Predictive Modelling — SMHcoders

AI SERVICE // PM-04

Predictive Modelling

Quality, emissions and equipment-health prediction from the instruments you already have.

Free AI Consultation

Talk to an engineer

30–60Existing tags as inputs

12–24 moHistorian + lab reference

SecondsEstimate update rate

OPC UAPublished like any instrument

// Overview

Data-driven decisions with predictive analytics

Our predictive analytics services use machine learning on your historical data to uncover trends and forecast future outcomes — taking the guesswork out of decision-making across operations, finance, energy and manufacturing.

Our data scientists work closely with your team to deliver customized, actionable models: optimize operations, reduce risk, allocate resources effectively and act on new data the moment it arrives.

>Robust across data types and scalable to very large datasets>Interpretable, transparent models you can audit and trust>Automated feature selection and real-time predictive scoring

PREDICTIVE ANALYTICS · PROCESS DATA

// What we offer

Inside this service

PRE-01

Soft sensors

Inferential measurement of quality, composition and emissions.

PRE-02

Equipment-health prediction

Degradation flagged with lead time to plan work.

PRE-03

Demand and load forecasting

Production, utility and network demand ahead of time.

PRE-04

Emissions estimation

Predictive emissions monitoring from process data.

PRE-05

Batch quality prediction

End-of-batch quality predicted mid-run.

PRE-06

What-if scenario models

Response surfaces for operating decisions.

// How it works in practice

What you actually get

PM-F1 · VALIDATION

Validated against your lab

Every model is benchmarked against your lab reference before go-live, and the accuracy is reported in your units.

>Trained on historian and LIMS data >Accuracy reported vs lab reference >Confidence bands on every estimate

QUALITY FORECAST // TI-204 FEED

HISTORYFORECAST ± BAND

RUNTIME PROFILE

PLANT-SIDE HARDWARE · NO CLOUD ROUND-TRIP

PM-F2 · REAL-TIME

Real-time, not batch

Estimates update every few seconds on plant-side hardware and publish like any other instrument.

>Updates every few seconds >Runs on plant-side hardware >Publishes via OPC UA tags

PM-F3 · TRANSPARENCY

Transparent models

You get feature importance, drift alarms and a retraining plan — not a black box.

>Feature importance reports >Drift alarms configured >Retraining plan at handover

FEATURE IMPORTANCE // QC-EST

TRAY 14 TEMP78%

REFLUX RATIO62%

FEED RATE41%

COLUMN DP24%

// More capabilities

Predictive methods we apply

METHOD	QUESTION IT ANSWERS	WHAT YOU GET
Regression	How does X depend on Y?	Relationships, sensitivities, what-if response
Time-series forecasting	What will demand, load or quality do next?	Horizon forecasts with confidence bands
Classification	Which category is this event, asset or record?	Labels with probabilities
Anomaly detection	Is this normal?	Deviation scores, ranked contributing tags
Transfer learning	Can we start before we have years of data?	Pre-trained models adapted to your unit
Real-time scoring	What is the estimate right now?	Live values to dashboards, alarms, DCS

// Tools and technology

Built with proven tooling

NumPy

pandas

scikit-learn

TensorFlow

PyTorch

PYTHON SCIKIT-LEARN XGBOOST PYTORCH OPC UA

// Process

How we deliver

STEP 01Historian auditTag coverage, sampling and gaps assessed.

STEP 02Feature engineeringProcess knowledge encoded with your engineers.

STEP 03Model trainingCandidate models compared on your data.

STEP 04Lab validationBenchmarked against reference measurements.

STEP 05Live deploymentPublished as tags, monitored for drift.

SMHCODERS // METHOD

A prediction you cannot audit is a guess. Every model ships with accuracy in your units and confidence bands.

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// FAQ

Common questions

How accurate are the predictions?+

Accuracy is reported in your units, benchmarked against your lab or reference data before go-live — with confidence bands, not a single opaque score.

What data do we need to start?+

Most engagements start with 6–24 months of historian, lab or document data. We audit coverage in the first week and tell you plainly if it is not enough — before you commit.

How long until we see results?+

A proof of concept on your historical data typically lands in 4–8 weeks. Production deployment follows once the KPI target is met and your team signs off.

Where does it run, and who sees our data?+

On your infrastructure — plant-side servers, your Azure or AWS tenancy, or an air-gapped network. Data never leaves your boundary, and nothing is used to train third-party models.

Who owns the result?+

You do. Handover includes source code, documentation, training sessions and a retraining plan.