Abstract
With the objective of delineating a clear understanding of the nature, intensity and geographic traits of the inter-relatedness between the component suppliers and automotive manufacturers, this chapter presents an analysis of the Indian automotive industry from a network perspective. Based on secondary data of the industry, we elaborated in the preceding two chapters on the characteristic features of the industry, viz., regional distribution of firms, distribution of products across firm size and regions, etc. The illustration, while equipped us with substantial information on the general trend and structure of the industry, does not lend adequate insight into the way firms interact, coordinate and how certain firms can potentially ply influence on others in the industry. Moreover, given that distinct regionalization is visible in the Indian automotive industry, a natural question arising in this context is whether the ‘relational structure’ of the auto-component and automotive firms in this industry is ‘localized’ in nature. This chapter tries to shed some light on this specific aspect although complementary analysis on the general structure, intensity and nature of the relation is also performed to get an insight into the structure of the Indian automotive industry. By thus invoking a concrete network framework to describe the industry structure and nature of interrelatedness, this chapter complements and extends our previous analysis (in the preceding chapter) in unravelling some of the underlying dynamics of the Indian automotive industry.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
As envisaged respectively as buyers and suppliers in this Chapter.
- 2.
Social network analysis is an interdisciplinary methodology developed mainly by sociologists and social psychology, further developed in collaboration with mathematics, statistics, and computing that led to a rapid development of formal analyzing techniques used widely in several disciplines like economics, marketing or industrial engineering (Scott 2000).
- 3.
Here, customers are automotive firms and suppliers are auto component firms. Networks have been used recently as metaphor to B-S relations since these relationships tend to involve many firms of different size and status, through both direct and indirect relationships of varying length (Martin et al. 1995). For fluidity of expression, the terms customers and buyers/suppliers and sellers are used interchangeably in the text of the Chapter.
- 4.
Vertical disintegration offers many advantages such as greater corporate efficiency, and profitability. They have assumed increased significance with the growth of complex manufacturing products.
- 5.
In the specific case of automotive industry, this unequal power balance becomes even more prominent due to the apparent size differences among the automotive manufacturers and their supplier firms.
- 6.
The term automotive is used in a broader sense including both the vehicle manufacturers as well as the tier-1 firms in the industry.
- 7.
Take for instance a horizontal network of co-authors where all the actors would probably have same tie strength thus implying equal balance of power.
- 8.
In 1980, 181,000 vehicles of all types were produced, 17% of which were passenger cars, whereas in the year 2000 the corresponding figures stood at 1.03 million and 50% respectively (Facts and Figures, ACMA 2001b, ACMA).
- 9.
Tata Engineering and Locomotive Company (TELCO) is the largest indigenous conglomerate in the Indian automotive industry. Known widely as Tata Motors, the company produces a wide range of Commercial Vehicles, Passenger Cars and Multi-Utility Vehicles.
- 10.
For example, MUL practises a production system called “Maruti Production System” that focuses on the elimination of wasteful activities in their manufacturing processes in which the company work with the vendors in areas such as improving their productivity, reducing the number of their components that are rejected, reducing materials handling, improving their yield from materials, and reducing their inventories etc. In some cases, MUL sets targets with vendors for cost reduction, and for the initial period of the cost reduction, it shares the benefits of the reduction with the vendor to provide an additional incentive for the vendor to reduce costs. In addition, MUL also has plans to integrate their vendors into the worldwide purchase system, or WWP, whereby a vendor may become the sole supplier for a Suzuki product in several countries including India. This would generate economies of scale for the vendor (Source: MUL document at http://www.myiris.com/shares/ipo/draft/MARUDYOG/MARUDYOG.pdf).
- 11.
The introduction of new models such as Matiz, Indica and Accent, and advance purchases in anticipation of increase in sales tax rates primarily contributed to this.
- 12.
In fact, the trend started from the early 1990s onwards when the multinational firms that entered the Indian market were required to achieve a high level of domestic content within a specified period (typically, 70 % within 3 years) which forced them to switch rapidly from a reliance on importing components to procuring them locally (Sutton 2004). This gave the vehicle manufacturers a strong incentive to work closely with their suppliers in order to ensure greater productivity and high quality standards.
- 13.
These include the world wide web (Albert et al. 1999), the internet (Faloutsos et al. 1999), power grids (Watts and Strogatz 1998), metabolic and protein networks (Jeong et al. 2000; Jeong et al. 2001), food webs (Montoya and Solé 2002), scientific collaboration networks (Amaral et al. 2000; Newman 2001a, b, c), and software architecture (Valverde et al. 2002) and the like.
- 14.
The research of Barabási's team on Hollywood connectivity and the structure of the World Wide Web yielded an important finding: neither Hollywood actors nor web sites are connected in the sort of random network assumed by Erdos and Renyi.
- 15.
