OFFICIAL ANNOUNCEMENT
FAVORIOT SDN. BHD.

Subject: Revocation of Malaysia Digital (MD) Status

FAVORIOT Sdn. Bhd. hereby acknowledges the formal notification issued by Malaysia Digital Economy Corporation Sdn. Bhd. (MDEC) dated 20 February 2026 regarding the revocation of the Company’s Malaysia Digital (MD) Status.

The letter, referencing BSD-BSS-LTR-MDCC-MDC (MD File ID: MD/0000184), states that the Malaysia Digital Status previously granted to FAVORIOT Sdn. Bhd. on 2 August 2023 has been revoked with effect from 1 August 2024. As outlined in the notice, the Company is no longer entitled to the benefits, incentives, privileges, or the use of the Malaysia Digital (MD) logo, effective from the date of revocation.

FAVORIOT respects MDEC’s decision and confirms that we will fully comply with all directives stated in the official correspondence.

We would like to assure our partners, customers, stakeholders, and the broader ecosystem that this administrative status change does not affect:

• Our operational continuity
• Our contractual obligations to clients
• Our ongoing projects and platform services
• Our commitment to delivering high-quality IoT and AIoT solutions

FAVORIOT remains fully operational and continues to focus on strengthening its technology offerings, expanding partnerships locally and internationally, and supporting customers across industries such as smart cities, agriculture, manufacturing, logistics, and education.

We remain committed to contributing to Malaysia’s digital economy and advancing the adoption of Internet of Things and AI-driven solutions through our platform, training programs, and ecosystem initiatives.

We thank our stakeholders for their continued trust and support.

For any further clarification, please contact us directly at:

FAVORIOT Sdn. Bhd.
CP3A30, Pusat Perdagangan IOI
No.1, Persiaran Puchong Jaya Selatan
Bandar Puchong Jaya
47100 Puchong, Selangor

Issued by:
Management of FAVORIOT Sdn. Bhd.
Date: 23 February 2026

Executive Summary

Many industrial workplaces expose workers to hazardous gases that are invisible and difficult to detect without proper instrumentation. Even short exposure beyond safe limits can cause serious injury or long-term health issues. Traditional gas monitoring systems rely mainly on threshold alarms that activate only when conditions become dangerous. While this approach supports compliance, it offers limited help in preventing incidents.

Smart gas monitoring combines safety-grade gas sensors, continuous data collection, and machine learning techniques to strengthen worker protection and compliance. By learning patterns and trends instead of reacting only to fixed limits, organisations gain earlier warnings, clearer insight, and better control through a cloud-based IoT platform.

Why Gas Monitoring Remains a Safety Challenge

Industrial gas hazards are difficult to manage because they are:

• Intermittent and location-dependent
• Influenced by ventilation, temperature, humidity, and work activity
• Dangerous even at relatively low concentrations
• Especially critical in confined or enclosed spaces

Common limitations of conventional gas monitoring include:

• Alarms are triggered only after limits are exceeded
• Minimal use of historical data
• Repeated alarms without a clear root cause
• Nuisance alerts that reduce worker trust

Meeting regulatory limits alone does not always translate into safer operations.

A Typical Smart Gas Monitoring Scenario

In a typical facility such as a factory, processing plant, or utility site, fixed gas sensors are installed across key areas, including:

• Production and processing zones
• Utility and equipment rooms
• Storage and loading areas
• Confined or enclosed spaces

These sensors continuously measure hazardous gas concentrations and support environmental conditions such as temperature and humidity. The sensors are calibrated for occupational use and generate time-stamped data that reflects both routine operations and abnormal situations.

System Architecture Overview

A smart gas monitoring solution typically follows a layered approach:

• Gas sensors continuously capture readings
• A gateway aggregates data and handles secure transmission
• A cloud IoT platform stores and visualises information
• An analytics layer applies rules and machine learning models
• Alerts, dashboards, and reports support timely decisions

This structure allows safety-critical detection to remain independent while enabling higher-level insight and analysis.

How Machine Learning Enhances Gas Monitoring

Machine learning shifts gas monitoring from simple limit checking to behavioural understanding.

Instead of asking:

• “Has the threshold been exceeded?”

The system can ask:

• “Is this behaviour unusual for this location and time?”

Key ML-driven capabilities include:

• Anomaly detection
  Identifies unusual gas patterns even when readings remain within safe ranges
• Trend analysis
  Highlights gradual increases that may signal leaks, ventilation issues, or process degradation
• Reduced false alarms
  Distinguishes short-term spikes from genuine risks
• Predictive alerts
  Estimates the likelihood of a future alarm based on current trends

These capabilities allow safety teams to act earlier and with greater confidence.

Benefits for Safety, Compliance, and Operations

A smart gas monitoring approach delivers value across several areas.

For worker safety:

• Earlier warnings reduce exposure risk
• Better visibility across zones and shifts
• Improved readiness for confined space work

For compliance:

• Continuous, auditable gas exposure records
• Easier preparation for inspections and audits
• Clear evidence of proactive risk management

For operations:

• Root cause analysis of recurring incidents
• Insight into ventilation and process performance
• Data-supported improvements rather than guesswork

Visualisation and Decision Support

Modern IoT platforms turn raw sensor readings into practical insight through:

• Real-time dashboards by zone and gas type
• Historical charts for exposure and trend review
• Alert timelines linked to operational activity

Platforms such as Favoriot provide a practical environment for ingesting sensor data via common protocols, configuring rule-based alerts, visualising trends, and supporting machine learning workflows. This allows organisations to begin monitoring and progressively introduce predictive insights without replacing existing safety-certified equipment.

