Big data is not the future!

Rather, it is the present AND future. Facilities and their service providers are already in the era of big data, making the most of it in terms of enhanced decision making,  revolutionising equipment management and hygiene standards. In this feature, we particularly discover how predictive maintenance, powered by IoT and big data analytics, shapes the future (especially in healthcare). Our experts, Bhimrao Dhoble, Head of Soft Services, Aster DM Healthcare and  Thomas Friswell, Associate Director, Emrill  share invaluable insights into how big data and predictive maintenance enhance operational efficiency and compliance in critical sectors.

Bhimrao Dhoble, Head of Soft Services, Aster DM Healthcare

Predictive maintenance (PdM) is a proactive approach to equipment management that leverages data analytics, sensors, and machine learning to predict and address maintenance needs before equipment failures occur. By integrating Internet of Things (IoT) sensors with big data analytics, businesses can significantly reduce downtime, extend the lifespan of equipment like vacuums or floor scrubbers, and optimize maintenance schedules.

Predictive Maintenance:

Here’s a breakdown of how IoT sensors contribute to predictive maintenance:

1. Real-Time Data Collection

IoT sensors embedded in equipment—such as vacuums or floor scrubbers—continuously monitor various parameters, such as:

• Temperature
• Pressure
• For equipment like scrubbers, measuring water or air pressure can help identify blockages or leaks.
• Motor current/voltage

These sensors collect vast amounts of real-time data about the operating conditions of the equipment, which is transmitted to a central system for analysis.

2. Big Data Analytics

Once the data is collected from the IoT sensors, it is fed into a big data platform for processing. Advanced analytics tools, including machine learning algorithms, analyze the data to detect patterns, trends, and anomalies. These tools can predict:

• When components are likely to fail
• Optimal maintenance intervals
• Performance degradation

3. Scheduling and Workflow Optimization

By predicting when maintenance is required, predictive maintenance systems can automatically schedule repairs or replacements. This reduces downtime by ensuring that maintenance is performed only when necessary, as opposed to relying on time-based schedules or reacting to failures. This can be done in several ways:

Automated alerts: When a sensor detects a parameter outside of normal operating range, it triggers an alert, notifying maintenance staff.

Integration with maintenance management systems: These alerts can be linked to a computerized maintenance management system (CMMS) that schedules tasks, assigns technicians, and tracks parts and inventory needs.

Proactive parts replacement: Certain wear-and-tear components (e.g., filters, belts, or brushes in vacuums and scrubbers) can be automatically flagged for replacement before they fail, reducing the likelihood of unplanned equipment downtime.

4. Extended Equipment Life

A major advantage of predictive maintenance is the ability to extend the useful life of equipment. By addressing small issues before they escalate into major failures, businesses can maximize the lifespan of expensive equipment like vacuums and floor scrubbers. Additionally, replacing components based on wear patterns—rather than on a fixed schedule—ensures that the equipment operates at peak efficiency for a longer period, preventing premature depreciation.

5. Cost Efficiency

Predictive maintenance reduces the need for reactive, emergency repairs, which tend to be more expensive due to the unplanned nature of the work. Additionally, it reduces the cost associated with under-utilized maintenance—performing repairs or replacements that aren’t yet needed. By shifting from reactive to predictive maintenance, organizations can optimize labor, reduce unnecessary parts procurement, and prevent costly downtime.

6. Avoiding Unscheduled Downtime

Unscheduled downtime is costly, especially for industries that rely on high levels of operational continuity. For example, in cleaning operations, a malfunctioning vacuum or floor scrubber can halt operations and affect service quality. Predictive maintenance helps identify potential problems before they lead to equipment failure, ensuring that repairs are scheduled during off-peak times, preventing operational interruptions.

Enhanced Hygiene Standards and Compliance Using Data Collection and Analysis

In sectors like healthcare, where hygiene is paramount to patient safety and overall health outcomes, adhering to strict cleaning protocols is non-negotiable. Regulatory bodies, such as the DHA & MOH, set stringent hygiene standards for healthcare facilities to minimize the risk of infections and improve public health. Meeting these standards can be a complex challenge, especially as the size of the facility and the number of surfaces and equipment increases.

Data collection and analysis, especially when combined with real-time monitoring, offer powerful tools to support and enhance hygiene standards, ensuring compliance with regulations while driving continuous improvement.

1. Real-Time Monitoring of Cleaning Activities

IoT sensors and smart devices embedded in cleaning equipment or installed on key surfaces (e.g., patient rooms, operating rooms, restrooms) can track hygiene-related activities in real-time. This can include:

Environmental Monitoring

Surface Cleanliness Detection

Equipment Usage Tracking

By continuously tracking these parameters, healthcare facilities can ensure that cleaning tasks are performed according to plan, without relying on manual logs or visual inspections alone.

2. Automated Compliance Reporting

Data collected from IoT devices and sensors can be automatically compiled into compliance reports, which are crucial for audits and inspections by regulatory bodies. These reports can include:

Cleaning Frequency Logs: Automated tracking of cleaning schedules—when and where cleaning was performed—ensures that cleaning intervals align with healthcare standards.

Verification of Cleaning Standards: Data from cleanliness sensors can verify whether hygiene standards were met. For example, the system might report that a particular area was cleaned but didn't meet the required cleanliness threshold (e.g., ATP level below a certain point).

Temperature and Humidity Logs: Continuous monitoring of temperature and humidity conditions can help ensure that they stay within safe ranges to prevent microbial growth. These logs can be stored and accessed instantly, providing evidence of compliance with environmental hygiene standards.

