Packaging Analytics
Packaging plants know their material waste number at month-end. What they don't know is which machine, which shift, which substrate, which operator caused it — and that's the only version of the number you can act on.
What we hear from operators
The problems we solve
Material variance is known monthly, not daily
Standard vs actual material consumption is reconciled in SAP at month-end. The number is reported. The head of operations reviews it. And then nothing changes because there's no way to trace it back to a specific machine run, a specific batch of substrate, or a specific operator. The variance is real. The cause is invisible. The month after, it happens again.
Machine OEE is inconsistent across lines
Some lines have automated OEE tracking from the machine controllers. Others rely on shift logs. Most packaging plants have a patchwork — some data is automated, some is manual, some doesn't exist. The result is an OEE number for the plant that's partly real and partly estimated, and nobody knows which parts are which.
Changeover time is tracked but not analysed
Changeover events are logged — start time, end time, SKU before, SKU after. But the data is almost never used to identify which changeover sequences are consistently slower, which lines have the most variation, or whether the standard changeover time in the production schedule reflects reality. It's data that exists and does nothing.
Who this is for
Who packaging analytics is built for
The roles that feel the problem first — and what we build for each of them.
Plant Manager
The problem
You know the material waste number at month-end, but not which machine, which shift, which substrate or which operator caused it — so the same variance happens again next month.
What we build
Machine controller data linked to SAP PP production orders in Microsoft Fabric, with per-run material variance surfaced in Power BI.
Material variance traced to the machine run within 24 hours
Operations Director
The problem
Plant OEE is a patchwork — some lines automated from the controllers, some from shift logs — so the number is partly real and partly estimated and nobody knows which.
What we build
Automated OEE from OPC-UA and SCADA through Azure IoT Hub into Microsoft Fabric, replacing shift-log estimates with measured data in Power BI.
15–20% OEE improvement
Quality Manager
The problem
Scrap is logged by cause code but not connected to the machine run or the substrate batch that produced it, so the root cause stays invisible.
What we build
Scrap-by-cause connected to the machine and substrate batch in Power BI over Microsoft Fabric, isolated within the shift it occurred.
60% faster root-cause analysis
CFO / Financial Controller
The problem
Material consumption variance is the most under-tracked cost driver you have, and it is only visible as a month-end aggregate after the waste is done.
What we build
A daily material-variance cost view in Power BI on Microsoft Fabric, tied back to the machine, SKU and shift driving the loss.
Variance seen daily by cost driver, not once at month-end
Measurable outcomes
What changes after implementation
Specific shifts from delivered packaging analytics work — the before, and the after.
Material variance visibility: monthly aggregate → machine-level daily tracking
Quality and production teams can trace a material variance event to the specific machine run within 24 hours of it occurring — not at month-end when the cause is already forgotten.
OEE accuracy: partly estimated → fully measured from machine data
Automated OEE collection replaces shift log estimates. The number becomes trustworthy enough to act on, and the line-by-line comparison reveals which equipment needs attention.
Changeover analysis: logged but ignored → optimised sequence scheduling
Changeover time analysis identifies the SKU transition sequences that consistently overrun. Scheduling adjustments based on actual changeover data reduce total changeover time across the plant.
By market
Packaging Analytics — market-specific pages
Each page below covers what packaging analytics looks like specifically in that market — the local ERP landscape, compliance context, and the operational patterns we actually see there.
Singapore & Malaysia
United Kingdom
North America
By industry
Packaging Analytics — industry-specific pages
How packaging analytics applies to the specific systems, metrics, and operational challenges of each vertical.
Technology stack
Start with a conversation, not a proposal
First call is 30 minutes with Amit. We ask about your systems, your team, and your most pressing operational problem. You get a clear view of where the gap is and what closing it looks like. No slides. No pitch deck. No obligation to proceed.