Continuous Improvement in Maintenance Strategies
Industries are increasingly paying attention to maintenance efficiency optimizing the level in reliability and availability of assets. Many of the improvements could be obtained using new technologies and strategies to maximize service level and to reduce the maintenance costs, as long as it is possible to identify the business areas where a leap in technology could render and optimize the maintenance processes.
One of Europe’s large elevator companies, ORONA, is an example on this trend. In their search for service excellence, starting from an optimal level in reliability and availability, ORONA, together with IK4, has worked in the development of a continuous improvement cycle, which is able to consider the quality of service as well as the maintenance efficiency in a coherent way.
The decission process, which has been in use in ORONA since 2009, is using common techniques and standards, together with a specific module developed for evaluation of different strategies, keeping always in mind the possibility to implement new technologies related to predictive maintenance.
Performance improvements in the maintenance and conservation activities of assets are measured by availability and operational reliability. They should be obtained preserving maximum quality and safety levels and minimizing the costs.
In the current scenario of competitiveness, improvement efforts are essential to reach high levels of effectiveness and efficiency in every company’s production or operational department. The purpose is to achieve competitive advantage (in products or offered services) based on different hard-to-copy aspects, i.e. know-how.
To obtain maximum performance, the organizations must be prepared for changes and there are three interconnected areas in the change concept :
- Processes, work fluxes to achieve the improvements (e.g. doing more preventive work instead of corrective work, etc.).
- Technologies to facilitate or enable some processes.
- Organization and people within the organization must validate any change, so there is a need of tools to ease changes.
Figure 1. Predictive actuation area for a normal probability distribution failure.
One of the approaches for improvement is to identify and to apply techniques and tactics using high-tech elements, which would help to identify anomalies with high reliability.
Applying advanced technologies for the improvement of operation and maintenance processes are usually nuanced or cancelled, partly because of a lack of tools for coherent evaluation of the effects to costs and profits of the application (how it affects the life cycle of the product or global productivity of the plant).
In this context, predicitive actuation is a current area of improvement. As show in FIGURE 1, habitual CBM systems are centred on the left area of distribution in order to mitigate critical systems failures before to the time based replacement (or repair). It is important to take into account the right area where more and more cost-effective predictive systems are able to extend the repair periods.
In the search of excellence in service, ORONA has launched a research programme focusing in three strongly related aspects:
- Product: Data acquisition with sensors connected to the control system, focusing especially on the failure modes of critical components.
- Process: Definition of the maintenance processes using a better formalization of these processes and personalized to each user.
- System: Allowing data exploitation to obtain information (historic data, control data, etc.) by lift monitoring.
In order to deal with productprocess aspects, a project called “product and strategies analysis of elevator conservation” was launched to establish improvement steps of products related to maintenance processes. The main goal of the project is to show a continuous search model of better processes using cost-effective technologies, particularly emphasizing predictive maintenance strategies. New sensors, communication systems, standards and protocols, concepts, etc. are continuously emerging to the market.
Moreover, a permanent cost reduction of mature technologies enables their large-scale implementation, following trend of improvement in the optimization of assets and maintenance processes.
In recent years, there has been a substantial improvement in predictive technologies for mechanical, thermal, electro- mechanical and more recently in electric and electronic system monitoring .
Maintenance should be a constantly improving activity, which enhances the quality of service and optimizes operating costs. Condition-based maintenance and predictive strategies based on cutting-edge technologies are arriving on the market and their continuous cost reduction opens wide opportunities, helping the operation and maintenance personnel to perform tasks more effectively.
There are cost-effectiveness studies of the different types of strategies [3,4,5,6], but it is difficult to measure the impact of predictive strategies.
A simple model was developed to solve the difficulty of showing how predictive maintenance could help in a cost-effective way. This model is based on the application of different existing tools (Balance Scorecard, RAMS optimization, FMECA, FRACAS,…), and it follows a six-step structure based on a Deming cycle to gradually improve each process or service adapted to maintenance needs.
The cycle is continuously fed with new information to achievean optimal maintenance working way in a cost-effective manner and keeping in mind strategies that are based on predictive technologies. The steps are carried out in a cyclical manner as shown in figure 2.
1 – Selection of the objectives
The first step is to establish the main objective. It is essential to know exactly the situation of the company in order to know what should be improved and to align the vision-mission-strategies- objectives-indicators. These objectives should be identified with KPIs (Key Performance Indicators) with different approaches: financial, learning or technical. There are many different techniques that can serve to achieve the correct alignment between the company and indicators, such as Balanced Scorecard.
2 – Identification of the most important products/processes
The next step is the identification of the main objects or processes where the improvements are going to be critical with respect to their impact on the selected KPIs. Results may include machinery parts (e.g. door mechanism), product types (e.g. semi-automatic doors) or target sectors (e.g. hotels) among others. Criticality tables are used in order to rank the results and select an appropriate subset for further analysis and simulation in the next steps.