This automated, data-driven approach makes it easier for facilities to demonstrate their commitment to hygiene standards and regulatory requirements without relying on manual record-keeping.

3. Predictive Analytics for Proactive Hygiene Management

Beyond simply tracking cleanliness, data collection can be used for predictive maintenance of hygiene protocols. For instance:

Identifying High-Risk Areas: Predictive analytics can identify areas of the facility that are at higher risk for contamination based on traffic patterns, prior cleaning effectiveness, or seasonal trends in infection outbreaks. This allows cleaning staff to prioritize high-risk zones and apply more intensive cleaning measures.

Optimal Cleaning Schedules: By analyzing patterns in patient traffic, staff presence, and infection rates, predictive models can recommend the optimal times for cleaning or disinfecting high-touch areas, such as door handles, bed rails, and toilets.

Alerting for Early Interventions: Predictive analytics can identify when hygiene standards might be slipping, such as when cleaning activities fall behind schedule or when sensors detect abnormal levels of contamination. This early warning system allows staff to intervene before hygiene becomes a major issue, avoiding potential non-compliance and improving infection control.

4. Ensuring Consistent Adherence to Cleaning Protocols

Human error or fatigue can sometimes lead to lapses in cleaning protocols, especially in large healthcare facilities with numerous rooms and common areas. Data analysis can address this challenge by:

• Monitoring Staff Performance
• Ensuring Equipment Usage
• Automated Feedback Loops 

5. Integrating Data with Compliance Management Systems

Healthcare facilities often use compliance management systems (CMS) to ensure adherence to regulatory standards and quality benchmarks. IoT data and real-time monitoring can be integrated into these systems, providing a seamless flow of information from cleaning equipment to compliance tracking software. This integration supports:

• Audit Trails
• Certification and Accreditation
• Regulatory Reporting

6. Ensuring Patient and Staff Safety

Ultimately, the goal of enhanced hygiene standards and compliance is to safeguard the health of patients, healthcare workers, and visitors. By integrating data collection, real-time monitoring, and predictive analytics, healthcare facilities can prevent Healthcare-Associated Infections (HAIs)and maintain trust and reputation. 

An FM service provider’s perspective

Emrill leverages cutting-edge predictive maintenance strategies to ensure the reliability and longevity of essential cleaning equipment, such as vacuums and floor scrubbers. Using big data and IoT technology, Emrill anticipates when equipment might need maintenance or replacement, reducing unexpected breakdowns and costs. IoT sensors capture essential data such as usage hours, motor vibrations, and temperature, enabling Emrill to monitor wear and tear in real time and proactively address issues.

Thomas Friswell, Associate Director, Emrill says, “through data analytics, we create tailored maintenance schedules that match each piece of equipment’s usage patterns, allowing for repairs or replacements at optimal times to minimise downtime. By tackling issues early, Emrill extends equipment lifespan and maximises capital efficiency by avoiding premature replacements while ensuring consistent service quality.”

This IoT-driven maintenance approach enhances operational efficiency, reducing equipment failures and service interruptions. With downtime minimised, Emrill can provide uninterrupted, high-quality service to clients. This proactive maintenance strategy supports sustainable practices, maximising equipment use and providing cost-effective solutions.

Enhanced Hygiene Standards and Compliance

As a BICSc member and accredited training centre, Emrill’s commitment to elevated hygiene standards, especially in sectors with stringent regulations like healthcare, is strengthened through data collection and analysis. Monitoring footfall improves service delivery, enabling the planning and allocation of resources to maintain hygiene standards. IoT sensors and Radio-Frequency Identification (RFID) technology on cleaning equipment enable Emrill to track and verify the completion and frequency of cleaning tasks in high-touch areas, such as patient rooms and waiting areas. This automated approach ensures that hygiene protocols are strictly followed, reducing reliance on manual checks and offering solid proof of regulatory compliance.

Through their Computer-Aided Facility Management (CAFM) system and work order (WO) tracking, Emrill’s teams can monitor cleaning tasks in real time, providing accountability and transparency. Digital logs document each cleaning activity, allowing management to audit processes and share records with regulatory bodies as needed. This level of transparency demonstrates Emrill’s adherence to strict hygiene standards and assures clients that their facilities are maintained in line with best practices.

The system also alerts management when tasks are overdue, triggering additional cleaning measures. This real-time response mitigates the risk of lapses in cleanliness, safeguarding the health of building occupants and maintaining Emrill’s reputation for consistent, top-tier hygiene standards.

Labour and Resource Optimisation

By analysing data insights, Emrill optimises labour allocation and resource usage to achieve greater efficiency and cost savings. In areas with fluctuating demand, such as washrooms, they utilise smart sensors to monitor footfall and adjust cleaning schedules accordingly. This targeted approach ensures that labour is deployed during peak times, improving service delivery while avoiding overstaffing during quieter periods.

Footfall data and activity peaks also guide task assignments, enabling Emrill’s teams to concentrate resources where they are most needed. This precision in scheduling reduces the risk of underuse or overuse of resources, ensuring that client facilities are consistently maintained without unnecessary expenditure.

Additionally, Emrill’s data-driven approach extends to supply management, where consumption trends by location or task are analysed to accurately forecast supply needs. Managers can order cleaning supplies in quantities aligned with actual demand, reducing excess stock and storage requirements. This approach not only cuts costs but also supports sustainable resource use, aligning with Emrill’s commitment to responsible facility management practices.