Financial planning for new products

61

Financial Planning for New Products

Financial Planning for New Products Part I-Basis

for Model Formulation*

America Albalq

In a previous paper the author presented a staged approach methodology for the evaluation and selection of R & D projects. The present paper is intended to serve as its complement. The aim is the formulation of a matching overall long range budget planning policy which will attempt to answer the questions of “how many projects to approve” and “how much to fund” on the basis of satisfying the company growth objective. Jo achieve the above purpose, a staged budget planning model has been developed which recognizes and utilizes the specific attributes of the various R 8 D stages. The project stage survival rate and project stage cost ratio concepts are discussed. A Ste8dy flow of projects through the various R & D stages is maintained. The aim of the program is to produce periodically a planned number of new products for commercial investment. This methodology me y apply to R & D programs with a sufficient number of moderately sized projects that justify the use of survival rate and cost ratio values. It should be construed as a tentative effort for possible use within the staged limitations.

Part I of this paper presents the basis for the formulation of a budget planning model. Part II is devoted to the presentation and discussion of the model itself.

Introduction Long term plans in growth-oriented companies are based on a product strategy which demands the ouput of a continuous stream of new products2$. To achieve this end, new ideas and projects must be generated and, in turn, evaluated, selected and budgeted. R & D project evaluation and selection has been the subject of extensive attention by researcher@ and

‘This article is based on the author’s working paper ‘Stage Approach for Developing A Budgeting Policy for R 8 0 Projects’, Technion Operations Research, Statistics and Economics Mimeograph Series No. 144, Technion, Haifa, Israel, January 1974. tThe Author is on the staff of the Israel Institute of Technology, Technion.

numerous models have been proposed for this purpose (see surveys mentioned in Ref. 2). Models on R & D budgeting decisions are much less abundant as can be observed from above surveys. Bobis’ has advanced the opinion that the allocation of funds represents a more complicated problem than selection methods. This may explain the scarcity of such models. The budgeting problem has been defined by Dean and Senguptal” and Ackoff’ as pertaining to allocation decisions on (a) overall R 8r D budget, (b) categories of projects and (c) individual projects. Rosen and Soude?* propose a similar breakdown, but they do not recognize the intermediate group (b). In essence, the question is related to the determination of budgeting levels resulting from a common budgeting policy. These levels represent interrelated phases originating in the project evaluation-selection decision. Project fund allocation decisions start mostly from the assumption of a ‘given’ total R & D budget. The budget-in turn-has been determined by judgment as a function of some relevant company economic parameters (profits, sales) or is based on the quantity of human resources available. Table 1 below shows a list of criteria currently employed for determining the overall R & D budget. The interested reader is referred to sources given for further details. Table 1. Criteria for Determination Budgets Criteria 1. Per cent of sales 2. Profitability measures Per cent of profits Projected rate of return 3. Growth rate standards 4. Industry wide averages or competitors expenditures 5. Personnel availability 6. Others

of Overall R & D

References 23, 26, 32, 39 26 33 33.39 39 33 39 16, 31, 32

62

Long Range Planning Vol. 10

August 1977 on the postulate that decisions throughout the R s( D process are of a sequential nature17*‘8*2e~zB.A consequence of this sequential characteristic is the ability to distinguish decision points at the completion of defined stages.

It may be observed from the above list that overall R & D budgets are the product of measures extracted from some supposedly relevant factors. The present paper is intended to be a contribution on the development of tools for improving the R & D budget planning decision process. The proposed model correlates the budget planning decision with the new product strategy referred to above. Part I of this paper formulates the basis of the model, while Part II presents the budget planning model.

The methodology was based on an approach that utilized the specific attributes of each project stage. This approach conduced to the selection of differentiated evaiuation techniques for each stage on the basis of‘the tool must fit the need’, as opposed to the use of a single model for the evaluation of the entire project.

Brief Review of-Some B&feting Models

A suitable budget planning policy must accompany the above evaluation and selection approach. The staged approach is now applied to develop a budget planning system that will allow the determination of a long term R & D policy to satisfy company growth objectives.

