Precision in Practice: How On-Demand Maintenance Elevates Organisational Resilience!

Explore the shift from scheduled to on-demand maintenance, leveraging IoT, AI, and machine learning for real-time insights and cost-effective operations in modern industries.

This blog delves into the intricacies of on-demand maintenance, exploring its implications for overall operational efficiency and cost savings in industry 4.0. SmartSense’s real-time monitoring and predictive analytics enhance efficiency, reduce downtime, and optimise resource allocation, showcased through a snippet of our successful case study in the banking sector.

In the dynamic landscape of industry 4.0, where efficiency is paramount and technological advancements redefine industrial processes, conventional pre-scheduled maintenance is challenged by the effectiveness of on-demand solutions. As we explore the era of interconnected systems and smart factories, the emphasis is shifting from routine checklists to real-time data analytics, enabling a more responsive and cost-effective approach. Leveraging cutting-edge technologies such as IoT sensors, AI algorithms, and machine learning (ML), on-demand maintenance is poised to revolutionise how industries manage their assets.

Scheduled maintenance

As the name suggests, it is nothing but performing planned checks on machinery or systems at regular intervals. Scheduled maintenance is conducted irrespective of identified issues, following original equipment manufacturer (OEM) recommendations, industry standards, or operational conditions. It involves routine inspections, lubrication, adjustments, and component replacements for optimal performance and preventing potential problems. By following a predetermined schedule, organisations reduce downtime, enhance reliability, and extend the lifespan of assets, which helps save time in the long run.

Though crucial for preventing unexpected breakdowns, scheduled maintenance has its share of disadvantages. A major drawback is that it can be expensive, especially if a facility has a multitude of equipment that requires regular maintenance. There’s also a possibility of over-maintenance, where components are replaced or serviced unnecessarily, leading to increased costs. Moreover, scheduled maintenance might not effectively address sudden, unforeseen issues that arise between planned intervals. Hence, balancing the benefits of regular upkeep with these drawbacks requires careful consideration and often a complementary on-demand strategy to address immediate needs.

On-demand maintenance

The meaning lies in the phrase – a responsive and adaptive approach to asset management, performed when needed rather than on a fixed schedule. It uses predictive analytics and AI-ML for real-time condition-based monitoring to detect early signs of equipment failures and anomalies before they snowball into costly instances.

Ecolibrium’s AI-powered decarbonisation platform, SmartSense, with over 61 advanced ML algorithms assimilating 25 million data points daily, merges seamlessly into on-demand maintenance strategies. By performing real-time condition monitoring and harnessing predictive analytics, SmartSense provides actionable insights to the site team. On detecting a sudden deviation or anomaly in the equipment performance beyond standard parameters, SmartSense initiates a predictive maintenance protocol with data-driven insights to schedule a repair and prevent potential breakdowns. This dynamic strategy enhances operational efficiency, increases equipment uptime, and optimises resource allocation.

According to a Deloitte study, predictive maintenance, on average, boosts productivity by 25%, slashes breakdowns by 70%, and reduces maintenance costs by 25%.1 In contrast, scheduled maintenance may risk over-maintenance or overlooking emerging issues, impacting productivity and costs differently.

Why on-demand maintenance?

Nowadays, organisations favour on-demand maintenance strategies for their adaptability and efficiency. Its real-time responsiveness, powered by predictive analytics, aligns with the demands of Industry 4.0. This approach enables businesses to address issues promptly, reducing downtime. Unlike rigidly scheduled maintenance, on-demand strategies accommodate the unpredictable nature of modern industries, ensuring that interventions are timed based on actual equipment performance. This flexibility enhances operational efficiency and proves cost-effective by minimising unnecessary maintenance tasks. A streamlined maintenance approach that aligns with the technology-driven nature of industrial processes.

Industries with critical machinery, such as manufacturing, energy, healthcare, banking, and cybersecurity, benefit significantly from on-demand maintenance. Additionally, the transportation, telecom, and utilities sectors leverage these maintenance approaches to enhance the efficiency of their operations, showcasing the broad applicability of predictive maintenance across diverse industries. The manufacturing industry, reliant on a web of interconnected components, reaps major benefits from on-demand maintenance. Real-time interventions amplify production by ensuring continuous uptime and elevating overall equipment efficiency (OEE). Furthermore, a Deloitte report states that organisations employing predictive maintenance witness a 5-15% reduction in downtime and up to a 20% surge in labour productivity.1

The Ecolibrium approach to on-demand maintenance!

A prominent Southeast Asian bank with a presence in 19 countries initiated a project with Ecolibrium to optimise the HVAC system at their 25-storey Singapore offices. The project aimed to extend the maintenance cycles of their decade-old system, reduce energy consumption, and enhance occupant comfort. The bank sought to transition from a costly monthly OEM-directed maintenance schedule to a more efficient and predictive bi-monthly regimen.

SmartSense guides prominent Southeast Asian bank’s transition to predictive HVAC maintenance.

Impact Summary

•   Identified a 5% energy-saving potential in AHUs through our proprietary ML algorithms.

•   Achieved a 75% reduction in HVAC maintenance frequency by shifting to bi-monthly schedules.

•   Forecasted 60% maintenance cost savings with a transition to quarterly predictive maintenance.

For further details, take a look at our case study here. If you are interested in discovering how SmartSense can redefine your organisation’s maintenance strategies, reach out to our team to schedule a personalised demo and consultation.


1 Schleichert, Bringmann, Kremer, Zablotskiy, Köpfer, 2017. Predictive Maintenance. Taking proactive measures based on advanced data analytics to predict and avoid machine failure. Deloitte Analytics Institute.

2 Bücher, 2023. Everything you need to know about predictive maintenance. Precognize. [online].

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