Idle machines, slow cycles, excess material, rework, unplanned downtime - every plant leaks output and rupees in places the monthly report never shows. Munshi connects your shop floor and your machines to surface those losses as they happen, with the cause attached.
What Munshi Covers
Output, cycle times and consumption by machine, shift and product - from operator reports and IoT - so slow cycles and idle time surface on their own.
Every stop timed from the machine, every spare traced to its component, and abnormal electrical draw flagged while it's still a reading - not yet a breakdown.
Every parameter checked against your range as it's entered - deviations flagged with severity, cause and recommended action, linked to the batch that produced them.
Live stock across stores, with consumption tracked per unit of output - so material drift shows up before it reaches the P&L.
Every parameter - measured or computed - checked against its range as it lands. The instant something drifts out of band, it's flagged with severity, direction and the likely cause.
Configurable thresholds and multi-tier escalation - the right person is notified at the right severity, automatically, before a small reading becomes a stoppage.
Real-time Plant, Production, Maintenance and QC dashboards - leadership sees where the operation is leaking without waiting for the month-end review.
Load, current, power factor and cycle data read continuously from the panel and PLC - so the machine's own signature feeds the same deviation engine the floor reports into.
Output leaks across production, maintenance, quality and consumption - and the cause usually sits between them. Munshi monitors all four for deviations, raises them in real time, and turns them into insight - so the loss and its cause show up together, not in four separate registers.
Why Munshi
Your ERP records what was transacted - tonnes in, tonnes out, money spent. It can't tell you the shift, machine or batch where the output and the rupees quietly leaked. That answer lives in the systems that actually run the floor: an MES, a QMS, a CMMS and a machine-monitoring layer. Most plants buy them separately - or never connect them at all. Munshi is all four in one, built ground-up for process manufacturing, so the loss and its cause finally sit in the same place.
IoT sensors read the machine automatically; operators submit structured reports from the floor. Machine truth and human context land in one record - so nothing falls into the gap between them, which is exactly where losses hide.
Munshi doesn't just show a number out of range. It flags the deviation with severity and direction, states the likely cause in plain language, and gives the recommended action - so the floor can act without waiting on a review meeting.
Every loss is traceable to where it happened and what produced it. A weak month leads straight back to the specific machine, shift and batch conditions that caused it - instead of an argument over whose number is right.
Foundries, forging, heat treatment, shot blasting - batch and continuous environments where deviations are expensive and most data still lives on paper. Munshi was built for these floors, not adapted to them.
Deep Capabilities
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 a normal-looking shift become visible, per machine and per cycle.
Explore Production →Breakdowns rarely arrive without warning - the electrical signature usually drifts first. Munshi monitors current and load continuously, times every stop from the machine itself, and ties each repair to the spare it consumed.
Explore Maintenance →A deviation found after the batch is finished is already scrap or rework. Munshi checks each reading against your range the moment it's entered, says what an out-of-range value means and what to do, and links the result back to the batch that produced it.
Explore Quality →Consumption gets logged and never analysed. Munshi divides every input by the output it produced and benchmarks it over time - so material drift surfaces by machine, product and shift, well before it reaches the P&L. Material is often half your cost per tonne; this is where the rupees leak first.
Talk to us →Set thresholds on any parameter - a low motor / turbine current, a QC deviation, an overdue maintenance task. Multi-tier escalation makes sure the right person is reached at the right severity, every time, with a full trail of who was told and when.
Talk to us →Top-Level KPIs
Output, availability, OEE, reliability, quality, material and energy - every high-level KPI leadership tracks, calculated automatically from the floor and the machines the moment a report is filed or a machine reports in. Nobody re-keys a thing. Illustrative data.
Sustainability
Munshi already reads three-phase current, voltage, power factor and load continuously. So energy per unit of output, and CO₂ per tonne using your grid emission factor, come from the same stream - broken down by machine, shift and product. We surface where energy and emissions concentrate; the savings decision stays yours.
Total kWh divided by the output it produced - trended over time and compared across machines, shifts and products, so an energy-hungry line stops hiding in the monthly bill.
Energy consumed × your grid emission factor, attributed to the tonne it produced - a defensible carbon figure per product and per period, not a once-a-year estimate.
Continuous PF and load monitoring flags poor power factor and load imbalance - the quiet causes of demand penalties and wasted energy.
Compliance
Munshi doesn’t certify you - it generates the records, traceability and audit trail an ISO audit asks for, as a by-product of daily work. When the auditor arrives, the evidence is already there.
Who recorded what, when, on which asset and batch - captured automatically. The record exists before anyone asks for it.
Every out-of-range reading becomes a logged nonconformance with severity, action taken and a resolved state - the corrective-action trail a quality system requires.
Manuals, certificates, calibration and warranty records held against the asset, with expiry tracking - so nothing lapses unnoticed.
Batch → machine → shift → operator, linked end to end. A finished lot leads straight back to the conditions that made it.
Energy monitored without manual logging - the baseline and review an energy-management standard expects, kept current automatically.
Removed records are retained with who removed them and when - nothing disappears from the trail.
Representative Findings
Illustrative of what teams uncover once production, maintenance and quality data sit in one place.
Utilization read 27.8% - not the figure the shift logs implied. The idle hours were real all along; they had simply never been measured against running 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.
A critical measurement drifted past its lower tolerance mid-batch. Munshi flagged it on entry - with the likely cause and the check to run - before it became a full batch of rework.
One machine's material consumption per tonne crept up 8% over three weeks - within ‘normal’ on any single shift, an unmistakable drift across them.
A motor / turbine's current drifted below its baseline days before the blades failed - the early signature was sitting in the data the whole time.
Rework was quietly running higher than rejection, but the two were lumped together. Separating them showed the real cost was in correction, not scrap.
Get Started
We sit with your team, take one line or one machine, and show you the losses Munshi surfaces - mapped to your operation, not a generic demo.
Email Us
aj@munshios.com
We respond within one business day.
Walk us through your floor
Tell us one line or machine where you suspect losses, and we'll show you what Munshi would surface there.