R& D ‘ program m es-co m petenci es’ matrix: anal yz i n g R&D expertise within the firm

Thomas Durand

Ecole Centrale Paris, Grande Voie des Vignes, 92295 Chatena y Malabry Cedex, France.

Abstract

The author suggests that the systematic identification of a firm‘s technologies opens up the possibility of evaluating, protecting, optim- izing, enriching and exploiting these technolo- gies to the full. A firm‘s technologies fall into two parts: those embodied and exploited in current operations and those evolving from the collective competencies of its R&D staff. The author’s aim is to evaluate and describe the firm‘s R&D competencies.

The identification of R&D competencies proceeds in three steps: tracing the background of the current and past programmes, construct- ing a programmes/competencies matrix, and deriving an expertise profile. The construction of the matrix is the key activity and requires much care in its execution, if competencies are to be properly defined and truly existing, and if it is to be exhaustive. The author sketches a procedure for constructing a matrix and discusses problems of implementation and draws attention to some of its limitations.

The author claims that such an inventory of expertise could improve the efficiency of the use of the R&D resource, identify a laboratory’s strengths and weaknesses, direct R&D into hitherto neglected channels, assist individuals to identify and evaluate their own expertise, justify obtaining funding for building expertise in shortage areas. The method should be considered as a starting point to formulate appropriate strategies to gain access to tech- nology.

I. INTRODUCTION

R&D programmes conducted within the firm have historically drawn much attention from management researchers.

The major issues addressed have dealt with improving research efficiency, evaluat-

R&D Management 18,2,1988

ing programmes, organizing and planning for R&D, etc. More recently the manage- ment of technology has become a field of its own within strategic management.

Along these lines, some researchers have advocated systematically identifying the many technologies embodied within the organization. Indeed, based on this inform- ation, it is then possible to evaluate, protect, optimize, enrich and exploit the firm’s technology portfolios (Morin, 1985). Consult- ants have suggested distinguishing between three categories of technology (key, pacing, basic). They have also constructed matrices to represent technology portfolios. Similarly, symbolic representations of the firm’s techno- logical assets have been put forward either as ‘technological bunches’ (SEST, 1983) or as Japanese Bonzai trees (Giget, 1984). See Figure 1. These different pieces of work might be regarded as ‘technecentred’. They have each emphasized the strategic value of technology as the basic resource of the firm. They all suggest a systematic exploitation of what they regard as an unsuspected gold mine. These approaches are worthwhile; very little is said, however, on how one should go about evaluating and exploiting that mine.

Introducing technology as a strategic resource requires some knowledge of the technological expertise which actually exists in the firm, but identifying and evaluating the technologies turns out to be quite difficult in practice. Concepts and techniques have been put forward in order to do so. Porter’s (1985) added value chain is a good way to characterize the many technological competencies attached to each added value step, especially in manufacturing.

The technologies of the firm are indeed embodied in its processes as well as in its products. These technologies are part of the existing stock of knowledge and expertise

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1 70 THOMAS DURAND

Figure 1.a.

held by the human resources in the organ- ization.

There is a second level of technological capability within the firm. The R&D work- force and its expertise represent another dimension of the firm's technologies. In some sense these competencies are congruent to a flow of future technologies, yet to be developed.

The technological assets of the firm may thus be described as twofold:

1) A stock of existing technologies. 2) R&D activity as one possible source of

technology to build up the stock. This paper concentrates on how to

evaluate and describe R&D competencies within the firm. Although this might be considered an old issue given the new trends in the management of technology (see for

example Durand, 1987), three major reasons bring the R&D management researcher back to this topic.

Innovative activity is essential in manage- ment. By definition R&D, as a flow, is directly related to innovation because it may modify the situation, i.e., the existing stock of technologies. (Allen, 1977) While the strategic management of technology goes much beyond internal R&D, it turns out that no major access to technology is possible - e.g. through subcontracting R&D, licensing in, acqui- sition, joint ventures or any other combination - without minimal internal scientific and technical expertise con- cerning the technology at hand. Any attempt to grasp the scientific

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R& D ‘programmes-competencies’ matrix 171

REPRESENTING THE FIRMS TECHNOLOGIES (GIGET 1984)

INTEGRATING THE GENERIC TECHNOLOGIES INTO INDUSTRIAL AND TECHNOLOGICAL EXPERTISE FOR THE FIRM

GENERIC TECHNOLOGIES HIGHLY RELATED TO BASIC SCIENCE

Figure 1.b

competencies in R&D labs usually ends up in wordy qualitative descriptions of some research activities mixing the methodology used together with the outcome as well as the R&D planning process. Annual reports are usually not explicit about what an R&D unit is really expert at, and therefore do not evoke quantified evaluation of the competencies that the unit may claim.

