Stop Describing
What Happened.
Start Predicting What's Next.
MDI builds predictive analytics and operational AI for manufacturing, FMCG, and supply chain companies. Demand forecasting that replaces the Excel model. Predictive maintenance that replaces the OEM schedule. Decision intelligence that acts — not just reports.
Trusted by operations teams at
The Problem
Why most operations are still running blind
These are the four patterns we see across almost every manufacturing, FMCG, and supply chain operation we work with. Not hypotheticals — conversations from the last 18 months.
Reports tell you what happened. Not what's about to happen.
Most operations teams I speak to have Power BI dashboards. Some have good ones. But they are all looking backwards — last week's OEE, last month's forecast variance, last quarter's spoilage. By the time the number is on the screen, the decision window has closed. The plant manager needed that signal three days ago.
Demand forecasting still runs in Excel, and it's always wrong by 20%.
A 20% forecast error in FMCG is not a planning problem — it's a cash problem. Too much inventory ties up working capital and ages out. Too little and you miss fill rate targets. Most companies we work with are doing weighted average forecasting in a spreadsheet that hasn't been reviewed in two years. The data to do this better already exists in SAP. It's not being used.
Maintenance is scheduled, not condition-based — and it costs more than it should.
Scheduled maintenance intervals come from the OEM manual, not from what the equipment is actually doing. A conveyor motor running double shifts in a humid environment needs attention sooner than the quarterly service schedule says. Sensor data from your SCADA or MES already captures vibration, temperature, and cycle count — but nobody is analysing it. Unplanned downtime costs 5-10x more than planned maintenance.
AI pilots get built. They never reach production.
The proof-of-concept graveyard is real. A data scientist builds a model in a Jupyter notebook on a sample dataset, it shows 85% accuracy, and then nothing happens for six months because nobody owns the deployment. The model needs a data pipeline, a serving endpoint, a monitoring layer, and an integration into the tool the operations team actually uses. Without that infrastructure, the model is just a slide in a deck.
What we build
Four solutions. Each one replaces something specific.
Not a platform. Not a framework. Specific, production-grade AI systems — each designed to replace a named manual process or a broken tool you are already paying for.
Demand Forecasting & Replenishment Intelligence
Replaces
The Excel weighted average model on one analyst's laptop — the one that is 15–25% wrong every quarter and nobody has touched in two years.
- Trained on 24+ months of SAP order, GRN, and consumption history — with seasonality, promotional uplift, and lead time variance built in from day one
- SKU-level 4–13 week forecast with confidence intervals, not a single point estimate. Finance and supply chain see the same numbers.
- Procurement shortfall signals surface in Power BI before stockout occurs — auto-alerts route to the right buyer when the model flags a coverage gap
- Automated retraining pipeline on Microsoft Fabric — the model updates on fresh data weekly, with drift monitoring to flag when accuracy drops
15–20% MAPE improvement. 30–40% stockout reduction. Working capital freed from overstocked slow-movers.
Predictive Maintenance & Asset Health Monitoring
Replaces
The OEM quarterly service schedule and the emergency callout at 2am when the conveyor stops mid-shift.
- Condition-based model on SCADA, OPC-UA, or MES sensor data — vibration, temperature, pressure, current draw, cycle count per asset
- Asset health score per machine updated continuously from live sensor feeds, displayed on a maintenance Power BI dashboard
- Failure warnings 48–72 hours ahead — enough lead time to schedule the work and order parts before breakdown, not after
- Maintenance notifications auto-raised in SAP PM or your CMMS — no manual logging, no missed shift handover alerts
15–20% unplanned downtime reduction. Planned maintenance costs 5–10× less than emergency callout (MHI, 2025).
Quality Anomaly Detection & Statistical Process Control
Replaces
End-of-batch QC that finds the deviation after the product is already packaged — when rework or reject are the only options left.
- SPC control limits set on live production parameters — fill weight, temperature, pH, viscosity, visual inspection scores — with real-time monitoring
- Deviations flagged as the batch runs, not at end-of-shift. Line supervisors and quality managers get the alert while corrective action is still possible.
- Alerts route via Teams or Power Automate with context — which parameter, which line, which shift — not just a number out of range
- Historical deviation pattern analysis to identify root causes across shifts, lines, raw material batches, and specific equipment combinations
Catch quality deviations 2–4 hours earlier than post-process QC. Typical 20–30% rework reduction on first deployment.
