The Cost of an Inefficient Asset Maintenance Strategy
One of the most significant issues in asset maintenance is identifying the right time to run maintenance tasks. Pulling an asset out of operations for maintenance earlier than needed is a waste of production time. Not doing it on time leads to asset failure and, consequently, downtime.
In 2017, Business Wire surveyed over 450 businesses globally across various sectors like IT, medical, oil and gas, telecoms, distribution, and logistics and found that 82% of these companies experienced at least one major downtime in a span of three years. On average, the cost of downtime across business sectors was estimated to be $260,000 per hour, but it can be much higher depending on the industry, going up to $22,000 per minute for the automotive industry!
The most common reason for unplanned downtime is an inefficient asset maintenance strategy. A Behrtech survey of businesses across industries found that 71% of companies don’t know when an asset should be scheduled for maintenance, 69% of companies don’t know when an asset is due for an upgrade, and 75% of companies don’t know when an asset should be replaced. These factors lead to unplanned downtime, which can be ten times costlier than a planned downtime.
The failure to create an optimized asset maintenance strategy comes from a lack of insight and information – insights into the asset’s current working condition, past maintenance data and maintenance activities, wear and tear level, environmental impact on the asset, temperature and pressure conditions, etc.
The lack of information is a consequence of a lack of digitalization. The primary role of technology in asset maintenance is going to be to eliminate this information gap. An AI-enabled asset maintenance solution monitors asset conditions using sensors that track parameters like temperature, vibrations, pressure, etc. It analyzes this data in real-time to deliver insights into the asset’s condition. This gives businesses all the information needed to plan maintenance tasks at the right time.
How AI and Predictive Maintenance is Transforming the Processes
Predictive maintenance technology uses asset monitoring sensors that track asset parameters in real-time. An AI-enabled analysis platform studies this tracked data along with historic data to give insight into the asset’s condition, detect anomalies, predict failures, and prescribe a course of action.
This allows businesses to perform proactive maintenance and service, i.e., upgrade or replace assets before they fail and cause disruption, but not before they really need to be removed from operations.
Businesses end up having a low and efficient asset maintenance frequency. Assets are serviced, upgraded, or replaced just in time – not too late so they don’t result in failure and not early so that planned downtime is minimal. This results in optimal costs for scheduled maintenance tasks, both proactive and reactive.
Several elements go into building a predictive maintenance solution. There are sensors that are installed at the location of the asset, and these sensors can monitor a range of parameters such as:
- Temperature – Measures asset and ambient temperature.
- Vibrations – The vibration patterns of moving parts like motors give many insights into their condition and help take proactive preventive maintenance action.
- Pressure – In the case of pressure-inducing systems like pumps and valves.
- Humidity – For example, in the case of ventilation and heating assets measuring humidity levels is important.
And at a central level, there are technologies like AI, ML, and IoT that are used to consume data transmitted by sensors, analyze it, compare it with historic data, and generate insights and action plans.
Together, the sensors and the central platform form the predictive maintenance system.
Business Implications of Digitizing Asset Maintenance
Businesses do not need to run manual tests, track maintenance dates, or assume potential asset failure once they digitize the asset maintenance process. A predictive maintenance solution gives insights that help determine the correct course of action – service, upgrade or replace, and the right time to perform the action. The entire maintenance strategy can be built around these insights.
The machine learning algorithm draws patterns from past data. It compares this to real-time data captured by sensors to detect any anomalies in parameters and indicates the condition of the asset and potential risks – also called prescriptive maintenance insights. Businesses can then make informed, proactive decisions. Businesses can:
- Get an idea of the current working condition of even remote assets.
- Identify potential asset failures and downtime.
- Prepare asset maintenance and risk mitigation plans based on asset condition data.
- Determine the most optimal, condition-based maintenance schedules for the least maintenance frequency.
- Nearly prevent any unplanned downtime.
How Eugenie Can Help You Implement an AI-Powered Asset Maintenance Process
Eugenie’s solutions Papillon and Ray-Finn provide end-to-end predictive analytics for all critical and non-critical assets within your facility. The principle behind our predictive maintenance products is Spot, Explore, and Exploit:
- Spot – Detect anomalies from the high-volume industrial data
- Explore – Know what caused the deviations, based on statistical evidence
- Exploit – Get optimal know-how through simulations
The added benefit of our products is the inclusion of Explainable AI – insights that empower decision-makers with data-driven conclusions rather than just charts and analytics. Contact us and get a demo of our predictive AI solution today.
To know more about Eugenie, get in touch with us.