High-Technology Clusters: Specialisation, Interaction and Transportation. G. M. Peter Swann Manchester Business School University of Manchester, UK

Transcription

1 High-Technology Clusters: Specialisation, Interaction and Transportation G. M. Peter Swann Manchester Business School University of Manchester, UK Workshop on High-Tech Business and Clusters Robinson College, Cambridge, 28th May 2003 A supporting paper is available from: pswann@man.mbs.ac.uk

2 Objectives To recognise the role of specialisation and interaction in the development of clusters. and therefore, to analyse the role of a cost-effective communication and transportation infrastructure in cluster development Illustrate with reference to personal computer manufacture.

3 What sort of Cluster are we discussing here? The term cluster has come to mean different things to different authors as Ron Martin has shown Here we discuss the highly specialised and narrowly defined clusters that emerge in some high-tech industries A value chain is broken into many different stages and companies (and indeed clusters) specialise in a small part of that value chain Illustrate this with reference to manufacture of personal computers:

4 Personal Computer (PC) Manufacture In PC manufacture, it is almost meaningless to ask in what country a PC is manufactured: Open up a personal computer and inspect the electronic chips: they read like the United Nations (Scott and Hayen, 1991) The various components are manufactured in many different countries, assembly may be done in more than one country, and the final "badge" may be added somewhere else again.

5 Origin of Components for Typical Personal Computer Brand: USA Final Dispatch: Ireland Main Box: Ireland Motherboard Chips: USA, Korea, Taiwan, Philippines Motherboard Battery: Philippines CD ROM Drive China (Japanese Parts) CD-R (consumables) Germany Hard Disk Drive: Singapore 3.5" Floppy Disk Drive: Philippines

6 Origin of Components. (continued) Modem Card: Netherlands (chips from USA, Korea, Taiwan) Graphics Card: China (chips from USA, Korea, Taiwan) Specialist Video Card: USA Monitor: UK (origin of components?) Keyboard: Mexico Mouse: Mexico Child's Mouse: Taiwan

7 Origin of Components. (continued) Loudspeakers: Malaysia Microphone: Mexico Inkjet Printer: Spain Zip Drive: Malaysia Scanner: Taiwan Webcam: China Power Supplies: Taiwan, China, Malaysia, Mexico Manuals: Scotland, Ireland, Wales, Germany Environmental Mark: Sweden

8 Internationalisation It is well recognised that Personal Computer industry is one of those in which internationalisation and intraindustry trade is furthest developed: vertical trading chains spanning a number of countries, each specialising in a particular stage of production (OECD, 2002) This splitting of production across countries may be most beneficial for complex manufactured products, involving many components (OECD, 2002) Krugman (1995) speaks of supertrading countries which depend on the slicing up of the value added chain

9 Internationalisation and Shocks This internationalisation of production means that a shock in any one country can have implications for a widely dispersed supply chain E.g. the major earthquake in Taiwan (September 1999) caused disruption to many parts of the high-tech industry: It will ripple through the whole supply chain (CNET News.com, 1999) This is an old idea, see for example, Jevons (1878): A second disadvantage of the division of labour is that trade becomes very complicated, and when deranged, the results are ruinous to some people.

10 Internationalisation and Clusters The processes of internationalisation and clustering are inter-related Particular companies and indeed particular clusters may become specialised in one (or a few) particular part(s) of the overall process In short, we have a process of specialisation and interaction which depends on transportation for its development

11 Simple Model: Key Concepts Cluster development proceeds along with division of labour and specialisation The cluster model of economic development requires interaction between different stages of supply chain, and hence a cost-effective communication and transportation structure This interaction may be the rich exchange of tacit knowledge of the (mythical?) Silicon Valley model... Or it may be the more mundane interaction required for completion of supply chain logistics

12 Figure 1 Geographical Maps of City, Towns and Villages

13 Figure 2.1 Dispersed: Production Co-located with Demand

14 Figure Vertically Integrated Towns

15 Figure Vertically Integrated Towns

16 Figure 2.4 Single City Cluster

17 Figure Specialised Clusters

18 70 Figure 3 Transportation to Customers Frequency by Length of Journey Frequency Clusters 1 City 4 Towns 8 Towns Dispersed

19 Model of Specialisation and Interaction Two Parts: Model of Production Costs - with economies of scale, scope and agglomeration Model of Interaction Costs - between production stages and final customers

20 Model of Specialisation and Interaction: (1) Production Costs Any production stage enjoys economies of scale: { } PC = X exp α X PC ij ij j ij i N = j= 1 PC ij Any cluster enjoys economies of scope: N PC = PC exp β X i i ij j= 1

21 Model of Specialisation and Interaction: (1) Production Costs, continued. Any cluster enjoys economies of agglomeration, but with a risk of congestion costs in very large clusters PC PC.exp Z X Z X 2 N N i = i γ1 i + γ ij + 2 i + ij j= 1 j= 1 PC = K i= 1 PC i

