Intelligent engineering: less downtime, more productivity
“A single failure is a source of knowledge we might not have gained in any other way.”
These are the words of Henry Petroski, Professor of Engineering and History at Duke University.
Failures, he argued, “reveal weaknesses in reasoning, knowledge and performance that successful designs may not even hint at.”
That’s the purpose of intelligent engineering: learning not just how but why things fail so you can design ways to stop them failing again. And in doing so you significantly reduce your overall downtime.
In short: you fix potential problems before they become real ones.
Here’s how it works…
No time for downtime
It’s true that failure can result in positive outcomes. But in the world of IT, failure often means downtime. And downtime can mean a loss in productivity and revenue and a serious dent to your brand.
But this predictive approach isn’t only about keeping systems running. It’s also about cutting the cost of any downtime that does occur and the engineering support that comes with it.
Dealing with incidents that have already happened is more costly. Costly in terms of time, fuel and parts. You have engineers zigzagging from one incident to another, in locations that could be tens of miles apart. It’s an inefficient and unreliable way to deal with problems.
But when you can predict failures and then schedule servicing, replacement parts or software upgrades accordingly, you can get the absolute best out of your engineers’ time.
It also means you can plan for the future with much more certainty. As a business this means you can operate with more confidence and be less worried about cash flow – a benefit you can pass on to your customers in the service they receive.
A mix of man and machine
So where does the ‘intelligence’ in engineering come from?
It’s generated by observing, correlating and understanding causality through data. Not just digital data but human, qualitative information that enables you to make changes with people in mind.
That human ‘data’ could come from anyone in your organization whose skills and knowledge could help you better predict problems. For us, it came from our engineers and the breadth of experience they have between them.
You can pool that data with information from social media and quantitative data from devices, creating a much richer and well-rounded picture of when and where failures are happening. And why.
If you used machine learning alone to create a dataset about device failures over time, you would end up with a huge amount of useful information. But it would take a long time to get to that point.
Adding that human layer means you can start being proactive much sooner.
Invisible demand – beyond engineering
When you break it all down, intelligent engineering is really just making the right decisions at the rights times.
It’s what we call ‘invisible demand’ – the ‘known unknowns’ and the ‘unknown unknowns’ you need to incorporate into your day-to-day strategy.
The technology people use every day provides more than just data about what those devices are doing. It can reveal deep insight about demand.
You can see whether those devices are robust enough to cope with that demand, whether you need more of them or perhaps even fewer of them.
And you can set the level of required engineering support off the back of that information.
This means service level agreements (SLAs) can focus on specific, localized needs rather than being ‘catch-all’ contracts that could end up costing the organization more in the long run.
And as a result of engineers being deployed more efficiently, you can focus your resources in other areas.
It’s a new way of thinking that goes beyond engineering.
An engineer’s core function is no longer to simply repair things. Today, they exist to help companies improve their performance by providing as much uptime as possible.
In a world that’s always on, that could be the difference between beating your competitors or watching them leave you behind.
Read our intelligent engineering white paper to learn more about our approach.
Related articles in Productivity
What makes collaboration work?
The debate is on: Does remote working work? Are we more or less productive working from home or somewhere other than the traditional workplace? In particular, what... Read more
People power: The key to staying agile while scaling
Guest authored by Simon Abrahams, Industry Analyst at Teknowlogy Group
Digital innovation - applying ideas that add business value
Executing digital innovation is one of... Read more
How Windows Virtual Desktop is Enabling Effective and Secure Remote Working
Tools for improving the employee experience, collaboration and innovation
From the start of the 2020 pandemic, the global economy... Read more
