Artificial Intelligence continues to transform industries by delivering results in the form of insights. AI helps streamline business processes by improving existing models and simplifying tasks. Of the several issues that AI has tackled, one major segment has been the growth of Predictive Maintenance to solve maintenance issues. According to the International Society of Automation, $647 billion is lost globally each year due to machine downtime. This shows that maintenance is an important area that can help companies cut down costs while increasing production value. Keep on reading this blog post to know how AI is adding significant value to businesses by enabling them to cut down maintenance costs.
Decoding Maintenance Costs
While dealing with existing legacy systems, what are the main areas that result in higher costs of maintenance operations? Let’s take a look at them.
Infrastructure: Legacy systems, as the name suggests, are systems that have been existing since the beginning of the business. As time goes by, it becomes a hassle to maintain the underlying infrastructure as they usually require a specific technical environment. Legacy data that is scattered across different databases presents another infrastructure issue. It is a cost-intensive task to gather legacy data and to transfer it to a new database.
Software Updates: Even the smallest update could result in conflicts across the systems as the legacy systems are monolithic and one cannot change or replace a system module. It is often risky to interfere with the source code and even making a small change requires a lot of time, effort, and money.
Staff Training: Support and maintenance of legacy systems require people with a specific set of skills. If the ones who built the software are not a part of the team anymore, it might present a bigger challenge for the business leaders. Thus training the staff for specific skills might be yet another expenditure.
Even with the businesses that do not rely on legacy structures, maintenance costs are an important cause of concern. A study by Deloitte states that unplanned downtime costs industrial manufacturers approximately a cost of $50 billion a year. This explains why organizations are leveraging AI and machine learning to optimize maintenance by reducing inefficiencies.
Planned Maintenance vs. Predictive Maintenance
The two prominent maintenance structures that exist today are planned preventive maintenance and predictive maintenance. Planned maintenance schedules maintenance when the machine is working to prevent unexpected downtime and to maximize the productivity of the equipment. Periodic assessments reduce the risk of failure and enable businesses to fix any problems before they get out of control. However, the downside of this method is that businesses employing planned maintenance run into the risk of over-maintenance of assets and loss of production time.
While planned maintenance is driven by time or events that require repair, predictive maintenance uses condition-based indicators and alerts. Leveraging AI capabilities, predictive maintenance ensures that repairs occur only when necessary. Businesses can anticipate errors in their machines before they happen using data that is collected from various sources and analyzed in real-time. By enabling businesses to foresee the future, predictive maintenance helps in reducing maintenance costs. As time goes by, predictive maintenance will help businesses identify the best method to fix repairs and will consequently result in zero downtime and increased efficiency. Predictive maintenance not only cuts down maintenance costs but also makes room for innovation by helping industries optimize their maintenance strategy.
Using sensors to monitor operational conditions, predictive maintenance significantly minimizes the possibility of downtime. Not only does it result in lowered costs, but it also enables increased longevity of equipment and efficiency of field technicians. A functional predictive maintenance program could help businesses to yield remarkable results. Businesses can supposedly witness a 25%-30% reduction in maintenance costs, tenfold increase in ROI, 35%-45% reduction in downtime, and 70%-75% decrease in breakdown. If savings incurred by businesses are to be expressed per labor hour, preventive maintenance costs $13 hourly per annum while predictive maintenance costs only $9 hourly pay per annum.
Reducing Maintenance Costs with AI
In 2019, the US Federal Government is said to have spent 80% of the IT budget on Operations and Maintenance. Only 20% of the fund was assigned to development and modernization as the maintenance of the aging legacy systems was more crucial. This unequal budget split is seen across several industries, and studies suggest that maintenance costs can exceed the original development budget in five years after the product release. This explains why it is important for businesses to leverage AI to gain insights and avoid unplanned downtime. Businesses that foresee the changes and equip themselves for the future will always have a competitive advantage over the others.