3 – Analysis of selected product/processes
An exhaustive analysis of selected products/processes is carried out to have a clear idea of their main important aspects.
Analyzing the most critical assets is very useful in order toobtain the selected objectives. The information of different tools: Failure Mode and Effect Analysis (FMEA), Risk Analysis (PHA, HAZOP), Failure Tree Analysis (FTA) or Failure Reporting and Corrective Action System (FRACAS) analysis can be used depending on the objectives and information available. In conclusion, a complete understanding of these assets gives a better way to make improvements.
4 – Development of the proper strategy for each critical product/ processes
This step consists of an analysis and an assessment of various maintenance strategies for the selected critical products/ processes. There are a number of different techniques for implementing and analysing these aspects.
The cost assessment simulation is done in this step and is the most critical part of the whole model. An ad-hoc optimization tool has been developed to better simulate the cost-effectiveness, and therefore this step will be further explained in this article.
5 – Implementation of actions
The selected strategy is implemented at least in one control group in order to evaluate the results. New procedures, hardware and software technologies will be deployed and tested.
6 – Final assessment
Using the initially defined indicators, the assessment will evaluate whether the initial objectives have been fulfilled or not. Steps 5 & 6 are run iteratively in order to compare the simulation results in step 4 with the real results obtained after the deployment of the new strategies. This deployment is normally progressive (concept complete design, lab trials, first installs on selected machines, etc.)
If the objectives are not fulfilled, i.e. there are large deviations from the simulated cost assessment, it is necessary to return back to the previous step and identify the deviations sources. If the objectives are being fulfilled, new objectives should be defined to follow the continuous improvement programme.
he cost-effectiveness analysis step is very important in this model, because it is the way to indicate if any profit or competitive advantage can be achieved by using more automatic maintenance tasks, especially predictive maintenance.
With this in mind, the analysis is done with a maintenance strategies simulator , which introduces the reliability aspects of predictive systems with other common data as corrective and preventive maintenance costs and reliability information. These aspects appeared in  and recently have been studied by other authors [10,11].
The key issues in simulation are to use a valid source of information, employ a relevant selection of key characteristics and behaviours, make approximations and assumptions when necessary and understand the fidelity and validity of the simulation outcomes.
For the simulation of maintenance strategies, it is assumed that the probability density distribution of failure for the equipment or component is known. This function describes the possibility that a failure occurs at an instant of time. This is typically established from data collected in trials, testing the items and noting the time when the failure occurs or with reliability data, for example supplied by the manufacturer. The confidence value of the simulation data is very important.
Figure 3. Multi-cycle optimization based in the scheme of figure 2.
Figure 4. ORONA’s door mechanism.
The improvement Cycle of Information Quality
Improvement cycle is not only focused on optimizing the maintenance processes, it is also related to the process of information acquisition and storage as well as in the identification of best indicators for finding deficiencies, which in other ways could be difficult to analyse.
Outcomes of the Improvement Process
Among other improvements, the analysis allowed to focus on different aspects of the cabin and floor doors considered most important at different cycles, such as certain door mechanism (FIGURE 4). Selecting these critical elements allowed a detailed study of the principal failures and causes, using information of FMEA combined with information analysis of the lift population.
The analysis was focused on technologies with potential to identify the failure before it happened and with the help of the work team knowledge different systems were found to solve failures.
The improvement analysis finishes with the estimation of the potential impact in the indicators with the current conditions of operation and maintenance. The impact of the different strategies (corrective, timebased, inspections) is measured in Euro’s comparing the maximum cost of the selected technologies and alternative strategies, considering several known variables such as frequency of the inspections, reliability, cost of failures, inspections and preventive actions.
In Figure 5 the y-axis describes the costs in Euro/month during the lift’s lifecycle, the online monitoring costs (installation, maintenance) are simulated in the x-axis to check the optimum cost for this type of CBM strategy. It is possible to check the maximum cost of the online monitoring system by comparing it with the other maintenance options. In this case, the graphic indicates different strategies and their cost-effectiveness. Comparison between different strategies including the partial costs of the different activities, are shown in figures 6 and 7.
Figure 5. Impact of the different strategies.
Figure 6. Inspection strategy with different frequencies shows how decreasing the inspection frequency is not the right approach because it could produce savings, but there will be a cost in quality of service (failures).
Outcomes of the information Improvement
The outcomes of the project are not only related to the identification of ‘hot spots’ where to apply new technologies and strategies. The continuous improvement had also an important impact in the quality of theinformation that is handled by ORONA services.
A new optimized visit report has been designed, both in manual and electronic versions. The main goal is to reduce the amount of incidences not yet linked with a clear type of failure. Vaguely defined fields of the work order were erased (“Other” type of failures) and now there are three codes to be filled in: failure code, cause code and made action code. Codes with little impact in the indicators were eliminated to have a manageable work order for the maintenance staff.
