Manufacturing Analytics Dubai —
built at the plant, not the boardroom.
Manufacturing Analytics for UAE plants — KIZAD, Hamriyah, Sharjah Industrial, RAK and Jebel Ali. OEE, predictive maintenance, production vs plan, quality and cost intelligence on Microsoft Fabric and Power BI. First production OEE in 6 weeks.
The Problem
Patterns we see in every engagement
UAE manufacturing runs on a stack that looks similar plant-to-plant. SAP S/4HANA or SAP ByD in the back office. Siemens / Rockwell / Mitsubishi PLCs on the floor. An MES of varying vintage. A Power BI workspace where OEE sits in a single tile nobody believes.
OEE believed by nobody, gamed by everybody.
Most boards see 85% OEE. The plant runs at 62%. The shift supervisor reclassifies changeover as planned. Quality reject goes into rework. The audit trail of every reclassification is what surfaces the gaming.
Predictive maintenance proposals that ignore signal coverage.
Vendors propose ML-based PM on equipment with 6 PLC tags. The model has nothing to train on. We start with condition-based monitoring (vibration, temperature, motor current) and graduate to predictive only where the data justifies it.
Production vs plan compared at end of day.
By end of day the plan is already wrong. We build production vs plan at the line, refreshed every shift change, with predictive trajectory to end-of-day.
Group-level rollup across UAE + Saudi + Oman has no consistent definition.
Group reviews argue about whose OEE is comparable. The standards project is the analytics project — without consistent reason codes, downtime categorisation and ideal cycle times, the rollup lies.
What we build
What we build for UAE plants
Six engagement shapes that map to UAE manufacturing reality.
Real-time OEE per line
Replaces
The shift-end OEE Excel that arrives Monday morning.
- OPC-UA / MQTT integration from PLC tag to Fabric Real-Time Analytics
- Six Big Losses Pareto per shift, per line
- Mobile shift-handover view — plant manager sees OEE on their phone
- Audit trail of every reclassification — gaming becomes visible
OEE moves from end-of-shift Excel to live dashboard. Decisions happen during the shift.
Predictive maintenance where signal supports it
Replaces
The PM vendor pitch that ignores whether the equipment has enough sensor coverage.
- Condition-based monitoring first — vibration, temperature, motor current
- Graduate to ML-based predictive only where signal coverage and history justify
- Azure ML model per asset class with explainability
- Maintenance team UX co-designed — dashboards they own, not theirs to ignore
PM programmes ship with realistic scope. False positives stay manageable. Maintenance team trusts the prediction.
Production vs plan with predictive trajectory
Replaces
The end-of-day Excel comparing actual to plan three hours after the shift closed.
- Plan vs actual at line, refreshed every shift change
- Predictive trajectory to end-of-day at current run rate
- Bottleneck identification per shift
- Drill from aggregate to station-level loss
Production team sees the gap during the shift, not after. Mid-shift intervention becomes possible.
Quality and cost intelligence
Replaces
The monthly quality Excel showing aggregate scrap with no per-line breakdown.
- First-pass yield per line per shift per crew
- Cost per unit produced — material, labour, energy at the unit level
- Scrap by reason code (Pareto) with material lot drilldown
- Energy intensity — kWh per unit, surfaces equipment efficiency drift
Plant manager sees quality and cost at the line level. Interventions are specific.
Group rollup across UAE + Saudi + Oman + Bahrain
Replaces
The plant-by-plant Excel that takes 5 days to consolidate into a group review.
- Standardised OEE definition agreed at group-level workshop
- Reason code taxonomy consistent across plants
- Group dashboard rolls up; plant dashboards drill down — same source
- Cross-plant benchmark with the best-shift-ever as stretch reference
Group reviews stop arguing about whose number is comparable. Best-practice transfer becomes evidence-based.
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 for stream
- OneLake for historian (5+ years granular)
- Power BI Direct Lake for semantic model and dashboards
- Azure IoT Hub for cloud-side device management at scale
One stack. One team. Maintainable. Not a five-vendor integration.
How we work
From plant walk to live OEE in 6 weeks
We walk the lines first. PLC tag inventory, stop-reason taxonomy, per-line target workshop — before we build any dashboard.
01
Discover — walk the lines, audit the tags
Two weeks. Walk every line in scope at your KIZAD / Hamriyah / Sharjah / RAK plant. Document PLC tags, MES integration points, edge gateway state. Facilitate per-line target-setting with operations.
02
Prototype — one critical line live
Two weeks. Build the OEE dashboard for one critical line. Refresh every 30 seconds. Parallel-run with existing reporting for 2 weeks. Operators see both.
03
Deploy — across the plant, then predictive
Four to six weeks. Roll out across remaining lines. Add predictive maintenance where signal coverage supports it. Train shift supervisors on the morning huddle workflow.
Technology stack
Edge & OT
Streaming
Lakehouse
Visualisation
PLCs
Operator UX
Common questions
What buyers ask us
Our MES already has an OEE module. Why this?
Most MES OEE modules do not expose the data outside MES. Plant manager cannot see it from the office. Group Operations Director cannot compare across plants. 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 Kepware or Ignition edge gateways. If the PLC has tags exposed via OPC-UA, Modbus or MQTT, we can read them. If it is a true island, we add an edge module first.
Do you have UAE references?
Yes — typical references include Hollandia Dairy and named UAE consumer-goods manufacturers under NDA. Specifics after the NDA.
Onsite or remote?
Onsite for Discover (1–2 plant visits per week). Mixed for build. Onsite for go-live moments. Amit is Dubai-based; delivery team across Dubai and Hyderabad.
How much does it cost?
Discover USD 10,000–14,000 fixed-fee. Prototype (one line) USD 22,000–32,000. Deploy 5–15 lines USD 50,000–140,000. Quoted precisely after Discover. Billed USD or AED.
Ready to move
Book a 30-minute Manufacturing Analytics diagnostic in Dubai
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.