The Impact of Control and Complexity on Supply Network Performance: An Empirically Informed Investigation Using NK Simulation Analysis

Abstract

Control over suppliers is a core issue for a buying firm. Despite the amount of research on the topic, how much of its supply network (i.e., scope of control) a buying firm should control, so as to increase supply network performance, is a question that has not received adequate research attention. The study addresses this research question by considering the supply network as a complex adaptive system and developing an empirically informed agent‐based simulation model using the NK fitness landscape framework, to examine how varying levels of scope of control influence supply network performance. We also investigate the direct effect of two supply network complexity dimensions (i.e., number of firms and level of supply interactions) on supply network performance and the moderating effect played by the scope of control. Results show that the relationship between scope of control and supply network performance follows an inverted‐U shape. Furthermore, we find that the complexity dimensions negatively affect supply network performance with the performance decrease depending on the scope of control. Based on these findings, we formulate different control strategies to mitigate the negative influence of complexity.

Team Resilience in Complex and Turbulent Environments: The Effect of Size and Density of Social Interactions

“…It is consistent with a long tradition of creating simple yet insightful models of the organizations as complex adaptive systems, by means of NK fitness landscape in both singlefirm [31,[33][34][35][36] and multifirm [37][38][39][40][41][42] contexts. In these stream of studies, the organization is supposed to search for high-performing combinations of N interdependent decisions (choice configurations) [43].…”

“…The higher K, the more complex the landscape is. Solving a NK Kaufmann combinatorial problem, that is, finding the optimum of the NK landscape, is classified for K > 2 as a NPcomplete problem [68] (for details about the landscape generation see [25,28,29,[37][38][39][40][41]69] …”

Revisiting the complex adaptive systems paradigm: Leading perspectives for researching operations and supply chain management issues

“…The CAS perspective calls for considering part “in vivo.” Investigating how changes in one part affect others and the behavior of the whole can help in assessing the inherent nature of CAS, such as supply networks. In this regard, this CAS perspective can help in further discerning how control and emergence operate in supply networks (Giannoccaro et al, ).…”

Innovation Search Scope, Technological Complexity, and Environmental Turbulence: A N-K Simulation

“…Since Kauffman [16] introduced the N-K model into the study of biological evolution, the model has been applied widely by innovation scholars to simulate the complex system of management [17][18][19]. Technology is also a complex system with interdependent components and hence, using the N-K model is suitable for use in simulating the technology innovation process [15]. Our formal baseline model is as follows:…”

“…In our paper, innovation search scope is operationalized as the number of technological components developed simultaneously during the innovation process. Each technology could be considered as a system with interdependent components and thus, it is suitable for use in analyzing the process of technology innovation by the N-K model [15], which allows us to compare the different levels of innovation search scope. Our argument starts with the observation that during the innovation process, some firms choose to treat technology components one by one, while others deal with several components simultaneously.…”