Manufacturing Analytics
Your Machines Are Talking. Your Teams Can't Hear Them.
Your ERP, MES, SCADA, and shop-floor systems are generating more operational data than most manufacturers know what to do with. We connect it, clean it, and turn it into something your plant managers, operations directors, CFOs, and leadership teams can actually act on - in real time, not next week.
Manufacturing Intelligence Hub
Live · Updated 47 seconds ago
Overall OEE
87.6%
+2.3%
Active Lines
5 / 6
1 alert
Prod vs Plan
96.2%
On track
Scrap Rate
1.8%
-0.4%
Live · Line avg
Line Status
L1
91%
L2
88%
L3
72%
L4
89%
L5
85%
L6
OFF
Shift Performance
Shift A
91%
Shift B
87%
Shift C
83%
Predictive Alerts
Pack Line 3 - Motor B
Est. failure in 18hrs
Filling A - Bearing
Est. failure in 52hrs
QC Station - Sensor
Est. failure in 5 days
The Real Problem
The data exists. The problem is it's everywhere and trusted by nobody.
Most manufacturers we speak to aren't short of data. OEE lives in the MES. Production schedules are in the ERP. Maintenance history is in the CMMS. Sensor readings are in the SCADA. Quality records are in a spreadsheet someone built in 2019 that everyone uses but nobody owns. None of it talks to anything else.
So the floor is data-rich and decision-poor. The morning meeting runs on gut feel, last week's report, and whoever argues loudest. There are dashboards - but the numbers never quite match, nobody can trace them back to source, and the moment something looks wrong, the first instinct is to check the data rather than act on it.
We fix the trust problem first. Not the dashboard problem. Not the AI problem. The trust problem. Once operations teams believe the number on the screen, everything else - predictions, automation, executive reporting - actually works.
Data trapped in 6–10 disconnected systems
ERP, MES, SCADA, CMMS, spreadsheets - each holding a fragment of the truth. None of them connected.
Dashboards exist but nobody trusts the numbers
Built once, never maintained, based on manual inputs. The people who should use them don't believe them.
Decisions made on data that is days or weeks old
By the time the weekly report is compiled, the situation has already changed. You're managing history.
The IT–OT gap no one has bridged
IT owns the ERP. OT owns the machines. Neither team knows how to connect them - and both get the blame when they don't.
Who Feels the Pain
Every team in your operation has a data problem. Each one is different.
Select your role to see the specific challenges your team faces - and exactly what MDI builds to solve them.
Plant Manager
“Running a floor full of data - and flying blind anyway”
What We Hear Every Time
Your OEE report tells you what you already lost
Shift ends. Losses banked. Report generated. By the time it reaches you, you're reading about downtime that happened eight hours ago - on equipment that's either been patched or is quietly building to another failure. OEE only matters when it's live. At machine level. Every minute. Anything else is a post-mortem.
Every breakdown was visible. Just not to anyone watching.
The vibration spike was there sixty hours before the machine stopped. Temperature had been trending wrong for three days. The PLC logged every reading - and none of it was connected to anything that would have raised a flag. You found out the machine was down when a supervisor called you. That's not a maintenance problem. That's a connectivity problem.
The night shift's crisis becomes the morning shift's mystery
The handover log says "issue resolved." It doesn't say which machine, what caused it, who fixed it, or whether the fix is actually holding. Morning crew starts with zero context from the twelve hours before them - and the same fault that caused three hours of downtime at 2am quietly resets for an encore.
What We Build For You
We take the data your machines are already generating - PLC feeds, SCADA outputs, MES records - and wire it into a single live view that updates every 60 seconds. Predictive models flag failure risk 48 hours before a breakdown, not 48 seconds after it happens. Shift handover becomes a structured data briefing: what happened, what's at risk, what the incoming team needs to watch. Your floor stops being something you react to and starts being something you manage.
OEE live by machine and line - before the shift ends, not after
Maintenance called before the breakdown, not because of it
Shift handover that doesn't lose context between crews
The MDI Approach
End-to-end. Not a dashboard bolt-on.
We don't start with the dashboard. We start with the data. Then we add AI. Then we close the loop with automated actions. In that order - every time.
01
UNIFY
One Trusted Data Foundation
We connect ERP, MES, SCADA, OPC-UA sensors, PLCs, and quality systems into a single governed Microsoft Fabric data layer. Every data point - from a machine vibration reading to a customer invoice - lands in one place, with full lineage and schema enforcement.
- ›ERP + MES + SCADA integration
- ›OT/IT convergence with OPC-UA & MQTT
- ›Real-time and batch ingestion in one platform
- ›Master data governance and cleansing
02
PREDICT
AI That Sees Problems Before They Happen
On a clean, unified data foundation, we deploy AI models built specifically for manufacturing - predicting equipment failures, detecting quality deviations at source, and forecasting demand and material needs before they become urgent.
- ›Predictive maintenance: 48–72hr failure warning
- ›Quality deviation detection at source
- ›Demand forecasting with production signals
- ›OEE anomaly detection and root cause AI
03
ACT
Decisions and Actions Without Delay
Insight without action is just reporting. We close the loop - connecting data signals to automated responses: maintenance work orders triggered automatically, replenishment alerts sent to procurement, and executive dashboards updated in real time.