This paper is an analytical attempt to address this issue.

11. THE RATIONALE BEHIND THE ANALYSIS

1. The ubiquitous experience curve concept of the seventies simply put forward that organizations which perform certain tasks slowly build up some specific abilities through the process of learning and innova- tion, as well as scale, though more indirectly.

The same logic may be applied to R&D. The scientific and technical expertise of an R&D unit may be regarded as the result of a learning process taking place over the years

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as the human resources in the unit work on different programmes.

The lab’s capabilities are a result of what each individual has done before, alone or together with other researchers, within or outside the R&D unit. An historical perspect- ive is therefore essential to our present concern. 2. R&D unit staff can usually describe in great detail their programmes, that is, the parts of their activity which are organized to respond to certain well defined objectives: developing a prototype, modifying a process, solving a technical problem, or even develop- ing human knowledge in basic science.

The information regarding the R&D programmes is not always complete nor fully available. However the R&D organization and its monitoring procedures usually provide a great deal of information and data. The basic idea of the approach developed here will be to build on that information in order to derive some of the many competencies that are embodied in the R&D organization. Given our present purpose, an analysis of the R&D programmes is therefore essential as a starting point.

THOMAS DURAND

means to obtain any exact budget or number of researchers per year-programme. Rather it is to roughly evaluate the relative weight of the many programmes that were under- taken in the lab over time.

As a test of validity of the evaluations, one may check that the sum of the elementary budgets or the workforce allocated to each programme matches the global budget or total workforce in the R&D unit year by year. Figure 2 presents the historical develop- ment of a young research unit since it was created in 1978. It shows the various programmes of the lab and their importance measured in person-years allocated to each of them. In other instances, although not presented here, it was necessary to go back as far as the early fifties to reconstruct the history of the past programmes of older research groups.

Although useful and worth analyzing, the graph cannot give any deep insight into the unit’s real expertise. 2. One may then want to have the research- ers or some of their representatives in the lab claim specific competencies which they feel the R&D unit masters. A list of all the detailed expertise they claim can thus be prepared.

At first, the classification of the competen- cies may not be precise enough. It may also be misleading, not explicit or not understand- able, too broad or too specific. It may often be non-exhaustive. One should thus spend time discussing what the researchers in the unit mean by the various words they use to define their competencies. New categories may be created, new labels may be given, new competencies may be thought of and included, etc.

Behind each formulation to represent a competency, there should be a precise definition leading to some sort of competen- cies dictionary. The step of defining the competencies is critical. It requires highly interactive sessions with the researchers. A technical and scientific background usually proves essential in conducting such discus- sions. More specifically, it is important to ask the researchers what other lab would have the same expertise, how different is their knowledge from their peer’s, who should be asked to assess the lab’s R&D

111. THE THREE STEPS

Given an R&D lab with anything between 10 to 200 people, one may follow the three steps below: 1. Trace the background of the R&D

programmes in the unit. 2. Construct the ‘Programmes-Competen-

cies’ matrix. 3. Derive the expertise profile. 1. The organizational setting of R&D labs usually makes it quite possible to quickly gather information on the number of people employed over the years, the programmes that were funded during that time, the budgets and workforce allocated to each of the programmes and its yearly evolution.

