• 08
  • Feb

The top 3 barriers to predictive maintenance

Senseye BlogThe top 3 barriers to predictive maintenance

Predictive maintenance sounds great – maintain your assets, before they show outward signs of failure and cause unplanned downtime (loss of revenue), whilst spending less money than you would for a preventative maintenance program – boosting profitability and throughput. The benefits are quite clear. Why then do relatively few companies have an active predictive maintenance program? 

Technology

Manufacturing companies are optimised for the flow and assembly of materials – they’re not traditionally experts in condition monitoring or data analysis and we don’t believe they should have to be. Until recently, successful predictive maintenance has required either deep pockets for external consultants or a team of experienced in-house condition monitoring experts. 

There’s also a general belief that to take advantage of predictive maintenance requires a heavy investment in the ‘Internet of Things’ / Industry 4.0. Whilst we believe that prognostics is the most interesting use of Industry 4.0, you’ll find that most automated equipment (e.g. industrial robots through their PLCs) collects good quality data already. The problem is rarely technology but accessibility; the data needs to be brought out and stored in a middleware layer. 

How to overcome this barrier

Start with bringing out the data you already have and make the fullest use of it before investing in additional flashy hardware.

 

Financial justification

A paradigm-shift in maintenance approach can be difficult to initially justify, given a fear of the unknown and the belief (often misplaced) that ‘what we have works’. Adopting predictive maintenance does require an initial outlay but looking at the short-term view must be discouraged - successful predictive maintenance is continuously demonstrated as saving upwards of 20% of operations and maintenance costs. The long-term view is to understand that a successful program will result in a reduced Total Cost of Ownership (TCO) as well as significantly less downtime (between 30-50%). 

External consultants and ‘box pushers’ of traditional condition monitoring hardware will be keen to inflate the scope of any predictive maintenance project as much as possible but that can be a recipe for unrecoverable costs and a return to inefficiency. Any maintainer will say it’s crucial to bring the right tools for the job, selecting the right predictive maintenance software and approach is no different. 

How to overcome this barrier

Start small, prove gains on select assets with minimal investment and then multiply those gains.

 

Cultural fit

Maintenance can often be seen as a fire-fighting activity, with maintainers doing the absolute best they can in situations where they’re given little to no warning of impending disaster. There’s a justifiable fear that changing anything at all could lead to further problems, this is why it’s absolutely key that any predictive maintenance program is introduced slowly and that the benefits to everyone (maintainer, manager, CXO) can be clearly and consistently demonstrated.

We sometimes encounter a concern that predictive maintenance could threaten job security for maintainers – we take the opposite view as we’ve seen evidence that maintainers with the right predictive maintenance tools have their powers multiplied and can contribute even more significantly to reducing unplanned downtime. 

How to overcome this barrier

Once predictive maintenance has been demonstrated on a small number of assets and the benefits have been clearly understood, cultural resistance tends to melt away.

Get a grip on downtime – Senseye

Reduce the Total Cost of Ownership of your assets, increasing throughput, saving money and improving profitability with Senseye, our automated condition monitoring and prognostics software to help you to enable predictive maintenance without the pain.

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Author: Hernán Piñera - Flickr License: Creative Commons ShareAlike 2.0 - https://creativecommons.org/licenses/by-sa/2.0/

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