Predictive maintenance

LOGISTICS 5.0

Anticipating errors with key data

At Smartlog our priority is to guarantee total availability and efficiency of logistics systems for our clients. We support them 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.

Predictive maintenance service: a service with no limits

We offer a local, personal service 24 hours a day, 365 days a year

Personalised attention and immediate response in the event of faults or emergencies

Our team of experts is available both by telephone and remotely to provide real-time diagnosis and send service technicians if necessary

Face-to-face and online training programmes and courses to keep our experts up to date

Global management of spare parts

What we offer includes after-sales service and corrective, preventive, predictive and proactive maintenance and repair packages, to keep a warehouse working at all times and adapt it to changes in the future

Contact our experts to request personalized consultation on your warehouse

Key benefits of predictive maintenance in logistics

Predictive maintenance in logistics and intralogistics offers key benefits in fostering efficiency, safety and profitability.

Minimising downtime

Preventing unplanned shutdowns by identifying faults before they occur is crucial to avoid delays in the supply chain and keep costs down.

Optimising maintenance planning

Using predictive analysis to schedule interventions efficiently, improving use of resources and avoiding interruptions during critical periods.

Prolonging the useful life of equipment

Avoiding premature wear and tear and unnecessary replacements by extending the life of machinery and vehicle and fostering sustainable operation.

Improving operating safety and control

Anticipating potential problems, improving safety at work and complying with regulations to reduce the risk of accidents. Offering precise information for occasional interventions, so reducing stress on logistics teams and boosting efficiency.

Cutting operating costs and making the best use of resources

Cutting down on costly repairs and improving energy efficiency, significantly reducing operating costs. Optimising management of resources and the supply chain.

Continuous improvement and machine learning

Allowing constant optimisation of processes through data analysis, improving precision in decision-making and revealing opportunities for improvement.

Predictive maintenance based on
Machine Learning (ML) and Artificial Intelligence (AI)

Our own warehouse management and data monitoring software

At Smartlog we anticipate the future. We have Galys, our own warehouse management platform and data monitoring and predictive maintenance technology.

Galys tools use advanced technologies like artificial intelligence (AI) and machine learning to analyse and learn from all automated processes.

Thus, the system can propose improvements, schedule maintenance, recommend new configurations and predict new scenarios. This capacity for anticipation allows us to offer total solutions and add intelligence to the whole supply chain, making an essential competitive difference.

This proactive, predictive approach makes all the difference, by guaranteeing operating efficiency in the long term and offering sustainable, resilient, cutting-edge logistics solutions.

Galys Monitor, turning data into predictive maintenance

Galys Monitor stands out as an integrated tool for managing digital assets; it is a powerful ally in implementing predictive maintenance strategies. With its knowledge and processing modules, the tool extracts valuable information from digital assets, to identify patterns and trends.

Galys Monitor offers an efficient, proactive approach to predictive maintenance, enabling companies to anticipate faults and maximise the availability of their assets.

Machine learning and artificial intelligence (AI) technologies allow the system to learn and evolve over time, constantly improving its ability to predict faults.
The use of predictive algorithms makes it easier to spot problems earlier and helps to optimise maintenance strategies.

Highlights of Galys Monitor include:

The ability to simulate scenarios and apply AI algorithms. This feature helps to identify possible faults, allowing companies to anticipate complex situations and assess how certain events could affect their assets.

By simulating a range of scenarios, organisations can proactively adjust their maintenance strategies to cope with possible challenges before they become real problems.

Galys Monitor does not just provide information; it also facilitates smart automatic actions in the field of maintenance. With its accumulated knowledge and predefined risks, the tool can trigger automated problem-solving processes, speeding up the response to potential faults and minimising the downtime of assets.

Contact our experts to request personalized consultation on your warehouse

Mitigation of operating risks and cost reduction

The ability of Galys Monitor to anticipate errors of faults also helps significantly to reduce the costs associated with unplanned repairs and downtime. By dealing with problems before they become emergencies, companies boost the availability and reliability of their assets, leading to long-term cost savings.

Interoperability and resilient, sustainable maintenance

Galys Monitor’s constant management of assets and collaborative approach boost maintenance efficiency, guaranteeing sustainable interoperability with a range of existing technologies and systems.

This capacity for adaptation and collaboration ensures that maintenance strategies evolve to meet the changing needs of the company and its environment.

Enhanced experience for customers and employees

Effective implementation of predictive maintenance also improves the experience for customers and employees. By avoiding faults and reducing downtime, companies can offer more reliable, satisfactory services, generating confidence and loyalty on their customers’ part.

Predictive maintenance strategy

Predictive maintenance is a cutting-edge strategy in asset management, using monitoring systems and machine learning algorithms to anticipate faults and optimise operations.

This maintenance strategy backed up by data analysis is an indispensable ally for companies seeking to optimise their operations, ensure customer satisfaction and gain a competitive advantage on today’s market.

It allows precise interventions based on the actual state of assets, optimising the assignment of resources and cutting downtime.

This paradigm boosts the reliability and operating efficiency of systems and equipment, raising the quality of service to the customer. Preventing faults ensures greater customer satisfaction and a notable reduction in operating and maintenance costs.

It works on the basis of three key components:

Connected sensors and devices
These compile data in real time about the status and performance of machines using Internet of Things (IoT) technologies.

Cloud software and storage
These allow analysis of large amounts of data through Big Data applications and data mining techniques.

Predictive models
These use machine learning algorithms to establish patterns, make comparisons, prepare fault predictions and schedule proactive maintenance.

Proactive or reliability-centred maintenance

This goes beyond predicting faults and sets out to identify and mitigate the root causes of potential problems. It involves exhaustive analysis of all possible failure points, including factors external to the original design of the system.