Attempting to answer the budgeting questions, Dean and Senguptall developed a computerized budgeting model which measures the fum’s performance by a relationship which combines cumulated product research, process research, production cost and sales, and administrative and general expenses.

This system will be based on assigning resources to the project stages as the basic budgeting units, instead of the individual project as proposed in the conventional approach.

Hess=, in turn, proposed to solve the budgeting problem as a sequential decision process. For this purpose, dynamic programming techniques were employed and a general model was pre ared assuming that there is no budget restraint. A mo Bel including this restraint was also discussed.

Table 2, below, resents the sequence of R & D budgeting levels Por both the conventional and the proposed staged budget planning approach.

Rosen and Soude?* redeveloped Hess’s approach and proposed a mathematical model combining project selection, resource allocation and budget determinations problems. Sa&a.?5 proposed a model which unites project search, pordolio generation and portfolio evaluation. The author calls it ‘a practical guide to management’. Freema@ presents a model directed to the determination of the optimal amount to be allocated for research on the basis of profit maximization criteria. On the other hand, Ash& proposed to optimize return to the company resulting from the utilization of a scarce resource such as manpower. It may be assumed that the manpower constraint may serve as a basis for overall R & D budget determination. A similar approach is proposed by Allen’.

The Project Stages

A summary description of project stages encountered in the chemical industries is presented below as a reference. Stage L-Exploratory Sco e of work: Preliminary

market surveys, process and Por product identification, raw material availability, other technical surveys, preliminary laboratory research, preliminary economic evaluation. .

Stage 2-Applied Research Scope of work: Laboratory

and bench-scale research directed to define technological characteristics of new process or product: market and economic studies. Stage 3-Development

A Staged Approach for R x D Budget Planning

The application of a new product strategy implies that new product planning and development is an essential part of the overall long range planning process of the company1a*1s*24, p. 136).

Scope of work: Pilot scale work, determination of operating and engineering design parameters, determination of detailed investment and operating costs, market testing, etc. In general, all data required for the preparation of a full-scale project feasibility study.

in a previous pape?, the author proposed a methodology for R & D project evaluation and selection based

The results of Stage 3-Development serve to complete the information needed for deciding upon the

Table 2. Sequentiality of R & D Budgeting levels

Budget unit

Conventional budgeting Project

Stage-Approach project sta$0

budget planning

Budget level sequence : First Second Third

Overall R & D budget Project category budgets individual project budgets

Project stage budgets Composite stage budgets Overall (constrained) R Et D budget

Financial Planning for New Products commercial investment. This is a ‘point-of-no-return’ decision as it involves the allocation of large sums of money necessary to meet engineering design, equipment, erection and start-up costs, and product commercialization. The staged approach for R & D evaluation and selection requires the project to be monitored at the completion of each stage. At all inter-stage points, a ‘go-no go’ decision is made regarding the succeeding stage. Some projects are ‘killed’ while others ‘survive’ and continue toward the next stage. The need for this approach has been stressed by Goalwin20, who insists that the project be authorized ‘ . . . as far ahead as we can reasonably see. When and if one phase is successful, the next phase is funded’. The proposed evaluation methodology attempts to satisfy this demand. Some specific characteristics can be seen in each R & D stage, and this observation suggests the possibility of an individualized analysis. In particular, differentiation becomes evident with regard to cost, time needed for completion, and the degree of risk and uncertainty characterizing the stage, as can be observed from the trends shown in Figure 1.

63

Project Mortality in New Product Development

Many ideas die as a consequence of the sequential R & D screening decisions. Serious project mortality occurs as shown in the corresponding reject survival curve of Figure 1. This curve is siJ * ar in shape to elsemortality (or survival) curves reported where 14*25.37.3E.The common characteristic is the assumption that project survival is best represented by curvilinear regressions, probably of the exponential type. Mortality is highest at the beginning of the process when the degree of work and uncertainty is at its greatest, and decreases as the amount of knowledge increases. A number of reports quantitatively confirm the severity of project mortality in various industries. Table 3 is a summarized presentation of available data on project survival rates. The most extreme case is found in the pharmaceutical industry where mortality in product testing was reported to be 97 per cent. Basesfor Developing a New Product Budget Planning Policy The Project Stage Survival Rate Concept. Characteristic

project mortality curves are the results of the R 81 D screening policies employed. These policies are manifested in different mortality curves, as illustrated in Figure 2, which shows three characteristic curves.

