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An alternative algorithm has been developed for computing the behaviour of flows within arbitrary ducts and channels. This technique requires a small number of downstream marches in the primary flow direction, employing, on each march, numerically efficient procedures originally developed for a single sweep non‐elliptic flow solver. The multiple sweeps allow the capture of effects such as upstream pressure influences and streamwise recirculation. The energy equation is also solved to allow for varying heat transfer between the fluid and the boundary walls. The numerical work is further complicated by considering flows within turning sections of ducts which demonstrate large transverse velocities and consequent distortion of the primary flow. The computations are validated by comparison with a number of fluid/heat transfer experiments. The majority of these are taken from studies of turning flows within circular arc ducts which display the various pressure and transverse flow phenomena for which this new algorithm was initially developed to represent.

The boiler pressure parts are a major asset of a power station, and the maintenance cost is often accountable for a huge portion of the annual budget. In the power generation industry, the outage costs due to loss of production, both planned or forced, are very significant and thus it is of interest to seek for a meaningful approach to the management of boiler pressure parts maintenance such that the enterprise performance is optimised. This paper aims to do this.

This paper proposes a framework that introduces the division of the enterprise objectives into the three decision dimensions. The framework is applied to the case of power station boiler pressure parts maintenance to optimise maintenance outages decisions for Loy Yang, a power station in Victoria, Australia.

The study finds that the framework provides meaningful approach to optimising maintenance decisions and is generic for application in different cases.

The paper provides a new insight and integrated approach to optimising asset maintenance for an enterprise with the use of a case study.

Maintenance is constantly challenged to increase productivity by maximizing up‐time and reliability while at the same time reducing maintenance expenditure and investment. Traditional reliability models are based on detailed statistical analysis of individual component failures. For complex machinery, especially involving many rotable parts, such analyses are difficult and time‐consuming. This paper aims to propose an alternative method for estimating and improving reliability.

The methodology is based on simulating the up‐time of the machine or process as a series of critical modules. Each module is characterized by an empirically derived failure distribution. The simulation model consists of a number of stages including operational up‐time, maintenance down‐time and a user‐interface allowing maintenance and replacement decisions.

Initial analysis performed on aircraft gas‐turbine data yielded an optimal combination of modules out of a pool of multiple spares, resulting in an increased machine up‐time of 16 percent.

The benefits of this methodology are that it is capable of providing reliability trends and forecasts in a short time frame and is based on available data. In addition, it takes into account the rotable nature of many components by tracking the life and service history of individual parts and allowing the user to simulate different combinations of rotables and operating scenarios. Importantly, as more data become available or as greater accuracy is demanded, the model or database can be updated or expanded, thereby approaching the results obtainable by pure statistical reliability analysis.

The model presented provides senior maintenance managers with a decision tool that optimizes the life cycle maintenance cost of complex machinery in a short time frame by taking into account the rotable nature of modules.

This research paper aims to develop and validate an enhanced business process improvement methodology (EBPIM) by integrating the DMAIC (define, measure, analyze, improve and control) and the comprehensive business process management (CBPM) methodologies.

A systematic literature review and analysis were conducted to prove the novelty of the research approach and identify the similarities, differences, strengths and weaknesses of the DMAIC and the CBPM methodologies. The EBPIM was proposed based on the analysis results. Then, a focus group approach was used to evaluate and validate the methodology.

The EBPIM consists of nine activities: preparation, selection, description, quantification, modeling, enactment, improvement opportunities selection, analysis and improvement and monitoring. The proposed methodology adopted the systematic and structured process of the DMAIC methodology by having one tollgate between every two activities to check the progress and authorize the team to go to the next activity. At the same time, it has the ability of the CBPM methodology to enhance the interaction between human activities and business process management systems (BPMS).

The EBPIM was evaluated and validated by a focus group of academic professors. However, the main limitation of the proposed methodology is that it is still theoretical and needs to be empirically tested. Therefore, future work will focus on testing the EBPIM in different industries and organization sizes.

From the theoretical perspective, the proposed methodology adds value to the knowledge in the scope of business processes improvement methodologies (BPIMs) by integrating the DMAIC and the CBPM methodologies. It takes advantage of and combines the strengths of the DMAIC and CBPM methodologies. From the practical perspective, the proposed methodology presents a valuable tool that can facilitate the organization’s mission to improve the areas that need improvement using a systematic improvement methodology that will effectively enhance organizational performance (OP).

The BPIMs literature analysis proved that most of the reviewed methodologies could not support all phases of the business process improvement (BPI) activities. It was concluded that integrating the DMAIC and the CBPM methodologies is a novel approach. The proposed methodology will enhance the efficiency of both methodologies, fill the gaps that may exist in both of them and lead to better results in terms of BPI.