Data Analysis & Processing

This course is designed to provide a comprehensive foundation in data analysis and processing techniques specifically tailored for Production Control and Operations (P&OC). Participants will learn how to effectively collect, clean, transform, and analyze large volumes of structured and unstructured data generated in various stages of the production process. The course covers statistical methods, data visualization, and machine learning techniques to extract valuable insights and support data-driven decision-making in production planning, scheduling, inventory management, and quality control.

• Understand the importance and challenges of data analysis and processing in Production Control and Operations

• Apply data collection, cleaning, and transformation techniques to ensure data quality and consistency

• Perform exploratory data analysis (EDA) and use statistical methods to gain insights from production data

• Create effective data visualizations to communicate findings and support decision-making

• Implement machine learning algorithms for predictive modeling and anomaly detection in P&OC applications

1. Introduction to Data Analysis and Processing in P&OC

• Overview of data sources and types in Production Control and Operations (e.g., sensor data, machine logs, quality reports)

• Data collection and integration techniques for structured and unstructured data

• Data quality assessment and data cleaning methods (e.g., handling missing values, outliers, and inconsistencies)

• Hands-on exercises: Collecting and cleaning a sample production dataset using Python or R

2. Exploratory Data Analysis (EDA) and Statistical Methods

• Descriptive statistics and summary measures for production data

• Univariate, bivariate, and multivariate analysis techniques

• Hypothesis testing and statistical inference for data-driven decision-making in P&OC

• Time series analysis and forecasting methods for production planning and inventory management

• Hands-on exercises: Conducting EDA and applying statistical methods on production datasets

3. Data Visualization and Communication

• Principles of effective data visualization and visual perception

• Types of visualizations for different data types and purposes (e.g., line charts, heatmaps, scatter plots)

• Interactive data visualization using libraries such as Matplotlib, Seaborn, or Plotly

• Dashboard creation and data storytelling for communicating insights to stakeholders

• Hands-on exercises: Creating data visualizations and dashboards for production data

4. Machine Learning for Data Analysis in P&OC

• Overview of machine learning techniques and their applications in Production Control and Operations

• Supervised learning algorithms for regression and classification tasks (e.g., linear regression, decision trees, support vector machines)

• Unsupervised learning algorithms for clustering and anomaly detection (e.g., k-means, DBSCAN, isolation forest)

• Feature engineering and selection techniques for improving model performance

• Hands-on exercises: Implementing machine learning models for predictive modeling and anomaly detection in production planning, scheduling, inventory management, and quality control

• Basic understanding of mathematics and statistics

• Familiarity with programming concepts and a language such as Python or R

• Knowledge of database systems and SQL is beneficial but not required

• Understanding of production control and operations management principles