PROGRAM OVERVIEW
The 4th Industrial Revolution is the 21st century convergence of digital, physical and bio technologies driving an unrelenting acceleration of human progress. Advances in computing power, artificial intelligence, IoT and machine learning are enabling companies to speed the pace of growth and create amazing experiences in retail, healthcare, smart cities and other vertical industries. The technology of the Fourth Industrial
Revolution is inseparably tied to the vast amounts of data needed to train artificial intelligence and other key forms of modern technology. The need for data has lead to exponential growth in gathering it and advanced analytics has gained massive momentum in the industrial sector. Its evolution and conquest of the markets is unstoppable, along with its presence and importance as an essential tool. The main objectives of this
program are presenting the scientific concepts and providing industrial case studies for different applications of advanced analytics, which can be grouped into three main areas:
- Descriptive Analytics: Its function is to describe, diagnose, and discover what trends and patterns occur in a given process, thanks to the real-time study of historical data.
- Predictive Analytics: Based on more advanced mathematical methods that include statistical analyses, data mining, predictive models,
and machine learning, among others. Its function consists of predicting events that can occur in the future, thanks to developing a predictive model. - Prescriptive analytics: Its function consists of defining the actions to take to obtain the best results in a process. It relies on predictive models, scenario simulations, localized rules, and technical optimization to transform data and recommends taking to obtain the desired result. This level of analytics is completer and more robust. It uses complex event processing, neural networks, heuristic learning, and “machine learning,” among others.
- Optimization: Optimization is one of the most important categories of advanced analytics.
KEY BENEFITS
The Program is aimed at providing:
- Identify where and how to apply advanced data analytics to improve energy efficiency, productivity, and reduce operations’
maintenance costs; - Industry executives with an understanding of the business value and applicability of different analytic approaches;
- Data analytics leads with a business framework in which to assess the value, cost, and risk of potential analytic solutions as well
WHO SHOULD ATTEND THIS PROGRAM
- Change Management
- Technology Transformation
- Business Management
- Organization Development
- Organization Transformation
- Marketing Management
- Project Management
- Business Strategy
- Business Transformation
- Brand
DOWNLOAD COURSE CATALOGUE
Starting Course
Topic 1:What is industry 4.0 o
- Definition and development
- Opportunities and Challenges
- Applications
- Design principles and goals
- Industial Revolution Governmental Program
Topic 2: What is Artificial Intelligence
- Categorization
- Applications
- Approaches
- Impact of AI on industry 4.0
o Predictive Quality and Yield
o Predictive Maintenance
o Human-robot collaboration
o Generative design
oMarket adaption/supply-chain
Topic 3: Industry 4.0, Key Areas of Focus
- Automation and Robotics
- synchronization of supply chains in real-time
- Internet of things (IoT) platforms
- Augmented reality/ wearables
- Smart sensors
- advanced analytics
- advantage of advanced analytics
Topic 1: What is advanced analytics?
- Data mining
- Machine learning
- Deep learning
- cloud computing
- Data-driven models
- Benefits
Topic 2: advanced analytics solutions with a high impact on each of the industrial value chain stages
- Design
- Process/production engineering
- Production
- Quality
- Maintenance
Topic 3: Maturity levels of analytical solutions
- Basic
- Medium
- Advanced
Topic 3: algorithms and analytics as the fundamental pillar of digital transformation companies
- companies that are using big data
- algorithms
Topic 3: Implement Advanced Data Analytics
- Where is Your Organization with Analytics Maturity?
- Steps to Becoming an AI-driven Organization
- Upscale your existing team
o Apply automated data science machine learning o Common Team roles
o Executive Sponsor
o Model Risk Analyst
o Data Sciencist
o Besiness Analytics Professional
o Data engineer
o Software Developer
Topic 4: approaches and methods to improve data-driven decision making
- Make data more applicable
- Make data more accessible
- Make data mo
Topic 1: Descriptive Analytics
o Real-time visualization of data.
o Advanced visualization of data (e.g., creation of benchmark tables offering
o flexibility in terms of variables, generation of ad hoc reports, etc.)
o Descriptive statistics of processes and detection through PCA (e.g., detection of production anomalies)
Topic 2: Predictive Analytics
o Prediction of anomalies and alerts.
o Demand estimation.
o Forecasting process outcomes based on the values of variables (e.g., model for detecting product quality issues).
Topic 3: Prescriptive analytics
o Generation of scenarios to recommend actions.
o Identification of the best results in an autonomous way.
o Proactive updating of recommendations for action due to changing events.
Topic 4: Optimization
o Process and scenario simulations.
o Analysis of the evolution and search for maximum and minimum key values.
- Topic 1: Application of AI to increase the energy efficiency in surface mining (Case Study 1)
- Topic 2: Application of AI to decrease the mine mobile equipment maintenance cost (Case Study 2)
- Topic 3: Application of AI to estimate the shipping cost (Case Study 3)
- Topic 3: Application of AI to predict and minimize the locomotive fuel consumption (Case Study 4)
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