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The end of unnecessary maintenance is nigh!

18 January 2021

Colin Crow argues the importance of the maintenance function as part of an increasingly digitised food factory. 

The unique set of circumstances faced by the food and beverage industry in 2020 has, undoubtedly, accelerated digital transformation within the sector. The impact of the Covid-19 pandemic on supply and demand has resulted in a greater need than ever for factory floors to increase efficiency, agility and resilience. At the same time, cost management remains a priority and business disruption is anathema.

It is vital that the maintenance function is part of the digital revolution because downtime costs money and can have a huge effect throughout the business . 

But, while the preventative approach 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. So, how can businesses achieve 'just right' maintenance?

The answer 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 technology in this way will also enable maintenance teams to focus only on site visits that add value.  

This may sound daunting but, thanks to a new generation of Enterprise Resource Planning (ERP) software solutions it is now much easier to move away from costly and time-consuming maintenance schedules.  

Embracing predictive maintenance
With engineering teams spending up to 25% of their time maintaining equipment that doesn't need servicing, a move to predictive, self-diagnosing technology has benefits. 

Modern devices can let you know when they are in trouble and provide 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 – targeting predicted failures before they happen keeps production moving. At the same time, avoiding unnecessary maintenance work saves time and money.

Today’s self-diagnosing technology can accurately tell you when it's time to swap out and when its time to retire apparatus.

Digitally optimised maintenance should not happen in silo. It should exist as part of a connected enterprise – connecting technology, processes and people. This helps to unearth a new level of insight, regarding the way in which each element of the business impacts upon overall performance.

Connecting across functions in this way 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. If each business function is joined up with the next, via data and reporting processes.

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

The business case for embracing digital transformation in food and beverage maintenance functions is clear. Not only can the introduction of predictive technology help to reduce unnecessary maintenance, 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 failure rates while ensuring the cost of doing so is kept low. As part of a connected enterprise, technology-optimised maintenance will demonstrate added value across a swathe of areas, from production and customer satisfaction, to energy consumption and profit margins.

Colin Crow is managing director at Sigma Dynamics.


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