Cluster support in the EU is being subject of several professional papers at European Commission level. It mostly have the character of strategic plans of support policy formulation or professional working documents. The European Union also supports the exchange of information processing and technical analysis through support associations to develop clusters. The most important is the establishment of European Cluster Observatory (ECO) in the INNO initiative, which making the analysis of clusters in the EU and policy analysis to support clusters. In 2008, the European Commission established the European Cluster Policy Group, in order to promote the emergence of more global clusters in the EU, examine tools to remove obstacles to cluster cooperation, identify future challenges for cluster policies in response to globalization.

 

 

Parameters evaluation of automotive clusters in the EU

According to [1] the development of cluster management depends on composition and interaction between participants and the quality of cluster management. Cluster mapping is a necessary condition to maintaining cluster competitiveness and based on the assumption that the benefits, resp. identification and elimination of potential errors is necessary to evaluate by certain way.

Comparison of selected automotive clusters in the EU is based on the basic indicators of statistical data of the European Cluster Observatory ECO dealing with statistical indicators for mapping clusters in the EU. ECO identifies cluster potential by localization coefficient based on regional data on employment collected by Eurostat.

During evaluation of clusters it is important to assess their intensity, i.e. if employment in the specific industry, which belongs to the category of the cluster in the region reached so called "specialized critical mass" necessary to create spillovers and accouplements, bringing positive economic effects. [5] ECO monitors by three indicators i.e. size, orientation and specialization, this critical mass and assigns each cluster so called "Stars" (from 0-3, depending on criteria). Top results are getting three stars rating. ECO not examine the direct link between companies and even force under which are clusters develop, but whether such a concentration reached the critical mass of specializations needed to develop spill over effects. Identifies so called detected clusters effects. Size indicator monitors, if employment in a regional cluster reaches a sufficient level to create economic effects. Rating of one star cluster meets the employment of more than 15,000 workers. Indicator of specialization monitor if the region in a specific cluster category is more specialized than the overall economy in all evaluated regions. It is probable that the cluster will attract related economic activity from other regions to this location. One star cluster receives if a specialized coefficient is greater than 1.75, i.e. 75% or more employees in the cluster as is the average of all regions. Indicator of dominance monitors if cluster achieves high proportion of total employment, if yes it is probable that the spillovers effects and linkages in a given cluster exists. Rating one star acquire regional clusters, reaching 7% or more sectoral employment in the area.

The following Table 1 shows a comparison of indicators by ECO in selected clusters: West Midlands Automotive Cluster in the UK, ACS - Automotive Cluster of Slovenia, MSAC-Moravian-Silesian Automotive Cluster in the Czech Republic, AMZ-Automotive Cluster Saxony in Germany and Automotive Cluster-western Slovakia (AKS ).

tab1m

Three stars and the best results obtained clusters AKS, AMZ, West Midlands. One star won the MSAC and ACS. According to the size indicator one star gained clusters with employment of more than 15,000 workers in the West Midlands, AMZ and AKS. Specialization indicator was higher than 1.75 in the cluster in the West Midlands, AMZ and AKS, which means that clusters should attract related economic activity from other regions to this location.

The disadvantage of ECO statistics is limiting the data only for the employment data. It can be concluded that for more qualified examination of the clusters performance should therefore more inclusive way, the combination of other economic, technological data, that are not yet available at the regional level. It would therefore be preferable to use the data on the volume of wages and salaries, productivity, or value added.

Proposal of evaluation for selected automotive clusters in the EU

The proposed evaluation methodology is based on the analysis of different approaches: Methodology ESCA (European Secretariat for cluster analysis) and CNG (Agency Kompetenznetze Deutschland) [2, 4], ECEI methodology (European Cluster Excellence Initiative) [4], the methodology CLOE (Cluster Linked over Europe) [3] methodology for the identification and characterization of clusters by Koschatzky & Lo [6], british methodology for evaluating clusters [6]. Based on these analyzes were selected indicators and multi-criteria decision making methodology for four selected automotive clusters together and ACS - 8 criteria (K1-K8). The following Table 2 are proposed as indicators selected criteria and data for individual clusters.

