PurposeThe purpose of this paper is to examine how certain limitations of the current approaches to planning and scheduling of aircraft heavy maintenance can be addressed using a single integrated framework supported by unified data structures.Design/methodology/approachThe “unitary structuring technique”, originally developed within the context of manufacturing planning and control, is further enhanced for aircraft heavy maintenance applications, taking into account the uncertainty associated with condition‐based maintenance. The proposed framework delivers the advanced functionalities required for simultaneous and dynamic forward planning of maintenance operations, as well as finite loading of resources, towards optimising the overall maintenance performance.FindingsExecution of maintenance operations under uncertainty involves materials changes, rectification and re‐assembly. It is shown that re‐scheduling of materials (spare‐parts), resources and operations can be taken care of by simultaneous and dynamic forward planning of materials and operations with finite loading of resources, using the integrated framework.Research limitations/implicationsAs part of adopting the proposed framework in practice, it needs to be guided by an overall methodology appropriate for application‐specific contexts.Practical implicationsThe potential direct benefits of adopting the proposed framework include on‐time project completion, reduced inventory levels of spare‐parts and reduced overtime costs.Originality/valueExisting approaches to aircraft maintenance planning and scheduling are limited in their capacity to deal with contingencies arising out of inspections carried out during the execution phase of large maintenance projects. The proposed integrated approach is, capable of handling uncertainty associated with condition‐based maintenance, due to the added functionalities referred to above.

The current understanding of project complexity is limited in that there is neither a widely recognized conceptualization of project complexity nor a convergent view on how to deal with its effects. Drawing on the extant literature concerning project complexity and complexity science, this article develops a coherent and holistic profile of project complexity and provides reflections on its implications for project management theory and practice. This profile serves as a touchstone for practitioners to better understand, assess, and address complexity in their projects and as an aid to researchers in framing their research efforts.

This thesis reports on an empirical investigation into manufacturing strategy (MS) formation in practice. The broad objective is to advance the understanding of MS processes through constructing consistent patterns in decision-making and action-taking relating to the manufacturing structure and infrastructure of the organisations studied. Using the "Grounded Theory-Case Study" approach, nine organisations within the metal products, machinery and equipment manufacturing sectors in Australia were studied, in order to address the following research questions: ORIGINALITY STATEMENT 'I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.'

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Considering the need for more effective decision support in the context of distributed manufacturing, this paper develops an advanced analytics framework for configuring supply chain networks. The proposed framework utilizes a distributed multi-agent system architecture to deploy fuzzy rough sets-based algorithms for knowledge elicitation and representation. A set of historical sales data, including network node-related information, is used together with the relevant details of product families to predict supply chain configurations capable of fulfilling desired customer orders. Multiple agents such as data retrieval agent, knowledge acquisition agent, knowledge representation agent, configuration predictor agent, evaluator agent and dispatching agent are used to help execute a broad spectrum of supply chain configuration decisions. The proposed framework considers multiple product variants and sourcing options at each network node, as well as multiple performance objectives. It also captures decisions that span the entire supply chain simultaneously and, by implication, represents multiple network links. Using an industry test case, the paper demonstrates the effectiveness of the proposed framework in terms of fulfilling customer orders with lower production and emissions costs, compared to the results generated using existing tools.