OEE is the most gamed metric in manufacturing.
We build the dashboard that surfaces the gaming.
Real-time OEE per asset, refreshed every 30–60 seconds via OPC-UA into Microsoft Fabric Real-Time Analytics. Six Big Losses Pareto, audit trail, mobile shift-handover view. Built on Power BI Direct Lake. First production OEE in 6 weeks.
Trusted by manufacturing, FMCG, packaging and logistics operations across India, the GCC, Singapore, the UK and North America.
50+
Clients delivered
150+
Projects
14+
Years in industrial data
5
Markets
Who this is for
OEE built around the role that reads it
Real-time OEE for the plant floor and the operations office — the number each leader acts on that shift, mapped to the decision they own.
Plant Manager
The problem
OEE is keyed in from paper logs a shift late, so by the time you see the number the shift that lost the hour has already gone home.
What we build
Live OEE — availability × performance × quality — by line, shift and SKU on Power BI Direct Lake, fed from MES and SCADA through Fabric Eventstream.
15–20% OEE improvement
Operations Director
The problem
Losses stay invisible until the month-end pack lands, and by then you cannot tell which plant, line or crew gave the points back.
What we build
One governed OEE model across every plant so the Monday review compares like for like, with drill from group OEE to the line and SKU behind it.
Every plant reports OEE the same way
Maintenance Manager
The problem
Unplanned stops get argued over in the standup because nobody can point to the actual downtime signal — only what someone wrote on the clipboard.
What we build
Downtime and the Six Big Losses read straight from PLC tags via OPC-UA, with a Pareto that names the cause before you send a technician.
up to 40% less unplanned downtime
Production Supervisor
The problem
You cannot drill from the plant OEE headline to the line or SKU causing the drop, so root-cause becomes a spreadsheet exercise the next morning.
What we build
Drill-through from the OEE tile to the run rate, stop reason and operator on shift — root-cause on the floor, not at the next day's desk.
60% faster root-cause analysis
The Problem
Patterns we see in every engagement
Most boards see 85% OEE. The plant is producing 62%. The line operator pads availability by reclassifying changeover as planned. The quality reject goes into rework instead of scrap. These are the four patterns we surface.
Availability gamed by reclassifying stops as planned.
Setup, cleaning, minor stoppages and changeover get moved into the 'planned production time' denominator. Availability looks fine. Real availability is 12–18 points lower. The dashboard logs every reclassification with timestamp, operator and reason — gaming becomes visible.
Performance gamed by lowering the ideal rate.
If the ideal cycle time gets edited down to match what the line actually does, performance always looks like 95%. The audit trail catches every edit to the standard. Anyone changing the rate has to log why.
Quality gamed by reclassifying scrap as rework.
If quality holds and rework do not count against quality rate, the number stays high. The dashboard tracks rework-as-percentage-of-scrap as a separate trend so quality gaming surfaces over weeks, not hides indefinitely.
Manual operator entry burden kills adoption.
If the operator has to log every stop on a clipboard, they will not, and the data will not be there. We design with the manual entry burden at 30 seconds per shift maximum — automated capture from PLC tags wherever the signal exists.
What we build
What we build
Six dashboards. Each tile maps to a specific decision a plant manager, shift supervisor or operations director makes that day.
Real-time OEE tile per asset
Replaces
The shift-end Excel OEE that arrives Monday morning, three days after the decisions that mattered.
- Refresh every 30–60 seconds via OPC-UA or MQTT from PLC tag into Fabric Real-Time Analytics
- Availability × Performance × Quality computed continuously — no shift-end Excel reconciliation
- Drill-through to current run rate, downtime cause and operator on shift
- Mobile-friendly tile so plant manager sees current OEE from anywhere in the plant
OEE visibility shifts from end-of-shift Excel to live dashboard. Decisions move during the shift, not at next morning's standup.
Six Big Losses breakdown per shift, per line
Replaces
The 'we know there's downtime but we don't know why' conversation in the operations review.
