Could maintenance help speed manufacturing recovery?

As 2021 dawned with a third national lockdown, hopes of a swift recovery from the economic downturn caused by COVID-19 faded fast.

It's clear that, despite the silver-lining offered by the coronavirus vaccine programme, the UK's manufacturing industry faces a drawn-out fight to survive the current crisis, before it can move on to a more prosperous future.

Supply chains have been impacted, workforces have been laid low and factory floors have had to adapt quickly to reams of new health and safety requirements.

In this challenging environment, where short-term survival tactics have to be balanced against medium and long-term recovery strategy, it can be difficult to prioritise where to cut costs and where to invest.

Tuning your maintenance, so that it's applied in the right place at exactly the right time, will minimise down-time and disruption – challenges that few businesses can afford in the current climate. By enabling a more efficient operation, 'just right' maintenance can also help to boost productivity and improve customer satisfaction – ultimately supporting a healthier profit margin. So, how can manufacturers achieve this balanced approach?

Don't just focus on preventative action

While preventative maintenance is designed to avoid major equipment failures and all of the disruption that comes with them, it can also waste resource. Preventative maintenance leads many manufacturers to set maintenance schedules at shorter than necessary intervals, to ensure failures do not occur. Maintaining equipment when you don't need to, however, costs money. It also eats up manpower.

On the flipside, nobody would recommend tackling maintenance issues on the back-foot.

The solution lies in employing the right combination of self-diagnosing devices, 4G and 5G technology, big data, machine learning and AI technologies to optimise maintenance, swap-out and retirements. Using tech in this way will also enable maintenance teams to focus only on the site visits that add value.  

This may sound daunting but, thanks to the new generation of ERP software platforms, it's never been easier to implement technology and move away from expensive and time-consuming maintenance schedules.  

Embrace predictive power

With engineering teams spending as much as 25% of their time maintaining equipment that doesn't need servicing, a move to predictive, self-diagnosing tech has obvious benefits.

Modern devices tell you their health, let you know when they are in trouble and provide important feedback such as cycle time and environmental conditions. They also provide data that allows you to determine when a device may fail, when it needs servicing and when it should be left alone.  

Predictive maintenance means that, with the help of AI, data analysis and machine learning, you can base decisions on the real time data that your self-diagnosing devices produce. These solutions provide predictive information that enable teams to prioritise maintenance on equipment that would otherwise risk a breakdown, and  to avoid costly, unnecessary activity.

The advantages are numerous here. Targeting predicted failures before they happen keeps production rolling – achieving positive results for both internal and external customers. At the same time, avoiding unnecessary maintenance work saves time and money.
On top of this, today's self-diagnosing tech also tells you when it's time to swap out and when its time to retire apparatus, with greatly improved accuracy.

Maintenance at the centre of your connected enterprise

Digitally optimised maintenance shouldn't happen in silo. It should exist as part of a connected enterprise, which connects technology, processes and people across the entirety of the business. This helps to unearth a whole new level of insight, regarding the way in which each element of the business impacts upon overall performance.

Connecting across functions in this manner leads to joined up data, shared processes and understanding. This constantly refreshed intelligence can then be turned into action, with the results clear to see via shared reporting dashboards.

In a connected enterprise, the impact of introducing predictive maintenance on the factory floor will create a positive ripple-effect on data processed in other connected hubs, such as finance or operations, for instance. If each business function is joined up with the next, via data and reporting processes, it will be easy to track how a switch to predictive maintenance has reduced operating costs, helped to boost customer satisfaction by speeding production or had an impact on the bottom-line.

Simplify implementation as far as possible

Digital transformation should be a phased process – an evolution of a business' existing systems and processes.

It's certainly not a case of out with the old and in with the new overnight. There's no need to break the bank to achieve big results, either. In the maintenance arena, existing equipment can often be upgraded, to enable data collection and analysis. Current data platforms can be migrated across into AI and analytics-friendly systems and new tech can be integrated into existing IT platforms.

Most importantly, specialist providers are available to manage every stage of the process smoothly and efficiently, based on the best practice solutions for your sector.

The business case for embracing digital transformation in maintenance functions is clear. Not only can the introduction of predictive tech help to reduce unnecessary maintenance to near 0%, it can also help to ensure the right preventative action is taken at the right time.

Proactive monitoring, maintenance and swap-outs guided by machine learning and AI can significantly reduce your failure rate while ensuring you don't spend too much time or money on maintenance.

As part of a connected enterprise, tech-optimised maintenance will demonstrate added value across a swathe of areas, from production and customer satisfaction, to energy consumption and profit margins. Implementation is simple, with the right expert support on board.

Stay ahead of the recovery curve

Businesses that have already embedded digitalised maintenance programmes will be at an advantage in terms of being able to adapt to changing levels of demand, operating more effectively and efficiently. This will place them at the front of the recovery curve.

And with maintenance set to become an increasingly complex beast as the IoT expands – currently there are 50 billion sensors in things, and that number is expected to increase to a massive two trillion in the next few years – managing, optimising and simplifying the process sooner rather than later makes sense.   

Manufacturers would be well advised to pursue investment in digitalised maintenance technology now, to reap the benefits ahead of the competition and stand them in good stead as business slowly but steadily gets back to normal.

Colin Crow is managing director Sigma Dynamics

http://www.sigmadynamics.co.uk