Manufacturing Analytics
Most plants we walk into have OEE measured by a shift supervisor in Excel, reviewed Monday morning, three days after the decisions that mattered. We build the infrastructure that changes that.
What we hear from operators
The problems we solve
OEE exists on paper only
The MES captures downtime. SAP captures production orders. Nobody has connected them. So OEE is calculated at shift end by whoever remembers to fill in the form — and by the time the plant manager sees Monday's report, the week's production schedule is already locked. The data that should drive decisions is arriving three days after they were made.
Scrap is measured, not managed
Month-end scrap reports show you what you lost. They don't show you which machine, which shift, which operator, which material batch caused it. You can't act on aggregated scrap data — you can only wince at the number. The fix is granular tracking at the point of production, connected to the ERP batch records.
Quality deviations arrive too late
By the time a quality deviation surfaces in the SAP quality module, the batch has already shipped or the downstream impact has already cascaded. The SPC data exists in the quality system. The production parameters exist in the MES. Nobody has built the bridge that catches the pattern before it becomes a non-conformance.
Planned vs actual has no teeth
Every plant has a production plan. Most have planned vs actual reporting — weekly, in a spreadsheet, presented in a meeting where everyone nods and moves on. The gap between plan and actual is accepted as normal. It isn't. It's a symptom of a scheduling system that isn't connected to real capacity data.
Who this is for
Who manufacturing analytics is built for
The roles that feel the problem first — and what we build for each of them.
Plant Manager
The problem
OEE is calculated at shift end in Excel and you see Monday's report three days after the decisions that mattered. You are running the plant on numbers that are already history.
What we build
A live OEE dashboard in Power BI Direct Lake over Microsoft Fabric, with the MES and SAP PP connected and the number refreshed every 15 minutes.
15–20% OEE improvement
Operations Director
The problem
Planned vs actual is reviewed weekly in a meeting where the gap is accepted as normal — a symptom of scheduling that is not connected to real capacity.
What we build
Exception alerts through Power Platform the moment production tracks more than 5% off plan, built on the Microsoft Fabric production model.
Live exception alerts before the week closes, not a Monday-morning post-mortem
Quality Manager
The problem
Deviations surface in SAP QM after the batch has shipped, and scrap only appears as a month-end aggregate you cannot trace to a machine, shift or material batch.
What we build
SPC and MES production parameters bridged in Microsoft Fabric, with per-batch scrap traceability surfaced in Power BI within the shift it occurred.
60% faster root-cause analysis
Maintenance Manager
The problem
Maintenance is reactive — there is no runtime or vibration signal until a machine has already stopped and taken the shift target with it.
What we build
Azure IoT Hub and OPC-UA feeds landed into Microsoft Fabric, driving runtime-based maintenance alerts before the breakdown.
up to 40% less unplanned downtime
Measurable outcomes
What changes after implementation
Specific shifts from delivered manufacturing analytics work — the before, and the after.
OEE visibility: end-of-shift lag → real-time or 15-minute refresh
Production teams stop waiting for reports and start managing the shift as it happens. Downtime decisions get made in minutes, not at the next morning's standup.
Scrap traceability: monthly aggregate → per-batch, per-machine, per-shift
Quality teams can isolate the root cause of a scrap event within the same production shift it occurred. Waste walk findings get backed by data, not gut feel.
Planned vs actual: weekly review meeting → live exception alerts
Plant managers get notified when production is tracking more than 5% off plan — while there's still time to respond, not after the week closes.
Month-end production close: 3-5 days → same-day or next morning
When SAP is connected to the MES, month-end production reconciliation stops being a manual exercise. Numbers close automatically against the system of record.
By market
Manufacturing Analytics — market-specific pages
Each page below covers what manufacturing 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
Manufacturing Analytics — industry-specific pages
How manufacturing analytics applies to the specific systems, metrics, and operational challenges of each vertical.
Manufacturing
Most manufacturing plants we walk into have four or five systems that don't talk to each other: SAP or Oracle for production orders, a separate MES for floor execution, a quality system that's often standalone, and spreadsheets filling every gap in between.
Explore →
Packaging
Packaging plants sit at the intersection of manufacturing analytics complexity and FMCG demand volatility.
Explore →
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.