Information Flow Structure in Large-Scale Product Development Organizational Networks


Cite as:

D. Braha, Y. Bar-Yam, Information Flow Structure in Large-Scale Product Development Organizational Networks, to appear in Smart Business Networks, Peter Vervest, et al Eds. Chap. 8 (Springer Verlag, 2004).


Introduction

On February 1, 1997, a major fire swept through one of Aisin Seiki’s plants supplying brake fluid proportioning valves (or P-valves) to all Toyota vehicles manufactured by Toyota-group plants in Japan (Reitman,1997; Nishiguchi and Beaudet, 1998). The sole reliance of Toyota on Aisin Seiki’s supply and the low inventory levels of the P-valves inventory due to a just-in-time (JIT) operating environment threatened to shut down Toyota’s 20 auto plants in Japan for weeks and damage local economies. Surprisingly, Toyota’s car factories succeeded to recover their operations in only five days after the fire. The admirable Toyota’s quick recovery can be attributed to the cohesive network structure of suppliers working with Toyota directly and indirectly. This enabled Toyota to rapidly reconfigure the supply chain network and pull together 36 suppliers, supported by more than 150 subcontractors, who produced small batches of P-valves on nearly 50 separate improvised tooling systems and production lines Reitman (1997). The above supply chain disaster recovery illustrates the importance of coordination and collaboration among supply chain partners (e.g., manufacturers, suppliers, and retailers) as a means for achieving greater strategic and operational value to the organization. Today, supply chain integration is further realized by complex business-to-business interactions via information technology, most importantly the Internet (Kambil and van Heck, 2002). In such supply chain networks partners are involved in an intricate web of information transfer such as demand data, inventory status, and shipment schedules.

The usefulness of understanding organizational network structure as a tool for assessing the effects of decisions on organizational performance has been illustrated in the social science and management literatures (Cross et al., 2002). There it has been shown that informal networks of relationships (e.g., communication, information, and problem-solving networks) -- rather than formal organizational charts -- determine to a large extent the patterns of coordination and work processes embedded in the organization (Cross et al., 2002). In recent years, networks have also become the foundation for the understanding of numerous and disparate complex systems outside the field of social sciences (e.g., biology, ecology, engineering, and internet technology, see Albert and Barabási (2002) and Newman (2003).

 

 

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