(c) Cumulative R and D COstsof Successful Project

id) Cumulative Commercial Investment Costs of SuCcessful

Risk and Uncertainty

Time, Months

Construction and _-& R and D Stages

Start-Up Mkt. Testing

Implementation Stages

L

Figure 1. Curves showing relevant trend relationships during the project R & D and implementation stages: (a) No. of surviving projects through R & D stages to allow one successful project for implementation; (b) Degree of risk and uncertainty of surviving projects; (c) Cumulative R & D costs of successful project; (d) Cumulative investment costs of successful project.

64

Long Range Planning Vol. 10

August 1977

Table 3. Survey of New Product Survival Rates in Various Business Fields New Product Survival Rate Business Field

Reference 4

As per reference

Pharmaceutical

Remarks

Percent

40 in 115000

0.3

From a Ph. M.A. 1958 survey. Number of new chemical entities resulting from number of compounds, beers and extracts tested.

5

Drugs

1 in 3000

0.03

As cited from J. R. Vane. Estimated success rate in clinical trials.

8

Supermarket

20%

20

As cited from T. L. Angelus. New Products meeting sales goals from a total of 9450 new products introduced.

2-3 in 100

2-3

Assumed figure.

-

1 in 32 1 in 243

Suggested ‘rule of thumb survival rate. (doubling or tripling rule throughout various R & D stages).

24. p. 135

Overall American Industry

1 in 500

5 0.7 > 0.2

14. p. 138

-

1 in 250

0.2

Fmm a mortality curve previously reported by the same author (Foster) in 1969.

Packaged foods

89% failure

11

From report bv Lippincot and Margulis.

3in 10

70

From a 1959 survey by Natural Industrial Conference Board. Major new products launched within last 5 years that failed to meet expectations.

1 in 540

o-2

From report by Commercial Chemical Development Assoc. Survfval of new product ideas from 20 chemical companies during 1965.

8 8 8

24. p. 137 18

From a curve by L Dooley 9 A. Ciapolla.

25

Chemicals

28

Chemicals

1 in20

5

Successful commercial products in Exploratory programs at DuPont.

30

Shipbuilding

4inl40

3

Number of fuQ development projects in relation to numbers of ideas received. As cited from S. Goodman.

36

1 in60

1.5

36

1 in20

5

37

Pharmaceuticals

1 in 6000

37

Pharmaceuticals

28 in 16800

o-01 5 0.17

37

Various

1 in40

2.5

17 in 2100

27,37

0.08

As reported by Ph.MA. As reported by Chas. Pfizer El Co. Marketable drugs resulting from testing number of substances. As shown in mortality curve by Boor-Allen and Hamilton showing decay of new product ideas (80 companies). As reported bv J. H. Whitney Et Co.

.

cuy3

2c - Lax screening

Curve 2b 2a Ear& Sued3nil _ Policy i~~~s~g.

l+-SBBo2

Figure 2 Typical Project Mortality

Curves.

Pofii

compromise Screening

Policy

Financial Planning for New Products p0 = No of original projects (ideas)

Curve 2a represents the case of a very severe screening policy being applied at the beginning of the R & D process. The aim of this policy is to approve only ‘safe’ projects and ‘kill’ all others before they consume significant resources. The final outcome may be, however, the sacrifice of many otherwise promising projects.