Although the data may not be immediately available, the information may be obtained by interviewing the older members of the R&D units, usually senior staff, as well as retired researchers and/or former managers of the R&D teams. The objective is by no

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RhD ‘programmes-competencies’ matrix

2

0

173

HEAT PUMP 1

_ _ SIROCCO PROJECT

HOT AIR MACHINE

INSA-LYON MECHANICAL ENGINEERIN6 DEPARTMENT

PROGRAMS I 11978-1986)

CONTRACTS WITH INDUSTRY

P 4 PERNOD/AFME CONTRACT

HEAT PUMP 2 V \-

Figure 2

expertise compared to that of other similar research units. Indeed, the ultimate objective is to identify the specific meaning of the R&D group’s competencies in great detail. The definition procedure is thus two-fold: (1) First, one has to identify subparts of a broad range of competencies around a major area of expertise: what do you mean by ‘combustion’? What type of ‘models’ are you using? Could you give examples of R&D programmes that drew upon those competen- cies? Is that expertise composed of different types of knowledge or competencies? Which of them are covered here, which are only somewhat related to the competency at hand, which are simply unrelated? (2) Second, one should attempt to compare the com- petencies claimed by the R&D group to that of other groups: how similar is their know- how in this field compared to yours? How does it differ? Why couldn’t you have conducted the same piece of research work? Why are they more known for that compet-

ency than you seem to be? In what sense is your expertise on this topic more applied, more specialized, more versatile, more recent, etc.?

The whole process is of course an iterative one. It may take time but it is essential to define the competencies properly.

Once a more complete and precise classification is obtained, it is possible to ask the researchers to help construct a matrix that matches the competencies to the programmes. Figure 3 presents such a ‘programmes-competencies’ matrix for the example illustrated here. It shows how programmes drew upon competencies, there- fore reinforcing and developing them. (Com- petencies are like muscles. The more they are used, the stronger they become).

Conversely it shows how existing and previously reinforced competencies made it possible for the unit to undertake certain programmes.

The existing competencies may be those

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174 THOMAS DURAND

INSA-LYON MECHANICAL ENGINEERING DEPARTMENT

PROGRAMSCOMPETENCIES MATRIX (1976- 19861 I I

5

P an THERMODYNAMICS

MATHEMATICAL MODELLING

COMBUSTION 0 0 - FLUID MECHANICS 0

SYSTEM ANALYSIS AN0 OPTIMIZATION

INDUSTRIAL OEVELOPMENl FOLLOW UP

Firure 3

that were initially available as input to the R&D unit - when it was set up or when it merged with other groups - and/or compet- encies that were developed over the years. The latter ones clearly appear on the matrix.

The rectangular boxes presented on the graph illustrate the importance of any competency for a programme. Their area is a measure (in man-years) of the workforce allocated to the research: the width of each column is proportional to the relative weight of the programme. The height of each box is proportional to the relative size of the competent workforce allocated to the corres- ponding programme.

In the process of constructing the matrix, competencies not originally thought of may be introduced. Tests for overall consistency are also possible, to check that the total man-year workforce subdivided by program- mes and by competencies corresponds to the unit’s total staff. Again, however, keeping in mind the real objectives of the analysis, one should not spend too much time looking for exact figures through extensive research in historical files. Approximations may be sufficient.

If one organizes the programmes in time

from left to right and similarly attempts to rank the competencies so as to approach a block diagonal matrix, then one may obtain a clear picture of the historical development of the R&D lab. On the example presented in Figure 3, our interviews confirmed the history told by the graph: early programmes were started, based upon a small background of initial competencies, that in turn were further developed. Then new programmes came about, requiring new competencies as well as older ones. At some point, contracted R&D was accepted as a way of exploiting the R&D competencies that had built up over the years, across the programmes. The contracted research also required totally new competencies that were not initially available and that were brought in, mostly by empirical learning in the present instance. More recently the R&D unit decided to look further for contracted research as a way to generate funding. Not surprisingly, these recent pieces of work mostly drew upon the core competencies of the unit.

3. Although quite meaningful in practice, the ‘programmes-competencies’ matrix is only one step of the analysis. One may next

R&D Management 18,2. 1988

R& D programmes-competencies ’ matrix want to focus on the R&D expertise profile of the lab. Keeping the same man-year unit to measure the cumulative workforce alloca- tion, one may then evaluate the amount of research time spent on each competency, across the programmes, over the years.

Figure 4 shows a bar chart presenting the lab’s expertise profile. This graph roughly illustrates what the researchers in the lab have been doing in the past and therefore what one might expect them to be best at.

IV. POSSIBLE USES AND IMPLICATIONS OF

MATRIX THE ‘PROGR AMMES-COMPETENCIES’

The above analysis is quite simplistic. It dares to address the sensitive issue of quantifying human competencies. We had the opportunity to implement the method in many different settings including R&D groups in various firms as well as labs in public research centres. All the data cannot be presented here. It should be stressed however that the method proved useful in all applications but turned out to be at most a starting point for further analysis and

175

discussion. It served many purposes, some of which are described below.

