Plant & Works Engineering
Making manufacturing maintenance pay
Published:  09 October, 2015

Predictive maintenance is revolutionising maintenance strategies of many capital-intensive industries. Yet despite its clear advantages over a costly ‘run until it breaks’ reactive strategy or an inefficient ‘fix it regardless’ preventative approach, predictive maintenance is yet to be widely adopted by manufacturers. PWE looks at the case for technology providers servicing adjacent industries to adopt their predictive maintenance solutions for manufacturers.

In an industry facing mounting pressure from squeezed margins, ageing assets and a reduced workforce, manufacturers are understandably targeting maintenance as an area to unlock greater productivity and profitability. While existing reactive and preventative solutions offer a degree of support in easing maintenance costs, significantly enhancing the performance of maintenance strategies requires a more pro-active approach supported by intelligent software. At present these solutions are limited which highlights that there is a gap for technology providers to fill if manufacturers are to have the trust and confidence to break away from traditional maintenance methods.

Limitations of current asset maintenance

Today's manufacturing facilities commonly make use of condition monitoring solutions for their critical assets that work by monitoring either one or several parameters of asset condition, such as vibration, used oil, thermograph or ultrasound. However, because many solutions typically focus on only one or two discrete data types, their ability to detect a comprehensive range of potential faults is limited. Furthermore, condition monitoring systems are restricted to merely flagging that data readings have gone outside of the specified normal range and do contain the highly intuitive functionality of predictive maintenance solutions to identify the severity or cause of the problem. By using asset condition data to predict failures and thereby only schedule maintenance when necessary, predictive maintenance solutions could add considerable value. For example, during a grinding machine failure, predictive maintenance will specify that a bearing in a spindle motor has worn and there is a 90% probability of machine failure if not replaced within a week.

Such workable examples of predictive maintenance are already delivering significant profitability and productivity improvements in sectors that share notable synergies with manufacturing, including oil and gas, wind, power generation and aerospace.

Castrol’s Bryan Rabenau told PWE the resultant benefits are clear; in a new report by Castrol’s innovation arm, Castrol innoVentures, and Roland Berger Strategy Consultants, energy companies eliminated on average 75% of breakdowns through the use of predictive maintenance techniques. Furthermore the average cost for power plants operating a predictive maintenance programme on their pumps was found to be 9 USD/hp per annum, compared to 18 USD/hp per annum for reactive maintenance and 13 USD/ hp per annum for preventative cost.

To support its findings and to understand the evolution and potential of predictive maintenance in more detail, Rabenau explained that Castrol explored several case studies from adjacent industries in its report ‘Predictive maintenance: Is the timing right for predictive maintenance in the manufacturing sector?’ In the oil and gas sector 40% of executives who responded to Castrol’s research said that failure of critical assets was the issue that had the greatest impact on operations and, through the adoption of predictive maintenance, downtime was reduced by between 35% and 45%, while maintenance costs were cut by by 25% to 30%. Furthermore, predictive maintenance increased production by between 20% and 25% and triggered a 10 fold increase on return on investment.

Rabenau said results were equally encouraging for the wind energy sector where predictive maintenance increased production revenues, resulting in return on investment in as little as six months. A global wind operator who took part in Castrol’s research also reported saving around a quarter of a million dollars in operation and maintenance costs through adopting a predictive software system. In a further example a wind farm operator used predictive maintenance software to locate and resolve an unintentional power limit that had been imposed on a one turbine within a wind farm. By resolving this issue, additional revenue was captured.

Overcoming practical barriers & challenges

Despite these tangible productivity and profitability benefits, manufacturers are yet to reap the widespread benefits of predictive maintenance solutions. This is in spite of some best-in-class organisations already introducing predictive maintenance and experiencing notable successes. For example, some manufacturers have reduced unscheduled downtime to around 5% below the industry average and achieved an increase in overall equipment effectiveness (OEE) of more than 8%. Furthermore, the report identified that manufacturers have an appetite to increase the time spent on developing predictive maintenance solutions from 15% to 33%, while reducing spend on reactive and preventative maintenance.

With these benefits and notable market interest reinforcing the relevance of exploring new predictive maintenance technologies in manufacturing, why are similar technologies yet to be widespread? The answer is multifaceted and driven by a number of practical barriers that are unique to the complexity of the manufacturing sector. These include the wide and complex range of critical equipment that is used as well as diverse and dynamic operating conditions, which can be technically challenging for solutions providers. Similarly, budgetary pressures and a cultural inertia towards reactive maintenance still need to be largely resolved in order achieve widespread adoption of predictive maintenance in manufacturing.

Furthermore before predictive maintenance solutions can be installed, manufacturers must establish a balance between the cost of retrofitting older machinery with new data collection capabilities, against the criticality of the machine in question. Risk must also be rigorously assessed and minimised in order to prevent equipment failure and subsequent losses of productivity during the installation and subsequent operation of predictive maintenance operation software.

Amid these challenges and although there is a growing awareness of the latent potential in predictive maintenance strategies, this is yet to translate into widespread behaviour as engineers are still commonly defaulting to traditional maintenance methods. However, with significant added value on the table; from reductions in unplanned downtime, to asset lifecycle extensions, elimination of unnecessary maintenance tasks and component replacement cost reduction, Castrol maintains that by transferring knowledge gained by the early adopters of predictive maintenance solutions and the right technology systems to support this, it will only be a matter of time before manufacturers too can drive efficiency and significantly lower costs on a more widespread level.

Inventive problem solving

Like any development opportunity, predictive maintenance requires ongoing investment if it is to be a success in the manufacturing industry. This is a challenge when there are many operational priorities that are competing for resources. To overcome this, Castrol is calling on entrepreneurial predictive maintenance providers to adapt their solutions to the manufacturing space and for those that do have an offering, to come forward and collaborate to increase trials and uptake. Based on the considerable efficiency savings achieved in analogous industries, developing predictive maintenance in manufacturing companies could deliver a win-win; opening up a new market for solution providers and driving increased profitability and productively that will enable manufacturers to remain competitive amid tough market conditions. With maintenance increasingly seen as a strategic business function by manufacturers that are looking for new opportunities to drive efficiency and lower costs, can technology providers afford not to initiative dialogue about new solutions that meet the varied and complex dynamics of manufacturing when so many benefits are at stake?

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