

The multiple correlation coefficient for predicting the performance period is 0.567, which is significant at the 0.99 confidence level. Again, the square öf the correlation coeffi­cient indicates the extent to which the factors correlating with the dependent variable have been identified. Thus factors correlating with about a third of the growth in time to complete a contract have been identified. They are as follows.

1)7: whether or not performance incentives are involved. When incentives exist, the contractor may attempt to increase

his chance of earning additional fees for a high-quality product.2 If possible, the contractor may seek additional performance time to help him attain the higher level of achievement.

2)L: whether or not there are "level of effort" limitations. More accurate planning of the effort seems to be possible when there is a specific limitation on the manpower for the contract.

3) P: the initially negotiated period of performance. There seems to be a greater degree of overoptimism about perfor­mance periods for the shorter perhaps simpler efforts than for the longer efforts.

4)Di', the common logarithm of the initially negotiated cost (in thousands of dollars). The performance period grows relatively more oh larger efforts.

5 ) ^ 4 : the name of the project branch. Much of the effort of the project branch designated A4 involves testing products developed under contracts handled by other branches. This branch's ability to meet schedules is hampered by delays in the other branches.

6) V: whether or not a significant change in scope of the effort is anticipated. Anticipated changes are over and above unplanned changes; accordingly, their presence affects growth in the performance period. However, the effect on growth in the performance period is markedly less than the effect on cost growth.

Individual primary cost growth factors show a statistical significance of better than 0.975, while individual primary factors for growth in completion time are barely significant at the 0„95 level. This leads to the conclusion that cost growth seems more consistent than growth in time to complete a contract.

CONCLUSIONS

Many administrative factors that are known at the start of the research and development programs correlate with the cost and time to complete a contract. For the organization studied, there is sufficient consistency in the cost factors to make reasonably accurate forecasts of the final cost, as had been hypothesized. The consistency of factors correlating with the completion date is sufficient for the regression to be used as a directional guide, but caution should be used in its application.

The results obtained here apply in detail to only one fairly small division in the Air Force. This approach is of general applicability, however, and similar analyses can be made in other sectors of the military or in commercial operations. If the approach is widely applied, a general pattern of relation­ships between administrative details and contract progress may emerge. Such a pattern would materially assist the formulation of a general model of research and development contracts.

This study takes account only of items that are available when a contract is negotiated. There are clearly other factors, administrative and otherwise, that appear or occur after the

2 A high-quality performance not only increases a contractor's profit, but it also places him in an advantageous position for future work.

IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. EM-19, NO. 4, NOVEMBER 1972 133

project is underway that influence project progress. An understanding of their impact is also required to formulate a comprehensive model of research and development contracts.

REFERENCES [1] B. V. Dean, S.J.Mantel, Jr., and L. A Roepcke, "Research

project cost distributions and budget forecasting," IEEE Trans. Eng. Manag. [Special Issue: Selected Papers by the Working Group on Research Management, 22nd Military Operations Research Symp. (MORS)/, vol. EM-16, pp. 176 -189, Nov. 1969.

[2J R. D. Doering, "Model for predicting risk in scheduling proposed R&D tasks," IEEE Trans. Eng. Manag., vol. EM-17, pp. 80-92, Aug. 1970.

[3] R.L.Perry et al, System Acquisition Experience, The RAND Corp., Santa Monica, Calif., RM-6072-PR, Nov. 1969.

[4] E. B. Roberts, The Dynamic of Research and Development. New York: Harper & Row, 1964.

[5 ] W. É. Souder, "The validity of subjective probability of success forecasts by R&D project managers," IEEE Trans. Eng. Manag., voL EM-16, pp. 35 - 49, Feb. 1969.

[6] — , experiences with an R&D project control model," IEEE Trans. Eng Manag., voL EM-15, pp. 39 - 49, Mar. 1968.

Allocation of Funds to R&D Laboratories by a Central Agency

P. S. NAGPAUL AND T. R. VASUDEVA

Abstract-The problem of allocating funds to a number of R&D laboratories under the control of a federal or central agency is considered, which is a two-stage decision-making process involving the optimal selection of projects within laboratories and allocation of budgets to the laboratories by the federal organization. An analytical method, based upon the concept of "equitable dissatisfaction," is presented for solving the problem. A numerical example is given to illustrate the application of the model.

INTRODUCTION

JT^ ENERALLY, the organization of government research %̂ ?and development (R&D) is federal in character. For example, in India, there are central organizations like the Council of Scientific and industrial Research (CSIR), Indian Council of Medical Research (ICMR), Indian Council of Agricultural Research (ICAR), etc., which finance and coordinate the activities of a number of research laboratories under their control. Similar patterns of organization of government R&D also exist in many other countries, e.g., U.K., Canada, Pakistan, etc. A central problem in these organizations is the allocation of resources to various con­stituent laboratories. This is usually done in an arbitrary ιιιαιΐ|ΐνι. i i i v v o u i i i a i v d υ ι Viviiiaxivi iiv/111 vaxivita ι α ι / υ ι ο ι , υ π ν ο ,

which may sometimes be exaggerated, are added together, and if the sum exceeds the overall budget available with the federal agency (which usually happens), arbitrary cuts are made. Evidently, the procedure is neither optimal nor rational, and it may lead to certain imbalances. Essentially, the problem involves decision making at two levels, viz., selection of

Manuscript received September 8, 1971; revised March 15, 1972. The authors are with thé Central Electronics Engineering Research

Institute, Pilani, India.

projects within the laboratories and allocation of funds to the laboratories for carrying out the projects.

The problem of R&D project selection and resource alloca­tion is well documented in literature [1], [2]. A number of models of varying degrees of sophistication ranging from Newton - Mottley's scheme of ranking projects according to a set of evaluative criteria to linear and dynamic programming have been proposed, but they pertain to decision making at one level, namely, within the laboratory. If we merge the two levels into one and make decisions at the federal level, we can straightaway apply these techniques, e.g., linear programming. We may thus choose an overall optimal set of projects, but the ol1rvr»otir\« r\Ç fiir»rlc ΤΤΙΟΛ/ t-»r\f K a Λ / Ί Ι Ι re may be situa­tions where some laboratories, which have more promising projects, may get a large share of money, while some others may be virtually starved of funds. The laboratories have to maintain a certain level of staff and other resources, and any allocation system which does not ensure this will not be feasible. We may introduce additional constraints in the linear programming model to ensure certain minimum levels of expenditure within the laboratories, but these will bring in an element of arbitrariness.

In this paper we would describe an allocation model based on the concept of "equitable dissatisfaction."

PROJECT CHARACTERISTICS

An i\otE) project consumes resources—manpower, equip­ment, materials, etc., widen can uC equateu to money, it aiso leads to certain benefits or has a certain value; otherwise there would be no raison d'être for undertaking the project. Both consumption of resources and benefits depend upon the time