22 Model of Specialisation and Interaction: (2) Interaction Costs Communication costs in a given supply chain: ( 1 θ ) [ ] h pq pq pq C C { } CC = Φ e + f.d i( p,h ),i( q,h ) Transportation costs in a given supply chain : h pq pq pq T T { [ ]} TC = θ Φ e + f.d i( p,h ),i( q,h )

23 Model of Specialisation and Interaction: (2) Interaction Costs, continued... Interaction costs are the sum of these two, summed over all nodes of the supply chain, and all supply chains IC = CC + TC h h h pq pq pq IC H N N = h= 1 p= 1 q= 1 IC h pq

24 Parameter Values and Model Outcomes (1) a simple chart of how the most efficient structure depends on the parameters (2) a radar chart of average parameter values amongst simulations with a particular outcome

25 Parameter Values and Model Outcomes: (1) Simple Chart a two-dimensional mapping from parameters to outcomes, under the following assumptions: Online communication costs are negligible (e C = f C = 0) Transportation costs within a cluster are negligible (e T = 0) Offline proportion (θ ) is fixed at 10% Congestion cost parameter (γ 2 ) is fixed at Scope economy parameter is half the value of the scale economy parameter (β = α /2) The agglomeration economy parameter is one tenth of the scale economy parameter (γ 1 = α /10)

26 Table 1 Total Communication/Transportation Distances, by Industrial Structure A B B C C D D FC 4 Specialised Clusters City Cluster Vertically Integrated Towns Vertically Integrated Towns Dispersed Production Colocated with Customers

27 Figure 4 Outcome by Parameter Values Transport Cost Parameter (x 10-1 ) Towns Dispersed (Production Co-located with Demand) 4 Towns 8 Towns Clusters 1 City Scale Economy Parameter (x 10-2 ) (also scope and agglomeration)

28 Table 2 Ranking of Different Industrial Structures by Efficiency, for certain parameter values Scale = 0 Scope = 0 Agglomeration = 0 Congestion = Scale = 0.05 Scope = 0.25 Agglomeration = Congestion = Scale = 0.1 Scope = 0.05 Agglomeration = 0.01 Congestion = Offline = 10% Comm Cost = 0 Transp Cost = 26 8 Towns (100%) Dispersed (75%) 4 Towns (68%) 4 Clusters (11%) 1 City (0%) Dispersed (100%) 8 Towns (40%) 4 Towns (29%) 1City (19%) 4 Clusters (3%) Dispersed (100%) 8 Towns (25%) 4 Towns (16%) 1 City (10%) 4 Clusters (1%) Offline = 10% Comm Cost = 0 Transp Cost = 1 8 Towns (100%) 4 Towns (73%) 4 Clusters (53%) Dispersed (43%) 1 City (0%) 4 Towns (100%) 1 City (95%) 8 Towns (51%) Dispersed (31%) 4 Clusters (21%) 4 Towns (100%) 8 Towns (69%) 1 City (69%) Dispersed (26%) 4 Clusters (9%) Offline = 10% Comm Cost = 0 Transp Cost = Towns (100%) 4 Towns (74%) 4 Clusters (73%) Dispersed (41%) 1 City (0%) 4 Clusters (100%) 1 City (53%) 4 Towns (39%) 8 Towns (14%) Dispersed (7%) 1 City (100%) 4 Towns (52%) 4 Clusters (14%) 8 Towns (7%) Dispersed (2%)

29 Parameter Values and Model Outcomes: (2) Radar Chart a 6-dimensional radar chart showing the average value for each parameter amongst all simulations with a particular outcome: compute model outcomes over a grid of 4 6 = 2 12 = 4096 different parameter combinations and outcomes for all simulations in which a particular structure is most efficient, we compute the average value of each parameter each axis runs from 0% to 100%, since each parameter average is expressed relative to the maximum value for that parameter

30 Table 3 Grid of Parameter Values Value 1 Value 2 Value 3 Value 4 Scale Economy Scope Economy Agglomeration Economy Congestion Costs Offline % 10% 40% 70% 100% Transportation Costs

31 Figure 5 Mean Parameter Values for Simulations with Different Outcomes Scale Transport Costs Scope Offline % Agglomeration 4 Towns 8 Towns 1 City 4 Clusters Dispersed Congestion Costs

32 Conclusions The specialised cluster strategy requires cost effective transportation and communication Codification may increase the proportion of transactions online, but cannot displace the face-to-face If transportation is too expensive to support specialised / distributed clusters, then the single city cluster may be the the only viable clustered structure But is the single city cluster desirable? Congestion costs Unbalanced development Very relevant to UK

33 Conclusions, continued Distributed cluster structures may be the only way to provide the mix of economies of scale, scope and agglomeration required for international competitiveness while avoiding congestion costs of single city cluster. But if the social cost of transportation is higher than private cost, then distributed cluster structures may be environmentally unsustainable