Ganesh W Jirkuntwar, National Manufacturing Head, Dalmia Cement (Bharat)

Ganesh Jirkuntwar is a seasoned global business leader with over 30 years of experience across India, the USA, the UK, and Canada. As National Manufacturing Head and EXCOM member at Dalmia Cement, he drives enterprise-wide operational excellence, digital transformation, and sustainability, positioning the company as a low-cost, low-carbon industry leader.

His career spans senior roles at UltraTech and Lafarge, where he delivered turnarounds, P&L ownership, and multi-country manufacturing leadership. Passionate about blending profitability with purpose, he champions alternative fuels, Industry 4.0, and resilient organizational cultures to shape the future of clean, sustainable manufacturing.

In a recent interaction with MR Yuvatha, Senior Correspondent, siliconindia, Ganesh W Jirkuntwar, National Manufacturing Head, Dalmia Cement (Bharat) Limited outlined how predictive intelligence transforms safety using real-time sensor and wearable data to prevent incidents while ensuring auditable, trusted governance.

For decades, manufacturing safety has been reactive, responding to incidents after they occur, analyzing root causes, and hoping to prevent repeats. That era is ending.

A powerful shift is underway. Predictive intelligence, powered by artificial intelligence and real-time data, is fundamentally rewriting the rules of workplace safety. Factories are no longer simply responding to accidents, they are anticipating and preventing them before any human steps into harm's way.

Today's intelligent systems use computer vision, IoT sensors, and machine learning algorithms to continuously monitor factory floors. They detect fatigue patterns, nearmiss trends, equipment anomalies, and environmental hazards in real time. When a risk is identified, whether an overheating bearing or an unsafe worker posture, the system alerts supervisors instantly, sometimes even shutting down equipment autonomously.

The results are transformational. Leading manufacturers are reporting dramatic reductions in lost-time injuries, unplanned downtime, and compliance violations. More importantly, they are building cultures where safety is not a checklist but an intelligent, living system.

It is happening now on factory floors across India and the world. Predictive intelligence is saving lives, protecting assets, and proving that the safest factory is not the one that reacts fastest but the one that never lets an incident happen at all.

Let's explore key questions on implementation, governance, and workforce acceptance to understand how predictive intelligence is being applied on the ground.

Predictive intelligence turns safety from a reactive checklist into an intelligent, living system that anticipates and prevents incidents before they occur

How is predictive intelligence turning real-time sensor and wearable data into early warnings that stop accidents before they happen in heavy manufacturing?

In any Heavy Industry like cement, metal and chemical etc., predictive intelligence is increasingly being used to identify unsafe conditions before they escalate and convert into big incidents. Real-time data from equipment sensors, early gas detectors, vibration monitors, thermal cameras, fatigue monitoring systems, and wearable devices can help detect abnormal trends such as overheating bearings, excessive vibration, unsafe gas exposure, worker fatigue, or unauthorized entry into hazardous zones.

By integrating this data into centralized monitoring platforms, plants can generate early alerts for supervisors, managers and control rooms, enabling preventive intervention before a near miss becomes a serious safety event/incident. This approach supports proactive safety management rather than relying only on lagging indicators after an event occurs.

Dalmia Cement is leveraging platforms like ‘KAVACH’ to enable supervisors to log observations, track unsafe conditions, and monitor action closures with location-tagged evidence. Such applications also provide employees and contractors with a platform to report hazards, submit near-miss data, and access standard operating procedures (SOPs) on the go.

As predictive models rise, how do you balance algorithm-driven safety decisions with human judgment on the shop floor without eroding worker trust?

In heavy industries like cement manufacturing, predictive tools always support operational judgement. But Safety decisions still require experienced human validation and re-validation because plant conditions can change rapidly and contextual understanding is very critical and important.

A balanced approach includes:

• Using predictive systems as advisory tools rather than autonomous decision-makers.
• Involving frontline teams in design and implementation of digital safety initiatives.
• Maintaining transparency on how alerts are generated.
• Encouraging operators and supervisors to challenge or validate system recommendations.

Employee trust improves when technology is positioned and implemented as a tool for protection and operational support rather than only ‘surveillance or performance policing’.

How has predictive maintenanceshifted from saving costs to preventing unsafe rushed repairs that put frontline workers at risk?  

Traditionally, predictive maintenance was viewed mainly as an efficiency and cost optimization tool. But nowadays in cement manufacturing, it is increasingly recognized as a critical safety enabler.

Companies like Dalmia Cement, safety is embedded in everyday operations through the alignment of technology, training, and trust driving the company’s vision of achieving Zero Harm.

Early detection of equipment degradation in kilns, crushers, conveyors, fans, mills, and electrical systems etc. helps plants schedule maintenance in a controlled manner instead of reacting during breakdown situations. 

This reduces:

• Emergency maintenance activities
• Off-time rushed interventions
• Exposure to high-risk confined spaces or hot work under pressure
• Human error caused by urgency and production stress.

Planned interventions significantly improve both operational reliability and workplace safety.

How do manufacturers fuse diverse data vibration, heat, gases, and worker biometrics to predict combined risks like equipment failure plus hazardous atmospheres?

Modern manufacturing safety systems are moving toward integrated risk intelligence platforms where multiple data sources are correlated together instead of being monitored independently.

• Rising bearing temperature + abnormal vibration may indicate imminent equipment failure.
• Simultaneous dust concentration or gas build-up in a confined area can elevate the consequence severity 
• Worker location tracking and fatigue indicators can further help assess exposure risk.

By combining operational, environmental, and human-factor data, manufacturers can identify ‘risk convergence scenarios’ that are often missed in silobased monitoring systems. This enables faster decisionmaking and targeted preventive controls.

What governance rules ensure predictive safety models stay auditable, unbiased, and compliant while earning worker confidence?

For any good organisation, ‘Governance’ is essential to ensure predictive safety systems remain credible and sustainable. Key governance principles must have:

• Clear accountability for data ownership and decision authority.
• Regular validation and calibration of predictive models.
• Cybersecurity and protection of worker data privacy.
• Transparent usage policies for wearable and biometric information.
• Alignment with occupational safety regulations and ethical standards.

Periodic review to ensure algorithms do not create biased or misleading risk prioritization. Most importantly, organizations must maintain open communication with employees and workforce representatives so that digital safety initiatives are seen as collaborative safety improvements rather than only monitoring mechanisms.

In the cement industry, long-term success will depend not only on technology capability but also on workforce acceptance, operational practicality, and leadership commitment to a proactive safety culture.