Purpose: The 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/approach: The “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.Findings: Execution 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/implications: As part of adopting the proposed framework in practice, it needs to be guided by an overall methodology appropriate for application‐specific contexts.Practical implications: The potential direct benefits of adopting the proposed framework include on‐time project completion, reduced inventory levels of spare‐parts and reduced overtime costs.Originality/value: Existing 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.

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 (SC) networks. The proposed framework utilises 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 SC 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 SC 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 SC 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.

Supplier evaluation and selection (SES) problems have long been studied, leading to the development of a wide range of individual and hybrid models for solving them. However, the lack of widespread diffusion of existing SES models in the industry points to a need for simpler models that can systematically evaluate both qualitative and quantitative attributes of potential suppliers while enhancing the flexibility decision-makers need to account for relevant situational factors. Furthermore, empirical validations of existing models in SES have been few and far between. With a view to addressing these issues, this paper proposes an integrated solution framework that can be used to evaluate both tangible and intangible attributes of potential suppliers. The proposed framework combines three individual methods, namely the fuzzy analytic hierarchy process, fuzzy complex proportional assessment and fuzzy linear programming. The framework is validated through application in a Turkish textile company. The results generated using the proposed framework is compared with the actual historical data collected from the company. Additionally, a feasibility assessment is conducted on the sample supplier selection criteria employed, as well as assessment of the results generated using the proposed model.

Purpose: The purpose of this paper is to explicate deeper structures in manufacturing strategy (MS) formation processes, in order to advance process understanding. This would be useful in identifying and nurturing appropriate forms of MS formation within specific organisational settings.Design/methodology/approach: Nine case studies in the Australian metal products, machinery and equipment manufacturing sectors, guided by the grounded theory approach.Findings: Deeper structures in MS processes represent linear and parallel, convergent and divergent, sequential and iterative progression of strategic initiatives across four broad phases identified as: initiation, consolidation, commitment and realization. The multiple progressions are explained by the nature of strategic initiatives, the causal relationships between the phases or modes and the influence of internal and external organisational contextual factors. The aggregate patterns are presented in the form of a conceptual model.Research limitations/implications: The conceptual model needed to be tested with a large sample of data using statistical techniques to improve its external validity. Causal relationships explored in this study may be further strengthened using longitudinal qualitative studies.Practical implications: The findings are grounded in empirical data. The model presented using simple formalisms is capable of providing rich insights in aggregate terms. As such, it is expected to hold a natural appeal to practitioners. If the findings can find their way into MS pedagogy, they can make a more substantial and progressive contribution to MS practice.Originality/value: The findings of this study have corroborated and explained the findings of several recent studies that have uncovered alternative forms of MS formation. The deeper understanding of MS process developed in this study contributes to theory‐building with the added significance that this study has successfully crossed the traditional analytical boundaries.

With the increasing significance of business model innovation, and due to the ongoing difficulties encountered in executing formally developed strategies, there is a strong case for studying the functional-level strategy processes from fresh perspectives. This paper examines the significance of alternative operations strategy (OS) processes and organisational context and explores how they relate to operations performance. The empirical evidence was drawn based on the statistical analysis of data collected through 242 survey responses from the senior management staff of the Canadian oil and gas industry. The evidence drawn from the empirical study confirmed that alternative forms of OS development do indeed exist in practice and that certain organisational factors have a direct influence on OS processes. The analysis discerned four strategy process configurations representing singular and multiple combinations of the three process modes identified through the literature review. The results explain the differences between these process configurations in terms of certain contextual factors and operations performance. The findings of this study help advance the understanding of OS processes and may contribute to theory building, particularly what is known as ‘mid-range’ theory, as the evidence was built through the analysis of a relatively large sample of data drawn from an industry sector that has not been reported on in previous studies.