**4. Conclusions**

During the life cycle of an asset or a production system, different costs are incurred, which span the purchase (initial investment) to the operation and maintenance costs that guarantee productive and financially worthy outputs for investors. The life cycle cost corresponds to the costs of both investment and operations inherent to the useful life of the asset.

Development and implementation of management models, applied to the maintenance of equipment, often present results in periods that exceed 1 year. This model, based on the ISO 55000 standard, presented immediate results mainly by, firstly, defining maintenance as a strategic activity for the collective benefit of the organization and, secondly, collecting all necessary information pertaining to defining the critical maintenance needs, in such a way so as to guarantee the high availability of the assets.

In this chapter a maintenance strategy model of the asset life cycle is proposed. It has a direct influence on maintenance management regarding decision-making, as well as the planning of preventive tasks and analysis of the equipment's useful life. Positive results are obtained in the overall development of this maintenance model. It is possible to notice a reduction of costs in the global execution, and so the average cost per work order has been reduced too. At the same time, an increase in the execution proportion of preventive tasks has been achieved. These findings may help other to implement the model successfully, even though the tasks performed and the model itself remain in continuous analysis and improvement.

The common maintenance budget models only present a general sum of costs; it does not provide enough information for decision-making. These results confirm the association between cost control, technical decisions and physical interventions, which have been and exemplified, and therefore, in this document, a new way of disaggregating the cost has been suggested.

Frequently the summation is separated monthly to fit in with the scale of the time series. These estimators of central tendency allow the visualization of how data variability alters this tendency according to the behavior of the series. It is also evident that the median is less susceptible than the average.

The result of the implementation has shown an increase of the availability indicator and a reduction of the general maintenance costs.

These preliminary results that cover a 12-month period suggest that in the long term/medium term, the availability may reach the level demanded by the company and may guarantee stable operation with lower maintenance costs. Finally, it is important to highlight that, without the support of the general management of the organizations, the initiatives to achieve operational excellence, or an adequate management of assets, may fail, causing loss and discouragement.

**23**

**Author details**

Colombia

Carmen Elena Patiño-Rodriguez1

provided the original work is properly cited.

*Maintenance and Asset Life Cycle for Reliability Systems DOI: http://dx.doi.org/10.5772/intechopen.85845*

EAM enterprise asset management ERP enterprise resource planning FAA Federal Aviation Administration

FMEA failure modes and effect analysis

OREDA Offshore and Onshore Reliability Data PID processes and instrumentation diagrams

SCADA Supervisory Control and Data Acquisition

RCM reliability-centered maintenance

IT information technology KPI key performance indicator MIS maintenance information system MSG maintenance steering group MTBF mean time between failures

PDCA plan, do, check, act

RBI risk-based inspection RBM risk-based maintenance

FERMA Federation of European Risk Management Associations

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

2 Department of Mechanical Engineering, Nacional University, Medellín, Colombia

1 Department of Industrial Engineering, University of Antioquia, Medellín,

\*Address all correspondence to: elena.patino@udea.edu.co

\* and Fernando Jesus Guevara Carazas2
