The course has as its main objective to provide students with the knowledge and skills necessary to manage and analyze volumes of data generated in industrial environments. In today’s digital era, technology is transforming all aspects of industry. The ability to collect, store, and analyze large volumes of data is essential to maintaining competitiveness and efficiency in the industrial sector. The adoption of advanced technologies such as the Internet of Things (IoT), Big Data, Artificial Intelligence (AI), and Lean Manufacturing is enabling industrial companies to improve their production processes, reduce costs, and increase the quality of their products. The aim is to train students in the use of advanced techniques and tools for data collection, storage, processing, and visualization, in order to optimize industrial processes and improve decision-making. This course is essential for students seeking to develop a career in the field of Industry 4.0, where efficient data management is key to innovation and competitiveness.
Titular Professors
Professors
It is recommended that students have basic knowledge of spreadsheet use, especially Excel, including data manipulation, the use of simple formulas, and the creation of tables and charts. It is also advisable to have some initial familiarity with data visualization tools and indicator interpretation, as these concepts will facilitate the understanding of analysis techniques applied in manufacturing environments. However, the course provides the necessary foundations to develop the required competencies throughout the program.
- Understand the basic and advanced concepts of data management in industrial environments.
- Develop practical and strategic solutions to optimize specific industrial processes, including processes across various departments such as engineering, quality, and maintenance.
- Develop the ability to make strategic and operational decisions based on real data, using analysis and visualization tools to extract relevant information.
- Apply Lean Manufacturing principles in data management to identify and eliminate waste and optimize industrial processes, based on the efficient use of available information.
- Address the growing importance of emerging technologies such as artificial intelligence and IoT in industrial data management, including the use of SQL as a tool for data extraction and preparation.
Introduction to Production Systems
- Basic concepts of manufacturing and industrial production systems
- Differences between Push and Pull systems and their impact on flow management
- Introduction to Kaizen and Kanban methodologies for continuous improvement
KPIs and Management Systems
- Definition and use of key performance indicators (KPIs) in industrial environments
- Analysis of Overall Equipment Effectiveness (OEE) and Total Effective Equipment Performance (TEEP)
- Data interpretation for operational decision-making
Introduction to Lean Manufacturing
- Principles of Lean Manufacturing and identification of the main types of waste
- Introduction to Six Sigma and the DMAIC improvement cycle
- Application of continuous improvement in industrial processes
Lean Manufacturing, JIT, 5S and Capacity Planning
- Implementation of the 5S methodology in production environments
- Concepts of Just-in-Time (JIT) and flow optimization
- Capacity analysis and planning using RCCP
SQL Fundamentals
- Basic query structure and principles of data retrieval
- Aggregation, filtering, and grouping of data for analysis
- Joining tables using join operations
- Use of window functions for advanced data analysis
The course combines theoretical sessions with practical activities aimed at applying the content in real manufacturing industry contexts. Classes are based on the explanation of key concepts, accompanied by the resolution of practical case studies and guided exercises that support progressive learning. SQL will be used regularly for data extraction, querying, and preparation, complementing the analysis with spreadsheets and visualization tools. Work is encouraged using examples based on real business situations, as well as active student participation in individual and group activities to consolidate the knowledge and skills of the course.
- Class participation: 25%
- Business cases: 25%
- Theoretical quizzes: 10%
- Midterm exam: 20%
- Final exam: 20%
The assessment of the course is based on the achievement of the defined learning outcomes, valuing both the understanding of theoretical concepts and the ability to apply them to real situations in the manufacturing field. The quality of the analyses carried out, data interpretation, problem-solving, the ability to make evidence-based decisions, and the effective communication of results through visualization tools will all be taken into account. Students must demonstrate appropriate use of the techniques and tools covered throughout the course, as well as active participation in the individual and collaborative activities proposed.
- Eliyahu M.Goldratt, "The Goal: A Process of Ongoing Improvement" - Eric Ries, "The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses" - Alasdair Gilchrist, "Industry 4.0: The Industrial Internet of Things" - Michael R.Bartolacci, "The Smart Factory: Responsive, Adaptive, Connected Manufacturing" - Jefrey K.Liker, "The Toyota Way"
- Eliyahu M.Goldratt, "The Goal: A Process of Ongoing Improvement" - Eric Ries, "The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses" - Alasdair Gilchrist, "Industry 4.0: The Industrial Internet of Things" - Michael R.Bartolacci, "The Smart Factory: Responsive, Adaptive, Connected Manufacturing" - Jefrey K.Liker, "The Toyota Way"