Complexity and Adaptivity of Buyer-Supplier Networks

Buyer-Supply networks are composed of multiple numbers of firms from a variety of interrelated industries. Such networks are subject to shifting strategies and objectives within a dynamic environment, guided by (micro factors) internal factors of the individual firms and also by the (macro factors) industry dynamics of the same. Today, supply chain management  involves adapting to changes in a complicated global network of organizations. As a result, buyer-supplier network decisions and the optimization of the same have become the center stage and concerns the scrutiny of the top level managers.

Two emergent challenges that managers frequently have to address when making these decisions are the structural intricacies of their interconnected supply chains and the need to learn and adapt their organization in a constantly changing environment to ensure its long-term survival. Complex interconnections between multiple suppliers, manufacturers, assemblers, distributors, and retailers are the norm for industrial supply networks. When decision making in these networks is based on non-complex assumptions problems are often hidden, leaving plenty of room for understanding and improving the underlying processes.

Along with managing the complexity inherent in the inter-connectivity of their supply networks, organizations have also started to learn the benefits of being adaptive in their behavior. Because organizations exhibit adaptivity and can exist in a complex environment with myriad relationships and interactions, it is a natural step to identify a supply network as a CAS. Research indicates that that supply networks should be recognized as CAS by providing a detailed mapping of each property of CAS to a supply network.

  1. A CAS consists of entities that interact with other entities and with the environment by following a set of simple decision rules (i.e., schema). These entities may evolve over time as entities learn from their interactions. In contrast to relational modeling, which tries to use one set of variables to explain variation in another set of variables, CAS examines how changes in an individual entity’s schema lead to different aggregate outcomes.
  2. A CAS is self-organizing. Self-organization is a consequence of interactions between entities. Self-organization is defined as a process in which new structures, patterns, and properties emerge without being externally imposed on the system. Because the behavior in complex systems comes from dynamic interactions among the agents and between the environment and the agents, the changes tend to be nonlinear with respect to the original changes in the system.
  3. A CAS coevolves to the edge of chaos, just like coevolution, positing that a CAS reacts to and creates its environment so that as the environment changes it may cause the agents within it to change, which, in turn, cause other changes to the environment.
  4. A CAS is recursive by nature, and it recombines and evolves over time. Furthermore, from a macroeconomic viewpoint, it can be posited that industry supply networks are interrelated within a national or international context and interact together as a CAS in a larger context.

Supply chain research has gained a lot ever since the conceptualization of buyer-supplier networks was done through CAS. What do you think should be the way ahead?

adapted from pathak et al.,2007
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Author: Kar

Dr. Kar works in the interface of digital transformation and data science for business management domains. Professionally a professor (IIT, IIM) and an alumni of XLRI, he has extensive experience in teaching, training, consultancy and research in reputed institutes. He is a Regular Contributor of Business Fundas and a blogging addict. Note: The articles authored in this blog are his personal views and does not reflect that of his affiliations.