AI Solutions & Delivery

Optimisation

Advanced algorithms and operational research models that maximise the performance of available resources — balancing cost, capacity and service level systematically.

Discuss your use case

AI Solutions & Delivery

Optimisation in AI means finding the best possible solution among millions of alternatives, within real operational constraints. It applies mathematical models — from vehicle routing (VRP) to inventory control — to make decisions that a human dispatcher, planner or buyer could never compute fast enough to act on.

Built with

Python

OR-Tools

SciPy

NumPy

PuLP

Azure

Operational research, not guesswork

VRP, MRP, safety stock and scheduling algorithms are grounded in decades of applied mathematics — adapted to your specific constraints and data.

Every constraint handled automatically

Time windows, vehicle capacities, SLAs, supplier lead times and compatibility rules are all considered simultaneously in every run.

Continuous, not one-off

Systems re-optimise dynamically as conditions change — route re-planning, real-time stock adjustment, live schedule recalculation.

Our approach

How we deliver it

We apply advanced algorithms and operational research models to solve complex logistics problems, maximising the performance of available resources. Our objective: systematise critical decision-making through applied mathematics, balancing cost, capacity and service level.

Operational research

VRP, MRP and inventory algorithms grounded in applied mathematics — not rules of thumb.

Constraint management

Time windows, capacities, SLAs and compatibility constraints handled automatically in every run.

Real-time recalculation

Systems re-plan dynamically as conditions change throughout the day — no manual re-work required.

Reference cases

Documented use cases

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

04Logistics · Construction Materials

Route Optimisation for Cement Silos

Minimise empty kilometres and maximise daily heavy fleet rotation.

ChallengeTrucks return empty after delivery with no real-time recalculation capability. Dispatcher decisions based on static rules generate systematic inefficiencies.

SolutionVRP algorithm dynamically generates optimal multi-stop routes (backhauling), keeping trucks loaded as long as possible with automatic handling of time windows and silo compatibility.

Fleet efficiencyEmpty KM reduction

05Supply Chain · Operations

Inventory and Safety Stock Optimisation

Minimise immobilised capital while guaranteeing service levels during demand peaks.

Challenge"Just in case" stock immobilises liquidity and space, while static rules fail during peaks or supplier delays, causing unexpected stockouts.

SolutionAnalytical models dynamically calculate the optimal reorder point for each SKU, based on real supplier lead time, historical demand variability and seasonality.

Capital releaseCost vs SLA balance

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

Optimisation

Advanced algorithms and operational research models that maximise the performance of available resources — balancing cost, capacity and service level systematically.

Discuss your use case

AI Solutions & Delivery

Built with

Python

OR-Tools

SciPy

NumPy

PuLP

Azure

Operational research, not guesswork

VRP, MRP, safety stock and scheduling algorithms are grounded in decades of applied mathematics — adapted to your specific constraints and data.

Every constraint handled automatically

Time windows, vehicle capacities, SLAs, supplier lead times and compatibility rules are all considered simultaneously in every run.

Continuous, not one-off

Systems re-optimise dynamically as conditions change — route re-planning, real-time stock adjustment, live schedule recalculation.

Our approach

How we deliver it

Operational research

VRP, MRP and inventory algorithms grounded in applied mathematics — not rules of thumb.

Constraint management

Time windows, capacities, SLAs and compatibility constraints handled automatically in every run.

Real-time recalculation

Systems re-plan dynamically as conditions change throughout the day — no manual re-work required.

Reference cases

Documented use cases

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

04Logistics · Construction Materials

Route Optimisation for Cement Silos

Minimise empty kilometres and maximise daily heavy fleet rotation.

ChallengeTrucks return empty after delivery with no real-time recalculation capability. Dispatcher decisions based on static rules generate systematic inefficiencies.

SolutionVRP algorithm dynamically generates optimal multi-stop routes (backhauling), keeping trucks loaded as long as possible with automatic handling of time windows and silo compatibility.

Fleet efficiencyEmpty KM reduction

05Supply Chain · Operations

Inventory and Safety Stock Optimisation

Minimise immobilised capital while guaranteeing service levels during demand peaks.

Challenge"Just in case" stock immobilises liquidity and space, while static rules fail during peaks or supplier delays, causing unexpected stockouts.

SolutionAnalytical models dynamically calculate the optimal reorder point for each SKU, based on real supplier lead time, historical demand variability and seasonality.

Capital releaseCost vs SLA balance

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.