Ford Pilot

XMANAI AI platform, manage and analyse the real-time and batch data acquired by Ford Corporative systems, and the scheduled data contained in the MP&L, maintenance and Tooling systems, in order to build novel AI models that contribute to the provision of recommendations to optimize the line throughput of the current and successive shifts. The hybrid and graph AI models to be explored, trained and evaluated in XMANAI, will allow created alerts of different uses cases: (a) Representation in real time of production and traceability; (b) simulation of some changes in the line production; (c) advices in the production batches in terms of size, mix and schedule; (d) the tool change strategy based on line efficiency and tool life; (e) buffer stock management; (f) maintenance tasks based on line efficiency, machine/people/tools availability.

Whirlpool Pilot

The white goods demonstrator for XMANAI project is focused on sales demand forecasting for a specific business channel. 
Currently, the overall demand forecasting process in Whirlpool, executed on weekly base within planning legacy systems (SAP Integrated Business Planning), drives the whole production planning and supply management for all the products within EMEA region.  

Partner Spotlight – Ford Motor Company

Name: Javier Colomer Barberá
Job title: New Technologies Engineer
Organization: Ford Motor Company
Bio: Javier Colomer is a Telecommunications Engineer from the Polytechnic University of Valencia.

XAI Model Guard: The XMANAI AI Models Security Framework

As manufacturing organizations are embracing the Industry 4.0 initiative that is revolutionizing the manufacturing sector towards the realization of smart factories, the adoption rate of technologies related to Artificial Intelligence (AI), machine learning, and analytics is also growing.

Explainable AI: a key to trust and acceptance of AI-based decision support systems

Explainable AI: a key to trust and acceptance of AI-based decision support systems Artificial intelligence is often based on complex algorithms and mathematical models that are difficult to understand. One of the characteristics of modern AI, based mainly on neural networks, is that it uses ‘black-box’ models, i.e. ‘boxes’ that make decisions without the user […]

Partner Spotlight – CNH Industrial

Name: Claudia Campanella
Job title: Manager of Ergonomics-HMI-VR-AR
Organization: CNH Industrial
Bio: Claudia Campanella graduated in Industrial Design at the Polytechnic of Turin, realizing a thesis in physical ergonomics. She started working at Fiat Auto in 2000 and at the same time, she attended the Master in Ergonomics in which she created a thesis in cognitive ergonomics.

7th XMANAI´s General Assembly

7th XMANAI´s General Assembly On the 22nd and 23rd of March, XMANAI held its 7th General Assembly in Lisbon hosted by Knowledgebiz.  This was an excellent opportunity to share project outcomes and plan the next steps.

Industrial Asset Graph Modelling in XMANAI

In the Industrial sector specifically, graph networks can describe pathways of IoT devices and sensor networks (Aggarwal, et al., 2017) in the framework of predictive maintenance, or represent associations between resources, daily workload and production in decision-making and dynamic scheduling problems (Hu, et al., 2020).

XMANAI Validation Environment for AI Models

The Explainable AI (XAI) in Manufacturing (XMANAI) project aims to provide a framework for the development and deployment of AI models in the manufacturing industry.

zExplAIn, Improving Manufacturing Processes with Explainable AI

In the new era of Industry 4.0, AI systems are becoming increasingly prevalent and cost-effective. With the ability to analyze vast amounts of data, factories can reduce expenses, boost productivity, and minimize waste.

XMANAI partner spotlight – Deep Blue

Name: Linda Napoletano
Job title: Head of the Manufacturing Area
Organization: Deep Blue srl
Bio: Linda Napoletano holds a Ph.D. in Human-Computer Interaction. Since 2002, she has been working on EU-co-funded projects aiming at designing and validating humans integration and interactions into highly innovative processes.

Technical and Socio-Business assessments of AI Maturity in pilots of Explainable AI

Technical and Socio-Business assessments of AI Maturity in pilots of Explainable AI The EU-funded XMANAI project focuses on Explainable AI, as the ability to make machine decision-making processes understandable; Explainability has proven to be a key element in stimulating the adoption of AI in various areas because it provides transparent and understandable information about algorithmic […]

XMANAI partner spotlight – UNIMETRIK

Name: Aitor San Vicente
Job title: General Manager
Organization: UNIMETRIK
Bio: Expert in advanced industrial metrology services, Aitor has extensive experience both in the field of advanced manufacturing processes, mainly in the die-cutting and stamping sector, and in the development of quality control and digitalization solutions and strategies.

XMANAI platform – Alpha release available

XMANAI platform – Alpha release available We are happy to announce that the XMANAI platform – Alpha release is now available. This is the first public release and provides the basis for the implementation and delivery of the upcoming versions of the XMANAI platform. In accordance with the XMANAI Description of Action, two additional releases […]

AI Algorithms Lifecycle Management and Collaboration

In XMANAI we have set out to develop robust and insightful AI pipelines that can assist manufacturers in their everyday operations and decision-making processes. To achieve our goal, we are creating a collaborative environment in which the explainability of the ML models’ decisions lies at the heart of our AI pipelines design, development and roll out. Needless to say, in order for these AI pipelines to be properly configured, trained, evaluated, deployed and applied, constantly monitored, assessed and refined as needed, numerous other processes need to be in place.