PurposeThis 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.Design/methodology/approachThe 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.FindingsThrough 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.Originality/valueThe 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.

It is important to be able to compare and evaluate different solutions early in development. This paper proposes a method for structuring historical data into a data model that can support the evaluation of new design concepts. The data is contextualized by linking it to a hierarchical decomposition of existing products. Two case studies were conducted to evaluate the value of using historical data when evaluating new concepts. The cases confirm that the proposed method is useful for evaluation of new concepts.

Maintenance is coming more and more into focus and in many areas maintenance costs are becoming increasingly important to running a profitable business. Performing the right maintenance is therefore important to many companies. The right maintenance is a balance of costs, impact on production, reduction of downtime, ensuring high safety and reducing environmental impact. The foundation of performing the right maintenance is an effective maintenance process. A maintenance process, as described in literature, includes identification of required maintenance; planning of the maintenance tasks; scheduling of the planned tasks; execution of the maintenance; and close-out of the maintenance job. Descriptions of the maintenance process in literature mainly follow a sequential process similar to that of sequential engineering. The sequential process is typically slow and inefficient and therefore does not support the need for running an efficient maintenance program. This paper presents a literature review that looks into how product development have met similar problems with a sequential development process and how this problem is solved through concurrent engineering. The maintenance process is analyzed through a literature review looking at different maintenance processes. The literature shows that the maintenance process is subject to large variation depending on the source, but that all the sources base their process on a sequential structure. It is also observed that maintenance is facing similar problems to the sequential product development. Based on the literature review, four initiatives for improving the maintenance process is suggested. The initiatives are based on the identified methodology from concurrent engineering. A case study is used to further understand the sequential issues in maintenance and to highlight how the initiatives can influence the maintenance process. The case study shows that implementing the initiatives gives a potential 12% cost reduction. This indicates a clear need for a more concurrent maintenance process, but to fully conclude the need and benefits of a concurrent maintenance process, more studies need to be conducted.

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PurposeCurrent 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.Design/methodology/approachThe paper uses the design research methodology utilizing a case study to validate the proposed method.FindingsThe 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.Practical implicationsThe 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.Originality/valueThe proposed method provides new insights into maintenance theory and possesses a huge potential to improve the current maintenance planning practices in the industry.