Big Data in Demand Planning equips participants with essential skills to leverage data analytics in optimizing inventory management and forecasting demand. This course emphasizes practical application through project-based learning, enabling participants to engage directly with real-world data sets and scenarios. By focusing on the intersection of big data and demand planning, attendees will learn to enhance decision-making processes and improve operational efficiency within retail and logistics sectors.
The curriculum is structured to provide a comprehensive understanding of demand forecasting methodologies, data analysis techniques, and the implementation of predictive models. Participants will explore advanced analytical tools and software, culminating in a final project that requires the development of a demand forecasting model using big data principles. This hands-on approach not only solidifies theoretical knowledge but also prepares participants to publish their findings in Cademix Magazine, fostering a culture of knowledge sharing and professional development.
Introduction to Big Data Concepts in Demand Planning
Data Sources and Data Collection Techniques
Statistical Methods for Demand Forecasting
Time Series Analysis and Forecasting Models
Machine Learning Applications in Demand Planning
Inventory Optimization Techniques
Case Studies on Successful Demand Planning Strategies
Tools and Software for Big Data Analysis
Developing Predictive Models for Demand Forecasting
Final Project: Creating a Big Data Demand Forecasting Model

Advanced Inventory Control Systems delves into sophisticated methodologies and technologies that optimize inventory management practices. This course is structured to equip participants with the analytical tools and practical skills necessary to enhance demand forecasting and planning. Through project-based learning, individuals will engage in real-world scenarios that simulate the complexities of inventory control, allowing for immediate application of concepts learned.
Participants will explore a variety of advanced topics, including data analytics for inventory optimization, the integration of automated systems, and the development of effective inventory strategies. By the end of the course, learners will not only have a comprehensive understanding of inventory control systems but also the capability to implement innovative solutions in their professional environments. Opportunities for publishing results in Cademix Magazine will further enhance their visibility in the field.
Key topics included in the course:
Fundamentals of Inventory Control Systems
Techniques for Accurate Demand Forecasting
Inventory Optimization Strategies
Role of Technology in Inventory Management
Data Analytics for Inventory Decisions
Automated Inventory Tracking Systems
Supply Chain Coordination and Inventory Flow
Risk Management in Inventory Control
Performance Metrics for Inventory Systems
Final Project: Developing a Comprehensive Inventory Control Plan

Time Series Analysis for Retail Demand equips participants with advanced methodologies to analyze and predict consumer demand patterns using historical data. The course emphasizes practical application through project-based learning, allowing participants to engage with real-world datasets and develop actionable insights that can drive strategic decision-making in retail environments. Participants will explore various forecasting techniques, statistical models, and software tools that are essential for effective demand planning.
The curriculum is designed to foster interactive learning, encouraging collaboration among peers and the sharing of findings through publication in Cademix Magazine. By the end of the program, participants will have a comprehensive understanding of time series data manipulation, model selection, and performance evaluation, culminating in a final project that demonstrates their ability to apply these concepts to real retail scenarios. This hands-on approach not only enhances theoretical knowledge but also builds practical skills that are highly sought after in the job market.
Introduction to Time Series Analysis and its Applications in Retail
Data Collection and Preprocessing Techniques for Time Series
Exploratory Data Analysis (EDA) for Time Series Data
Seasonal Decomposition of Time Series
Autoregressive Integrated Moving Average (ARIMA) Models
Exponential Smoothing Methods
Advanced Forecasting Techniques: Prophet and Machine Learning Approaches
Model Evaluation Metrics and Forecast Accuracy
Implementation of Time Series Models using Python/R
Final Project: Developing a Comprehensive Demand Forecasting Model for a Retail Product