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The purpose of this paper is to better identify, define and model complexity within the field of project management in order to manage better under conditions of complexity (and manage better complexity‐induced risks).

An extensive literature review enlightens the lack of consensus on project complexity and thus provides a broad view and a critical analysis of the underlying concepts. A project complexity framework and definition are then proposed. After underlining the stakes of project complexity in accordance with these proposals, a project complexity model is then built notably due to systems analysis.

Proposal of standard project complexity framework and definition. Proposal of a synthesis of the relationships between the concepts of project uncertainty and project complexity. Proposal of a project complexity model (and validation due to industrial application).

The literature review and project complexity framework tries to be exhaustive even though it is likely to be completed. The final version of the model is still to be computed and tested.

Avoiding confusion when defining and managing a complex project, particularly between project team members (and as a consequence improving communication and information sharing), improves the assessment of the propagation of a change within the project.

The paper proposes an original framework and a definition of project complexity. The complexity model permits the navigation from any element of the project to any other (when detail is needed) and is, as a consequence, original and complementary with traditional project management models and tools.

The purpose of this paper is to present how complexity on retail supply chains should be recognized and its relationship with the performance. Different supply chain structures and planning horizons have been analyzed to support practitioners taking action on the short, mid and long terms. Confronted complexity in the supply chain has been categorized as system, perceived and value adding. This would also help practitioners to understand the sources of the complexity and if the complexity is useful for the system or not.

Three different retail supply chain scenarios – each concentrating on different planning horizons – have been simulated on system dynamics software STELLA. Using the new classification scheme for complexity and suggested performance metrics, a multi-perspective analysis has been performed on the STELLA output.

The results and the methodology can be easily applicable in practice to support decision-making process and to answer “what-if” type scenario analysis on systems design and configuration. Using the selected complexity metrics, complexity of the system considering time factor – static and dynamic – and different information levels – system, perceived and value adding – has been evaluated. Used complexity metrics indicate the problematic areas in the systems to be distinguished.

This paper uses system dynamics modeling in retail supply chains to derive insight about dynamic behavior and to represent the complex interactions and a new classification scheme for system complexity.

The purpose of this paper is to enhance the understanding of what project complexity is, what drivers and factors that influence complexity and how consequences for organizational performance can be assessed.

The research is explanatory and based on literature review, model development, interviews and case studies. The model is validated through a case study.

The findings are a model for identifying and analyzing complexity drivers and complexity factors. The model starts with generic complexity drivers such as ambiguity, uncertainty, unpredictability and pace. These drivers are in each project influenced by nature and by socio-political, economic and technological surroundings to result in complexity factors that are specific to the project analyzed. The model can be used to analyze project complexity and to define requirements for the organization of the project and guidelines for the execution.

The research is limited to large projects with a technical delivery of some kind of facilities.

The model can be used to assess the required capability of the organization for successful project execution.

The contribution of the research is a new model for understanding project complexity. The distinction between project complexity drivers and factors is essential as well as the taxonomy for the factors building on and adding to already published research.

This paper aims to propose a multi-dimensional model on the basis of the key factors of the flexibility and the complexity through structural equation modeling (SEM). Dimensions of the flexibilities and complexity, including 16 main factors and 34 sub-factors, are investigated. The sampling of the research is accomplished using both academic and industrial experts.

A huge electronic questionnaire analysis, including 1,250 samples from which 1,036 were returned, was accomplished in various universities and manufacturing companies throughout the USA, Europe and Asia. Partial least square-SEM (PLS-SEM) is used to test the hypotheses through confirmatory factor analysis.

The results reveal insightful information about the impacts of different dimensions of flexibility on each other and also the effect of the flexibility on the complexity. Finally, system of linear mathematical equations for flexibility-complexity trade-off is proposed. This can be applied to realize the trade-off among dimensions of flexibility and complexity.

Flexible manufacturing systems are formed to meet the needs of the customers. Such systems try to produce products in appropriate quality at the right time and at the specified quantity. These, in turn, require flexibility and will cause complexity. Although flexibility and complexity are both important, there is no comprehensive framework in which the multi-dimensional relationships of the manufacturing flexibility and complexity, as well as their dimensions, are demonstrated.