- Breakdown, setup, minor stops, reduced speed, defects, startup losses — categorised per shift per line
- Operator stop-reason entry via Power Apps tablet form, sub-30-second log time
- Pareto chart of which causes drive 80% of the loss — drill to which crew, which time of day, which material
- Trend over 30 / 90 / 365 days so seasonal patterns surface
Maintenance and operations stop guessing. The Pareto names the cause. The next intervention has a target.
Audit trail of every reclassification
Replaces
The shift supervisor who quietly reclassifies a 90-minute changeover stop as 'planned downtime' so OEE looks acceptable.
- Every stop reclassification logged with timestamp, user, original code, new code, reason
- Daily exception report to plant manager surfaces unusual reclassification patterns
- Quality hold-to-rework conversions tracked separately so quality gaming is visible over weeks
- Read-only audit log — even admins cannot back-date or rewrite the original capture
Gaming does not stop because operators are watched. It stops because gaming is visible to the person who reviews the number.
Target vs actual per line, per crew
Replaces
The 'we hit target this week' narrative that compares actual to a target that was already lowered last month.
- Per-line OEE targets locked at quarter-start with formal change control
- Crew-level trending — same line, same shift pattern, three crews, real comparison
- Bench-mark vs the best-shift-ever value as a stretch reference
- 30 / 90 / 365 day trend so quarter-end target-game-the-system patterns surface
Operations leadership stops congratulating the line for hitting a soft target. The conversation shifts to closing the gap to the best crew.
Mobile shift-handover view
Replaces
The clipboard handover where the outgoing supervisor briefs the incoming one in 90 seconds and 60% of context gets lost.
- Plant manager sees yesterday's OEE on their phone before the morning huddle
- Shift handover form on Power Apps tablet — open issues, quality holds, maintenance backlog
- Auto-summary of the previous 12 hours with named exceptions and current line state
- Read directly from the same Fabric Lakehouse — no duplicated data, no sync lag
Shift handover quality improves. Open issues do not get dropped between crews.
Stack — Microsoft Fabric end to end
Replaces
The mix of MES OEE module, Excel pivot, in-house SQL warehouse and Power BI that nobody can reconcile.
- Fabric Real-Time Analytics (KQL database) for stream
- OneLake for historian / time-series — 5+ years of granular history
- Power BI Direct Lake for the semantic model and dashboards
- Azure IoT Hub if you need cloud-side device management at scale
One stack. One refresh strategy. One semantic model. Maintained by a small team — not a five-vendor integration project.
Business outcomes
What changes once OEE runs live
Ranges are from delivered MDI manufacturing work; capability outcomes apply to every live-OEE build.
15–20%
OEE improvement
Delivered range, live-OEE builds
up to 40%
Less unplanned downtime
PLC-fed loss capture
60%
Faster root-cause analysis
Pareto on the Six Big Losses
6 weeks
To first production dashboard
MDI standard cadence
One governed OEE definition across every line and plant
Live OEE on Direct Lake — no shift-end Excel reconciliation
Automated capture from MES and SCADA — under 30 seconds of operator entry per shift
Audit trail that surfaces reclassified stops, not just the headline number
How we work
From plant walk to live OEE in 6 weeks
We do not start with the dashboard. We start with the PLC tags, the operators, and the stop-reason taxonomy nobody has agreed on yet.
01
Discover — walk the lines, audit the tags
Two weeks. We walk every line in scope, document which PLC tags exist for stop reason, run rate and counts. We facilitate the per-line target-setting workshop with operations. We score which assets have enough signal for automated OEE — and we are honest about which do not.
02
Prototype — one critical line live
Two weeks. Build the OEE dashboard for one critical line. Refresh every 30 seconds. Run it for 2 weeks in parallel with whatever exists today. Operators see both numbers. Trust gets built before scale.