P1 = No of projects surviving for Stage 1 pz = No of projects surviving for Stage 2 Pa = No of projects surviving for Stage 3 P, = No of projects desired for commercial investment implementation and

On the other hand, curve 2c indicates a lax screening policy for Stages 1 and 2, with valuable resources being wasted by prolonging the life of poor-risk projects.

rrio = PI/PO= PSSR at pre-Stage 1 decision point rail = Pa/P, = PSSR at decision point between Stages 1 and 2

A compromise policy is represented by curve 2b, which allows a ‘middle-of-the-road’ approach, where a balance is obtained between the concepts of efficient resource management and creative generation of ideas.

r3t2

P() =

PSSR =

pi hl,oh,,,

rif3

Some published information suggests that project stage costs are progressively more expensive25 and that the shape of the corresponding cost curve, as shown in Figure 1, may be of the exponential type. For example, Hitchock suggests that the ‘cost per project is in the Screening

P2

Stage 3 Projects

PSSR =

P,=pi

pi h7-1j h3/2 hi/3

t 5-P

P3

omm.lnv creening

PSSR =

Projects Approved for Comm. Invest.

P,

h i/3

P3pEih3/2

b3

hV3

t

7 F-p2

7 P,;?

pz- P3

t

/ Unsuccessful

Figure 3. Diagram showing stage-by-stage

rif3

p3 = 5 hf3

h 3,*

_p,= h3j2hi;3

tjf2

rif3

The Project Stage Cost Ratio Concept. The ratio between the project budget cost at a given stage and that of the previous stage shall be used as the second element for setting the budget based on a staged approach. This value will be termed the project stage cost ratio (PSCR).

h '10

Ox

r3f2

Stage 2 Projects

Pl

pz = A-

hf2

The project survival concept is graphically represented in the three-stage screening diagram presented in Figure 3, where various decisions points (screening points) are noted.

*A generalized approach for a n-stage condition will be developed in Part II of this paper.

PO

r2fl

pi r2fl

to represent PSSR.

Stage 1 Projects

pi u.

p1 =

Conversely, a company characterized by the opposite conditions may be prone to adopt a more relaxed PSSR policy by keeping a larger proportion of the original ideas alive through the various screening barriers in the hope that some of them may prove successful.

Initial Ideas

Pa/P2 = PSSR at decision point between Stages 2 and 3

For convenience, PO,PiI P2 will be expressed as functions of the number of successful projects desired for commercial investment. It follows that:

PSSR is the result of the particular conditions and policies governing the R & D process. An R & D oriented company enjoying a large reservoir of new product ideas competing against a limited R 81 D budget, may embark on a low PSSR policy by tightening the screening devices. Thus, the chances of final success for the few remaining projects are increased.

Project Stage

=

ri/3 = Iz/& = PSSR at decision point between Stage 3 and commercial investment

The shape of the project mortality curve is determined by the project stage survival rate (PSSR), defmed as the fraction of projects remaining alive at the inter-stage screening point relative to those surviving for the precedent stage, or the Project Survival Function as proposed by Edge et ~1.‘~. PSSR is the first element for setting the R & D budget based on a stage approach.

Simple equations can be developed In a three-stage process*, let

65

Projects

screening accordin, u to the various project stage survival rates selected.

66

Long Range Planning Vol. 10

August 1977

rations of 1, 10, and 100 respectively, in each phase’. Cranston9 and Millne?O give figures in the same ratios. It is probable that the ratios depend on the technological nature of the R & D project. For example, in a threestage process, R3j2 (defined below) may be lower for some chemical projects requiring conventional pilot scale distillation towers than for advanced aircraft projects involving the development and testing of highly sophisticated and expensive prototypes. Let c, = average of the various project budget costs for Stage 1 C, = average of the various project

The simplifications are described below. (a) Assume that r1,o, r,/, and r3/2 are functions of each other. Make similar assumption for R,jl and R312. If the data allows, further simplification achieved with the following assumption

R 3/z =

r,l, = rzll = r,~, = r,:, = r and R2il = R3i2 = R. Referring the calculations to a ‘per unit of successful project’ basis, i.e., Pi = 1, the P values shown previously become

budget costs for

G/C2

be

(b) Make

po = !.-; PI = $; p2 = f; p3 =E1 r

Stage 2 c, = average of the various project budget costs for Stage 3 R 2/l = C2/G

may

Realistic values for r and R should be derived from an examination of actual company objectives, screening policies, and R & D budget and project reservoir limitations.

