AI in Logistics and Manufacturing
This comprehensive workshop is designed to give participants the ability to harness the power of AI in manufacturing. The program explains the core concepts of AI and Machine Learning and explores their fundamental differences and practical applications within a production context. With the addition of real-life case studies and exercises, attendees will learn methods to apply AI to their own processes.
Duration
24 Hours.
Format
Online training with an in-person option (groups of up to 15 people).
Target Audience
This workshop is ideal for:
- Manufacturing professionals, including engineers (production, quality, maintenance), operations managers, plant managers, supervisors, and process improvement specialists.
- IT and OT professionals, responsible for data infrastructure, automation, and technology integration within manufacturing environments.
- Data scientists and analysts seeking to apply their skills in a manufacturing context and understand industry-specific AI challenges.
Workshop Objectives
Upon completion of this workshop, participants will be able to:
- Understand basic AI and machine learning concepts and their relevance in a production environment, including the differences between supervised and unsupervised learning.
- Recognize diverse applications of AI technologies (e.g., vision systems, neural networks) across various industries such as automotive, food, and pharmaceuticals.
- Understand methods for collecting and visualizing production data from sources like IoT sensors and MES systems, and identify potential areas for optimization.
- Apply AI for defect detection, micro-damage identification, and accident prevention through predictive safety systems.
- Utilize AI for analyzing production flows, simulating processes, and employing digital twins to enhance efficiency, reduce costs, and improve order fulfillment times.
- Choose appropriate AI tools and technologies, plan and manage AI projects from pilot to full integration, and address change management and employee training.
- Understand the critical aspects of data protection, responsibility, and transparency in AI applications within manufacturing.
Key Takeaways
- Solid understanding of how AI and machine learning are revolutionizing production processes.
- Real-world case studies will demonstrate the tangible benefits of AI in diverse manufacturing sectors.
- Know how AI enables predictive maintenance, reduces downtime, and significantly improves overall production efficiency.
- Competence in how AI can automate quality control, detect subtle defects, and bolster safety protocols in industrial environments.
- Practical knowledge on selecting AI tools, managing implementation projects, and navigating the associated organizational changes.
Training Agenda
Module 1: Introduction to AI in Manufacturing
- Basics of AI and Machine Learning in Production Context
- Difference between supervised and unsupervised algorithms
- AI technologies used in industry (e.g., vision systems, neural networks)
Module 2: Case Studies
- Examples of AI applications in various industries
- Automotive industry: quality prediction and production control
- Food industry: sensory analysis and supply chain control
- Pharmaceutical industry: optimization of packaging processes and quality monitoring
Module 3: Production Data Analysis – Identifying Potential Optimization Areas
- Data collection: IoT sensors, MES systems
- Data visualization methods: dashboards, analytical tools
- Practical exercise: Identifying opportunities for improvement based on sample data analysis
Module 4: Predictive Maintenance and Process Optimization
- Using AI to predict machine failures and prevent downtime
- Predictive models: application of failure forecasting algorithms
- Real-time data collection from machines
- Case Study: reducing downtime in manufacturing plants
- Optimization of production processes
- Analysis of production flows using AI
- Impact of optimization on production costs and order fulfillment time
- Optimization tools: process simulations, Digital Twin
- Real-time monitoring of production parameters
- Implementation of monitoring systems
- Automatic response to deviations and prevention of quality losses
- Workshop: configuration of an exemplary monitoring system
Module 5: Quality Control and Safety Assurance
- Automation of quality control – defect and faulty product detection
- Application of AI in detecting micro-damages
- Examples from various industries: automotive, electronics, pharmaceuticals
- Application of AI in safety systems
- Accident prevention through predictive AI systems
- Examples of AI safety system implementations
- Intelligent robots and automation
- Cobots – human-robot collaboration
- Optimization of assembly processes using AI-assisted robotics
Module 6: AI Implementation in a Manufacturing Company
- Choosing the right AI tools and technologies
- Criteria for selecting AI tools for industry
- Overview of popular AI platforms and tools
- AI project implementation and management strategies
- Key implementation steps: from pilot to full integration
- Change management and employee training
- Case Study: successes and challenges in AI implementation
- Data security and ethical aspects of AI
- Data protection in AI systems
- Ethics in AI applications – responsibility and transparency
Workshop Format
The workshop is conducted online in a highly interactive manner. Participants can join from any location using a camera and microphone, ensuring maximum engagement and learning effectiveness. This format allows for direct interaction with instructors and fellow participants, fostering the exchange of experiences and promoting dynamic knowledge sharing. The training program includes case studies and practical examples of tools, demonstrated through simulations and live demonstrations. In the case of offline classes in a training room, the trainer will divide the participants into groups in which project work is carried out.
Pricing Options
- €3,400