Resilient Supply Chain Podcast: Efficiency Before Electrification

Heavy-equipment electrification is often framed as a battery problem, but the machinery consuming that electricity may be the more fundamental constraint. In this episode of the Resilient Supply Chain Podcast, host Tom Raftery speaks with Hiten Sonpal, CEO of RISE Robotics, about the relationship between machine efficiency, downtime, predictive maintenance and the gradual shift from analogue equipment towards digitally controlled and eventually semi-autonomous operations.

For supply chain and operations leaders, the discussion raises a broader question: whether resilience investments are being aimed at visible technologies while underlying mechanical inefficiencies remain untouched. The full episode is available at www.resilientsupplychainpodcast.com

Efficiency Before Battery Capacity

The central tension is economic rather than technological. Electrifying heavy machinery can require large batteries because traditional hydraulic systems waste substantial amounts of the energy supplied to them. Sonpal argues that hydraulics are typically around 25% efficient, meaning much of the electricity stored in an expensive battery never becomes productive mechanical work.

That changes the investment equation. Increasing battery capacity may compensate for inefficient machinery, but it also increases vehicle cost, charging requirements and infrastructure demand. Improving the efficiency of the equipment itself can reduce those downstream requirements.

This matters particularly for ports, mining, construction and other operations where equipment utilisation is high and electrification must compete against established diesel economics. The strategic question becomes less about whether electrification is technically possible and more about whether the entire system can deliver an acceptable operating cost.

Downtime Is the Hidden Resilience Cost

For operators, the strongest business case may have little to do with emissions. Asked to identify the biggest hidden cost in heavy machinery, Sonpal’s answer was direct: “Lost revenue due to downtime.”

Hydraulic systems introduce failure points through hoses, valves, seals and pressurised fluid. RISE Robotics’ alternative uses electrically measurable steel reinforcement inside its belts to assess deterioration and indicate when servicing is required.

That shifts maintenance from reaction towards prediction. For supply chain leaders, the implication extends beyond one particular actuator technology. Equipment resilience increasingly depends on knowing the condition of critical assets before they interrupt production, loading, warehousing or transport operations.

The difference is governance as much as engineering. Maintenance decisions supported by condition data can be scheduled, budgeted and audited. Unexpected failures cannot.

Industrial AI Needs Industrial Data

The conversation also challenges assumptions around autonomy. Sonpal argues that industrial AI is running ahead of the physical infrastructure needed to support it.

Heavy machines cannot simply acquire autonomous capabilities through better algorithms. They first need digital controls, feedback mechanisms and operating data. Electrification and drive-by-wire systems make it possible to capture information on position, force and orientation, creating the foundations for digital twins, teleoperation and eventually autonomous behaviour.

The constraint is therefore data availability. Large language models benefited from enormous quantities of existing digital information. Heavy-equipment operators do not have an equivalent historical dataset describing how machines behave across thousands of operating conditions. As Sonpal puts it, “that last 20% is actually a lot of work.”

Autonomy Without Removing Accountability

Rather than forecasting widespread removal of operators, Sonpal expects automation to amplify them. One operator might supervise several machines, intervening remotely when unusual circumstances defeat autonomous systems.

That model carries important implications for operational governance. Responsibility does not disappear as automation increases; it moves upwards, from continuous manual control towards supervision, exception handling and rules governing what machinery is permitted to do.

For supply chain leaders, the broader lesson is that electrification, resilience and automation cannot be treated as separate technology programmes. Machine efficiency affects electrification economics. Digitalisation affects maintenance visibility. Data quality affects autonomy. And autonomy changes where accountability sits.

Industrial transformation is therefore likely to advance fastest where these dependencies are addressed together. The resilient operation will not necessarily be the one with the largest battery or the most ambitious AI strategy, but the one that reduces wasted energy, detects failure earlier and builds reliable digital control into the physical assets on which production depends.