There has been an equation at the forefront of every machine-reliant business leader’s mind for decades. How do you reduce costs and overheads, whilst increasing workforce and machine productivity, all the while maintaining service excellence? This ongoing battle finally has a resolution.
The current landscape
At present, the best plans to lower cost and increase productivity can be scuppered in an instant. All it takes is for a machine to break down and costs are instantly incurred, productivity is hit and customer satisfaction is damaged.
Recent research found that machines fail, on average, five times a year. This results in a loss of 49 hours per annum, costing businesses an average of £31,000. This expense is due, in part, to the most common cause of failure: internal technical faults. The complexity of many machines and the lack of immediate technical support, means it can take time to identify the true cause of the failure and the extent of the damage caused.
This delay incurs a vast expense which, up until now, business leaders have just needed to try and lessen the blow. Traditionally this has been done through preventative maintenance. Manual checks at regular points throughout the year are carried out in an attempt to keep machinery running smoothly.
As an added preventative measure, machines are often replaced at a time when the engineers believe they are starting to show excessive signs of wear and tear. On average, the research found that businesses replace a machine once a year.
For three quarters of businesses, this preventative solution is outsourced to machine maintenance experts. Although their expertise should bring a quicker resolution, it is expensive, on average costing £120,000 per annum.
When you consider all the costs and inconvenience caused by machine downtime, you quickly see that the preventative maintenance model has significant flaws and impedes any plans to reduce costs and increase productivity.
The new landscape
The irregularity and uncertainty surrounding machine failures cannot be resolved without taking a different approach.
The rise of the Internet of Things means that we are now able to place sensors within machines to monitor every aspect of the system on a daily basis to anticipate failures before they happen. By then linking this data with a field service management system, businesses can precisely identify an issue and resolve that issue before it causes costly downtime.
The sensors collect data, such as temperature or vibration levels, which is fed back to a centralised cloud dashboard. Using artificial intelligence (AI), the software analyses this data, monitoring it continuously to identify any exceptions to normal service. For example, whether a machine is quicker or slower than normal or if vibrations are above the acceptable level. Alternatively, a video camera can be installed inside the machine, which along with sophisticated neural networks can analyse the machine’s performance to the nth degree.
When unusual activity is identified, an alert is created that pinpoints the exact place where the abnormality occurs. The central dashboard then schedules a maintenance engineer who has access to the exact history of the system as well as the necessary skills to fix the problem. Crucially, this takes places before complete system failure occurs.
Thanks to AI-enabled predictive models, intelligent appointment scheduling and real-time visibility, businesses can now streamline their business and optimise productivity and customer service. Predictive maintenance reduces the chance of the entire system going down and increases efficiency, thus saving thousands of pounds.
To find out more about how you can move away from the traditional rigid reactive and preventative methods and instead, embrace a predictive software solution, click here.