Traditionally, maintenance functions in asset-intensive organizations are expected to cope with their maintenance problems without seeking to operate in an optimal manner. For example, we found that many maintenance organizations, put their preventive and predictive maintenance policies into practice with only a little bit of quantitative analyses. As a consequence, although the tasks might be the right tasks for the identified failure modes, in most cases the intervals/frequencies of these maintenance tasks were at best sub-optimal, to downright making the maintenance tasks ineffective.  In other cases, many maintenance tasks are canceled as they are perceived as too expensive because they are done way too frequent.

 

Hence, asset managers who wish to optimize the life-cycle value of the physical assets must consider optimizing 3 key decision areas:

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Working together with our partners using highly practical and proven tools that are based on sound reliability

engineering and statistical methods, we support our clients in optimizing the decisions they made in those key areas above. Below are brief summaries of our decision optimization services

 

Optimizing the interval for preventive maintenance 

While predictive maintenance is much more preferable than preventive maintenance, in some cases, the later could still be the more cost-effective maintenance policy. The challenge remains, however, that if we do know that the risk of failure increases as the equipment aged, given its replacement costs, what would be the optimum interval for this preventive maintenance? Utilizing our simple but yet proven and beneficial tool, we help clients explore and arrive at the optimum interval for the 4 variations of preventive maintenance policies (deterministic deterioration, replace only on failure, constant interval, and age-based). If you are interested to understand more about the application of this tool, please contact us or click the application page for OREST. 

 

Optimizing MRO inventory stockholding level that effectively support maintenance  
  
When the maintenance function is still struggling in a reactive environment, it requires a large MRO spares inventory to support that practice. When the function has successfully achieved improvements in its reliability process, one of the payoffs is that it no longer requires excessive MRO spares in stock. However, we recognize that the path to optimum proactive maintenance practice may be bumpy and may require some time. And that in the process, the supporting MRO inventory may suffer from the adjustment process, all the time. Frequently, the inventory stock level goes up and down cyclically. Alternating between the cycle of inventory reduction exercise and excessive maintenance buying due to frequent stock-outs. And costing millions in the process. 

If you wish to get out from this process quickly. Shedding your excessive stocks without sacrificing maintenance, please contact us. We employ various spares optimization tools from our partners that can help you improve your MRO inventory performance.

Optimizing Asset Life-Cycle Cost

Asset Life-Cycle cost analyses is something that comes as a natural tendency for maintenance funtion to look at as maintenance costs for their equipments are creeping up. As equipment purchases in physical asset intensive environment involved substantially large amount of capital, it is extremely important for the maintenance function to understand how to do life cycle costing analyses properly to support effective decisions. Relogica helps clients in understanding and applying the right principles and assumption to correctly analyze asset life cycle costs. We utilize 2 separate tools namely Perdec and Age-Con which are aimed at plant and fleet environement, respectively. 

 

Optimizing the Predictive Maintenance Program

There is little doubt these days that predictive maintenance is the preferred maintenance policy over the others. Indeed, many have also implemented advanced predictive technologies such as vibration monitoring, thermography, tribology, and ultrasound. However, there are also numerous organizations which have failed to enjoy the benefits these new technologies may provide. This is often because of poor program setup and lack of good understanding of the relationship between the compiled various indicators and the actual failure of the equipment. This situation may results in bad maintenance decisions. Many maintenance professionals perhaps meticulously record equipment conditions but despite of early failure indications, lack the clue on when exactly the equipment will fail. Will it last another period until the next service / turn around? Given the expensive costs of parts, they may defer the replacement decisions, thinking that they may still have some time before it fails. Only to find out that it then fail when they least expect it. Hence, the early warning system, while gave them the warning, did not enable appropriate decision making.

 

In more cases, bad decisions often result in inefficient maintenance practices costing the company millions of wasted dollars.  In a study undertaken by Anderson et all in the 1980s, it was found that 50% of aircraft engines that were removed due to indicators in oil sampling analyses were found to be still in perfect working condition. A similar study by Christer in 1999 indicated that while gearbox breakdowns were successfully reduced by 90% due to CBM program, 50% of those gearbox removed as a result of the CBM program, were later found to be free evidence of gearbox fault. Hence, in this regard, CBM can be very effective but also inefficient at the same time.
 

To overcome these difficult conditions, we help clients in establishing the relationship between predictive indicators to the actual failure of the equipment. Through the advance reliability tools, EXAKT from our partner OMDEC, we help clients establish the co-relationship and improve the predictability of failures using the combination of several indicator variables as well as blending the effect of equipment aging. The resulting model is extremely useful in estimating the Remaining Useful Life (RUL) of a component. And thus assist in the replace or delay decisions for corrective maintenance. For case studies, please download here. 

Additionally, as the application of the optimization tools above requires good historical data, organizations aiming to utilize them, must establish proper reliability engineering practice. As such, we also assist our clients in establishing the right data structure for their work order management system/failure history collection. As well as training in the use of basic reliability engineering and Weibull analyses to derive component life parameters required for the optimization analyses.