It’s an interesting question. Probably 85% to 95% of samples are normal in a typical fleet of machines. But what if you are consistently getting normal samples, perhaps 98%. Then you may start asking the question do you really need oil analysis or indeed any other condition monitoring technology?
It’s the most common reason I hear people say they don’t want to consider oil analysis and with the “my machines don’t fail” reason.
A couple years back I did a pseudo survey of both our customers, and also people I would like to be our customers but were just not ready yet to take the plunge on oil analysis. Now in sales the one thing to ensure the phone being put down on you is to say “I just need to ask you 10 questions, it won’t take too much of your time.” I try to be the opposite and try to hear people out as it’s not an easy job cold calling someone. In fact I’m generally quite tolerant of sales calls, but I realise a lot of people are not and when you have had your 10th call from O2, EE, Vodafone etc saying they can lower your monthly bill for the day when you use none of these networks I can understand why. So I have simply over the past couple of years been taking note of one simple question. What is that question? Well that’s the purpose of this article.
What percentage of your machines are not working?
That’s essentially my question I ask. Well I use a few variations on this question, which I know will get me into trouble with puritan statisticians as varying the question can create some bias on larger studies but you try dropping survey questions into normal sales calls without getting told not interested. Basically the point is to find out if the customer might benefit from oil analysis, is benefiting from oil analysis or could tweak their oil analysis programme to improve things.
I however noticed two stark distinctions in the perceived amount of problems within the same organisation depending on the position they held.
Generally the more senior you are the less you think there are problems with your machinery. Directors, senior managers, even those with huge fleet uptime lists and really invested in this information were very much of the opinion the uptime availability is 99% or more with less than 1% not working. The most common answer was it was very rare and was a freak or rare occurrence. However there was a much larger spread of issues with people actually using or maintaining the machinery.
So why the perceived difference?
Well it’s the FOB effect as I like to call it, or Fear Of Bothering someone more senior with something believed to be below their pay grade. It’s strange but a lot of people don’t want to bother their boss or even fear doing so. Even in very open organisational cultures this can happen. Take for instance a case study in my own organisation. Now anyone who knows me will know I’m quite techy. This example will illustrate how fear of looking foolish can lead to issues not being raised.
Case study of information not passing up the chain of command
A common test we do on every sample is a photograph of the bottle top and bottom on receipt. Now this involves a couple of cameras in a camera rig plugged into a PC. These cameras as they are the exact same model have identical identifiers when you plug them in the PC, so the logic of the software just looks at order they load on the usb on the motherboard. This is how the computer identifies which camera is which. Very occasionally the way the PC boots up it decides to find the ports in the opposite order. So I created a little setting file of 1 or 0 to change to flip the camera order. I showed this trick to sort the problem to the team and thought problem sorted. However, I showed switching from 0 to 1 and assumed people knew it was 0 or 1, but instead the message came across as you increase by 1. An easy confusion to make, but it meant that the fix didn’t work as 1 was the typical boot order. What I didn’t realise was when the fix didn’t work rather than coming to ask me why it didn’t work people were clicking the restore factory defaults which involved a long restart process and that usually fixed the issue. However it was wasting about 20 min instead of 30 seconds. This apparently went on for a while before I eventually had to cover for the test on a busy day and realised what had been happening. I asked why it was never raised and I was told the rebooting fix worked so the staff didn’t feel they needed to raise it. Now our organisation is a relatively small one and I’m a very hands on as a boss, so let me tell you if I can not be aware of a problem then this can happen in any organisation. FYI I updated the training documentation in the lab to explain the increase by 1 was a toggle from 0 to 1 in the screenshot and only two options exist.
In the case study above I had the perception the appearance machine constantly worked and any downtime was mere seconds to fix but in reality I was unaware of hours of time the instrument could not be utilised. In this case my perception was wrong until I realised myself the issues that were occurring.
I suspect this may explain the glaring difference between the senior and junior or hands off and more hands on people to be precise.
Hence my hypothesis is the senior people don’t know machines are not working because people fix them before it’s a catastrophic issue, but downtime and repairs still cost money and it’s a hidden cost many just accept in an organisation. Now I accept that perhaps the more hands on person may just have oversight of a smaller number of machines and perhaps the more senior person has a larger view especially on costs. However this is where the issue of hidden costs come in.
Let me start with another case study to explain this point.
