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

Abstract

This paper presents a conceptual model for a renewed consideration of the complex adaptive systems (CAS) perspective in operations and supply chain management research. A literature review identifies the approaches taken in published research to examine issues such as complexity, adaptation, and emergent behavior. We present a revised conceptual framework that offers directions for embracing key tenets from CAS research so as to gain deeper insights into pertinent issues within the field. We introduce the articles that are part of this special issue and highlight how these articles relate to the conceptual framework proposed in the paper. We also propose some methodological directions that can help in undertaking rigorous investigations of some important aspects that have theoretical and managerial significance.

Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak

“…Ecological modelling is a research area concerned with the analysis of ecosystems in dynamics (Gross, Ebenhöh, andFeudel 2004, 2009). Recent literature point to resemblance of the SCs to ecosystems (Byrne et al 2018;Gross, MacCarthy, and Wildgoose 2018;Demirel et al 2019;Nair and Reed-Tsochas 2019). The applications of ecological modelling to SC uncertainty have been mostly focused on the stability analysis and multi-echelon SC synchronisation in terms of balancing the demand and inventory levels (Anne, Chedjou, and Kyamakya 2009;Demirel et al 2019;Mondal 2019).…”

Theorizing Supply Chains with Qualitative Big Data and Topic Modeling

“…We believe that qualitative Big Data would be particularly helpful in theorizing supply chains as complex adaptive systems (CAS), which have been largely understudied (Nair & Reed‐Tsochas, 2019). Quantitative Big Data has helped to study supply chains as CAS through dynamic modeling.…”

“…The “ complexity ” of the supply chain system is determined by the number, quality, and patterns of interconnections among the organizations (Choi, Dooley & Rungtusanatham, 2001; Nair & Reed‐Tsochas, 2019). The bases for these connections are many, some of which are visible such as the flow of products, services, and money, and some of which are not‐so‐visible, such as the exchange of information and knowledge, the existence of dense social ties, and the awareness of others’ competitive actions and connections (Johnson et al, 2018; Lu & Shang, 2017).…”

“…Most supply chain researchers see supply chains as well‐defined bounded structures and focus on a single level of analysis at a single point in time and on specific organizations, products, or services (Miller et al, 2018). For the most part, supply chain researchers have modeled average, linear, causal behavior of “small causes lead to small effects” (Nair & Reed‐Tsochas, 2019: 89). Whereas a bilateral exchange between two organizations is relatively easy to see and measure, it is much more challenging to collect and analyze data that reveal patterns among interconnected elements across levels and over time.…”

A Mathematical Modelling for Workflows

“…It is necessarily understood that the time occurs only if the information is processed, so the probability of time is dependent on the probability distributions of the information and the processing. However, the probabilistic distributions of I and T assume behavior in a sample space that does not have fixed intervals, since they come from complex adaptive systems [37] and with a degree of freedom for any resultant that varies from individual to individual [1]. In this way, it is possible to assume that every learning process as well as cognitive information processing derives not from a predefined sample of information given.…”