The project is focused in the Valencia Engine Plant (Ford Spain). The engine plant is currently the only Ford plant in Europe which is responsible for manufacturing the Duratec Ecoboost 2.0 and 2.3L engines that are used to equip the vehicles that Ford will assemble in its assembly plants worldwide. The average annual production of the plant is 324K Engines, which are supplied to the vehicle assembly plants of Valencia, Hermosillo, Saarlouis, Louisville. In addition to shipping engines, it also ships mechanized engine components like a Cylinder Block, Cylinder Head, Crankshaft and Camshaft (600K sets / year) to send the Cleveland engine assembly plant at USA.
To manage the variants manufacturing complexity, the plant is working with weekly batches managed manually using the experience of MPL and production personnel. Currently, the assembly line is manufacturing 25 different derivatives depending on the vehicle where it will be installed. Apart from the 4 engine components manufactured in Valencia, each of these derivatives needs different components such as the engine crank case, petrol pump, oil pump, and clutch, and this manufacturing complexity is managed manually using the experience of the MP&L and production personnel and static restrictions of line production. We need a system that feed with real time data and update the restrictions of the line production and advice what is the best batch and mix of production.
Currently, the performance of the whole plant depends on the right batch planification and MIX sequence that production and MP&L departments have scheduled based on client demand and their experience. On the other hand, production of the lines depends on the decision that the shift foreman is taking to minimize the planned stoppages and faults occurred during the shift.
To improve this scenario and achieve a better plant performance, Ford is considering that an AI system with Simulation and capabilities will help the operators to choose the right decision at every moment improving the line availability and efficiency by offering the best simulation of the different alternatives.
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:
- Representation in real time of production and traceability;
- Simulation of some changes in the line production;
- Advices in the production batches in terms of size, mix and schedule;
- The tool change strategy based on line efficiency and tool life;
- Buffer stock manement;
- Maintenance tasks based on line efficiency, machine/people/tools availability.
The actionable results of the AI algorithms will be leveraged in the Ford-specific application (“Digital Twin & AI recommendation App”) that will be developed in XMANAI in order to communicate to the operators and the personnel (blue-collar and white-collar workers), intelligent automatic alerts (using wearable equipment) and recommendations (through email and a dashboard showing in real-time and in the past the line/machinery status, the line availability and efficiency, and the events occurred during the shift that has altered the line availability or efficiency)