ASimplijedApproachfortheApplicationofPSSRandPSCR

For example, as said, a generous R & D budget and a lax screening policy would point to high J values, while a large inventory of new product ideas would allow the application of strict screening policy (low r values).

The appiication of PSSR and PSCR may be facilitated by making certain simplifications, provided the number of tojects is suf5ciently large and assuming the vali s,‘ty of the approximately exponential shape of the project survival and cost curves as shown in Figure 1.

It can be argued that a higher T value is justified for the interstage Development-Commercial implementation screening point than for the previous interstage points. The work carried out in the Development Stage differs

R 2,1 and hi1 become, thus, the PSCR relative Stage 2 over Stage 1 and Stage 3 over Stage 2.

to

Project Stage Survival Rates

.6 E$stagel+-s

Figure 4. Project Survival Trends Through

12

18 Time, months tage 21-_tage

the Various R & D Stages.

24 3

30

Financial

Planning

conceptually from the tasks accomplished during previous stages. The Development Stage emphasizes engineering questions which are normally of a conventional nature, while exploratory and research work always involves a much higher degree of risk and uncertainty. Hitchcockz2 considers that ‘as the research project crosses the barrier into development . . . the risk of chances of success are deemed at least eq,ual to, and probably better than, the chances of failure.

for New Products

Number

of Original

No. of Projects

67

Ideas

SurvkJing

of Projects

Surviving

In turn, the election of a realistic value for R may be based on past project cost history of a s&ciently large number of previous projects. The r and R values selected shall be used as guidelines to orientate the ‘go-no go’ decisions and the approval of project stage budgets at the various screening points. Project stage evaluation and selection methods (see Ref. 2) shall be applied in a manner such that the selection decision will be directed to sustain ‘on the average’ the selected values of r and R.

$4030 -

‘$ 0.25

If the lack of data correlation does not allow the election of meaningful single r and R values, it is preferable to abandon the simplified approach and use independent values for each one of the various stages. Graphical Illustratior~ of the Application of the PSSR and PSCR Concepts. Several graphs have been prepared to illustrate the application of PSSR and PSCR to a three-stage R & D process. The unit simplified approach has been employed in the preparations of the graphsi.e., (a) All values refer to P* = 1 (completion of one project for commercial implementation and (b) single values have been used for r and R, respectively, in a given curve for the three R & D Stages. Figure 4 presents a family of PSSR curves picturing the number of surviving projects at each stage. The r values range from 0.25 to O-50. Stage duration times have been assumed to progress geometrically (see Figure 1) from stage to stage for an assumed total planned project time of 30 months.

/ 0.35

0.30 Project

0.40

Stage Survival

c.45

L 0.50

Rates

Figure 5. Unit program cuves showing number of projects surviving for each stage as a function of the project stage survival rates. The PSCR values vary from 1 to 10. Conversely, Figure 7 presents a family of PSCR cuves showing the respective cumulative project costs as the project advances through the R & D process. This article represents Part I of a two-part paper concerned with the application of the staged approach for formulating a policy and methodology for long term new product budget planning. Part I refers to the bases for the formulation of a budget planning model. Part II, in turn, to be presented subsequently, shall propose and discuss the model itself.

A variation of Figure 4 is Figure 5 where the curves depict the number of surviving projects at each stage plotted against the various PSSR. Attention is drawn to the number of original ideas needed for r = 0.25. The resulting value is 288 (not shown in the curve), per single project desired for commercial investment. However, as previously discussed, a higher r may be used at the post-Stage 3 screening point, and this will substantially reduce the number of required new ideas. For example, if rIlo = r,,, = r,iz = O-25 and if rij3 = O-5, the above number is decreased to 144 (still a very high figure indeed!). Figures 6 and 7, in turn, illustrate the application of PSCR-also presented in simplified fashion. The values shown refer to c1 = 1. Figure 6 presents a family of project stage cost curves drawn as a function of PSCR.

