AI Solutions & Delivery

Predictive Analytics

Anticipate demand, risk and behaviour by processing your most impactful historical data — and generate measurable ROI without manual intervention.

Discuss your use case

AI Solutions & Delivery

Predictive analytics uses historical data, statistical algorithms and machine learning to forecast future outcomes with operational precision. Rather than describing what happened, it tells you what is likely to happen — and with enough confidence to act on it before it does.

Built with

Python

scikit-learn

XGBoost

Apache Spark

Azure ML

LightGBM

From description to prediction

Traditional reporting tells you what the data shows. Predictive analytics tells you what to expect next — demand volumes, equipment failures, supply shortages.

Models that learn from your data

Algorithms train on your operational history and external signals, continuously self-correcting as real outcomes come in.

Actionable at the operational level

Outputs integrate directly into planning, procurement and scheduling systems — not dashboards that require analysts to interpret.

Our approach

How we deliver it

We establish the foundations of predictive analytics by processing the most operationally impactful historical data, to anticipate demand and generate immediate ROI. Our objective: systematise demand projection and recurring predictive scenarios without manual intervention.

Historical data processing

We select and structure the most operationally impactful data, removing noise and bias before any model is trained.

Iterative model training

Algorithms continuously self-adjust from recent deviations — precision improves as operations evolve.

Proactive monitoring

Constant supervision and early alerts on business evolution let teams act before problems materialise.

Reference cases

Documented use cases

Each case below is a system delivered in production — with a measurable outcome attached to it.

01Energy · Distribution

Residential Energy Consumption Forecasting

Optimise grid generation and transport — to the megawatt and the hour.

ChallengeRigid forecasting based on static profiles generates energy waste from poorly distributed grid injection. No capacity to respond to sudden demand changes.

SolutionML models combining consumption history, weather forecasts and temporal variables produce hourly D+1 demand curves segmented by zone, enabling dynamic flow adjustment.

−18%distribution lossesROI · 4 months

02Manufacturing · Cement

Cement Sales Volume Forecasting

Weekly AI forecasting that restores adaptability and protects corporate growth.

ChallengeLack of precision generates unnecessary stock accumulation or stockouts, limiting planning and putting growth at risk compared to more agile competitors.

SolutionPredictive models integrating seasonality, working calendar and climate variables with weekly-level output. Continuous self-adjusting algorithms and proactive monitoring.

Production optimisation

03Automotive · Supply Chain

Sales and Demand Forecasting — Supply Chain

Anticipate order volatility and minimise the impact of cancellations on production.

ChallengeUnpredictable OEM order flow with last-minute cancellations causes immobilised inventory and operational stress across the entire supply chain.

SolutionML noise-filtering models discriminate statistical noise from real net volume, with early identification of orders at risk of cancellation to dynamically adjust MRP.

−32%immobilised component stockROI · 6 months

Standards

Secure, accountable delivery.

Recognised standards for information security, quality management and responsible data protection.

ENS Alto

ISO 27001

ISO 9001

GDPR

Contact

Talk to MCCM about your AI roadmap.

Tell us what you want to improve. We will help you shape a practical route from strategy to production.

AI Solutions & Delivery

Predictive Analytics

Anticipate demand, risk and behaviour by processing your most impactful historical data — and generate measurable ROI without manual intervention.

Discuss your use case

AI Solutions & Delivery

Built with

Python

scikit-learn

XGBoost

Apache Spark

Azure ML

LightGBM

From description to prediction

Traditional reporting tells you what the data shows. Predictive analytics tells you what to expect next — demand volumes, equipment failures, supply shortages.

Models that learn from your data

Algorithms train on your operational history and external signals, continuously self-correcting as real outcomes come in.

Actionable at the operational level

Outputs integrate directly into planning, procurement and scheduling systems — not dashboards that require analysts to interpret.

Our approach

How we deliver it

Historical data processing

We select and structure the most operationally impactful data, removing noise and bias before any model is trained.

Iterative model training

Algorithms continuously self-adjust from recent deviations — precision improves as operations evolve.

Proactive monitoring

Constant supervision and early alerts on business evolution let teams act before problems materialise.

Reference cases

Documented use cases

Each case below is a system delivered in production — with a measurable outcome attached to it.

01Energy · Distribution

Residential Energy Consumption Forecasting

Optimise grid generation and transport — to the megawatt and the hour.

ChallengeRigid forecasting based on static profiles generates energy waste from poorly distributed grid injection. No capacity to respond to sudden demand changes.

SolutionML models combining consumption history, weather forecasts and temporal variables produce hourly D+1 demand curves segmented by zone, enabling dynamic flow adjustment.

−18%distribution lossesROI · 4 months

02Manufacturing · Cement

Cement Sales Volume Forecasting

Weekly AI forecasting that restores adaptability and protects corporate growth.

ChallengeLack of precision generates unnecessary stock accumulation or stockouts, limiting planning and putting growth at risk compared to more agile competitors.

SolutionPredictive models integrating seasonality, working calendar and climate variables with weekly-level output. Continuous self-adjusting algorithms and proactive monitoring.

Production optimisation

03Automotive · Supply Chain

Sales and Demand Forecasting — Supply Chain

Anticipate order volatility and minimise the impact of cancellations on production.

ChallengeUnpredictable OEM order flow with last-minute cancellations causes immobilised inventory and operational stress across the entire supply chain.

SolutionML noise-filtering models discriminate statistical noise from real net volume, with early identification of orders at risk of cancellation to dynamically adjust MRP.

−32%immobilised component stockROI · 6 months

Standards

Secure, accountable delivery.

Recognised standards for information security, quality management and responsible data protection.

ENS Alto

ISO 27001

ISO 9001

GDPR

Contact

Talk to MCCM about your AI roadmap.

Tell us what you want to improve. We will help you shape a practical route from strategy to production.