Unnecessary fans
Now I have used this case study a few times before so apologies if you have already heard it before, but I had a customer who had a large lubricated system for their production plant which generated a lot of heat. The system had a lot of pipework and cooling fins built into the design by the OEM, but the system required large industrial fans blowing across the cooling fins to help in the heat transfer to the outside air. These fans were on all the time and even the field engineers assumed the fans were part of the design. However, one of our customers (the filter supplier) used to work in the Middle East for the OEM and knew this was an add on. When we analysed the oil post them installing some new filters which we have the contract to do the analysis we noticed the acid number was very high (4.8) when the new oil was around 0.6 mgKOh/g. We recommended a varnish potential and got 65 by MPC. (Normal is less than 15). After a few discussions we came to the conclusion the varnish was depositing on the inside of the cooling fins and insulating the system making overheating worse and entering a looping cycle of varnish formation. After the system was cleaned with a solvent flush and then a varnish removal filter the cooing became so efficient the fans were turned off permanently a few months later. Now this was a cost that was not even realised was unnecessary and had likely been an issue for years to the point that the original reason the fans were bought in had been forgotten. A classic treating the symptom and not the cause.
The above example over years had cost possibly millions in electricity that was not even needed. However nobody thought there was an issue. It was just accepted it was something you had to do. The entire testing to diagnose the problem was less than £150 which was likely recouped in a matter of hours for these fans electricity. So I come back to the concept of when you say everything is working you assume just because it’s not failing it’s working at 100% of its potential.
Enter the underutilised asset
In the realm of industrial maintenance and machinery management, the term “underutilised asset” refers to equipment that, while operational, is not achieving its full potential in terms of efficiency, productivity, or longevity. This underutilisation often stems from a lack of comprehensive maintenance strategies, including regular condition monitoring and oil analysis. The paradox lies in the fact that machinery may appear to be functioning adequately from a superficial standpoint, yet, upon closer examination, is found to be operating in a suboptimal state due to unseen wear, contamination, or degradation.
Oil analysis serves as a critical tool in the identification and rectification of such underutilisation. By offering a detailed glimpse into the internal condition of machinery through the “bloodwork” of the equipment – its lubricating oil – this technique can highlight issues well before they manifest as outright failures or inefficiencies.
Case Study: The Silent Efficiency Thief
Consider a hypothetical scenario where a manufacturing facility relies on a fleet of high-value production machinery. Regular oil analysis might reveal a trend of increasing wear metal concentrations in several units, suggesting abnormal wear despite no apparent decline in performance. Further investigation, prompted by these findings, could uncover misalignment, improper lubrication, or early-stage component failure, all of which silently degrade efficiency and capacity as well as production quality. Addressing these issues not only prevents catastrophic failures but also optimises the machinery’s performance, effectively increasing its output and efficiency without significant capital investment.
The benefits of addressing underutilisation through proactive maintenance strategies like oil analysis are multifaceted:
- Cost Savings: Early detection of potential issues leads to cost savings by avoiding expensive repairs, downtime, and extending the life of the equipment.
- Efficiency Gains: Optimising machinery operation enhances efficiency, reduces energy consumption, and can increase production capacity.
- Sustainability: By maximising the use of existing assets, organisations can reduce their environmental footprint, aligning with sustainability goals.
- High quality and reduced waste – if you manufacture anything you will realise the tolerances required can be miniscule, which means you need your machinery at its best to achieve this. Suboptimal machinery can lead to Suboptimal product, rejects and raw material wastage. Keeping your machinery and lubricant in good condition improves quality of the products you manufacture.
The disparity in the perception of machinery performance between senior management and operational staff highlights a critical gap in understanding. Oil analysis, alongside other condition monitoring techniques, plays a vital role in bridging this gap, offering tangible data that can inform both strategic decisions and day-to-day operations. By embracing a culture of continuous improvement and proactive maintenance, organisations can transform their underutilised assets into sources of competitive advantage and operational excellence.
The concept of the underutilised asset challenges the complacent assumption that if machinery isn’t failing, it’s performing at its best. Through strategic application of oil analysis and condition monitoring, hidden reserves of efficiency, productivity, and capacity can be unlocked, revealing the true potential of industrial equipment. It’s not merely about preventing failure; it’s about aspiring for unparalleled excellence in machinery performance.
For those intrigued by the potential of uncovering hidden efficiencies within their operations, the journey towards optimised asset utilisation begins with a commitment to regular, comprehensive maintenance strategies, including oil analysis. Feel free to reach out for more insights or to discuss how oil analysis can transform your machinery from merely operational to optimally efficient. The ‘Contact Us’ button on this page is your gateway to unlocking the untapped potential of your equipment.