Large research centres undergoing massive reorganization have used it to rationalize their departments through team grouping around similar competencies.

Public agencies have used this scheme to analyse various R&D labs before launching financial support for major research pro- grammes.

Companies have used the approach to evaluate their internal R&D capacity when they were considering diversification through alternative strategies to gain access to technology, namely external development and acquisitions.

Firms have also drawn upon similar analyses to assess their relative R&D strength with respect to competitors and public research centres. It led to identifying partners with which joint ventures should be started. It also helped them to identify external experts to be hired and, conversely, internal key researchers to be protected from being hired by competitors.

Our experience working with the method led us to the conclusion that managers find it easier to grasp from the graphs what R&D

INSA-LYON MECHANICAL ENGINEERING DEPARTMENT

R E 0 EXPERTISE PROFILE I (1978-19861

Lo

COMPETENCIES

Figure 4

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176 THOMAS DURAND

Vertically, one stresses the applicability of the knowledge to specific programmes: this is typically the view of the marketing and other business-oriented members of the firm. The indicators of success from that perspect- ive deal with the ability to develop a prototype that performs well under working conditions, at low cost, on schedule, etc.

It should be noted, however, that it might be difficult to apply the method to some research units specialized in basic science. Indeed, in some cases, the research pro- grammes may be colinear to the competen- cies. This happens when the aim of the lab is primarily to develop human knowledge in the competencies. The use of the method should thus be restricted to situations where the R&D programmes differ from simply strengthening the competencies. - Exploiting the firm’s technological assets, including its R&D expertise, may imply launching R&D programmes that draw upon competencies that exist internally in the organization as well as other competencies that are not available. The example shown in Figure 3 and described above illustrates the situation in as much as the competencies required for some of the new programmes had not all been developed over the years and were not initially available in the lab. (Our experience in using the method led us to face many different situations: in some cases, new programmes even required R&D expertise that was totally new for the company. The ‘programmes-competencies’ matrix was then completely block diagonal).

This clearly implies that exploitation of internal technology may in turn lead to diversifying the R&D expertise profile: a policy of exploitation of purely internal resources may thus imply substantial diversi- fication and enlargement of the R&D scope for the organization.

units are expert at; in turn, heads of the labs appreciate the possibility of summarizing their competencies as well as the history of their R&D unit in a somehow concise and visual way.

Beyond this immediately practical per- spective, the approach presented makes it possible to address several other issues. - Privately-owned research centres con- ducting contracted research are known to have a hard time surviving without govern- ment grants. One of the key aspects of their difficulty becomes clearer here. The research centres need to obtain funding to create and maintain advanced expertise on many differ- ent topics. Their clients do not expect to pay for that competency development. They simply want to buy the small pieces of expertise that are necessary for their own projects.

Without adequate funding, there cannot be an accumulation of man-years per competency. Without cumulated man-years, there is no marketable expertise, which means there is no sustainable activity in contracted research.

This might explain why the Battelle Institute, SRI, AdL, The Fraunhofer Institute in Germany, Bertin in France are all highly dependent on government funding. - The best R&D expertise profile may not just be the profile of labs including the most famous and advanced researchers for some competencies. Some R&D units may be excellent at some tasks precisely because they are average at many different competen- cies, thus making it possible to choose among the many different technical solutions which are available. In contrast, the highly-special- ized research group focussed on one specific technology may impose the only solution it may think of, without considering alternative technologies that might be more appropriate to the problem at hand.

- The ‘programmes-competencies’ matrix may be viewed from two different perspect- ives: horizontally, one stresses the importance of the competencies that the unit masters: this is typically the view of the scientific researcher evaluating his peers. The indica- tors of success and efficiency are the number of publications, scientific recognition, etc.

V. DISCUSSION

Several comments and qualifications need to be made about the approach presented here. - When attempting to evaluate the degree of expertise, the above analysis uses human

R 1 D Management 18,2, 1988

R&D ‘programmes-competencies’ matrix resources allocated to each competency as a measure of the accumulated scientific and technical experience. This assumption raises many questions.

Why not include the equipment available in the lab for the expertise evaluation?