AI Agents & Conversational Decision Intelligence
Replaces
The BI analyst who spends 60% of their time answering ad-hoc data questions by pulling reports and emailing them back.
- Azure OpenAI + Copilot Studio agents connected directly to your Microsoft Fabric data layer — operational queries answered in plain English or Arabic
- "What is our OTIF for this customer this week vs last week?" answered in seconds from live data, not a manually pulled report
- Autonomous agents that act on signals — low inventory drafts a purchase order, a quality exception raises a SAP material hold, a forecast shortfall alerts procurement automatically
- Deployed in Microsoft Teams — where your operations team already works. No new interface to learn, no separate login.
Self-service analytics without the BI backlog. Ops, supply chain, and finance get answers in seconds, not 3-day report requests.
How we work
From data readiness to model in production
Every engagement starts with a diagnostic. We do not propose architecture before we understand your data, your systems, and the actual operational problem.
01
Data readiness assessment — where you stand before we write a single model
We map your data sources, assess historical data quality and completeness, and identify which use cases are actually feasible with what you have today. Some companies have 3 years of clean SAP transactional history and can go straight to a forecasting model. Others need 6-8 weeks of data foundation work first. We tell you which category you are in on day one — not six months into a project.
02
Use case prioritisation — highest ROI problem first, not the most technically interesting one
Demand forecasting, predictive maintenance, quality anomaly detection, logistics ETA prediction — these are the four use cases that pay back fastest in manufacturing and supply chain. We score each against your data readiness, business impact, and time-to-value, and start with the one that delivers a measurable result in the shortest time. The ROI from the first model funds the roadmap for the next three.
03
Build, deploy, integrate — model in production, not a notebook
We build the data pipeline, train and validate the model, deploy it as a serving endpoint on Microsoft Fabric, and integrate the output into Power BI or your operational system. Your team gets a working, production-grade predictive model — with monitoring, retraining logic, and documentation — not a proof of concept that lives on a data scientist's laptop.
Technology stack
Platform
Forecasting & ML
Agents & NLP
Data Layer
Visualisation
Source Systems
Common questions
What buyers ask us
Do we need clean data before we can start using AI analytics?
Yes — and this is the most honest answer you will get from any AI consultant. Every AI analytics project we have seen fail comes back to the same thing: the model was trained on dirty, inconsistent, or incomplete data. Before we build any predictive model, we run a data readiness assessment. If the foundation is not there, we fix it first. Typically that means connecting your ERP, MES, and SCADA into a governed data layer before a single model gets trained. We do not skip this step.
How long before we see results from AI analytics?
A demand forecasting model on clean historical data can be live in 4-6 weeks. A predictive maintenance model on sensor data from a single line takes 6-10 weeks depending on data availability and labelling quality. The timeline is almost always governed by data readiness, not model complexity. If you have 2+ years of clean transactional history, you are closer than you think. We scope this precisely in the initial diagnostic.
We have a data science team internally — where does MDI fit?
Internal data science teams are good at models. They are rarely resourced to also build data pipelines, govern the platform, integrate with SAP, and deploy to production. MDI handles the infrastructure layer — data connectivity, feature engineering, model serving, and Power BI integration — so your team can focus on what they are good at. We work alongside internal teams regularly, not instead of them.
Can AI analytics connect directly to our SAP system?
Yes. We integrate SAP B1, SAP ByDesign, SAP S/4HANA, and SAP ECC into Microsoft Fabric using Azure Data Factory and Fabric Pipelines. Transactional data from SAP lands in the lakehouse in near-real time, feeds into the feature store, and drives both predictive models and Power BI dashboards from the same governed layer. No manual exports. No CSV uploads. The model retrains on fresh data automatically.
What is the difference between predictive analytics and standard BI reporting?
Standard BI tells you what happened. Predictive analytics tells you what is likely to happen next — and in some cases triggers an action before the problem occurs. The practical difference in operations: a BI dashboard shows your stockout rate last month; a demand forecasting model tells you which SKUs will stock out in the next 14 days so procurement can act today. Predictive maintenance replaces scheduled maintenance intervals with condition-based alerts — you service equipment when sensors say it needs it, not when the calendar says so.
Ready to move
Start with the data readiness check — not the model
First call is 45 minutes. Bring your source systems list and the operational problem you most want to predict. We will tell you exactly what your data can support today — and what the path to production looks like.