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Commercial Success of ECLAIR Programme
Overview - 3. Assessment of Commercial Success |
Preface
Executive Summary
Overview
1. Introduction
2. Information Collection
3. Assessment of Commercial Success
4. Sectors, Technology and Markets
5. Industrial Participation
6. Impact of Science and Technology on Commercial Development
7. Political and Legislative Environment
8. ECLAIR in the Context of European Research and Development
9. Conclusions
3. Assessment of Commercial Success
On the basis of the information collected, the projects were assessed in terms of the current state of development, within one or more of the categories illustrated in Figure 1, that is:
These projects are further classified on the basis of the original ECLAIR programme grouping into the following activity sectors or clusters. The projects are in each cluster are indicated by colour in Figure 1 and the abbreviation used to denote each cluster in Figures 2-7 is shown in parenthesis:
| Cluster 1 Oleochemicals (O) |
| Cluster 2 Lignocellulose (L) |
| Cluster 3 Carbohydrates (S = sugars) |
| Cluster 4 Crop Adaptation (C) |
| Cluster 5 Food Processing Technology (F) |
| Cluster 6 Animal Health (A) |
| Cluster 7 Aquaculture (W = water) |
| Cluster 8 Biological Pest Control (B) |
The original ECLAIR documentation included the Bioraf Pilot Plant as a separate cluster (biorefinery). However, since a single project was difficult to fit into the subsequent numerical analysis of performance, this project AGRE-0061) was included in the carbohydrates cluster, since at the end of the ECLAIR programme, the work was focused on wheat.
Table 2 lists the projects and their contract number by cluster as used for the purposes of this report (note: the grouping of projects varies between EU publications, hence any comparison should be made referring to the project number and this Table).
Figure 1. Level of commercialisation of ECLAIR Projects (indicated by contract number)
In order to provide an empirical classification system the level of attainment, under the current situation, was assigned a numerical value as shown in Table 3.
In order to determine an overall score for a given sector (cluster), the scores of each individual project were summed and the total divided by the number of projects in the cluster. Other parameters used for this analysis included an assessment of the strength of commercial or industrial involvement in the original project. Again this was done by assigning arbitrary values to organisations, with higher values assigned to those with a greater commercial emphasis (Table 4).
| Status at time of this report | Score |
| work ceased | 1 |
| continuing research | 2 |
| trials, pilot plant and/or prototype built | 3 |
| near market | 4 |
| products marketed | 5 |
| Organisation status | Score |
| university | 1 |
| research institute | 2 |
| trade association, etc | 3 |
| small or medium company | 4 |
| large or multinational company | 5 |
The commercial involvement for a given project was determined by assigning these numbers as appropriate to each of the participants. Since, it might be assumed that the coordinator had a greater degree of influence on the overall project development, the score assigned to the coordinator was doubled. The total was then divided by the number of participants in the project.
The impact of continuing research was evaluated by awarding a project a score based on the nature of research carried out subsequent to the ECLAIR funding. A value of 1 was scored for precompetitive research (such as that funded under national, international and EC programmes such as FAIR, AIR, etc); a value of 2 was assigned for near market research (such as that funded under the Eureka or Innovation programmes); and a value of 3 was assigned to in-house research. The maximum score of 6 could be achieved if all three types of research had been carried out subsequent to ECLAIR.
Other parameters used in this analysis included the total number of participants in a project and the score given to the coordinator alone (not doubled on the relevant graph, so that the axis had a comparable range to the other Figures). One project (EC wheats, AGRE-0052) had over 25 participants, addition of which had a disproportionate effect on the analysis. However, this project was organized into three separate areas of activity, covering industrial processes, functional components and biochemical genetics. Hence, for this project only the scoring system was modified, analysing the results on the basis of sections, rather than participants.
Figure 2 shows the impact of the level of industrial involvement in the project on the current position concerning nearness to market, when all the project results are taken and averaged. In view of the wide range of different areas of activities, project formats, aims and objectives, as well as the empirical methods used in evaluation, the results are striking. They clearly indicate that the greater the industrial participation in the initial project team, the more likely it was that the project had produced a marketable product.
In Figure 3 the same data has been re-calculated, but this time separating the projects on the basis of the cluster. This Figure indicates that the areas closer to farming (animals and crops), had less industrial involvement (attracting applications from the many agricultural related research institutes and university departments) and did not get as close to market as those areas covering processing (lignocellulose, carbohydrate, oleochemistry), or those closer to the consumer (food, aquatic and biopesticides). In addition, projects in the latter group, closest to the consumer, are nearer to market despite the fact that they did not involve the highest level of industrial participation.
However, due to the diverse nature of the projects in each group, and the way in which they were classified, these results require some further clarification. This is presented in the following section, cluster by cluster, starting with the area of greatest commercial success and working back towards those areas in which less success was achieved.
Figure 2: Correlation between degree of market development and level of industrial participation classified by level of commercialization
Figure 3: Correlation between degree of market development and level of industrial participation classified by sector (see
Table 1 for key)The next correlation to be considered was that of the impact of the coordinator alone on the ultimate level of commercialization. When all the projects were considered together (Figure 4), it was found that they fell into three groups. The projects that had not progressed further in general did not have a high level of industrial coordination. Although having an industrial coordinator was not a guarantee of commercial success, projects were more likely to reach the market if they had a higher level of industrial coordination. This could suggest that near-market products might have reached the market place by now, but lack of industrial coordination had held them back. Projects that had succeeded in obtaining further research money from the EC, as well as those those with products on the market place, in general scored more or less the same in terms of level of industrial coordination.
Figure 4: Correlation between degree of market development and industrial status of coordinator classified by level of commercialization
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Figure 5: Correlation between
degree of market development and industrial status of coordinator
classified by sector (see Table 1 for
key)The impact of the coordinator was much less obvious when the results were considered on a sectorial basis (Figure 5). In part the results reflect the nature of the industries involved. Projects in the carbohydrates (S) cluster gave the highest score for industrial status of coordinator, no doubt reflecting the fact that most of the sugar and starch in the EU is controlled by a few large multinational companies, or by the processing arms of large farmers cooperatives. The lower value for oleochemicals, in contrast, reflects the fact that proposals were put in by associations and research institutes. The animal projects had a greater proportion of universities and research institutes, as did the lignocellulose area, whereas in contrast, the crop sector involved more associations and the food sector more companies.
Analysis of the effect of the number of participants on the level of commercialization indicated that smaller projects fared better in the move towards commercialization (Figure 6). However, a strong commercial participation and sector-related factors (Figure 7) overrode this size effect when the product came to be placed on the market.
Figure 6: Correlation between degree of market development and number of participants classified by level of commercialization
Figure 7:
Correlation between degree of market development and number of participants classified by sector (see Table 1 for key)Finally, analysis of the impact of research on the current level of commercialisation indicated that projects that had reached the market had probably benefitted to a greater extent from applied (in-house) research (Figure 8). It was also observed that projects in a near-market situation had not undertaken as much in-house or applied research since the end of ECLAIR. This might suggest that these products might have reached the market place by now, but for a lack of industrial follow-up. When correlated by cluster (Figure 9), there is a clear indication that those clusters in which more commercial-oriented research had been carried out since the end of the ECLAIR programme, had achieved a higher level of commercialisation.
Figure 8: Correlation between degree of market development and commercial level of continued research classified by level of commercialisation
Figure 9:
Correlation between degree of market development and commercial level of continued research classified by sector (see Table 1 for key)Contacts
Author
© Copyright 2006 Policy Statements
Updated
by CPL Press:
03/07/2007
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