Bachelor in Business Intelligence and Data Analytics

Become an expert in data analysis and business decision making in a technological ecosystem and with great networking opportunities

Data Applications in Manufacturing

Description
The subject of Data Applications in Manufacturing aims to provide students with the necessary knowledge and skills to manage and analyze volumes of data generated in industrial environments. In the current digital era, technology is transforming all aspects of the industry. The ability to collect, store, and analyze large volumes of data is essential to maintain 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 course seeks to train students in the use of advanced techniques and tools for data collection, storage, processing, and visualization, with the aim of optimizing industrial processes and improving decision-making. This subject is fundamental for those students who seek to develop a career in the field of Industry 4.0, where efficient data management is key to innovation and competitiveness.
Type Subject
Optativa
Semester
First
Credits
5.00
Previous Knowledge
Objectives

- Understand the basic and advanced concepts of data management in industrial environments.
- Develop practical and strategic solutions to optimize specific industrial processes, including processes from various departments: engineering, quality, maintenance...
- Develop the ability to make strategic and operational decisions based on real data.
- Apply Lean Manufacturing principles in data management to identify, eliminate waste, and optimize industrial processes.
- Emphasize the growing importance of emerging technologies such as artificial intelligence/IoT in industrial data management.

Contents

1. Fundamentals of Data in Manufacturing
2. Introduction to production systems
Production Planning and Control Systems
The production process-> Case 1: Line balancing: define better sequence of operations (efficiency)
3.Introduction to production systems: KPIs
KPIs -> Case 2: Analyze data and define the correct KPIs. Understand the meaning and define actions to improve it
4. Lean Manufacturing
Introduction
Pull systems -> Case 3: Kanban, ConWIP vs POLCA. Compare pull systems and calculate number of kanban cards
Just in time
5s -> Case 4: Toyota as a well-known and successful manufacturer
Kaizen and A3
PDCA and quality tools -> Case 5: Quality issue and create 5 why's analysis
VSM (Value Stream Maping) -> Case 6: Real example VSM
5. IoT solutions for Manufacturing industry -> Case 7: AI applications/ IoT implementation in manufacturing
6. Importance of IIoT Devices
Alongside the cases and presentation slides covered during class sessions, the instructor will intermittently provide students with additional readings. Extra readings or assignments will be distributed during class meetings, and students may also be tasked with locating specific readings in the library databases.

Methodology

The course is designed to foster both individual and group learning through various methods:
1. Active Case Study Discussion:
- The selected cases are closely aligned with the content of the course and demonstrate the application of theoretical concepts.
- Students are expected not only to read and prepare thoroughly but also to actively participate in class discussions.
- Beyond preparation, students are required to contribute with interventions during group sessions.
- It is crucial to emphasize that the learning process of the case method requires active participation and deep discussion.

2.Idea Exchange and Critical Thinking Development:
- The main objective is to collectively present and discuss the conceptual frameworks and fundamental tools of the course.
- The discussion process will be facilitated, highlighting key concepts and lessons.
- However, each student is individually responsible for formulating their own synthesis, based on conceptual readings, class attendance, active participation, and class discussions.
- To effectively follow the course, students must read and prepare the assigned readings before each class.
- The course content is structured sequentially, with each session serving as a foundation for the next. Therefore, thorough preparation and class attendance are crucial for comprehensive understanding.

Evaluation

Your final grade will be determined by three components:
1. Class Participation: 30%.
2. 2 Group Projects: 25% each, totaling 50%.
3. Midterm exam: 20%.
Hand-in dates for individual and group assignments will be discussed, and failure to adhere to these deadlines will result in a percentage deduction. This course mandates the submission of a hard copy of your work during the designated session. The assignments will evaluate course outcomes, including analytical abilities, comprehension of theory, and the application of management functions to practical cases. Additionally, the assessment will consider the use of an appropriate language style (scholarly) and formatting.
Group project:
Students are expected to work on two group projects. For the projects, students will need to gather in groups of 3 people and present a project based on real data from a multinational company that will encompass all the concepts and methodologies explained in class. The objective is to simulate a real project that can serve as a first real-world experience in the industrial field.

Evaluation Criteria
Basic Bibliography

- 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"

Additional Material