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This paper aims to investigate how the product architecture and service architecture methodology can be applied in strategic maintenance optimization to reduce the non-value-adding variance of maintenance, decrease the complexity and ensure alignment in maintenance practices in asset-intensive companies. The proposed maintenance architecture model will make it possible to make data-driven decisions regarding how the equipment should be grouped and maintained.

The research approach is considered exploratory, and the main research strategy is a case study. The maintenance architecture model is developed based on the product architecture methodology and then tested in three different cases in the oil and gas sector.

Through the maintenance architecture model, it is possible to pair a quantitative data-driven approach with qualitative understanding of dependencies between equipment, maintenance actions and maintenance work management processes, enabling a more holistic and top-down data-driven approach to improving maintenance, than what currently exists in literature.

The proposed model provides a contribution to the understanding of maintenance and is positioned at a detailed level, different from other maintenance improvement models. This model is focused on the main drivers of maintenance that can be utilized at the strategic level compared to optimization of maintenance for individual pieces of equipment.

Current industry practices illustrate there is no standard method to estimate the number of hours worked on maintenance activities; instead, industry experts use experience to guess maintenance work hours. There is also a gap in the research literature on maintenance work hour estimation. This paper investigates the use of machine-learning algorithms to predict maintenance work hours and proposes a method that utilizes historical preventive maintenance order data to predict maintenance work hours.

The paper uses the design research methodology utilizing a case study to validate the proposed method.

The case study analysis confirms that the proposed method is applicable and has the potential to significantly improve work hour prediction accuracy, especially for medium- and long-term work orders. Moreover, the study finds that this method is more accurate and more efficient than conducting estimations based on experience.

The study has major implications for industrial applications. Maintenance-intensive industries such as oil and gas and chemical industries spend a huge portion of their operational expenditures (OPEX) on maintenance. This research will enable them to accurately predict work hour requirements that will help them to avoid unwanted downtime and costs and improve production planning and scheduling.

The proposed method provides new insights into maintenance theory and possesses a huge potential to improve the current maintenance planning practices in the industry.

The main purpose of present study is to model the replacement policy under uncertainty for managerial application based on grey-reliability approach by considering the subjective views of quality control circle (QCC). The study objectively links the optimality between individual replacement and group replacement policies for determining the minimum operational costs. The integrated framework between QCC, replacement theory, grey set theory and supply chain management is presented to plan replacement actions under uncertainty.

The study proposes the concept of grey-reliability index and built a decision support model, which can deal with the imprecise information for determining the minimum operational costs to plan subsequent maintenance efforts.

The findings of the study establish the synergy between individual replacement and group replacement policies. The computations related to the numbers of failures, operational costs, reliability index and failure probabilities are presented under developed framework. An integrated framework to facilitate the managers in deciding the replacement policy based on operational time towards concerning replacement of assets that do not deteriorate, but fails suddenly over time is presented. The conceptual model is explained with a numerical procedure to illustrate the significance of the proposed approach.

A conceptual model under the framework of such items, whose failures cannot be corrected by repair actions, but can only be set by replacement is presented. The study provides an important knowledge based decision support framework for crafting a replacement model using grey set theory. The study captured subjective information to build decision model in the ambit of replacement.