Indeed R&D activities are highly dependent on up-to-date materials, de- vices, measuring, testing and computing equipment. Is a technician equal to a graduate researcher; is junior staff as competent and qualified as senior staff; should the worst productive researcher be counted for as much as the best? Is a man-year of R&D activity in 1960 equivalent to a man-year spent in 1987? Memory losses, personnel turnover (e.g. retirement, death, departure, etc.), tech- nology evolution make it difficult to consider that the learning process which

177 took place 30 years ago is as important today as more recent investigations. In some sense, one might think of applying inverted discounted evaluation techniques to measure the accumulated man-years which are to be taken into account for competency assessment.

These questions are legitimate. However, coming back to the example used earlier, the most developed expertise in the lab under study - when measured in accumu- lated man-years spent on the competency - turned out to weigh no more than 20 man- years at most.

In contrast Figure 5 shows the expertise profile of another lab specialized in the same field. The major expertise of the lab scores way above lo00 accumulated man-years. How could anyone argue that the relative scientific and technical knowledge of the second lab should not outperform the first?

t

NUCLEAR RESEARCH CENTRE-GRENOBLE HEATTRANSFERRESEARCHGROUP

R&D EXPERTISE PROFILE ( 1960- 1985)

0

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Figure 5

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It is not surprising to find that the second lab has an international reputation for that specific competency.

THOMAS DURAND

conducting similar historical analyses of the R&D units they previously belonged to.

- Scientific and technical knowledge stands as a resource for the firm that is highly dependent on human resources: technological knowledge is held by the members of the organization. The manage- ment of technology in turn has much to do with human resources management.

Therefore it should be argued that describing the R&D expertise profile of an R&D unit also requires an analysis of its population and its potential: age distribution, seniority, turnover, mobility, career path, distribution of maximum positions attainable, qualifications, academic degrees held, etc.

- Along the same lines one may also study the population in the R&D lab from an individual perspective. What knowledge does a person have that is not part o f hidher past work experience? What hobby. what interest'?

It might also be extremely useful to characterize the researchers to identify creative personalities, pragmatic business- oriented individuals, manager-like behaviours, etc. Those individual profiles are also part of the unit's competencies. Works conducted by psychologists may then be used, e.g. Guilford's (1967).

- It was suggested to limit the use of the method presented to R&D groups ranging from 10 to 200 people. The workable size, however, clearly depends on the nature of the research activity in the group: develop- ment work with 24 hour pilot monitoring requires many technicians and engineers on a 3-shift basis. However, it may not be as complex to analyze as a more theoretically oriented, smaller research group. Our experi- ence suggests implementation of the method with groups of about 50 persons. There is no real limit on size except for a very large group, which makes it difficult to clearly grasp, summarize and present the complexity of a large body of research with many different programmes and competencies involved. The 200 person limit is thus only mentioned as an indication of what should be regarded as a big group, given the method presented. Conversely, a very small unit makes it difficult to evaluate the accumula- tion of man-years spent on each competency. The first example presented here in Figures 2, 3, 4 should be typically regarded as somehow in the lower range.

These tentative limits on size, however, should not directly apply to accumulated man-years as presented in Figures 4, 5 since the time dimension is taken into account in the graph. - Regarding the turnover issue as a limit of the approach, one might also point out that, in most research centres, mobility is extremely i f not surprisingly low, thus legitimizing the analysis. Obviously the method should not be applied in firms where the R&D division is in charge of training newly-recruited members.

In certain instances the yearly turnover in the labs may peak at around 20%. Thus the cumulative process of expertise built-up described above can no longer take place.

Conversely, the case o f a large number of researchers having joined the R&D unit over the years is easier to handle. One may attemot to evaluate the accumulated exDert-

- Volume. i.e. size of the workforce, turns o u t to be a major source of competitiveness of R&D.

However it is possible to point out small R&D units that are dynamic and highly productive. Outstanding researchers with creative abilities and with a limited number of ccinvestigators may be specialized enough to know as much in their narrow field of interest as any big research centre. One can then think of a U-shaped curve describing the competency of R&D units as a function of the unit size. This approach would thus be similar to what Porter (1980) claimed for experience curves.