In today's globalized business environment, growing supply chain complexity (SCC) is arguably a major threat to the firm's business continuity with an adverse impact on the firm's competitive advantage and business performance. Researchers, though, investigated the impact of SCC drivers on a firm's operational performance, but the key question “Which supply chain complexity drivers severely impact the supply chain performance (SCP)?” remains largely unanswered from empirical research. The present study aims to decompose the SCC into four major constituting sub-categories (upstream, operational, downstream and external) to explore the causal impact of SCC drivers on SCP in direct and mediated manner.

The indicators applied for measuring constructs in the “Measurement model” are obtained from existing literature to increase the validity and reliability of the model. First, a pilot survey involving 25 SC managers from various manufacturing firms was conducted for indicator refinement and content validation. Second, the large-scale response data were collected through extensive surveys. This research explores the causality by testing the hypothesis applying Structural Equation Modeling (SEM) based on the responses received from 246 firms.

The study investigates the impact of SCC drivers on SCP through direct and mediation effect. The results indicate that upstream and operational SCC drivers play a mediating role in managing SCP. The findings reveal that upstream and operational SCC drivers adversely impact the SCP. Furthermore, the impact of downstream complexity on SCP is moderated through operational complexity drivers. The result explains the theoretical relation among SCC drivers supported by empirical validity.

The outcome offers practical relevance to supply chain (SC) managers in SCC and SCP management. Knowing the effect of SCC drivers among themselves and on SCP will facilitate the SC managers in devising the right strategies. The study provides a framework for prioritizing the resource in addressing the SCC issues among many.

The study addresses the apparent gap in the literature by modeling the impact of SCC drivers on SCP, which remained largely unexplored. First, it contributes to developing complex relationships among SCC drivers. Second, the direct and mediated causal effect of the SCC drivers individually and combinedly on SCP are explicated.

The purpose of this paper is to propose the use of graph theoretic structural modeling for assessing the possible reduction in complexity of the work flow procedures in an organization due to lean initiatives. A tool to assess the impact of lean initiative on complexity of the system at an early stage of decision making is proposed.

First, the permanent function-based graph theoretic structural model has been applied to understand the complex structure of a manufacturing system under consideration. The model helps by systematically breaking it into different sub-graphs that identify all the cycles of interactions among the subsystems in the organization in a systematic manner. The physical interpretation of the existing quantitative methods linked to graph theoretic methodology, namely two types of coefficients of dissimilarity, has been used to evolve the new measures of organizational complexity. The new methods have been deployed for studying the impact of different lean initiatives on complexity reduction in a case industrial organization.

The usefulness and the application of new proposed measures of complexity have been demonstrated with the help of three cases of lean initiatives in an industrial organization. The new measures of complexity have been proposed as a credible tool for studying the lean initiatives and their implications.

The paper may lead many researchers to use the proposed tool to model different cases of lean manufacturing and pave a new direction for future research in lean manufacturing.

The paper demonstrates the application of new tools through cases and the tool may be used by practitioners of lean philosophy or total quality management to model and investigate their decisions.

The proposed measures of complexity are absolutely new addition to the tool box of graph theoretic structural modeling and have a potential to be adopted by practical decision makers to steer their organizations though such decisions before the costly interruptions in manufacturing systems are tried on ground.

Noting the scarcity of complexity techniques applied to modelling social systems, this paper attempts to formulate a conceptual model of decision‐making behaviour within the information systems evaluation (ISE) task, against the backdrop of complexity theory.

Complexity theory places an emphasis on addressing how dynamic non‐linear systems can be represented and modelled utilising computational tools and techniques to draw out inherent system dynamics. In doing so, the use of fuzzy cognitive mapping (FCM) and morphological analysis (MA) (hence a fuzzy‐morphological approach), is applied to empirical case study data, to elucidate the inherent behavioural and systems issues involved in ISE decision making within a British manufacturing organisation.

The paper presents results of applying a combined FCM and MA approach to modelling complexity within management decision making in the ISE task: both in terms of a cognitive map of the key decision criteria; a matrix of constraint criteria; and a synthesised model that provides an indication of the linkages between technology management factors and organisational imperatives and goals. These findings show the usefulness of viewing the topic in complexity science terms (emergent behaviour, non‐linearity and chaotic response).

