MEASURING THE PERFORMANCE OF BRAZILIAN DISTRICTUAL SYSTEMS

Transcription

1 MEASURING THE PERFORMANCE OF BRAZILIAN DISTRICTUAL SYSTEMS João Amato Neto, Ph.D. University of São Paulo, Brazil

2 AGENDA : Research aims Research questions Theoretical approach The fundamental presupposal Research methodology Integrated set of matrices The cases (clusters of SP state - Brazil) Main conclusions

3 RESEARCH AIMS: Development of a reference model (*) for management of local production and innovation systems, from a selected small and medium enterprises (SMEs) clusters set in the São Paulo state - Brazil. (*) A systematic set of concepts, principles and indicators which can guide public and private actor s actions in order to promote and improve such kind of productive arrangements.

4 RESEARCH QUESTIONS (I): 1.) What are the basic requirements to transform small and medium enterprises (SMEs) clusters in local production and innovation systems? 2.) What is the relationship degree among enterprises that belong to a local production and innovation system (the frequency and the intensity of their joint actions)?

5 RESEARCH QUESTIONS (II): 3.) What are the different governance structures and settings which could be found in such systems, and its local and regional links with other global regions? 4) What is the role of the Science, Technology & Innovation National System (including the Basic Technology Industry requirements - Standards, Metrology and Industrial Certification) in the SMEs modernization process inserted in local production and innovation systems?

6 BORDERS AND VERTICES OF ANALYSIS (Epistemological pole) REGIONAL ECONOMICS INDUSTRIAL CLUSTERS & COOPERATION NETWORKS INDUSTRIAL ORGANIZATION PRODUCTION ENGINEERING

7 THEORETICAL APPROACH 1. NEW ECONOMIC GEOGRAPHY, Krugman, (1991, 1998), Marshall (1920): clusters are induced by the presence of local external economies (incidental). 2. COMPANIES STRATEGIES (Porter, 1998), locational strategies are a part of wide strategies of companies defined according to the business branch. They are also market forces which determine the clusterted producers performance. 3. REGIONAL ECONOMY (Scott, 1998), differential element: the recognition of the extra-market coordination and public policies importance. 4. COLLECTIVE EFFICIENCY (Schmitz, 1989), emphasizes the deliberated forces which intensify the enterprises competitive capacity, which come from the cooperation, consciously sought among private ans public agents.

8 THEORETICAL APPROACH 5. INDUSTRIAL DISTRICTS ( Belussi, F. 2000; 2005; Gottardi,G. 2000, Lombardi,M., 2003) Learning and knowledge transfer process at Local Production Systems. Models of localized technological change. 6. SOCIAL CAPITAL (Silva, 2006; Marteleto e Silva, 2004; Bourdieu, 1998; Degenne, 2004). Interpersonal or intergroup relationship cooperation networks in which its members are governed by certain rules,systems, beliefs, values, feelings (like recognition, respect, friendship) and confidence.

9 FUNDAMENTAL PRESUPPOSAL JOINT ACTIONS (companies, government, development agencies, universities, technical schools, other social agents) POSITIVES EXTERNALITIES (qualified work-force and abundant raw material supply, among others) COLLECTIVE EFFICIENCY strengthening of clusters Local and regional development

10 RESEARCH METHODOLOGY Mapping clusters in the state of SP through survey. Research Instrument (I) : self-administered questionnaire in order to examine some characteristic aspects of the different clusters. Instrument Research (II): qualitative research, supported by the multiple case study method (GIL, 1995; YIN, 1998), using semi-structured questionary.

11 RESEARCH PLAN Documentary research with public agencies, local institutions of support, trade unions, NGOs, SEBRAE (Brazilian System to Support SMEs), universities, research centers and so on.; Clusters analysis (their externalities; joint actions; degree of cooperation, links with institutions and external agents, etc.). Clusters comparative analysis.

