Intelligent or predictive intralogistics

LOGISTICS 5.0

Anticipating the future, optimising the present

In the era of global competition, optimising internal logistics is crucial for gaining a market advantage. Predictive intralogistics, powered by artificial intelligence (AI), is revolutionising the management of warehouses and distribution centres.

Predictive intralogistics is the future of supply chain management. It combines asset and operations monitoring with AI algorithms to provide a comprehensive, real-time view of the logistics chain. This technology enables the prediction of future scenarios, such as demand spikes or equipment failures, allowing for quick and efficient responses.

We support our clients through every stage in the life cycle of the logistics project, from data and needs analysis through to subsequent monitoring, predictive maintenance, constant optimisation and adaptation to new needs.

Our predictive intralogistics technology makes all the difference.

We have our own system for monitoring and predictive management of facilities. We include technologies like total connectivity, Big Data, Artificial Intelligence and Machine Learning to go even further.

Advantages of predictive intralogistics

Demand prediction

Uses historical and current data to anticipate product demand.
Optimises inventory levels and reduces the risk of overstocking or stockouts.

Route optimisation and scheduling

Plans efficient routes for vehicles and tasks, reducing travel time and fuel consumption.

Predictive maintenance

Predicts equipment failures through data analysis, allowing for maintenance to be scheduled before breakdowns occur.

Warehouse space management

Optimises space utilisation and product layout, improving material flow and reducing storage costs.

Personnel management

Analyses work patterns to optimise shift scheduling, enhancing efficiency and reducing labour costs.

Safety and compliance

Identifies potential risks and improves safety measures, reducing the risk of accidents and ensuring regulatory compliance.

Sustainability

Contributes to reducing environmental impact by optimising energy use and minimising emissions.

Contact our experts to request personalised consultancy for your warehouse

Artificial intelligence in predictive logistics

AI in intralogistics not only monitors and manages data but also acts as a digital director, coordinating all resources and assets.

Our own software suite, such as Galys Monitor and Galys Intelligence, integrates advanced technologies like IIoT, Big Data, Artificial Intelligence, and Cloud Computing to offer an automated and resilient management platform.

Galys Monitor: Efficiently responds to anomalies and optimises processes.
Galys Intelligence: Learns and adapts operations, proposing continuous improvements and anticipating future issues.

Our warehouse management software and monitoring technology, based on data exploitation, AI, and Machine Learning, enable the system to learn from the operation of all automated processes. It proposes improvements, schedules maintenance, recommends new configurations, and predicts new scenarios.

This approach allows us to anticipate future challenges, provide global solutions, and bring intelligence to the entire supply chain.

This technology represents a key competitive advantage.

Predictive management in intralogistics

Predictive analytics uses algorithms and statistical models to anticipate future events based on historical and real-time data. In the field of intralogistics, it is used to forecast product demand, delivery times, inventory levels, and other critical aspects of the supply chain.

Advantages of predictive analytics in logistics

Improved planning

It allows companies to plan their logistics operations more accurately, including demand forecasting, transport route scheduling, and inventory management.

Cost reduction

By anticipating demand and optimising logistics processes, companies can minimise costs related to excess inventory, waiting times, and delivery delays.

Enhanced customer satisfaction

By forecasting and anticipating customer needs, companies can improve delivery timeliness and provide better service.

Predictive management is another application of artificial intelligence in logistics, focusing on anticipating and preventing problems before they occur. By using machine learning algorithms and real-time data analysis, predictive management helps companies foresee and mitigate supply chain disruptions, such as equipment failures, production delays, and inventory issues.

Advantages of predictive management in logistics

Predictive maintenance

Allows companies to monitor the condition of supply chain assets, such as machinery and equipment, and predict failures or maintenance needs before they occur, preventing unplanned downtime and reducing costs associated with breakdowns and urgent repairs.

Supply chain optimisation

By predicting and preventing supply chain issues, predictive management optimises logistics processes, reduces waiting times, and improves operational efficiency.

Adaptability and resilience

Helps companies anticipate and adapt to market changes, disruptions, and unexpected events, making them more resilient and able to respond quickly to challenges.

Both predictive analytics and predictive management are essential aspects of artificial intelligence in logistics. They enable companies to optimise operations, reduce costs, and enhance customer satisfaction. By using algorithms and statistical models, we help businesses forecast demand, anticipate problems, and make more informed decisions within their supply chains.

Contact the technical department for personalised advice