03
Deploy — across all lines, then predictive
Four to six weeks. Roll out to remaining lines. Train operators on the audit trail. Train shift supervisors on the morning huddle workflow. Add predictive maintenance, energy intensity, scrap analytics — same Lakehouse, same Power BI workspace.
Technology stack
Edge & OT
Cloud Streaming
Lakehouse
Visualisation
Operator UX
Source Systems
Why MyData Insights
Why choose MDI to build your OEE dashboard
We start at the PLC tag, not the dashboard
The first two weeks are spent walking the lines and auditing which tags exist for stop reason, run rate and counts — the honest test of which assets can carry automated OEE and which need an edge module first.
Microsoft Fabric end to end
OPC-UA or MQTT into Fabric Eventstream, OneLake for the time-series history, Power BI Direct Lake for the model. One stack your IT team can support — not a five-vendor integration nobody can reconcile.
We surface the gaming
Every reclassified stop is logged with timestamp, operator and reason. The dashboard shows where availability, performance and quality get padded — so the number the board sees is the number the plant actually ran.
Built for the floor, not just leadership
Operator stop-reason capture on a Power Apps tablet stays under 30 seconds a shift, and the mobile handover view means the plant manager sees yesterday's OEE before the morning huddle.
Senior practitioners only
The person who walks your lines in discovery builds your model. No junior hand-off, no learning exercise funded on your budget.
First production OEE in 6 weeks
A working dashboard on one critical line, run in parallel with whatever exists today so operators trust the number before it scales — not a 50-slide roadmap.
Common questions
What buyers ask us
We already have an OEE module in our MES. Why a separate dashboard?
Most MES OEE modules do not expose the data outside MES. The plant manager cannot see it from the office. The Group Operations Director cannot compare across plants. The cross-line comparison does not exist. We pull the same data MES is calculating and put it in Power BI alongside cost, quality and supply chain data. Same OEE number — more useful.
Our PLCs are 20 years old. Can we still do this?
Often, yes. We have connected to PLCs that predate Ethernet via a Kepware or Ignition edge gateway. The honest test: if the PLC has tags exposed via OPC-UA, Modbus or even MQTT, we can read them. If it is a true island with no comms layer, we add an edge module first.
Can the dashboard read from Siemens MindSphere / Rockwell FactoryTalk / AVEVA?
Yes. We connect to MindSphere, FactoryTalk and AVEVA via their APIs, land the data in OneLake, and the dashboard sits on top. Vendor lock-in works the other way after we ship — your OEE history is now portable.
How much does it cost?
We work in fixed-scope, fixed-fee phases — Discover, Prototype, Deploy — never open-ended time and materials. The fee for each phase is quoted precisely after Discover, once we have seen your line count, PLC count and edge integration scope, because those are what actually move the number. What we commit to upfront is the timeline: first working output in 6 weeks, not a 50-slide roadmap. Book 30 minutes with Amit and you will leave with the shape of the engagement and what it takes to scope it.
What if our operators do not want to log stop reasons?
We design the experience so logging takes under 30 seconds per shift. The bigger blocker is usually trust — if the data has been used to punish operators historically, getting buy-in takes longer. We facilitate the operator workshop and we design the audit trail to surface gaming by supervisors, not operators. The dashboard becomes a tool for the floor, not just leadership.
Engagement
Free OEE Dashboard Scoping
Thirty minutes with Amit on how OEE is actually captured on your lines today — where the number gets padded, which PLC, MES and SCADA signals exist, and where a live dashboard would land first. No slides, no obligation.
What you get
- A read of how OEE is captured on your lines today
- The losses your current number cannot see
- A data-source and integration readiness check on your MES and SCADA
- A 6-week roadmap to the first live OEE dashboard
Ready to move
Book a 30-minute OEE diagnostic
30 minutes with Amit. No slides. No pitch deck. No obligation to proceed. We walk through your current OEE definition, your PLC signal coverage, and where the dashboard would land first.