ESG-Aware Supply Chain POC

Overview AI Forecasting Trade-off Engine Virtual Twin RL Optimizer Roadmap

Research-to-Execution Blueprint

A cutting-edge solution to link demand forecasting, emissions optimization, and a 3D virtual twin for resilient logistics.

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Probabilistic Forecasting

Quantile forecasts (P90) feed an optimization model that includes monetized carbon cost in its decision-making.

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Carbon-Cost-Time Frontier

Interactive simulation to map out Pareto-optimal solutions, making ESG trade-offs explicit and quantifiable.

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Hyper-Automated Logistics

Reinforcement Learning agents optimize carrier selection and routing within the live Virtual Twin environment.

AI Forecasting with Emissions-Aware Optimization

This system uses Temporal Fusion Transformers (TFT) for granular demand prediction, providing a full probability distribution (quantiles) rather than a single point forecast. This allows the Decision Layer to set emissions-informed safety stock by balancing potential stockout costs against inventory carbon footprint.

Safety Stock & Reorder Point Calculation

Adjust the sliders to see how the desired service level (P-level) and the assigned cost of carbon influence the calculated inventory policy.

Visualization of the risk coverage (higher coverage = more safety stock).

€0 €50 €200

OPTIMIZED DECISION

Reorder Point

135 units

This value ensures a 90% service level, balancing stockout probability against the increased inventory carrying cost and associated carbon footprint due to storage and potential obsolescence.

Based on P90 forecast of 120 units and safety stock of 15 units.

Trade-off Simulation Engine: Cost-Time-Carbon Frontier

The simulation engine provides a Pareto-optimal set of solutions for logistics decisions. This chart visualizes the conflicting goals of minimizing cost vs. minimizing carbon. Hover over the points to see the associated lead time and select a business rule to highlight the optimal strategy.

Strategy Pareto Frontier (Cost vs. Carbon)

Business Rule Selection

Selected Strategy

Strategy Name:

Standard Road Freight

Total Cost (M):

€1,200,000

Lead Time:

5 days

Carbon Footprint (tCO₂e):

85

3D Virtual Twin: Real-time Disruption Simulation

The Virtual Twin links to live external data (weather, traffic, port status) and runs disruption scenarios to predict impacts. This 2D representation simulates a simple logistics flow, allowing you to trigger events and see the immediate visualization of stress and KPI degradation.

Scenario Controls

Select a scenario to visualize its immediate effect on the network and business metrics.

Live KPI Impact

Service Level (SLA): 98%

Total Emissions (tCO₂e): 450

On-Time Delivery (OTD): 95%

🏭 Factory A
⚓ Port A
📦 Warehouse B
🚚 Hub C
🏠 Customer D

Reinforcement Learning for Multi-Objective Optimization

RL agents learn optimal, non-linear policies for routing and carrier selection by training within the Virtual Twin simulator. The goal is to maximize a custom multi-objective reward function that rigorously penalizes both monetary cost and carbon emissions.

RL Formulation Components

State (S)

Inventory levels, vehicle locations, live weather/traffic, current demand predictions.

Action (A)

Carrier selection (low-carbon vs. high-speed), transport mode, rerouting decision, scheduling adjustment.

Constraints

Hard limits on budget, mandatory regulatory compliance, and minimum service levels.

Multi-Objective Reward Function (Pseudocode)

function calculate_reward(state, action): # Monetary cost (negative) C_Monetary = -action.monetary_cost # Carbon cost (monetized emissions, negative) C_Carbon = -action.co2_emissions * CARBON_PRICE # Service level penalty (negative) P_Delay = -calculate_expected_delay(action) # Tunable weights for business priority w_cost = 0.5 w_carbon = 0.3 w_delay = 0.2 total_reward = (w_cost * C_Monetary + w_carbon * C_Carbon + w_delay * P_Delay) # Large penalty for violating hard constraints (Safe RL) if violates_budget(action) or violates_regulation(action): total_reward -= 5000 return total_reward

Phased 16-Week POC Roadmap: Week-by-Week Breakdown

A detailed, weekly sprint plan ensuring technical rigor and measurable progress toward the final stakeholder demonstration.

Phase 1: 📊 Foundation & Emissions Mapping (Weeks 1-4)

Weeks 1-4

Phase 2: 📈 Model Prototype & Baseline Simulation (Weeks 5-8)

Weeks 5-8

Phase 3: 🌐 Virtual Twin MVP & RL Training (Weeks 9-12)

Weeks 9-12

Phase 4: ✅ Integration, Evaluation & Handoff (Weeks 13-16)

Weeks 13-16