Spatial Modeling Approach to Clustering theFurniture Industry and Regional Development             in Jepara, Indonesia  M...
Structure Introduction Method Result   Spatial distribution   Spatial analysis Conclusion
Introduction• 95% furniture industry managed by small-  scale and medium enterprises (SMEs)• Jepara, Central Java, long hi...
Introduction – cont.• Dropped to 11,981 enterprises in 2010  – 96% independent (focus on specific activities, e.g.    work...
Introduction – cont.• SMEs in Jepara formed natural clusters   – Not efficiently distributed in obtaining raw materials   ...
Method• Two sets of data were used  – Spatial census 11,981 enterprises  – Detailed intensive survey 2,000 enterprises• Up...
Method – cont.• The efficiency will affect the industry’s  revenue  gross revenue• Efficiency  reduced operation costs a...
Results – spatial distribution
Results – spatial distribution CEK
Results – spatial distribution
Result – spatial analysis                          Distance                 Distance                          Annual      ...
Result – spatial analysis35302520                                                                                         ...
Result – spatial analysis
ConclusionFurniture industry in Jepara has:• Different downstream and upstream efficiency• significance correlation   • fu...
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Spatial Modelling Approach to Clustering the Furniture Industry and Regional Development in Jepara, Indonesia

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About 95% of the furniture industry in Indonesia is managed in small-scale and medium enterprises. These enterprises naturally form clusters, but are not necessarily efficiently distributed in terms of obtaining wood material and marketing. This inefficiency can make the industry less competitive. In this presentation, CIFOR researcher Rubeta Andriani provides spatial analysis of small- and medium-sized furniture enterprises in Jepara, Central Java, which contribute 10% to Indonesia’s national furniture export value (US$1.5 billion). The spatial analysis provides options for making more efficient enterprise clusters for regional development. She gave this presentation at the MODSIM International Congress on Model and Simulations held on 12–16 December 2011 in Perth. The conference took the theme ‘Sustaining Our Future: understanding and living with uncertainty’.

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Spatial Modelling Approach to Clustering the Furniture Industry and Regional Development in Jepara, Indonesia

  1. 1. Spatial Modeling Approach to Clustering theFurniture Industry and Regional Development in Jepara, Indonesia Modelling and Simulation Society of Australia and New Zealand 13 Dec 2011
  2. 2. Structure Introduction Method Result  Spatial distribution  Spatial analysis Conclusion
  3. 3. Introduction• 95% furniture industry managed by small- scale and medium enterprises (SMEs)• Jepara, Central Java, long history of significant furniture industry player• Provide livelihood for + 5m people (direct/indirect) through 15,271 associated enterprises (2005)
  4. 4. Introduction – cont.• Dropped to 11,981 enterprises in 2010 – 96% independent (focus on specific activities, e.g. workshops, sawmills) – 4% integrated (integrate 2 or more activities, e.g. workshop and showroom, log yard and sawmill)• 92% are small scale producers• Furniture industries contributed 27% of Jepara district’s income (2009); accounted for 10% national export value (US$1.5 billion)
  5. 5. Introduction – cont.• SMEs in Jepara formed natural clusters – Not efficiently distributed in obtaining raw materials and marketing• Large number of small workshops were established during the export boom era in 1997/98• Many exited soon after the boom; due to inefficiency: – Unable to cope with increasing raw material price – Unable to fulfill market demands• Aim to analyze the spatial context of efficiency based on the industrial location theory – Total reduction in production costs, including minimizing transportation costs
  6. 6. Method• Two sets of data were used – Spatial census 11,981 enterprises – Detailed intensive survey 2,000 enterprises• Upstream efficiency – Distance from producers (workshops and warehouses) to suppliers (wood)• Downstream efficiency – Distance from producers (workshops and warehouses) to retailers (showrooms)
  7. 7. Method – cont.• The efficiency will affect the industry’s revenue  gross revenue• Efficiency  reduced operation costs and time – Less transportation costs  more efficient
  8. 8. Results – spatial distribution
  9. 9. Results – spatial distribution CEK
  10. 10. Results – spatial distribution
  11. 11. Result – spatial analysis Distance Distance Annual to to Wood Road gross Sub-district Furniture Supplier density revenue (in retailers (km) million Rp.) (km) Bangsri 1.46 10.48 0.0038 231,152 Batealit 0.78 0.79 0.0052 1,308,344 Donorojo 15.64 25.22 0.0033 6,788 Jepara 0.75 1.04 0.0043 1,312,824 Kalinyamatan 1.88 3.14 0.0058 23,528 Kedung 0.46 0.62 0.0055 402,600 Keling 10.58 21.24 0.0029 3,372 Kembang 3.16 14.68 0.0039 24,924 Mayong 1.78 4.16 0.0037 27,978 Mlonggo 0.90 7.22 0.0052 572,754 Nalumsari 8.11 10.92 0.0039 8,374 Pakisaji 1.02 3.24 0.0043 321,344 Pecangaan 0.76 1.22 0.0057 467,858 Tahunan 0.29 0.17 0.0066 3,306,500 Welahan 4.16 6.19 0.0059 7,380
  12. 12. Result – spatial analysis35302520 Distance to Wood Suppliers (km)15 Distance to Furniture10 retailers (km) Annual gross revenue 5 0 Pakisaji Welahan BATEALIT Keling Kalinyamatan Nalumsari Pecangaan TAHUNAN Mayong Donorojo Kembang JEPARA Kedung Bangsri Mlonggo
  13. 13. Result – spatial analysis
  14. 14. ConclusionFurniture industry in Jepara has:• Different downstream and upstream efficiency• significance correlation • furniture workshop  retailers • furniture workshop  suppliers• Furniture industry in Jepara is more buyer driven• Future development of Jepara needs to consider: • Spatial configuration of furniture retailers and wood suppliers • Road network
  15. 15. THANK YOU

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