- Along similar lines, the evolution of technology may have an impact on the R&D exDertise mofile similar to the one it is

ise thi t individuals or groups brought t& the kn'own to Lave o n the firm's manufacturing lab when joining it. This essentially requires , facilities and organizational know-how as

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R& D ‘programmes-competencies ’ matrix 179

described in Abernathy and Utterback‘s theory (1978).

Indeed, as a new technology evolves, R&D specialists start training new researchers. They soon start setting up specific units to develop and enhance their technology, in turn reinforcing their expertise profile. When radical innovation occurs, it may draw upon other generic competencies. The strong R&D expertise profile that has been devel- oped over the years suddenly appears vulnerable and may soon be useless.

In some sense, R&D expertise undergoes the same process as the fluid/transitional/ specific stages described by Abernathy and Utterback. What would now be the use of mastering the finest know-how in analog technology in electronics? Considering this evolution, firms might choose alternative strategies to gain access to technology so as to remain flexible and reactive. They should be careful, however, not to cut all their R&D expenditures.

Indeed, along the lines of Williamson’s transaction cost theory (1973, it appears that R&D subcontracting, as well as tech- nology acquisitions require some level of internal expertise to take the best out of it.

- The analysis presented here is in some respect similar to Porter’s added value chain (1985). In Porter’s model the products flow through the added value chain and require specific added value steps. In our model, the programmes flow through the ‘programmes- competencies’ matrix in as much as they require specific technological and scientific competencies.

This comparison suggests that support activities may be included in our model as human resources management or procure- ments are included in Porter’s model. It is for instance possible to put forward compet- encies other than purely scientific and technical ones e.g. organizational ability to work with marketing staff, innovative and/or entrepreneurship behaviour, ability to protect the classified R&D results, ability to attract talented young researchers, ability to draw upon outside consulting competencies in academia and public research centres, ability to operate within an interactive network of researchers, etc.

The parallel with the added value chain is

worth drawing because it also points out the logic of the so-called ‘economy of scope’ behind the model: different products within the firm may require the same added value steps. Similarly, different R&D programmes may require the same competencies, thus sharing the accumulated investment made on the expertise.

VI. CONCLUSION

The approach developed here addresses the issue of expertise profile description in R&D labs. It suggests a three step analytical scheme yielding concise and visual repre- sentations. The method has proved useful in practice in several instances and may help firms and research centres in defining their R&D strategies.

Although conceptually as simplistic as the added value chain model, the scheme also leads to interesting findings regarding the management of R&D and technology.

Several comments are made concerning the limits of the model presented. Comple- mentary analyses are suggested regarding individual behaviour, characteristics and knowledge, human resources management, equipment available in the lab, etc.

The rationale behind the model is clearly similar to the cumulative volume logic that prevails for the experience curve concept and the added value chain. Similar limits are thus suggested regarding a U-shaped curve for R&D expertise as a function of size. R&D strategies which mostly focus on constantly reinforcing competencies in the dominant technologies are also considered vulnerable to radical innovation as in Abernat hy and Utterback‘s theory.

The model presented is of interest from at least two different perspectives: from the theorist’s viewpoint, it is possible to grasp the process through which R&D labs build up their cumulative expertise. From the practitioner’s standpoint, the method requires some data gathering and processing, but yields concrete visual results. The time and energy spent obtaining the necessary inform- ation usually worries the analyst. It should be noted, however, that no more than a week of work is necessary for an auditor to go through the three steps of the scheme for

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R&D units including about 50 staff members. Firms are often reluctant to invest in

reconstructing the history of their past activities. It must be emphasized that the analyses suggested should by no means be considered finished with the construction of the three graphs presented. The 'program- mes-competencies' matrix should rather be considered as a starting point for in-depth analyses of the core expertise of the lab, through internal discussions, peer interviews, systematic comparisons with competitors' R&D expertise profiles, etc.

The method presented merely provides the skeleton; more work is needed to add the flesh around it, thus yielding some understanding of the whole body.

Finally one may consider applying the approach advocated here to individuals. Can we not all attempt to list and sort out the research work - and other types of work - we have undertaken as well as the competen- cies we claim or think we have? We should then be able to match the two lists, thus constructing our own 'programmes-compe- tencies' matrix and our expertise profile as well.

THOMAS DURAND

When put into practice for an individual, the model presented in this paper gives some insight into how one really views him/herself thus stimulating hidher thinking. After all, that is what analytical models are for.

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