First available results showed a great improvement of the identification of failures with the three codes: failure/cause/action, there is now a clear identification of the failures which is essential for suggesting improvements and speeding up work order fulfilment (less notes are needed in the work order).
During the first steps of the project there was only one indicator related to the reliability of the product. During the project development new indicators were discovered and selected as the need to consider other lift costs and quality of service related aspects became evident. Currently there are four indicators to identify the impact of the potential improvements in maintenance.
The need of better information quality enabled identifying improvement points for infrastructures associated to information access, transmission and processing. These improvements are being handled by different groups in the company.
Impact in the Organization
The potential impact of the technologies is often reduced due to organizational issues:
- Even if the company has a dynamic organization, there is a rejection of new technologies by maintainers, because it is seen as a risk to their job.
- New processes are difficult to fit into the organizational structure
In general it is very important to involve people from all levels of an organisation when an improvement is implemented. Orona Elevator Innovation Centre’s e-conservation team is working to integrate people from all areas of the maintenance business to adjust the processes related to maintenance technologies.
The described process is expected to be a useful tool in different sectors (energy, transport, production, SME…), where it is difficult to show the obtainable improvements when implementing new processes or products, particularly in predictive maintenance (e.g. wear control, inspection system improvement, etc.).
Although predictive strategies are shown to be cost-effective for companies, the lack of understanding these techniques, initial costs and reticence for maintenance culture change, require tools to prove the cost-effectiveness.
In conclusion, this type of research is relevant in the area of continuous technological improvement, where today’s experimental technology could be tomorrow’s competitive advantage ensuring the optimal level in reliability and availability with the lowest possible cost.
Figure 7. Smart combination of on-line monitoring and on-site inspections (right) ensures maximum quality of service (less failures) while notably reducing overall costs.
»»References ››  Bengtsson M & Salonen A. “On the Need for Research on Holistic Maintenance”, Figure 7. Smart combination of on-line monitoring and on-site inspections (right) ensures maximum quality of service (less failures) while notably reducing overall costs. Proceedings of 22nd International Congress on Condition Monitoring and Diagnostic Engineering Management 2009, pp. 165–172. ››  Vichare N & Pecht M. “Prognostics and Health Management of Electronics”, IEEE Transactions on Components and Packaging Technologies, Vol. 29, No. 1, March 2006, pp. 222–229 ››  B. Al-Najjar. Economic Criteria To Select A Costeffective Maintenance Policy, Journal of Quality in Maintenance Engineering, Vol. 5 Number 3, 1999 ››  H. Fromm, S. Heck & M. Buderath. Cost Benefit Analysis Of A Health Management System, 1st CEAS European Air and Space Conference, 2007. ››  Feldman K, Jazouli, T & Sandborn P. (2009) A Methodology for Determining the Return on Investment Associated with Prognostics and Health Management. IEEE Transactions on Reliability, Vol. 58, Nº 2, pp. 305–316. ››  Jardine, A.K.S. & Tsang, A.H.C. “Maintenance, replacement and reliability: Theory and Applications” CRC Press 2005 ››  Parida A. “Study and analysis of maintenance performance indicators (MPIs) for LK - AB” Journal of Quality in Maintenance Engineering Vol. 13 No. 4, 2007 pp. 325–337 ››  Conde E, Fernandez S, Arnaiz A., Maintenance methodology for selecting the most cost-effective predictive strategies. The 37th ESReDA Seminar, Octubre 2009 Baden (Suiza). ››  Jardine, A.K.S.; Joseph, T.; Banjevic, D. “Optimizing condition-based maintenance decisions for equipment subject to vibration monitoring”, Journal of Quality in Maintenance Engineering, Vol. 5 No. 3, pp. 192–202, 1999 ››  Al-Najjar B. (2009) A computerised model for assessing the return on investment in maintenance; Following up maintenance contribution in company profit. Proceedings of the Fourth World Congress on Engineering Asset Management (WCEAM) 2009, pp. 137–145, Greece, Athens, Sep. 2009-09-08, Springer-Verlag London Limited. ››  Berdinyazov A, Camcj F, Sevkli M and Baskan S, “Economic Analysis of Maintenance Policies for a System”. IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2009. SDEMPED 2009.
We have all read about it: leak detection should be a top priority since, if no leak detection program has been implemented, leaks can account for 30 to 40% of consumed volume. So, why is this issue still on the table? Why is it difficult to change things in the field?
A growing number of companies want to use big data analytics in their predictive maintenance and are also investing in the resources needed for this. Of the companies already using this technology, no less than 95 percent say that they have already achieved concrete results. This is the conclusion of a follow-up study conducted by PwC and Mainnovation among 268 companies in the Netherlands, Germany and Belgium.