{"id":1386,"date":"2020-02-03T10:49:32","date_gmt":"2020-02-03T02:49:32","guid":{"rendered":"https:\/\/www.sage.com\/en-sg\/blog\/?p=1386"},"modified":"2025-10-17T19:07:38","modified_gmt":"2025-10-17T11:07:38","slug":"how-predictive-maintenance-can-help-manufacturers-save-money","status":"publish","type":"post","link":"https:\/\/www.sage.com\/en-sg\/blog\/how-predictive-maintenance-can-help-manufacturers-save-money\/","title":{"rendered":"How predictive maintenance can help manufacturers save money"},"content":{"rendered":"
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\n\t\t\t\t\t\t\tTechnology & innovation<\/a>\t\t\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t
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\n\t\t\t\t\t\tHow predictive maintenance can help manufacturers save money\t\t\t\t\t<\/h1>\n\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t

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What is predictive maintenance? All industrial machines are subject to wear and tear. Whether its abrasion, corrosion or fatigue, the health of your machinery will be affected and you will inevitably lose money due to downtime.<\/p>\n\n\n\n

What can you do to mitigate an issue that naturally occurs?<\/p>\n\n\n\n

You could already be pursuing a strategy of preventative maintenance \u2013 periodic inspection of machinery could identify problems before they happen, reducing the time spent on stoppages. However, this still won\u2019t tell you exactly when issues occur.<\/p>\n\n\n\n

However, as we\u2019ve entered the fourth industrial revolution<\/a>, predictive maintenance is a way to prevent you from losing money that downtime causes.<\/p>\n\n\n\n

Through the industrial Internet of Things (IoT), you can connect sensors to machinery that will collect data, and use advanced analytics to take this data and create meaningful insights.<\/p>\n\n\n\n

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How manufacturers can benefit from predictive maintenance<\/h2>\n\n\n\n

Analysts believe predictive maintenance will be big business \u2013 according to Big Market Research (BMR), the global predictive maintenance market size will be worth $24,015.7m by 2026<\/a>.<\/p>\n\n\n\n

As a manufacturer, why should you be interested? The answer is value-added services and servitisation.<\/p>\n\n\n\n

Certainly, predictive maintenance has the capability of powering new as-a-service models, in which the manufacturers of capital equipment can bundle in maintenance and repair as part of the overall cost of a system.<\/p>\n\n\n\n

There is excellent value in the data you can collect from predictive maintenance, but you need to look closely at the back-office expertise you need to make the best use of it. You need to have the right data available, frame your problems appropriately and evaluate the predictions properly.<\/p>\n\n\n\n

1. Algorithms<\/strong><\/h3>\n\n\n\n

Best practice dictates that every sensor on your machinery must have an algorithm aligned with the monitored piece of equipment. If not, you won\u2019t be able to report individual red\/amber\/green situations effectively.<\/p>\n\n\n\n

The algorithm is usually defined by the equipment\u2019s manufacturer.<\/p>\n\n\n\n

2. Insights<\/strong><\/h3>\n\n\n\n

The next step is to collate multiple instances of fail scenarios to understand why they occur and anticipate them. Many manufacturers successfully collect data to understand why numerous instances of their products fail.<\/p>\n\n\n\n

However, it would be more effective if users worked together to collect similar data across all of their estates, so they can understand why chains of equipment from different manufacturers might fail.<\/p>\n\n\n\n

3. Machine learning<\/strong><\/h3>\n\n\n\n

The next step in maintenance optimisation is to apply machine learning and predictive analytics<\/a> to these cases, but machine learning is exponentially dependent on data quality.<\/p>\n\n\n\n

Optimising a complex operational chain requires increasing numbers of variables and parameters to be analysed in real time for incremental degrees of benefit.<\/p>\n\n\n\n

Using predictive maintenance for the circular economy<\/h2>\n\n\n\n

The circular economy is a system that looks to eliminate waste and the continual use of resources.<\/p>\n\n\n\n

As opposed to a traditional linear economy in which we make, consume and throw away, moving circular is all about creating a circle where we reduce waste and pollution by keeping products and materials in use for as long as possible and finding ways to create new resources from what we discard.<\/p>\n\n\n\n

Being part of the circular economy is a good thing from a moral standpoint. But, more to the point, you may be getting pressure from customers and regulatory bodies to do more sustainable business that is better for the environment.<\/p>\n\n\n\n

Customers are increasingly appreciating and looking for links with companies that show they care, and the long-lasting relationships you can build like this are like gold dust.<\/p>\n\n\n\n

According to the Ellen MacArthur Foundation, the manufacturing industry could save between 10% to 15% on direct materials required for production.<\/p>\n\n\n\n

Managing the economics of the circular economy demands that your business understands and remodels your supply chain. You must become experts in predicting failure rates, both individual units and at scale.<\/p>\n\n\n\n

And you must create new reverse supply chains, from the consumer back to base or to redistribution centres. Both of these are new disciplines that benefit from predictive maintenance.<\/p>\n\n\n\n

How to start using predictive maintenance<\/h2>\n\n\n\n

Predictive maintenance could help you unlock the ability to reuse product profitably in an environmentally friendly way while maintaining a positive customer experience.<\/p>\n\n\n\n

For predictive maintenance, you will need the systems and equipment in place to collect data from connected devices to report back to base. You then have the option to proactively service a unit and extend its lifetime, or to recommend replacement and then bring a unit into its secondary market economy.<\/p>\n\n\n\n

Through predictive logistics, you can put this foresight to work, optimising reverse supply chains, and turning them from reactive to proactive. And according to analysts Bain & Co<\/a>, shippers with optimised distribution networks could expect to increase margins by up to 10%.<\/p>\n\n\n\n

In the words of Talking Logistics<\/a>: \u201cShippers and service providers must operate smarter, not bigger. This is where profit will be made, and where the leaders will separate from laggards.\u201d<\/p>\n\n\n\n

Core to predictive maintenance is collecting, storing and analysing data sets. On a high level, predictive maintenance relies on three layers:<\/p>\n\n\n\n

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  1. Collection<\/strong>\u00a0\u2013 the accumulation of data, which is likely to come from sensors used on devices through the Internet of Things (IoT)<\/li>\n\n\n\n
  2. Interpretation<\/strong>\u00a0\u2013 data transferred through the collection is stored, analysed and interpreted<\/li>\n\n\n\n
  3. Prediction<\/strong>\u00a0\u2013 you can predict events through the calculation of event entry probabilities.<\/li>\n<\/ol>\n\n\n\n

    Here are some simple steps to getting started:<\/h3>\n\n\n\n