Project

Stage

Cost

Ratios

Figure 6. Project stage costs as a function stage cost ratios for a value of c, = 1.

of project

68

Long Range Planning Vol. 10

August 1977

Time, ~~~Jlf-stage

~-i-_stage

Months

2_1_tage

3

Figure 7. Cumulative project costs through the various R & D stages for various PSCR for a value of c, = 1.

The objective is to offer a matching budget plamring model as a culmination of the R & D process comprising project search, evaluation, selection and fund allocation. The R & D planning function is conceived as a part of the entire company long range planning approach. For growth-oriented companies, long term plans are the result of a product strategy based on a continuous output of new products. This paper uses this strategy for developing a fitting budget planning model. To satisfy this strategy the proposed budget planning methodology recognizes and utilizes the specific attributes of the various R & D stages. The project stage survival rate and project stage cost ratio concepts are proposed and utilized. A number of corresponding curves are presented to illustrate use for future applications.

(2) A. Albala. Stage Approach for the Evaluation and Selection of R Er D Management

Projects, IEEE Transactions on Engineering EM-22, (4). 153-164, November (1975).

(3)

0. H. Allen, Optical Selection of a Research Project Portfolio Under Uncertainty. The Chemical Engineer, 238 CE27B-284 (1970).

(4)

D. T. Asher, A Linear Programming Model for the Allocation of R 8 D Efforts, IRE Transactions on Engineering Manegement, EM-3, (4), 154-157, December (1962).

(5)

Baines et a/.. Research in the Chemical Industry. Elsevier, Amsterdam, p. 83 (1969).

(6)

A. G. Beged Dov, Optimal Assignment of Research and Development Projects in a Large Company Using an Integer Programming Model, IEEE Transactions on Engineering Management, EM-12, (4), 13B-142, December (1965).

(7)

A. H. Bobis, T. F. Cooke and J. H. Paden, A Funds Allocation Method to Improve the Odds for Research Success. Research Mun8gement, 14.34-39 (1971).

(B) H. Bogaty, Development of New Consumer Products-Ways to improve Your Chances of Success, Research Management. 17,26-30 (1974).

Part II of this paper shall employ these concepts for developing the budget planning model.

(9)

The proposed methodology may be adequate for new product programs comprising a sufficiently large reservoir of projects.

R. W. Cranston, First Experiences with a Ranking Method for Portfolio Selection in Applied Research, IEEE Transactions on Engineering Management, EM-21 , (4). (14B-152) November (1974).

(10)

B. V. Dean and S. Sengupta, On a Method for Determining Corporate Research Development Budgets, in Managament Sciences, Models. and Techniques, 2, 210-225. ed. C. W. Churchman and M. Verhulot, Pergamon Press (1960).

(11)

B. V. Dean and S. S. Sengupta, Research Budgeting and Project Selection, IEEE Transactions on Engineering Management, EM-a, (4). 15&l 69, December (1962).

(12)

J. H. Dessauer. Some Thoughts on the Allocation of Resources to Research and Development Opportunities, Research Management, 10, 77-89 (1967).

References (1)

Ft. L. Ackoff, Specialized Versus Generalized Models in Research Budgeting, in The Science of *lanagina Organized Technology, ed. M.Cetron and J. Goldhar. Goid& &&each, NY. pp. 1371-i 384 (1970).

Financial Planning for New Products (13)

H. A. Edge, F. C. Roesler and E. Sherlock, The Survival Probability Function : A Useful Concept in the Evaluation of Innovation Projects, R B D Management, 2, 91-96 (1972).

(14)

D. W. Foster, Planning for Products and Markets. Longman Group, London, p. 138 (1972).

(15)

R. J. Freeman, A Stochastic Model for Determining the Size and Allocation of the Research Budget, IRE Transactions on Engineering Manegement, EM-7, (1). 2-7, March (1960).