12 INTEGRATED SET OS MATRICES analysis of the main features of clusters reference for public and private policies. Stakeholders: Market sector associations Clusters responsables agents Public institutions Local associations and offices Local governments (public offices) Local industry associations

13 Clusters in SP state (Brazil)

14 Set of Matrices

15 Set of Matrices Main goals: Contemplate all characteristic dimensions Be independent of time, industry or original geography Results that range between 0 and 4 pts Be self-explainning and user-friendly Identification Code G1 Characteristic evaluated Proximity of raw material sources Matrix Distance (in km) between the main raw material sources and the cluster 0 points 1 point 2 points 3 points 4 points More than 200 Between Identification Code I3 Between Between Characteristic evaluated Less than 20 Marketing and Advertisement actions Matrix Participation and availability of marketing institutions 0 points 1 point 2 points 3 points 4 points Don t exist Work partially Not sufficient Exist and sufficient Work completely

16 Possible Classifications Development Stage Embryonic Emergent Expansion Maturity Organizational Level INFORMAL ORGANIZED INNOVATIVE

17 Main analysis São José dos Campos ICGG IGG 4,00 3,00 IEG 2,00 IGovG 1,00 0,00 IIG SJ dos Campos IIntG ISG IAG ITG

18 Main analysis São Carlos ICGG IGG 4,00 3,00 IEG 2,00 IGovG 1,00 0,00 IIG S Carlos IIntG ISG IAG ITG

19 Main analysis Americana IGG 4,00 ICGG 3,00 IEG 2,00 IGovG 1,00 0,00 IIG Americana IIntG ISG IAG ITG

20 Main analysis Indaiatuba ICGG IGG 4,00 3,00 IEG 2,00 IGovG 1,00 0,00 IIG Indaiatuba IIntG ISG IAG ITG

21 Main analysis 4,00 3,50 3,00 2,50 2,00 1,50 1,00 0,50 0,00 Indaiatuba Americ ana Ibitinga C erquilho/tiete Tabatinga IGG IEG IIG ISG ITG IAG IIntG IGovG ICGG

22 Main analysis CLASSIFICATIN GRAPHIC Limeira - Machines Limeira Jewelry Comparision among industries (same region) CLASSIFICATION GRAPHIC Comparision among regions Ourinhos Birigui Jaú Franca Comparision among different industries CLASSIFICATION GRAPHIC Furniture average Red Ceramic average CLASSIFICATION GRAPHIC Cluster evolution (per year) 2004 High tech production average

23 Clusters classification Cluster/ APL APL Índice Global Cluster/ APL APL Índice Global 1 SJ dos Campos 0,81 13 Franca 0,59 2 Plástico/ABC 0,75 14 Mirassol 0,59 3 Birigui 0,72 15 Tabatinga 0,58 4 Americana 0,72 16 Porto Ferreira 0,55 5 São Carlos 0,71 17 Tambaú 0,55 6 R.M.S.P /Móveis 0,70 18 SJ Rio Preto 0,52 7 Ibitinga 0,69 19 Salto/Itu 0,51 8 Ribeirão Preto 0,69 20 Itatiba 0,51 9 Jau 0,66 21 Cerquilho/ Tiete 0,47 10 Santa Gertrudes 0,63 22 Limeira 0,47 11 Vargem Grande do Sul 0,62 23 Indaiatuba 0,43 12 Sorocaba 0,60

24 Clusters evolution levels Organizational levels Informal Organized Innovator Q9 Q10 Q11 Q12 Q5 Q6 Q7 Q8 Q1 Q2 Q3 Q Embryonic Expanding Emerging Maturity Clusters development stages

25 Main conclusions Limitations for the application: Partiality of some qualitative matrices Knowledge of a specialist of the cluster Data availability Main implications: Extension of the study to clusters of other countries Statistical study to determine the exact correlation between the classifications and the size or format of the ocurrence zone

26 THANKS SEBRAE /SP Staff Paulo Eduardo Stabile de Arruda ( Diretor Técnico) Caio César Massao Ito Joaquim Batista Xavier Filho Renata Moreira Contatori dos Santos Rose Mary Estácio Post - Graduated Program in Production Engineering (EPUSP) Students Cláudio Roberto Leandro Luciana Pereira Luciana Telles

27 THANKS Profa. Sandra Rufino dos Santos (PRO EPUSP) Undergraduate studentes (Production Engineering - PRO - EPUSP) Fábio Bortolotti (2005) Ricardo Santa Maria Correa da Fonseca (2006) Thiago Miranda (2007) Trainee Vivian de Souza Pontes

28 Thank you! RESEARCH GROUP : COOPERATION NETWORK AND KNOWLEDGE MANAGMENT Coordinator : João Amato Neto, Ph.D. http/ amato@usp.br