- ›Automated maintenance work order creation
- ›Replenishment alerts to procurement
- ›Shift and line performance notifications
- ›Executive and board-ready live reporting
Capabilities
Six analytics capabilities that move
the metrics that matter.
60 sec
update frequency
Real-Time OEE Tracking
OEE that's live at machine level - not compiled at shift end. Availability, Performance, and Quality tracked separately so you know exactly where the loss is coming from. Shift-to-shift comparison without touching a spreadsheet.
48–72hr
ahead of failure
Predictive Maintenance
Sensor data - vibration, temperature, cycle time, current draw - continuously compared against failure signatures learned from your own equipment history. Maintenance gets the alert 48–72 hours before the breakdown, not 48 seconds after.
Live
not end-of-day
Production vs Plan Analytics
Live output tracked against schedule - by shift, by line, by SKU. Every gap is categorised automatically: planned downtime, unplanned stoppage, speed loss, quality reject, changeover overrun. The weekly review stops being a blame session.
Real-time
COGM by product
Cost of Manufacturing Intelligence
COGM visible during the period - not three weeks after it closes. Scrap attributed to specific runs, not rolled into a total. Standard vs actual variance explained in language that operations and finance can both work with.
< 1hr
to root cause
Quality & Root Cause Analytics
Quality events linked to machine state, material batch, environmental conditions, and shift team at the time of production. A customer return becomes a traceable production record in minutes - not the start of a week-long investigation.
Daily
reconciliation
Material & Inventory Intelligence
Consumption reconciled against output daily - not at month-end. Live production schedules feed procurement demand signals automatically. Replenishment triggered before the stockout, not because of it.
Client Proof
Real results from real manufacturing engagements.
Hollandia Dairy
Dairy / FMCG Manufacturing · USA
Challenge
Demand data was fragmented across trade channels - no consolidated view of sell-out versus sell-in by SKU. Spoilage and stockouts were managed reactively, after the fact. Replenishment decisions were based on gut feel and phone calls, not data.
Solution
We unified ERP, distributor channel feeds, and cold chain logistics data into a single demand intelligence layer. A forecasting model built on actual sell-out signals - not sell-in estimates - drove automated replenishment alerts. Spoilage became predictable before it became a write-off.
Results
5–15%
Sales Uplift
20–40%
Stockout Reduction
10–30%
Spoilage Reduction
Kim Moten, Program Director
Verified · Clutch.co · 2024
Korpack
Packaging Manufacturing · GCC
Challenge
Order management, production scheduling, and dispatch operated in separate systems with no shared data layer. On-time delivery performance was tracked manually - in Excel, weekly, always late. Material yield by run was unknown. When an order slipped, nobody could pinpoint where in the process it had gone wrong.
Solution
We connected order management, production planning, and dispatch into a live end-to-end pipeline. OTD reporting automated from source data. Material yield tracked by run and by material grade - giving production and procurement a shared number to work from instead of arguing about whose system was right.
Results
Live
Order-to-Ship Visibility
Automated
OTD Reporting
Eliminated
Manual Tracking
Operations Team, Korpack
Direct Client
Common Questions
Questions operations teams ask before they engage us.
How long does it take to get a live OEE dashboard for our manufacturing plant?
Typically 6–8 weeks from discovery to live dashboard. Week 1–2: data discovery and source connection (MES, SCADA, ERP). Week 3–4: data model and transformation in Microsoft Fabric. Week 5–6: Power BI dashboard build and validation with plant team. Week 7–8: training, handover, and first production review cycle.
Can you connect to our existing ERP system without disrupting operations?
Yes. We connect to SAP (ByDesign, S/4HANA), Oracle, NetSuite, Microsoft Dynamics, Epicor, Sage, Acumatica, and other ERP platforms using read-only data extraction - no changes to your ERP configuration. The analytics layer sits on top of your existing systems. Production operations are never touched.
Do you work with legacy PLCs and older shop-floor equipment?
Yes. Legacy OT connectivity is one of our core specialisms. We use OPC-UA and OPC-DA protocol bridges, MQTT brokers for IoT devices, and historian database connectors (OSIsoft PI, Ignition, Wonderware) to extract data from equipment that predates modern connectivity standards. Older equipment does not need to be replaced to become data-connected.
What data sources do you typically integrate for manufacturing analytics?
ERP (SAP, Oracle, NetSuite, Microsoft Dynamics, Epicor, Sage, Acumatica), MES, SCADA and DCS systems, OPC-UA and OPC-DA sensor feeds, PLCs, quality management systems, CMMS maintenance platforms, and warehouse management systems. Most manufacturers have 6–10 systems that have never communicated - we connect them into one governed data layer.
What is a realistic Phase 1 scope and budget for a manufacturer new to analytics?
Phase 1 is typically scoped as a 90-day sprint: data foundation (connect 2–3 source systems), one primary use case (OEE or production vs plan), and an executive reporting layer. This gives you proof-of-value with real data before committing to a full programme. We discuss indicative investment on the first call - we do not send proposals before we understand your environment.
Start Here
Book a 30-minute Manufacturing Data Assessment.
We'll review your current data environment, identify the two or three places where better data would move your most important operational metric, and tell you plainly what a Phase 1 looks like in your specific context. No slides. No generic pitch deck.
30 minutes - your ops environment, your data challenges
We tell you what's feasible in your timeline and budget
You leave with a clear Phase 1 outline - whether you engage us or not
No sales script. No deck. Just a direct technical conversation.