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- W2801341753 abstract "Operational effectiveness is the means by which market leading value propositions are delivered to customers. The contribution of operational effectiveness in manufacturing to competitive strategy has been established. The operational effectiveness approach has been based on scientific reductionism, defining itself by list of best practice tools, techniques and philosophies. This thesis argues that this had led to loss of the operational effectiveness whole, causing variegated success in application of these tools. Complex systems SCience, with its origins in communications, control theory and non-linear dynamical systems, has provided previously unavailable insight into real-world systems. The applications to date in manufacturing have been in soft systems appearing either as metaphors or computationally difficult optimisation-type problems. This thesis argues that manufacturing companies are complex systems. As a result measures for complex behaviour and structure in these systems will yield fresh insight currently unavailable through the reductionist worldview of operational This research proposes a new framework of agents that connects the operational effective and complex systems views, without the loss of system richness of the complexity worldview. This framework comprises interrelated sets of input/output Entities, produced through Activities carried out by Resources. Novel measures of flow efficacy and the structure of these set-based relationships are then obtained based on this common framework by the use of Entropy and QanalYSis respectively. A methodology for the creation of the framework, its measurement and validation is then proposed and tested. Three case studies have been carried out in the actual processes of a manufacturing company. The studies have been able to show a consistent benefit from the use of complexity-based measures over and above what would have been available from operational effectiveness. Correlation and independence have been observed in the concurrent use of both complexity measures on the frameworks, demonstrating their independence and interdependence." @default.
- W2801341753 created "2018-05-17" @default.
- W2801341753 creator A5080133316 @default.
- W2801341753 date "2005-01-01" @default.
- W2801341753 modified "2023-09-24" @default.
- W2801341753 title "Measures from complexity science provide manufacturing companies with insights previously unavailable to them" @default.
- W2801341753 doi "https://doi.org/10.21954/ou.ro.0000d4b3" @default.
- W2801341753 hasPublicationYear "2005" @default.
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