(16)

(17)

(18)

(19)

(20)

(21)

(22)

(23)

(24)

J. R. Freeman, A Survey of the Current Status of Accounting in the Control of R b D, IRE Transactions on Engineering Man8gement. EM -8, 179-l 81, December (1962). A. E. Gear, A Review of Some Recent Developments in Portfolio Modelling in Applied Research and Development, IEEE TIanS8CtiOnS on Engineering Manegement, EM -21, (4). 119-l 25, November (1974). A. E. Gear and A. G. Lockett, A Dynamic Model of Some Multistage Aspects of Research and Development Portfolios, IEEE Tt8nS8CtiOnS on Engineering Management, EM-20, (1). 22-29, February (1973). G. T. Gmitter, Towards a Better Understanding of Industrial R Er D and Cost Control, in The Science of Managing Organized Technology, eds M. Cetron and J. Goldhar. Gordon Et Breach, N.Y., (1970). pp. 1385-l 394. R. T. Goalwin, Comments on Performance Control in Government R b D Projects: The Measurable Effects of Performing Required Management and Engineering Techniques, IEEE Trensactions on Engineering Management. EM-M, (l), 93, February (1973). S. W. Hess, A Dynamic Programming Approach to R & D Budgeting and Project Selection, IRE Tt8nS8CtiOnS on Engineering M8n8gement. EM-g, (4). 170-l 79, December (1962). L. 8. Hitchcock, Selection and Evaluation of R Et D ProjectsPart 1, R8seatch Management, 11,231-244 (1968). Regression Models for Company Expenditures on and Returns from Research and Development, IRE Ttana8CdOnS on Engineering Manegement, EM-7, (1). 8-13, March (1960). D. E. Hussay, Corporate Pergemon, Oxford (1974).

Planning,

Theory and Pmctics.

69

(26)

R. W. Jones, Management of New Products, Journal Industrial Engineering, IX, 429435 (1958).

(26)

D. M. Kiefer, Winds of Change in industrial Chemical Research, Chemical Engineering News, 42, 89-l 09 (1964).

(27)

Ph. Kotler, Marketing Management. Analysis, Planning and Control. Prentice-Hall of India, p. 318 (1969).

(28)

A. G. Locket and A. E. Gear, Representation and Analysis of Multi-Stage Problems in R 8 D, Management Science, 19, 947-960 (1973).

(29)

0. G. Locket and A. E. Gear, Multistage Capital Budgeting under Uncertainty, Journal of Financial and Quantitative Analysis, 21-35, March (1975).

(30)

C. G. Millner, Innovation in Shipbuilding, R & D Manegsment, 2, 41-51 (1971).

(31)

A. L. Minkes and J. M. Samuels, Allocation of Research and Development Expenditure in the Firm, Journal of Management Studies, 3, 62-72 (1966).

132) .

B. N;islund and B. Sellstedt, An Evaluation of Some Methods for Determining the R Et D Budget, IEEE Ttanssctions on Engineering Msnagement, EM-21, (1). 24-29, February (1974).

(33)

J. B. Quinn, Budgeting for Research, in Hsndbook of tndustriel Research Msnsgement. Reinhold, N.Y., pp. 281-313 (1968).

(34)

E. M. Rosen and W. E. Souder, A Method for Allocation R Et D Expenditure, IEEE Tt8nsactions on Engineering Management. EM-12, (3). 87-93, September (1965).

(35)

R. C. Salazar and S. K. Sen, A Simulation Model of Capital Budgeting under Uncertainty, Management science. 16, B-161-B-179 (1968).

(36)

8. Sellsted and B. Naslund, Product Development Operations Research Quarterly, 23, 497-510 (1972).

(37)

G. Steiner, Top Management pp. 555-582.

(38)

J. C. Van Horne, The Analysis of Uncertainty Resolution in Capital Budgeting for New Products, Management Science, 16, B-376-B-386 (1969).

(39)

A. S. West, How to Budget R 6 D Expenses, Ch8miCSl Engineering Progress, 66, 13-l 7 (1970).

Plenning. MacMillan,

of

Plans, London,