AI logistics platform development: 20% carbon emission reduction

Innowise built a logistics optimization platform with AI-powered route planning, real-time analytics, and sustainability tracking for a major logistics provider.

Der Kunde

Branche

Logistik

Region

Kunde seit

2023

Our client is a prominent global logistics company that partners with retail, healthcare, and manufacturing businesses. With over 25,000 employees and a massive fleet, they’re moving millions of shipments every year. Known for their drive to innovate and cut down on environmental impact, they’re always looking for smarter ways to boost efficiency and shrink their carbon footprint.

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Herausforderung

Overcoming logistics inefficiencies, cutting carbon footprint and keeping deliveries on time

The client reached out to us with a few major issues that were holding back their operations and green efforts:

Inefficient route planning: Their logistics were mostly manual and static, which led to inefficient paths, more fuel use, and frequent delivery delays.
High carbon emissions: Running such a large operation meant a big environmental impact, making it tough to hit their sustainability targets as emissions kept rising.
Fragmented supply chain coordination: Different regional hubs were using separate data streams, causing delays in managing inventory, tracking in real time, and coordinating deliveries.
Missed delivery windows: Traffic jams and unpredictable weather often threw schedules off, resulting in missed or delayed shipments.

As a forward-thinking company aiming to cut down on its environmental footprint, the client wanted a scalable, tech-driven solution to supercharge logistics and hit sustainability targets aligned with the UN’s Sustainable Development Goals (SDGs).

Lösung

AI-driven platform for smarter routing and smooth supply management

To tackle these issues, we built a logistics optimization platform powered by machine learning. The solution uses smart routing algorithms, real-time data analytics, and seamless API integrations to upgrade delivery routes, cut fuel usage, and boost overall business efficiency.

Key features of the platform

Our team packed the platform with essential features to boost service delivery rate and amp up sustainability. These key functions work together to provide easy data integration, real-time insights, and predictive analytics across the entire supply chain.

AI-based route optimization: the system tweaks delivery routes on the fly, using real-time info like traffic jams, road conditions, and weather updates. The ML-Modell keeps getting smarter, which helps to further cut down delivery times and save on fuel.
Geospatial data integration: GIS mapping provides all the important details such as traffic conditions and terrain features. Then, our AI systems take that info to find the best and most eco-friendly routes, making everything run smoother and greener.
Predictive delay analytics: the platform predicts potential delays using historical and current data and automatically reroutes vehicles to make sure deliveries arrive on time.
Automated data sync: APIs keep all the order, inventory, and delivery data updated in real time across the client’s ERP, WMS, and TMS systems — eliminating manual coordination and reducing delays.
Sustainability metrics: the solution tracks key environmental stats like carbon footprint per delivery, total fuel use, and emissions reduction rate. These insights help the client stay aligned with sustainability goals and maintain transparency for stakeholders and auditors. The system also generates reports that meet GRI and ISO 14001 standards.

Carbon emission reduction

We built a smart routing algorithm that combines GIS and machine learning to optimize delivery routes. It factors in things like road congestion, elevation, traffic patterns, and vehicle type to prioritize fuel-efficient paths and cut down on unnecessary stops and idling.

Supply chain integration

Using APIs, we linked the platform to the client’s ERP, WMS, and TMS, so inventory levels, order updates, and delivery schedules stay in sync in real time. Data pipelines handle loads of supply chain data, keeping warehouse management, inventory control, and deliveries running smoothly together.

Real-time route improvement

Der AI-driven platform constantly tracks both historical and live data like traffic and weather, learning from it to reroute deliveries if potential delays come up. We implemented predictive analytics that not only flags potential issues but also suggests better routes to hit tight delivery windows.

Inventory management and throughput

By connecting the platform to the client’s WMS, we helped create a steady flow of goods. Real-time updates on stock, deliveries, and restocking made inventory turnover faster and reduced bottlenecks in warehouses and transport hubs — dramatically boosting throughput.

Technologien

Cloud Infrastructure

AWS (Lambda, EC2, S3, RDS)

Datenverarbeitung

Apache Kafka, Spark

Machine Learning Models

TensorFlow, scikit-learn

API Integration

RESTful APIs, GraphQL

Mapping and GIS

Google Maps API, Mapbox

Data Analytics and Reporting

Power BI, Tableau

Monitoring and Alerting

Prometheus, Grafana

Verarbeiten Sie

The client chose to stick with the Waterfall model, splitting the project into clear steps: requirements gathering, design, development, testing, and deployment. We checked in with them after every phase to make sure we were all on the same page and everything was going according to plan.

Our project manager held regular check-ins to share progress, gather feedback, and secure approvals at key milestones. By sticking to this setup, we stayed in sync, dodged risks, and delivered exactly what the client was after and right on time.

Team

Projektmanager

Business-Analyst

Datenwissenschaftler

ESG Consultant

Software Engineers

DevOps-Ingenieur

GIS Specialist

Ergebnisse

Reduced carbon emissions and boosted delivery speed

Thanks to real-time route tweaks, the client sped up deliveries by 30%, so customers always got their orders on time. They also cut carbon emissions by 20% just by optimizing routes and using less fuel. These changes cut fuel costs by 15%, while smoother inventory flow and better scheduling lowered operational costs by 10%. The real-time flow and faster decision-making bumped up inventory throughput by 18%. As a result, customer satisfaction soared, with a 25% increase in trust and loyalty.

Built for growth, this solution keeps up with the client’s sustainability efforts. Future updates will add metrics like vehicle wear-and-tear and plans for route electrification. With ongoing improvements to ML models and data integrations, we’re setting the client up to cut their carbon footprint by 50% over the next five years, right in line with the EU’s Green Deal goals.

Projektdauer

Juni 2023 - Fortlaufend

20%

cut in carbon emissions

10%

drop in operational costs

Fallstudien

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