**3.2 Case study**

*Reliability and Maintenance - An Overview of Cases*

• Planning and programming: It is the authors' view that adequate planning and programming should include short-term activities in the planning and scheduling of preventive maintenance. Activities of greater complexity can be addressed through root cause analysis. Likewise if, say, 80% of the total activities is scheduled in adequate time, then this may be regarded as demonstrating a stable maintenance operation. Another important point is to try planning in

the long term and scheduling in the short term as much as possible.

the asset for maintenance [45]. Planning and programming involve:

ners and programmers and maintenance and operations

nating modifications in the allotted time

• To assign engineering as a support to planners

• Measuring delay times and operating hours

frequencies of the assigned activities

recommendations of the manufacturers.

assets is defined in the generation stage of the baseline.

needs of both parties

Requirements for proper planning and programming include understanding the need to respond, properly preparing a work order with appropriate prioritization, and integrating operations to reduce programming delays due to nonavailability of

• Assigning a programmer and planner to review the pending work and coordi-

• Establishing roles, responsibilities, rules, and lines of authority between plan-

• Conducting daily meetings between programmers and operations to align the

• Holding meetings to level the needs of programmers and planners alike

• Notifying maintenance or purchasing leaders of material and resource needs

• Considering routes for maintenance personnel and analyzing the tasks and

The improvement cycle of planning and programming begins with an analysis of the existing maintenance plans and ends with a new plan, whose effectiveness is measured from the mean time between failures classified as critical systems. The implementation is progressive from the identification of the most critical systems, considering relevant indicators such as mean time between failures (MTBF) as input variable. The improvement process at its starting point cannot ignore the

• Establishing a defined level of service for materials and resources

*3.1.4 Critical maintenance task (CMT) list and regular maintenance task list*

The process to generate critical roadmaps and regular route sheets for maintenance tasks begins in accordance with asset ranking, that is, the severity of the impact of their failures within the production process. The hierarchical level of

Roadmaps, by definition, are documents designed to direct maintenance activities by minimizing the level of human error on the part of operators. They were developed by the aeronautical industry in the 1970s within the framework of technical recommendations pertaining to reliability in maintenance [46]. Roadmaps

**18**

The proposed model was implemented at an organization with 54 years of experience in providing home cleaning services and complementary activities in the city of Medellin and five nearby municipalities. The company has 767,668 users, among the residential, commercial, and industrial sectors. Service delivery in the residential sector is carried out twice per week (Monday–Thursday, Tuesday–Friday, Wednesday–Saturday), for a total of 104 services per year. Frequencies in the commercial and industrial sector may vary between 1 to 7 times per week, depending on the waste generation of each subscriber, which leads to a total of 104 to 365 collections per year. The main activities of the organization are collection and transport of solid wastes, sweeping and cleaning of roads and public areas, grass cutting and pruning of trees in public areas, and washing off roads and public areas. The range of services extends to the collection of special wastes, among which are waste generated at events and mass shows, points of sale in public areas, dead animals, construction and demolition wastes (C&D), hospital wastes, mattresses, vegetables, furniture, carpentry wastes, and collection (dismantling and installation) of public baskets.

As part of the solid waste collection strategy, the organization has a diversity of vehicles with different dimensions in order to access areas with adverse geographic conditions. To allow great maneuverability in limited-access roads, the company has model 2009 Kenworth vehicles with only two axes (simple) and smaller vehicles such as NPR model 1998 and 2012. In general, to meet the demand, the organization


#### **Table 2.**

*Potential errors in the inspection process.*

has its own fleet broken down as follows for each type of service. Collecting wastes from hospitals are carried out by using three vehicles equipped with containers and UV light, to ensure crew condition and reduce biological risk. The vehicle fleet has two skid-steer loaders transported by dump trucks for the collection of C&D waste. The organization operates light vehicles (NPR) for transportation of baskets in poor condition for waste disposal. Besides, the vehicle fleet has two series of equipment that allow the provision of collection services for special containers. Both use a lifting system equipped in the back part of the truck which is called lifter. One of the series of vehicles is employed for collection of big containers, whereas another series is used for the collection of buried containers. An availability indicator is generated, based on the data of the information system reports and the work orders of the equipment maintenance activities. All of this for a 30-day operation period during 2018 is shown in **Figure 4**.

In **Figure 4a**, it is possible to observe the increase in availability of the fleet of light vehicles per quarters during 2018, from 78% in the first quarter to 89% in the fourth quarter showing signs of stabilization. This improvement may be attributed to the reduction of the occurrence of failures, which in turn is the result of the implementation of a preventive maintenance program, and critical maintenance tasks. These availability standards are appropriate for a program of operational excellence. During 2018 one may see that the month having the best behavior in terms of availability was November showcasing a growing trend due to the implementation of the excellence model.

Despite the improvement in terms of availability that was evidenced in this study, it is the authors' view to analyze the specific behavior of the fleet vehicles. This is because being an average, the availability of the light vehicles could be

**21**

**Table 3.**

**Figure 5.**

*Availability indicator for critical light vehicles.*

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

these works are taken into consideration.

agreement with the terms of the contract.

information (**Table 3**).

affected by extreme values. **Figure 5** shows the behavior of each of the vehicles that make up the fleet. The vehicles that have worse average availability are #313 and #416. However, if the growth of the availability of these vehicles is observed, the positive impact of the implementation of the model of excellence can be verified. To evaluate the impact of the excellence model implementation, availability indicators besides indicators like efficiency must be considered. Next, the data related to the maintenance cost is compared, through the execution of work orders. The amount of orders generated, their typology, and the effective cost paid for

It is pertinent to mention that each work order carried out carries with it the corresponding audit report, which gives information of the tasks executed in detail, the report of time units, and the quality of spare parts used. These data are essential for the administrative review phases, in case of any type of claim, and verify the

Additionally, the maintenance work of the first 10 months of the year 2018 is analyzed and compared with the same period for the year 2017. A reduction of 5.06% in the sum of the maintenance costs is evidenced. In addition to this, the average cost per work order generated was reduced by 12.94% thanks to the recommendations of good management practices in the processing and management of

**# OTS % OTS prev OTS value**

2017 5755 6.23 −12.96%

2018 6277 10.12 Difference (%) −5.06% 3.89

*Budget execution in maintenance cost 2017 vs. budget execution in maintenance cost 2018.*

**Figure 4.** *Availability indicator for light vehicles per 2018 quarters.*
