Search results

The purpose of this article is to compare design choices and assess the structural complexity of six manufacturing supply chains (SCs) of the Brazilian wind turbine industry.

The research method is quantitative modeling. This study adopts the social network perspective to provide a broad set of network metrics for comparative analysis and characterization of the structural configuration and complexity of SCs. Transaction costs and the risk of disruption supported the metrics employed in the study. Network size, network density, core-size and centralization metrics stem from transaction costs, whereas constraint and betweenness centrality stem from risk of disruption.

The main conclusion is that, in the Brazilian wind manufacturing industry, increasing the SC structural complexity by adding redundant ties to minimize disruption risks, even implying higher transaction costs, increases the capacity to win orders.

Only the Brazilian wind turbine industry was studied. Therefore, findings are not general, but specific, to the case.

Managers and practitioners of the Brazilian wind turbine industry should focus on increasing the complexity of their SCs, even if it increases transaction costs, to ensure due dates compliance in orders.

To the best of the available knowledge, there is no commonly accepted or shared measurement for SC complexity, and this study proposed an alternative approach to bridge this research gap, the structural perspective of social networks. Traditional measures were complemented by new metrics, and the power of the application of social network analysis to SC investigations was empirically demonstrated in different levels of analysis.

This paper aims to propose a logistical network design framework with robustness and complexity considerations.

The paper defines robustness, complexity, and normalised efficiency of a logistical network. A mathematical model is then constructed based on the conceptual framework and applied to a hypothetical case study with varying robustness requirements. The mathematical model is formulated as an Mixed‐Integer Linear Programming problem. Furthermore, the paper introduces a graph‐theoretic view of the logistical network and presents its topological properties such as average path length, clustering coefficient, and degree distribution.

The results show that logistical network configurations can be obtained with desirable robustness levels whilst minimising cost. The relationships of robustness versus normalised efficiency and complexity are also presented. The results show that relationships between logistical network topological properties and robustness exist, as in other real world natural and man‐made complex networks.

Logistical network design is one of the earliest strategic decisions in supply chain management that supply chain managers have to make. Practitioners and researchers typically focus on optimising efficiency and/or responsiveness of logistical networks. It is argued that logistical network designers should also consider robustness and complexity as they are important characteristics of logistical network functionality. The logistical network design frame work successfully incorporates robustness and complexity into design considerations.

This paper newly introduces other important performance measures, robustness and complexity, into the logistical network design objective. The design framework is highly relevant and adds value to logistical network designers and managers.

The purpose of this paper is to explore the challenges facing companies that operate within complex supply networks and the strategies they use to manage such complex supply networks.

The paper uses mixed methods by combining in-depth case studies with an executive forum with those of senior industry stakeholders. The two in-depth supply network case studies were carried out through multiple interviews with focal (or ‘developer’) firms that supply energy through offshore wind power and key suppliers such as wind turbine manufacturers.

The findings show the challenges the offshore wind power industry faces because of complex supply networks, including attempts by several actors to exert their power and control. Despite the networks facing similar complexities and challenges, two distinctly different strategies for orchestrating and governing supply networks are uncovered: one strategy resembles an interventionist strategy, while the other is based on delegation.

Based on the findings, the authors identify and develop a classification of complex supply network divided into intervention and delegation strategies, thereby adding to existing research on ways to manage complex supply networks.

The authors identify strategies for focal firms for managing in complex supply networks, based on control and intervention or coordination and delegation.

Existing research on supply network strategies has largely focused on non-complex contexts. This paper draws from complex adaptive systems and organisational behaviour perspectives to contribute original insights into supply network strategies in complex supply networks.

In the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper is to review and compile the current AI methods used for cost-risk assessment in the construction management domain in order to capture complexity and risk interdependencies under high uncertainty.

This paper makes a content analysis, based on a comprehensive literature review of articles published in high-quality journals from the years 2008 to 2018. Fuzzy hybrid methods, such as fuzzy-analytical network processing, fuzzy-artificial neural network and fuzzy-simulation, have been widely used and dominated in the literature due to their ability to measure the complexity and uncertainty of the system.

The findings of this review article suggest that due to the limitation of subjective risk data and complex computation, the applications of these AI methods are limited in order to address cost overrun issues under high uncertainty. It is suggested that a hybrid approach of fuzzy logic and extended form of Bayesian belief network (BBN) can be applied in cost-risk assessment to better capture complexity-risk interdependencies under uncertainty.

This study only focuses on the subjective risk assessment methods applied in construction management to overcome cost overrun problem. Therefore, future research can be extended to interpret the input data required to deal with uncertainties, rather than relying solely on subjective judgments in risk assessment analysis.

These results may assist in the management of cost overrun while addressing complexity and uncertainty to avoid chaos in a project. In addition, project managers, experts and practitioners should address the interrelationship between key complexity and risk factors in order to plan risk impact on project cost. The proposed hybrid method of fuzzy logic and BBN can better support the management implications in recent construction risk management practice.

This study addresses the applications of AI-based methods in complex construction projects. A proposed hybrid approach could better address the complexity-risk interdependencies which increase cost uncertainty in project.