For the multi-criteria decision making method was used decision matrix. The proposed variant solution consists in evaluating weight (importance) of eight criteria by point scale method from 1 up to 10 while grade 10 were assigned the greatest weight and level 1 the lowest weight. The same scale was evaluated by the fact that each automobile clusters comply with selected criteria, which means. Stage 1 does not comply up to the level 10 meets perfect.

tab2m

Based on the weighted of the sum ranking was determined. The result of the decision-making method is the fact that automotive cluster West Midlands the best meets selected criteria with regard to the performance of the cluster. The second is the automotive cluster Saxony (AMZ), then Moravian-Silesian Automotive Cluster, Automotive Cluster of Slovenia and the last is the automotive cluster western Slovakia.

Summary

Through the analysis of four selected automotive clusters were identified several factors that have a decisive influence on the development of automotive clusters. It is important to note that the automotive cluster is amended and the objective is to minimize impact of these changes and support the growth of the cluster. Final Table 3 describes the important success factors for the growth of the automotive cluster.

Measurement of cluster performance and cluster initiatives through benchmarking tools and models formulate conclusions and recommendations for automotive clusters. Statistical mapping of clusters is one of the newest approaches serving to obtain a better assessment of clusters. It is a powerful tool that helps to identify the formation, existence, growth and decay of clusters in the region. Automotive clusters MSAC, ACS and ACS are involved in the project AutoNet aimed at cooperation in order to promote innovative environment for the creation of new processes, products in the automotive industry.

Implementation of clusters in the automotive industry leads to the performance of companies and their supply chains. Cluster initiatives are adequate and effective tools for concentrating resources and means to achieve critical mass and to accelerate knowledge transfer of know-how.

tab3m

Implementation and coordination of cluster initiatives and networks have become an important tool in the hands of regional authorities to support and promote the development of economic growth as in the technology advanced, both in traditional sectors of the economy.

The contribution was prepared in the framework of the grant project No. KEGA. 004TUKE-4/2013 "Intensification of modeling in teaching II. and III. degree in the the study field 5.2.52 Industrial Engineering ".

References

[1]   ESCA: Benchmarking as a Tool for Cluster Analysis. CLuster Excellence makes the differnce. ESCA leaflet MArch. 2012. Available on: http://www.cluster-analysis.org/downloads/ESCA_leaflet_March 2012.pdf

[2]   ESCA: Benchmarking of Cluster Organisations a Tool for Improving Cluster Managment. s.4. Available on: http://www.cluster-analysis.org/downloads/BenchmarkingPaperKergel_v4.pdf

[3]   CLOE: Cluster Management Guide – Guidelines for the Development and Management of Cluster Initiatives.. 2006. Available on: http://www.clusterforum.org/en/cluster_management_guide.html

[4]   GAMP T. L. – KOCKER M. – CHRISTENSEN T. A.: Clusters are individuals - creating economic growth through cluster policies for cluster management excellence.[online]. 2011. Available on: http://fivu.dk/en/publications/2011/files-2011/clusters_indhold.pdf

[5]   KAČÍRKOVÁ, M.: Zhlukový potenciál v regiónoch nových členských krajín Európskej únie. 2008. Available on: http://ekonom.sav.sk/uploads/journals/WP07.pdf

[6]   KOSCHATZKY, K. – LO, V. 2007. Methodological framework for cluster analyses. Institute Systems and Innovation Research. Karlsruhe 2007. ISSN 1438-9843. Available on: http://www.isi.fraunhofer.de/isi-media/docs/isi-publ/2007/isi07a24/framework-cluster-analyses.pdf

[7]   SOLVELL, O. – LINDQVIST, G. – KETELS, CH. 2006. Zelená kniha klastrových iniciatív. Czechinvest. 2006, Available on: http://www.czechinvest.org/data/files/zelena-kniha-klastrovych-iniciativ-64.pdf

 

TEXT/PHOTO Peter TREBUŇA, Dušan SABADKA