Asset management is critical for the continued success and productivity of your business.

The breakdown of any piece of equipment can lead to temporary halts in operations and an erosion of customer or client trust. That’s why having a first-rate maintenance team on hand, who can leap into action should something go wrong, is an important precaution.

If you can accurately monitor your vital company assets for signs of inconsistency, you can reduce downtime – as opposed to waiting for things to go wrong. And how do you do this? Predictive analytics.

Predictive analytics is an important asset when it comes to scheduling maintenance. Predictive analytics allows business to be proactive rather than reactive when it comes to scheduling maintenance.

What is predictive analytics?

Predictive analytics is a form of analytics used to forecast future events. It uses a combination of machine learning, data mining and statistics modelling to firstly answer the question “what happened before?”, then use the data to formulate predictions.

Predictive analytics allows users to make forecasts on unknown future events, and prepare accordingly.

In the context of maintenance scheduling, historical data is linked to a number of different variables, in order to make a prognosis on the lifespan of a machine, and the chances of it malfunctioning. This means that businesses can organise repairs on assets before they’re broken.

Predictive maintenance vs. preventative maintenance

Both of these terms speak to a forward-thinking approach to asset management, but are in fact very different. Preventative maintenance simply refers to the practise of regularly servicing equipment to prevent the likelihood of malfunction.

The key difference between this and predictive maintenance is the absence of data in the former. While it has its merits, in some ways, preventative maintenance is a shot in the dark, with no hard statistics to back when and in what ways a particular asset should be serviced to best keep it on track for optimal performance.

Predictive maintenance is more efficient than preventative maintenance. The core difference between predictive and preventative analytics is the use of data.

Why is predictive analytics important?

1. Saving money

Unplanned maintenance can cost three to nine times more than planned maintenance, according to SolvedFM. By contrast, when you’re able to work on equipment before it completely packs up, the job is usually quicker and easier – and therefore cheaper.

On top of the reduced expenditure in completing the maintenance itself, by acting before a serious technical issue arises, you can reduce costly downtime which comes when assets are out of action for prolonged periods.

2. Saving time

Take printing, for example. Nearly 25% of all calls to IT help desks relate to printer issues, says ImageOne. Persistent printer-related problems could point to the need for maintenance on this particular asset – or better user training.

Unplanned maintenance can cost three to nine times more than planned maintenance.

Predictive analytics remove the guessing game. With hard data to back up the performance of your printer, you can save time for both your IT staff and the workforce at large by performing proactive maintenance.

3. Better asset performance

There’s a big difference between broken and breaking.

Predictive analytics is an easy way to optimise your systems.Using predictive analytics to schedule maintenance means you can ensure systems are continually optimised.

HVAC systems, for example, may outwardly appear to be operating normally, while they’re really harbouring slight maintenance issues. For instance, a slight sensor miscalibration can lead the central device to make wrong assessments of how it needs to adjust temperature conditions.

Predictive analytics can help businesses ensure that all assets are operating at peak efficiency.

However, predictive analytics would monitor factors such as the energy consumption of your HVAC device, and compare it to historical data. If, suddenly, you’re seeing a spike in how much electricity is being used to maintain the same temperature, this is a good indication that it’s time to schedule maintenance to get this asset back to optimal performance.

4. A safer work environment

One of the hardest situations to plan for at work is injuries resulting from malfunctioning assets.

But with predictive analytics behind your maintenance scheduling, you can have greater peace of mind that the devices your staff use are in working order and are less likely to cause accidents.

Predictive analytics has an important part to play in workplace health and safety.By ensuring your assets are properly maintained, you can help minimise the risks of workplace injuries.

5. Minimising spare parts inventory

Because predictive analytics allows you to have a constant eye on when a specific asset is likely to need maintenance, you can reduce the number of spare parts you have lying around. This has two key benefits:

  1. Saving space – Rather than having your spare parts inventory clogged up with random parts you may require at any given time, you can keep things simple by referring to your analytics to determine what you likely to need, and when.
  2. Cutting costs – By only ordering spare parts as and when you need them, you can also streamline your stocking processes and eliminate unnecessary shipping costs.

How to setup predictive analytics in your business

Here’s an easy-to-follow guide for implementing predictive analytics:

1. Define your objectives

Ascertain which devices you’re most interested in targeting. You might select these based on how important they are to the business, how often they’re used or if a certain asset has a bad history of malfunctioning. For example you should probably keep tabs on the devices that are shared.

2. Data collection and mining

Predictive analytics relies on data from multiple sources to build forecasts. This involves standard data collection as well as mining. Data mining refers to an interdisciplinary approach to search for trends, usually in large data sets.

Generally speaking, data mining draws on tools such as AI, statistics and machine learning, and uses computer software to bring all the necessary information together.

Data mining is integral to predictive analytics. Data mining refers to the process of finding trends within large data sets.

3. Analysing your data

After you’ve identified patterns within your data, create models that provide insights on the expected lifespans of your various assets. You can then subject these models to statistical analysis to test your assumptions and look for any misinterpretations of the data.

4. Implementation

With your models ready to go, see how they play out in real life. This is your opportunity to discover whether the maintenance schedules you’ve devised offer all the benefits we’ve mentioned in this article.

5. Model reviews

You should regularly review your predictive analytics to see whether your forecasts were accurate. If not, it’s time to have a look at your data collection and mining and see how the information you’re feeding into your models can be improved. As your assets change or are updated, you should also ensure that this is reflected in your predictive analytics workflow.

For more information on asset management, and how you can make your workplace more productive, get in touch with Brother today.


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