The new dynamics of strategy: sense-making in a complex and complicated world

Kurtz, Cynthia; Snowden, David J.

doi:10.1109/emr.2003.24944

Cited by 161 publications

(304 citation statements)

References 14 publications

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“…Alternatively, knowledge based less on practical experience and more on abstract ideas, emerged in collegial brainstorming or discussion of hypothetical conditions. Snowden 44 terms the "Complex domain" in which uncertainty increases but potential answers can be generated through collegial exchange. It is in this domain where complexity science is located.…”

Section: Discussionmentioning

confidence: 99%

Team Science Approach to Developing Consensus on Research Good Practices for Practice‐Based Research Networks: A Case Study

Campbell-Voytal

Daly

Nagykáldi

et al. 2015

Clinical Translational Sci

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Using peer learning strategies, seven experienced PBRNs working in collaborative teams articulated procedures for PBRN Research Good Practices (PRGPs). The PRGPs is a PBRN-specific resource to facilitate PBRN management and staff training, to promote adherence to study protocols, and to increase validity and generalizability of study findings. This paper describes the team science processes which culminated in the PRGPs. Skilled facilitators used team science strategies and methods from the Technology of Participation (ToP ® ), and the Consensus Workshop Method to support teams to codify diverse research expertise in practice-based research. The participatory nature of "sense-making" moved through identifiable stages. Lessons learned include (1) team input into the scope of the final outcome proved vital to project relevance; (2) PBRNs with diverse domains of research expertise contributed broad knowledge on each topic; and (3) ToP ® structured facilitation techniques were critical for establishing trust and clarifying the "sense-making" process.

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Section: Discussionmentioning

confidence: 99%

Team Science Approach to Developing Consensus on Research Good Practices for Practice‐Based Research Networks: A Case Study

Campbell-Voytal

Daly

Nagykáldi

et al. 2015

Clinical Translational Sci

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“…The Cynefin Framework [19][20][21] is best construed as a sense making tool. In this way it helps us understand that the systems we are engaged in (Global Health Security context) have an inherent dynamic complexity.…”

Section: Cynefin Frameworkmentioning

confidence: 99%

“…For more details on the iceberg model, 19 the reader is encouraged to review Senge, Kleiner, Roberts, Ross, and Smith, " The Fifth Discipline Fieldbook" [60] which is an applied perspective of the background and theory of systems thinking presented in "The Fifth Discipline" [61].…”

Section: Recognize You Are Dealing With a Systemmentioning

confidence: 99%

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Global Health Security Innovation

Stikeleather

Masys

2020

Advanced Sciences and Technologies for Security Applications

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“…This is partly due to the complex nature of the products under development (Sheard and Mostashari, ). Complex project behavior is very hard to explore using a simple response metric based on historical data according to arguments presented by Kurtz and Snowden (), who suggest that any reliance on historical data will insufficiently prepare projects for future development. Project management metrics traditionally measure historical performance to schedule and cost, an approach that has two key drawbacks: metrics that are lagging in nature; and therefore metrics that are unable to show the incremental design maturity required (Sillitto, ; Frenz, ; Walworth et al., ).…”

Section: Introductionmentioning

confidence: 99%

Estimating Project Performance through a System Dynamics Learning Model

et al. 2016

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms Estimating project performance through a system dynamics learning model AbstractMonitoring of the technical progression of projects is highly difficulty, especially for complex projects where the current state may be obscured by the use of traditional project metrics. Late detection of technical problems leads to high resolution costs and delayed delivery of projects. To counter this, we report on the development of a new technical metrics process designed to help ensure the on--time delivery, to both cost and schedule, of high quality products by a UK Systems Engineering Company.Published best practice suggests the necessity of using planned parameter profiles crafted to support technical metrics; but these have proven difficult to create due to the variance in project types and noise within individual project systems. This paper presents research findings relevant to the creation of a model to help set valid planned parameter profiles for a diverse range of system engineering products; and in establishing how to help project users get meaningful use out of these planned parameter profiles. We present a solution using a System Dynamics model capable of generating suitable planned parameter profiles. The final validated and verified model overlays the idea of a learning 'S--curve' abstraction onto a rework cycle system archetype. Once applied in System Dynamics this matched the mental models of experienced managers within the company, and triangulates with validated empirical data from within the literature. This has delivered three key benefits in practice: the development of a heuristic for understanding the work flow within projects, as a result of the interaction between a project learning system and defect discovery; the ability to produce morphologically accurate performance baselines for metrics; and an approach for enabling teams to generate benefit from the model via the use of Problem Structuring Methodology.

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The new dynamics of strategy: sense-making in a complex and complicated world

Cited by 161 publications

References 14 publications

Team Science Approach to Developing Consensus on Research Good Practices for Practice‐Based Research Networks: A Case Study

Team Science Approach to Developing Consensus on Research Good Practices for Practice‐Based Research Networks: A Case Study

Global Health Security Innovation

Estimating Project Performance through a System Dynamics Learning Model

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