Teams that already operate gas sensors can consider connecting selected data streams to an IoT platform to gain visibility, historical insights, and early-warning capabilities with minimal disruption.

Key Implementation Considerations

Successful deployment depends on a few important principles:

• Clear separation of roles
  Safety-certified sensors handle detection and alarms, while the IoT and ML layer focuses on insight and prediction
• Scalable rollout
  Begin with high-risk areas, then expand coverage as data volume and confidence grow
• Data security and integrity
  Secure communication, access control, and audit trails are essential for trust

This approach supports progress without introducing compliance risk.

The Road Ahead

Smart gas monitoring is evolving toward systems that are:

• Context-aware and adaptive
• Integrated with ventilation and facility systems
• Linked to maintenance and operational planning
• Increasingly predictive as data accumulates

As machine learning models mature, safety teams can prevent incidents rather than respond after they occur.

Conclusion

Gas monitoring no longer needs to stop at alarms. By combining safety-grade sensors, continuous data collection, and machine learning, organisations can protect workers more effectively while strengthening compliance and operational understanding.

A cloud-based IoT platform with analytics and ML capability offers a practical path toward proactive safety. Organisations looking to move beyond basic monitoring may consider connecting their gas monitoring systems to platforms such as Favoriot to gain deeper insight, earlier warnings, and a stronger foundation for intelligent safety management.

ESG is no longer driven by intention statements or annual summaries. Today, organisations are expected to show evidence. Regulators want proof. Investors want consistency. Customers want transparency.

At the centre of this shift sits one critical enabler: IoT.

IoT transforms ESG reporting from a compliance obligation into an operational capability by capturing real-world data directly from assets, facilities, and environments. Without this layer of measurement, ESG metrics are often based on assumptions rather than facts.

ESG Needs Measured Reality, Not Estimates

Many organisations still depend on:

• Periodic meter readings
• Manual logs
• Spreadsheets are updated once a quarter or once a year

These methods struggle to survive audits and increasingly fall short of modern disclosure expectations. ESG today demands data that is:

• Continuous
• Verifiable
• Traceable to source

IoT fills this gap by collecting information automatically, consistently, and in real time.

How IoT Supports Each ESG Pillar

Environmental: Where IoT Plays the Largest Role

Environmental indicators are the most measurable and the most scrutinised. IoT enables direct monitoring of key environmental metrics such as:

• Energy usage
  • Electricity consumption by machine, line, or facility
  • Peak demand and load behaviour
  • Renewable energy contribution
• Emissions and air quality
  • CO₂ concentration
  • Particulate matter
  • Indoor air quality in controlled spaces
• Water consumption
  • Inflow and discharge volumes
  • Leak detection
  • Process water usage
• Waste tracking
  • Waste volumes
  • Recycling rates
  • Hazardous material handling

These measurements underpin carbon accounting, energy intensity reporting, and environmental risk management.

Social: Protecting People Through Data

IoT contributes to the Social pillar by improving visibility into workplace conditions, especially in operational environments.

Typical applications include:

• Monitoring temperature and humidity on production floors
• Detecting gas leaks or unsafe exposure levels
• Identifying equipment conditions that could lead to accidents

In sectors such as manufacturing, construction, and energy, these indicators are closely linked to legal and ethical responsibilities.

Governance: Building Trust Through Data Integrity

Governance is not measured by sensors, but it depends on the quality of the data behind decisions.

IoT strengthens governance by:

• Reducing manual intervention in data collection
• Creating time-stamped, tamper-resistant records
• Supporting audit readiness with clear data trails

When ESG figures are backed by operational data, governance moves from declarations to defensible accountability.

What ESG Monitoring Is Commonly Expected

While ESG rules vary by country and industry, several monitoring areas are widely treated as baseline requirements.

Organisations lacking reliable data in these areas often face delays, higher audit costs, and increased scrutiny.

Example: ESG Monitoring in a Manufacturing Factory

Consider a medium-sized factory operating multiple production lines.

Environmental Monitoring

• Smart meters track electricity usage at:
  • Incoming power supply
  • Individual production lines
  • High-energy equipment such as compressors
• Water flow sensors monitor:
  • Process water consumption
  • Cooling systems
  • Discharge points
• Air quality sensors measure:
  • Indoor CO₂ levels
  • Particulate concentration
  • Ventilation effectiveness

This setup allows the factory to calculate energy intensity per unit produced, detect abnormal consumption early, and support environmental reporting with confidence.

Social Monitoring

• Temperature and humidity sensors ensure safe working conditions
• Gas detectors provide early alerts before exposure becomes dangerous
• Equipment monitoring helps reduce accidents caused by malfunctioning machinery

Threshold breaches trigger alerts, enabling prompt corrective action.

Governance Enablement

All collected data is:

• Logged automatically
• Stored securely
• Visualised through dashboards
• Exportable for audits and ESG disclosures

This gives management visibility not just into outcomes, but also into actions taken when issues arise.

Turning IoT Data into ESG Insight

Raw sensor data alone is not enough. It must be structured, contextualised, and aligned with ESG indicators.

This is where an IoT platform becomes essential. Platforms like Favoriot help organisations manage data from multiple sensors, locations, and systems while presenting ESG-relevant insights through dashboards, alerts, and historical views. This makes ESG monitoring scalable across factories, buildings, and regions without adding operational complexity.

Closing Thoughts

ESG expectations continue to rise, and tolerance for estimates is shrinking.

IoT provides the foundation for:

• Measurable environmental performance
• Safer workplaces
• Stronger governance backed by evidence

For organisations serious about ESG, monitoring is no longer optional. It is the starting point for trust, accountability, and long-term credibility.