The purpose of this paper is to contribute to refine the conceptual framework of complexity. For such a purpose, a number of epistemologically oriented remarks are provided, arguing about the relevance of second-order considerations for complexity and the importance of pluralism in scientific research.

At first, the paper focuses on one of the topical areas of complexity research, i.e. network theory, but uses this for drawing the attention to more general issues. The underlying assumption is that scientific and philosophical research might complement each other, and that this is especially crucial for the advancement of complexity.

The paper suggests three ways for refining the scheme of complexity: analyzing it at the right level, i.e. not focusing on single principles or theories (e.g. network theory), but rather on the overall frame; including both ontological and epistemological considerations; and recognizing how the epistemological implications of complexity foster the adoption of a pluralist stance in scientific research (and beyond).

The way in which science (complexity) is portrayed, i.e. as “perspectival” and inclined to pluralism, could impact on how it is thought, designed and socially perceived.

Complexity is one of most promising fields of contemporary science, but still lacks of a coherent frame of analysis. This requires an investigation from different point of views, as an object of interdisciplinary cooperation. The main paper’s value consists of providing second-order considerations which puts scientific findings in perspective and can contribute to a better understanding of their meaning from a philosophical standpoint too.

The purpose of this paper is to further advance an existing supplier evaluation model for the purpose of identifying those supplier relations which predominantly threaten or worsen a company's performance. A defined basic set of parameters to determine complexity facilitates the identification of critical locations within a supply network (SN) under certain business conditions.

This paper is based on a structured literature review in scientific periodicals in logistics/supply chain management between 2000 and 2009. Articles are analysed based on a structured framework and the identified complexity parameters are operationalised using quantitative and summable measures. The conceptual model is applied within a multiple case study in the Austrian agricultural industry.

This paper illustrates how complexity in SNs can be operationalised in a company‐specific configuration in order to achieve concrete managerial recommendations. Hence, the model allows evaluating SN‐partners based on selected parameters to determine the contribution of a single partner to the overall complexity.

Due to the literature review executed and the case study approach chosen, the research may lack generalisability. Therefore, continued validation by means of implementing a greater amount of use cases in other companies and industries is advisable.

Applying the model, a company is able to determine tier‐1 to tier‐n suppliers which are predominantly affecting its business from a complexity perspective.

Unlike typical current complexity evaluation approaches, the proposed model respects rapid and continuous applicability, profound conceptualisation and practical feasibility.

The purpose of this paper is to propose a data-driven scheme for identifying critical project complexity dimensions and establishing the trade-off across multiple project performance criteria.

This paper adopts a hybrid approach using Bayesian Belief Networks (BBNs) and Artificial Neural Networks (ANNs). The output of the ANN model is used as input to the BBN model for prioritizing project complexity dimensions relative to multiple project performance criteria. The proposed process is demonstrated through a real application in the construction industry.

With a number of nonlinear interactions involved within and across project complexity and performance, it is not feasible to model and assess the strength of these interactions using conventional techniques. The proposed process helps in effectively mapping a “multidimensional complexity” space to a “multidimensional performance” space and makes use of data from past projects for operationalizing this mapping scheme by means of ANNs. This obviates the need for developing a parametric model that is both challenging and computationally cumbersome. The mapping function can be used for generating all possible scenarios required for the development of a data-driven BBN model.

This paper introduces a data-driven process for operationalizing the mapping of project complexity to project performance within a network setting of interacting complexity dimensions and performance criteria. The results of the application study manifest the importance of capturing the interdependency across project complexity and performance. Ignoring the underlying interdependencies and relying exclusively on conventional correlation-based techniques may lead to making suboptimal decisions.

Large‐scale systems have been developed with increasing requirements of high quality and performance. It would be indispensable to define the reliability of more complex systems, considering their complexity. This paper defines a complexity of redundant systems as a logarithmic function of paths, using the concept of an entropy. Further, a reliability function of complexity is given, and the reliabilities of series and parallel systems with complexity are computed. As one of typical redundant systems, a majority voting system is analyzed, and an optimal number of components is derived. Finally, a complexity of network systems is also proposed.

The purpose of this chapter was to describe the development of an early childhood learning network and to understand that development through the lens of complexity theory and trans-organization development. It explores the unfolding dynamics and emerging meaning that became central to facilitating the design, development, and implementation of the complex multi-stakeholder network.

The chapter identifies 12 implications from complexity theory for practitioners of trans-organization development, and then applies those implications to the process and understanding of development stages of an NP’s early childhood education network.

The use of complexity theory as a framework allowed for identifying five key observations and conclusions, in particular the significant role that meaning-making dialogue holds as a driver of common understanding and engagement among stakeholders. Additionally, the use of building responsive processes, understanding contextual shift, impact of power as an attractor pattern, and capacity of flexibility and adaptability all become essential elements of complex network leadership.

The findings of this chapter will help trans-organization development practitioners and leaders alike. Both the implications and lessons learned will assist in building the capacity of leaders and practitioners as a means of improving effectiveness in dealing with emerging dynamics and leading in unknown contexts and complex contexts environments.