This research is limited in applying the given technique to a single case study organisation in the UK manufacturing sector, where the sample size is limited. Since this is the first time that such a combined MA‐FCM technique has been used in this field known to the authors, future research needs to validate and explore the implications of this approach in a wider context (multiple organisations and viewpoints).

The paper highlights the need for those involved in analysing managerial decision making to include aspects of complexity theory in their evaluations – namely uncovering inherent inter‐relationships that may exist between stakeholders, processes and systems. In doing so, expanding the manager's understanding of how to achieve congruence between driving forces and factors, which may exhibit non‐linear, chaotic or feedback behaviour.

The given research brings together both artificial intelligence and operational research techniques, applied in the socio‐technical milieu of information systems evaluation, within the context of complexity theory, in order to describe the rich detail within the ISE decision‐making task.

Project complexity has been comprehensively investigated over the last two decades, resulting in many descriptive frameworks and models. The common layout is a multidimensional construct. While the perception of the complexity of projects is essential for a managerial approach, only scant research has been conducted into how practitioners perceive project complexity. The purpose of the paper is to fill this gap.

This paper is a quantitative study based on a large survey among managers of projects with more than 1,000 participants. The questionnaire is designed based on a review of research literature on project complexity.

The findings indicate that practitioners' mental models are concentrated on only a few dimensions of the many found in descriptive models. Further, the findings indicate that the mental models are much influenced by the project role of the perceiver and less so by the type of project and sector.

This paper discusses the differentiation of concepts of perceived project complexity and provides a framework for a survey of the topic. The contribution of the paper is an increased understanding of practitioners' perceptions of project complexity as a concept very different from the descriptive frameworks that have been the focal point for research in project complexity thus far. The project complexity might be in the eye of the beholder; however, the findings indicate that the eyes are very much influenced by the project role of the beholder.

Certain elements of Hayek’s work are prominent precursors to the modern field of complex adaptive systems, including his ideas on spontaneous order, his focus on market processes, his contrast between designing and gardening, and his own framing of complex systems. Conceptually, he was well ahead of his time, prescient in his formulation of novel ways to think about economies and societies. Technically, the fact that he did not mathematically formalize most of the notions he developed makes his insights hard to incorporate unambiguously into models. However, because so much of his work is divorced from the simplistic models proffered by early mathematical economics, it stands as fertile ground for complex systems researchers today. I suggest that Austrian economists can create a progressive research program by building models of these Hayekian ideas, and thereby gain traction within the economics profession. Instead of mathematical models the suite of techniques and tools known as agent-based computing seems particularly well-suited to addressing traditional Austrian topics like money, business cycles, coordination, market processes, and so on, while staying faithful to the methodological individualism and bottom-up perspective that underpin the entire school of thought.

The purpose of this paper is to provide a systematic classification for frameworks, methods, and models of in-project quantitative risk analysis (IQRA) for the last 30 years.

An extensive literature review is conducted to identify pertinent IQRA works. Identified IQRA frameworks/methods/models are then classified on the basis of commonalities in key attributes and assumptions. Linkages between each category of IQRAs and dimensions of complexity are also observed.

Around 70 key publications on IQRAs are identified. Major attributes for each work are described. Five distinct categories of IQRAs emerge with unique linkages to complexity dimensions. An analytical framework in the form of a matrix is presented to illuminate evolution on modeling characteristics and to indicate a relationship between respective category and dimensions of project complexity.

The research coverage is intended to be comprehensive but it is by no means exhaustive. This study highlights research opportunities in IQRAs and the possible extension toward in-project quantitative complexity analysis (IQCA).

The proposed matrix provides guidance to practitioners to select the appropriate category of IQRAs for a specific project complexity type in a contingency fashion. The study highlights lessons from development and utilization of IQRAs. Outstanding issues from IQRAs are discussed to avoid similar drawbacks for IQCAs.

This study provides an original framework/matrix to classify extant works in IQRAs. It also establishes an association between IQRAs and the emerging conceptual works of complexity.