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Intro
Maintenance of your data infrastructure isn’t something most people think about much. And that’s a positive. If your users never have to worry about their critical technology, someone in the IT department is doing a great job.
But there’s more than one method for doing maintenance … and some have a far better business case than others.
If you cheerfully overhaul all your equipment and systems every year – long before anything is likely to break down – your vendors will love you, but your CFO will soon come visiting with questions about ROI; you’re taking preventive maintenance too far. While if your team leaps into action whenever there’s a fault, everyone will think you’re a hero … but workflows will suffer as capacity drops, work backs up, and inefficiencies arise. Here you’re doing reactive maintenance, waiting until disaster strikes when the risks should have been obvious long before.
Here at Strypes, where we maintain technology for some of the world’s most demanding clients, we have a clear preference. Our vote goes to predictive maintenance: foreseeing the future based on data, and upgrading infrastructure at the right time to avoid problems ever happening. It reduces wasteful timeouts, while making the most of each piece of equipment’s useful lifespan.
But there’s an art to predictive maintenance, too. In fact, if you do it wrong, it’s every bit as costly and resource-hogging as other methods. So in this blog, we’d like to share how the right type of maintenance avoids equipment failure while reducing maintenance costs and unplanned downtime.
Let’s look at some maintenance strategies, and help you choose the one that works.
While predictive maintenance is our top choice, note there is nothing fundamentally wrong with other approaches – the differences are in the detail. It’s about how you match your approach to maintenance with business considerations like cost effectiveness, human resources, and the size and complexity of your data infrastructure. Here are the pros and cons of the three types.
Reactive maintenance: a series of never-ending panics
Let’s be honest: all IT professionals have taken a reactive approach at some time in their careers. Maybe they worked for a small startup that hadn’t formalised its processes yet. Maybe their infrastructure had grown piece-by-piece, instead of with a systems approach, and it was prone to unexpected feature interactions. Or perhaps the budget simply didn’t anticipate maintenance would ever be needed. (It’s hard for a new company to realise its shiny new servers will one day go wrong.)
And this is where a reactive approach is (sometimes) the right one. For a small company where server uptime isn’t mission-critical, or a less technologically adept one where everybody’s using their own laptop or phone, simply responding to failures as they occur carries one clear cost benefit: maximising the useful lifecycle. You got every last second of uptime out of that device, because it was in use up to the moment it died.
But it’s easy to see the downside here. For the user, a laptop suddenly becoming a useless desk ornament wastes time while IT replaces it. It wastes effort, if data was lost in the failure. And it wastes human resources, as the user sits at his desk twiddling his thumbs while IT unwraps a new device.
The principal problem: in many companies – even smaller ones – the cost of recovering the optimum situation can exceed the cost of running that device to failure. That laptop may have lasted the user five years instead of three. But if a day or two of downtime costs more than a few hundred Euros, all that extra benefit has been lost.
There’s a secondary problem, stemming from a lack of data. If the laptop (or server, or router, or cloud API) simply failed without supplying any data about the cause, the problem’s going to happen again – and probably sooner than you think. Replacing the device doesn’t solve the underlying problem.
That’s why we can’t recommend reactive maintenance as a cost effective strategy for any but the smallest, least technology-dependent companies. There are too many risks involved – risks that can be mitigated easily with a change of thinking.
So what about preventive maintenance?
Preventive maintenance: sometimes simple, often wasteful
This approach is more like the one your car salesman recommends. Note he doesn’t just sell you the car; he wants to sell you an annual servicing plan, windscreen cover, a schedule for oil changes and tyre pressure checks. It’s preventive maintenance, looking to head problems off before they happen.
In technology, it’s a common approach, since equipment vendors offer data about the expected lifetimes of their wares. (You’ll see an MBTF figure for storage, “Mean Time Before Failure”; batteries have thresholds for the number of charges they’ll hold before performance drops below 80%; cooling fans and power supplies all have maximum operational hours.)
A preventive approach to maintenance takes note of these numbers, and aims to replace or service aging technology before it’s likely to fail. But there’s trouble ahead here, too.
First, understand that those numbers are just estimates based on testing. The manufacturer doesn’t know the conditions you’ll use your equipment in. If your technology sits in a tropical greenhouse, or a dusty factory floor, its MBTF will be a lot lower than advertised. And they are, in any case, averages: by definition, half of all equipment will fail before its MBTF due date, while half will fail after.
On the whole, though, preventive is a more sensible approach than reactive – and may well be fit for purpose in SMEs where the overall technology investment is small. But again, the lack of data leads to waste and inefficiency. At 19,000 hours, you don’t want to know that the manufacturer estimates a useful lifecycle at 19,500. You want to know whether the machine will actually fail, based on its condition today.
And that’s where predictive maintenance comes in.
Predictive maintenance: connecting data for a better business case
Both reactive and preventive maintenance share a shortcoming: live data. Meaningful information about the part or unit, drawn from sensors that “watch” its performance on metrics like temperature, usage patterns, and environmental factors. It lets IT professionals make decisions based on what’s actually happening, in the real world.
This combines the one good idea of the reactive approach – maximising the operational lifespan of a device – with the many good ideas of the preventive one, such as scheduling updates and services when they’re least interruptive and always replacing items before they fail catastrophically.
That’s not all. It adds a special ingredient: because the predictive approach relies on real-time data, it fine-tunes those maintenance schedules to make the most of each individual piece of equipment. After all, the lifetime of a desktop machine in a dust-filled workshop will be a lot lower than one in a clean, cool office. Predictive maintenance is condition-based maintenance: it acts on quality information specific to each asset.
And as usual with good data, this approach gives something more: insights. Not just which machines need maintenance, but why. Giving CTOs the information they need to adjust strategy and allocate resources where they’re needed most. And to solve problems at their root, rather than treat symptoms as they appear. Which makes every Euro stretch further, and the C-Suite a happier bunch.
That’s why we take a predictive view of maintenance at Strypes. Best of all, the benefits start at SME size and scale up to the enterprise. So our next question: are you intrigued by what predictive maintenance offers, and can we help?
Reactive maintenance vs preventive maintenance vs predictive maintenance: and the winner is …
Strypes plans and executes predictive maintenance for a wide variety of clients, from agricultural world leaders to the biggest names in technology. Every company is different, but many share the same goals for their tech: lower costs, longer lifetimes, less downtime and more satisfied customers. So why not explore its potential?
To learn more about our Remote Diagnostics, Monitoring and Predictive Maintenance solutions, click here, or contact us directly:
Remote Еquipment Мonitoring and Diagnostics for the Agriculture Industry
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