The list of non-automotive firms is actually very few in the data which therefore does not pose a potential problem to our focus on the automotive industry. In fact most of the non-automotive firms are actually the firms producing farm equipments such as tractors, earth moving machinery as well as other agricultural machinery. Hence in that sense our data set can be quite safely treated to represent only the automotive industry only.
- 16.
We cover only the organised sector of the industry, as there is no comprehensive database on the unorganised segment. From the list of auto component firms and their citations of their customers in the domestic market, we have created a database on supplier-customer network. It may be mentioned here that this list of customers includes only the firms that have been self-reported by the auto component firms as their principal customers. This is compiled by ACMA in their annual publication ‘Buyers Guide’.
- 17.
It may be pointed out here that since the data matrix was primarily constructed from the citations of the auto component firms (416 in our case), the rows for the customer firms (i.e., for the 202 firms) contains only 0’s as we don’t have any information on their customer base.
- 18.
This artificial random network is generated in Pajek which is a program under UCINET to analyse and visualize large network datasets.
- 19.
Centrality, also used synonymously with ‘prominence’ in the social network analysis, refers to the identification of the ‘most important’ actors in the network. In the SNA literature there are a variety of measures designed to quantify the prominence of individual actors embedded in the network, viz., degree, closeness, betweenness etc., (see Wasserman and Faust 1994 for definitions of the various measures).
- 20.
We have used the discrete version of the core-periphery model (see Borgatti and Everett 1999 for a discussion of these concepts) using UCINET 6.2. To test the robustness of the solution the algorithm has been run a number of times from different starting configurations. It shows that there is good agreement between these results which ensures our finding that there is a clear split of the data into a core-periphery structure.
- 21.
This is based on an arbitrary cut-off value of 40 which was considered for our convenience. This was purposefully chosen to see if there are some component firms in the core list of firms in the industry. The list of top firms can be bigger or smaller depending on the cut-off.
- 22.
For example, Motor Industries (known as Motor Industries Company Ltd, MICO and is a subsidiary of Robert Bosch, Germany), has strong presence in the Indian automotive components business with a virtual monopoly in the Diesel Fuel Injection Equipment, Spark Plugs segments and also in the Electric Power Tools segment. Similarly Lucas TVS (a joint venture of Lucas Industries, UK and T V Sundaram Iyengar & Sons or TVS) is one of India's top twenty largest industrial houses with twenty-five manufacturing companies and a turnover in excess of US$ 1.3 billion. The company has a strong presence in the design, manufacture and supply of advanced technology systems, products and services to the world's automotive, diesel engine and aerospace industries. The other firm, Fenner India (called as Fenner (India) Limited) is the largest manufacturer and market leader of Industrial and Automotive Oil seals and Power Transmission Accessories in India.
- 23.
There are several ways to find the subgroups in the network which are broadly classified into agglomerative measures and divisive measures (see Wasserman and Faust 1994 for details). Newman and Girvan (2003) algorithm is particularly helpful when the community structure is already known. In these kinds of approaches, one starts with the whole network and iteratively removes the edges, which are least similar, thus dividing the network progressively into smaller and smaller disconnected sub networks within the large network.
- 24.
A number of definitions for identifying cohesive subgroups, within networks have been developed in social network analysis literature (for review, see Wasserman and Faust 1994). The most common definitions for cohesive subgroups within symmetric networks include cliques, n-cliques, n-clans, K-cores, K-plexes etc.
- 25.
This feature of non-overlapping lambda sets makes them a better way of representing subgroups than other concepts like cliques in which overlap is often very large.
- 26.
Since this measure is concerned with any connection between members, the directions of ties are ignored (i.e. either an out-tie or an in-tie constitutes a tie between two actors).
Bibliography
ACMA. (2000). Facts and figures. New Delhi: ACMA.
ACMA. (2001a). Buyers guide. New Delhi: ACMA.
ACMA. (2001b). Facts and figures. New Delhi: ACMA.
ACMA. (2001c). Status of the Indian automotive industry. New Delhi: ACMA.
ACMA. (2002). Status of the Indian automotive and auto-component industry. New Delhi: ACMA.
ACMA. (2003a). Status of the Indian automotive industry. New Delhi: ACMA.
ACMA. (2003b). Facts and figures: Automotive industry of India, 2001-02. New Delhi: ACMA.
ACMA. (2004). Facts and figures: Automotive industry of India, 2002-03. New Delhi: ACMA.
Albert, R., & Barabási, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74, 47–97.
Albert, R., Jeong, H., & Barabási, A.-L. (1999). Diameter of the world wide web. Nature, 401, 130–131.
Amaral, L. A. N., Scala, A., Barthélémy, M., & Stanley, H. E. (2000). Classes of small-world networks. PNAS USA, 97, 11149–11152.
Asanuma, B. (1989). Manufacturer-supplier relationships in Japan and the concept of relation-specific skill. Journal of the Japanese and International Economics, 3, 1–30.
Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.
Borgatti, S. P., & Everett, M. G. (1999). Models of core/periphery structures. Social Networks, 21, 375–395.
Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for Windows: Software for social network analysis. Harvard: Analytic Technologies.
Calabretta, R., Nolfi, S., Parisi, D., & Wagner, G. P. (1998). A case study of the evolution of modularity: Towards a bridge between evolutionary biology, artificial life, neuro- and cognitive science. In C. Adami et al. (Eds.), Proceedings of the Sixth International Conference on Artificial Life (pp. 275–284). Cambridge, MA: MIT Press.
D’Costa, A. P. (2004). Flexible practices for mass production goals: Economic governance in the Indian automobile industry. Industrial and Corporate Change, 13(2), 336–367.
Das, S., & Rao, Ch. S. (2004). Trade liberalization, imported inputs and factor efficiencies: Evidence from the auto components industry in India (Discussion Paper Series, No: 04-05). Delhi: Indian Statistical Institute.
Dyer, J. H. (1997). Effective interfirm collaboration: How firms minimize transaction costs and maximize transactional value. Strategic Management Journal, 18, 535–556.
Erdös, P., & Rényi, A. (1959). On random graphs. Publicationes Mathematicae, 6, 290–297.
Faloutsos, M., Faloutsos, P., & Faloutsos, C. (1999). On power-law relationships of the internet topology. Computer Communications Review, 29, 251–262.
Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40, 35–41.
Garton, L., Haythornthwaite, C., & Wellman, B. (1997, June). Studying online networks. JCMC, 3(1).
Helper, S. (1991). Strategy and irreversibility in supplier relations: The case of the U.S. automobile industry. Business History Review, 65, 781–824.
Huberman, B. A., & Adamic, L. (2003). Information dynamics in the networked world. In B.-N. Eli et al. (Eds.), Complex networks. Lecture notes in physics. Heidelberg: Springer.
Jeong, H., Mason, S., Barabási, A.-L., & Oltvai, Z. N. (2001). Lethality and centrality in protein networks. Nature, 411, 41–42.
Jeong, H., Tombor, B., Albert, R., Oltavi, Z. N., & Barabási, A.-L. (2000). The large-scale organization of metabolic networks. Nature, 407, 651–654.
Kathuria, S. (1996). Competing through technology and manufacturing: A study of the Indian commercial vehicles industry. New Delhi: OUP.
Kotabe, M., Martin, X., & Domoto, H. (2003). Gaining from vertical partnerships: Knowledge transfer, relationship duration, and supplier performance improvement in the U.S. and Japanese automotive industries. Strategic Management Journal, 24, 293–316.
Martin, X., Mitchell, W., & Swaminathan, A. (1995). Recreating and extending Japanese automobile buyer-supplier links in North America. Strategic Management Journal, 16, 580–619.
Montoya, J. M., & Solé, R. V. (2002). Small world patterns in food webs. Journal of Theoretical Biology, 214, 405–412.
Newman, M. E. J. (2001a). Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E, 64, 016132.
Newman, M. E. J. (2001b). The structure of scientific collaboration networks. PNAS USA, 98, 404–409.
Newman, M. E. J. (2001a). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, 64, 016131.
Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45, 167–256.
Newman, M. E. J., & Girvan, M. (2003). Finding and evaluating community structures in networks. arXiv: cond-mat 0308217.
Olhager, J., & Prajogo, D. (2012). The impact of manufacturing and supply chain improvement initiatives: A survey comparing make-to-order and make-to-stock firms. Omega, 40(2), 159–165.
Rahaman, A., & Bennett, D. (2009). Advanced manufacturing technology adoption in developing countries: The role of buyer-supplier relationships. Journal of Manufacturing Technology Management, 20(8), 1099–1118.
Rives, A. W., & Galitski, T. (2003). Modular organization of cellular networks. PNAS USA, 100(3), 1128–1133.
Scott, J. (2000). Social network analysis: A handbook (2nd ed.). London: Sage.
Sutton, J. (2004). The auto-component supply chain in China and India- A benchmarking study. London School of Economics and Political Science: The Toyota Centre Suntory and Toyota International Centres for Economics and Related Disciplines.
Valverde, S., Cancho, R. F., & Solé, R. V. (2002). Scale-free networks from optimal design. Europhysics Letters, 60, 512–517.
Wasserman, S., & Faust, K. (1994). Social network analysis. Cambridge: Cambridge University Press.
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Diebolt, C., Mishra, T., Parhi, M. (2016). Dynamics of Inter-firm Linkages in Indian Automotive Industry: A Social Network Analysis. In: Dynamics of Distribution and Diffusion of New Technology. India Studies in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-32744-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-32744-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32743-3
Online ISBN: 978-3-319-32744-0
eBook Packages: Economics and FinanceEconomics and Finance (R0)