Does this need new plant hardware?

No. At Sanjeevani Group effective throughput moved from roughly 4,500 toward 7,500 TCD without new hardware, because the constraint was visibility into the process rather than capacity on the floor.

What does better recovery actually come from?

Live control of the pan station and evaporation, with deviation alerts when the process drifts. Recovery moved from 8.0 to 8.3 percent on the same cane, which is a process-control result rather than an agronomic one.

How do you find where downtime is really going?

By logging it against a cause and a duration instead of a total. That exposed 266.8 seasonal downtime hours at Sanjeevani, with 69% traced to just two assets, which turns a vague maintenance problem into two specific ones.

Can procurement be made auditable?

Yes, through vendor scoring and a traceable requisition-to-payment trail. The same engagement surfaced a 300 crore audit gap that manual review had not.

Solutions

AI for sugar mills

Connect process control, downtime, procurement and the ERP so a sugar complex runs on live, traceable operational data.

Let's Speak

How can AI improve sugar mill operations?

MarsLab starts at the operating constraint: data that is written by hand, downtime that is not linked to a cause, procurement decisions that cannot be audited, and an ERP that does not reconcile with the floor. At Sanjeevani Group, it connected PLC and SCADA data, vendor workflows and the ERP around live control of the pan station and plant operations.

What we build

01
Live pan-station and evaporation monitoring with deviation alerts
02
Downtime logging by cause and duration to expose recurring failures
03
Vendor scoring and traceable requisition-to-payment workflows
04
Connected ERP across vendor, stores, process and accounts

Documented outcomes

/Sugar recovery moved from 8.3% toward 9.5% on the same cane
/Effective throughput moved from roughly 4,500 toward 7,500 TCD without new hardware
/266.8 seasonal downtime hours made visible, with 69% traced to two assets

The proof

Named work behind this solution

Sanjeevani Group · Sugar, ethanol, power & biogas manufacturing
One cane, seven revenue streams, and no live view of any of it
We spent weeks on the floor, then rebuilt procurement, process control and the ERP so the plant runs on data instead of trust.
9.5%
Sugar recovery, up from 8.3%

Questions we get asked

Does this need new plant hardware?
No. At Sanjeevani Group effective throughput moved from roughly 4,500 toward 7,500 TCD without new hardware, because the constraint was visibility into the process rather than capacity on the floor.
What does better recovery actually come from?
Live control of the pan station and evaporation, with deviation alerts when the process drifts. Recovery moved from 8.0 to 8.3 percent on the same cane, which is a process-control result rather than an agronomic one.
How do you find where downtime is really going?
By logging it against a cause and a duration instead of a total. That exposed 266.8 seasonal downtime hours at Sanjeevani, with 69% traced to just two assets, which turns a vague maintenance problem into two specific ones.
Can procurement be made auditable?
Yes, through vendor scoring and a traceable requisition-to-payment trail. The same engagement surfaced a 300 crore audit gap that manual review had not.

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We want your hardest problems.

We collaborate with ambitious organisations ready to move beyond AI experimentation. If you're looking to transform how your business operates, we'd love to build with you.

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