Discussing the Use of Complexity Theory in Engineering Management: Implications for Sustainability

Abstract

What is the state-of-the-art literature regarding the adoption of the complexity theory (CT) in engineering management (EM)? What implications can be derived for future research and practices concerning sustainability issues? In this conceptual article, we critically discuss the current status of complexity research in EM. In this regard, we use IEEE Transactions on Engineering Management, because it is currently considered the leading journal in EM, and is as a reliable, heuristic proxy. From this journal, we analyze 38 representative publications on the topic published since 2000, and extrapolated through a rigorous keyword-based article search. In particular, we show that: (1) the adoption of CT has been associated with a wide range of key themes in EM, such as new product development, supply chain, and project management. (2) The adoption of CT has been witnessed in an increasing amount of publications, with a focus on conceptual modeling based on fuzzy logics, stochastic, or agent-based modeling prevailing. (3) Many key features of CT seem to be quite clearly observable in our dataset, with modeling and optimizing decision making, under uncertainty, as the dominant theme. However, only a limited number of studies appear to formally adhere to CT, to explain the different EM issues investigated. Thus, we derive various implications for EM research (concerning the research in and practice on sustainability issues).

Keywords: complexitytheory; engineeringmanagement; management; sustainability; conceptual

1. Introduction

What is the state-of-the-art literature regarding the adoption of the complexity theory (CT) in engineering management (EM)? What implications can be derived for future research and practices concerning sustainability issues? In EM, addressing these questions through a critical discussion of extant findings is relevant if we consider two, intertwined aspects.

First, in general, the adoption of approaches based on CT has become, in the 21st century, increasingly popular and highly supported. Concerning sustainability related issues, in particular, this is seemingly evident, especially when research grants, funding opportunities, and/or public tenders are released on themes regarding, for example, technology management, open innovation, circular economy, green procurement, or, more generally, sustainable ecosystems [1].

Second, as also highlighted by our analysis in this article, in the 21st century, the use of complexity approaches recurs in decision-making problems, regarding how to improve the effectiveness and efficiency of new product development (NPD), project management (PM), and supply chain management (SCM), or team organization. We know that these aforementioned problems have always been considered as key themes in EM. At the same time, we are confident that, to date, they also represent key challenges towards more sustainable business models [2].

As an example, in addressing a central issue for technology management research, i.e., understanding the nature of the industry environments in which firms play, Ndofor et al. [3] argue that “if the microfoundations of industry environments are indeed strongly impacted by nonlinear relationships, then the industry environment would evolve with chaotic dynamics, as opposed to equilibrium systems” (p. 200). Relatedly, as maintained by McCarthy et al. ([4], p. 437), “early research on NPD has produced descriptive frameworks and models that view the process as a linear system with sequential and discrete stages. More recently, recursive and chaotic frameworks of NPD have been developed, both of which acknowledge that NPD progresses through a series of stages, but with overlaps, feedback loops, and resulting behaviors that resist reductionism and linear analysis.”

In the same vein, as stated by Amaral and Uzzi ([5], p. 1034), “a design engineer may know about the reliability of individual parts but find it difficult to estimate how failures in one part of system are tied together or how errors might cascade through the system when apparently separate components have a low probability of failure.” Likewise, as posited by Baumann and Siggelkow ([6], p. 116), “should a product design team always consider all components simultaneously, searching for designs that have high overall performance? Or should it first experiment with a subset of components and expand this set gradually in the course of the design process?”

On this premise, starting in the 1960s, several contributions to CT have arisen from various science disciplines, such as biology, mathematics, physics, chemistry, and information technology [7,

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