Production · Operations Intelligence

The losses hiding inside a normal-looking shift.

A day's total can look fine while idle hours, slow cycles and creeping consumption quietly eat into it. Munshi reads each machine's load signature and pairs it with operator reports - so the losses inside ‘normal’ output become visible, per machine and per cycle, while the shift is still running.

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Where the losses hide

If this is your floor, you are reconstructing the losses, not measuring them.

Output sits in handwritten shift registers. Comparing two shifts means someone retypes both into a spreadsheet.

Cycle-level performance is invisible - you see the day's total, and only after the day is gone.

A slow shift looks identical to a fast one on paper, right up until the month-end review.

Cycles that ran but never got reported simply vanish from the numbers - output and consumption both.

What Munshi does

Monitor for deviations. Surface the loss. Attach the cause.

Automatic cycle detection

Munshi reads each machine's load and current signature and detects every start, stop and cycle on its own - no operator input. Cycle duration is benchmarked against the running average, so the slow ones stand out the moment they happen.

44 cycles today→6 ran 30%+ over average→all one shift, after 2 PM→throughput paid for, not received

Output across every dimension

Throughput per hour, shift, week and month; output by machine, product and shift - rolled up across the plant or drilled to a single component along your asset tree (Plant → Process → Line → System → Machine → Component).

Consumption per unit of output

Every input logged against the output it produced, divided automatically. Material is often half your cost per tonne - this is where rupees leak, and where drift shows up first, long before the P&L.

Shift-vs-shift comparison

Output, consumption and quality compared across shifts and machines automatically - so a persistent gap between two crews stops being a hunch and becomes a number.

Material flow between machines

Define what each machine consumes and produces, and Munshi tracks material as it moves down the line - so a shortfall at the end traces back to the stage that caused it.

Nothing missing, flagged

Unreported cycles and incomplete reports surface on their own - so the data you decide on is actually complete, and the gaps are visible instead of assumed away.

Insights & Alerts

What this looks like on the floor.

Live examples of the production losses Munshi surfaces - teal is normal, maroon is flagged. Illustrative data.

Cycle Duration · minutes6 slow cycles

Output per Day · tonnesbelow avg

Shift A vs Shift B · outputShift B lags

Consumption / Unit · weekly+8% drift

Normal / in-spec Flagged by Munshi In-spec band-- threshold / average

Representative findings

The losses hiding inside ‘normal’ output.

Illustrative of what teams uncover once the data sits in one place.

Production · Cycle Time

Six of forty-four cycles ran more than 30% over the running average - all on one shift, all after 2 PM. The slow cycles were invisible in the day's total.

Detected from load signatures

Benchmarked cycle by cycle

Production · Unreported

Cycles ran that never appeared in any report - output and consumption that simply were not in the numbers anyone was reviewing.

Surfaced automatically

Reconciled against IoT

Inventory · Consumption

One machine's consumption per tonne crept up 8% over three weeks. Within ‘normal’ on any single shift, it was an unmistakable drift across them.

Consumption per unit, benchmarked

By material, machine and shift

For the Production Head

You are accountable for output. Munshi gives you the data to defend it - and to find the time you are losing.

Live production status across every machine and shift
Cycle-time variability spotted before it shows up in the day's total
Shift-vs-shift on output, consumption and quality - without a spreadsheet
Unreported cycles and incomplete reports flagged the moment they go missing