Introduction

Accurate inventory forecasting is the backbone of efficient supply chain and inventory management. As demand fluctuations become more unpredictable due to factors like global supply chain disruptions, seasonality, and shifting consumer preferences, businesses must leverage advanced forecasting models to stay ahead.

This 5-day advanced course will explore a variety of quantitative and qualitative forecasting models, emphasizing the use of machine learning (ML), time series analysis, and AI-driven forecasting tools. Participants will learn to implement cutting-edge forecasting methodologies that will optimize inventory levels, reduce stockouts and overstocking, and improve overall supply chain efficiency.

Course Objectives

By the end of this course, participants will be able to:
✅ Implement advanced forecasting models including time series, regression, and machine learning algorithms.
✅ Analyze historical data to identify patterns, trends, and seasonal variations for more accurate forecasts.
✅ Apply probabilistic forecasting methods for uncertain demand scenarios and fluctuating supply conditions.
✅ Leverage AI and machine learning tools to predict inventory needs and automate decision-making processes.
✅ Understand the impact of forecast accuracy on inventory optimization and financial performance.
✅ Optimize safety stock levels, reorder points, and lead time calculations for better inventory control.

Who Should Attend?

This course is ideal for:

• Supply Chain Managers
• Inventory Control Analysts
• Demand Planners
• Logistics and Operations Managers
• Data Analysts and Data Scientists
• E-commerce and Retail Operations Professionals
• Financial and Operations Planners

Day 1: Introduction to Advanced Inventory Forecasting

• Importance of Accurate Forecasting in Inventory Management
  • The impact of inventory forecasting on supply chain efficiency, cost reduction, and customer satisfaction
  • Cost of stockouts, overstocks, and excess inventory
• Types of Forecasting Models
  • Overview of traditional models: Moving averages, exponential smoothing, and ARIMA
  • Introduction to advanced techniques: Machine learning (ML), neural networks, and Bayesian forecasting
• Understanding Data in Forecasting
  • Collecting, cleaning, and preparing historical data for accurate forecasting
  • The role of external factors (market trends, weather, etc.) in adjusting demand forecasts
• Quantitative vs. Qualitative Forecasting
  • How to combine data-driven and expert-driven methods to improve accuracy

Day 2: Time Series Forecasting Techniques

• Time Series Decomposition
  • Breaking down demand data into trend, seasonality, and residuals
  • Identifying and removing noise from demand patterns
• Autoregressive Integrated Moving Average (ARIMA)
  • Understanding the ARIMA model and its components: AR (AutoRegressive), I (Integrated), MA (Moving Average)
  • Model building, parameter selection, and model validation for accurate forecasting
  • ARIMA vs. Exponential Smoothing Models: When and why to use them
• Seasonal ARIMA (SARIMA)
  • Incorporating seasonality in time series forecasting
  • Practical examples and hands-on case studies for SARIMA model tuning
• Forecasting with Trend Models
  • Linear regression models for forecasting trends and seasonal demand
  • Polynomial regression for more complex patterns

Day 3: Machine Learning and AI-Based Forecasting Models

• Introduction to Machine Learning in Forecasting
  • Key machine learning algorithms used for demand prediction: Random Forest, Support Vector Machines (SVM), and Gradient Boosting
  • Supervised vs. Unsupervised learning in the context of inventory forecasting
• Neural Networks and Deep Learning
  • Using deep learning techniques such as artificial neural networks (ANNs) for highly accurate forecasts
  • Training and evaluating deep learning models for demand prediction
• AI-Powered Forecasting
  • Leveraging AI tools for demand sensing and real-time inventory prediction
  • Case Study: Walmart’s AI-based demand forecasting
• Hybrid Models
  • Combining traditional forecasting methods with AI models to improve overall forecast accuracy
  • Ensemble models for enhanced prediction performance

Day 4: Probabilistic and Scenario-Based Forecasting

• Probabilistic Forecasting
  • The role of probability distributions in demand forecasting
  • Forecasting using Monte Carlo simulations and Bayesian models
  • Using stochastic models to account for uncertainty and variability in demand
• Scenario-Based Forecasting for Uncertain Demand
  • Applying what-if scenarios to account for demand variability and supply disruptions
  • Modeling and simulating different market conditions, including market shocks and economic downturns
• Demand Sensing vs. Demand Forecasting
  • Demand sensing: Adjusting forecasts based on real-time data (e.g., sales data, market activity)
  • How to blend real-time sensing and traditional forecasting for more accurate predictions
• Forecasting Accuracy Metrics
  • Understanding the importance of Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Bias
  • Measuring forecasting errors and applying continuous improvement techniques

Day 5: Forecasting Optimization and Implementation

• Safety Stock Optimization
  • How to calculate and adjust safety stock levels based on forecast accuracy and lead time variability
  • Integrating forecasting with inventory policies to minimize stockouts while avoiding excess inventory
• Lead Time and Replenishment Planning
  • Forecasting the lead time and aligning it with replenishment cycles
  • Optimizing order quantities, reorder points, and supply chain capacity
• Forecasting Automation and Integration
  • Integrating forecasting models with Warehouse Management Systems (WMS), Enterprise Resource Planning (ERP), and Supply Chain Management (SCM) software
  • Automating forecasting processes and improving decision-making capabilities
• Case Studies and Best Practices
  • Reviewing successful implementations from companies like Zara, Unilever, and Nestlé
  • Practical examples of forecast optimization across different industries
• Capstone Project & Certification
  • Participants will create and present a custom inventory forecasting model for their organization or industry
  • Final assessment and feedback session
  • Certification: Certified Advanced Inventory Forecasting Specialist (CAIFS™)

Certification

Upon successful completion, participants will receive the Certified Advanced Inventory Forecasting Specialist (CAIFS™) certification, recognizing their expertise in advanced inventory forecasting models, demand prediction, and AI-powered forecasting tools. This certification validates their ability to use sophisticated forecasting techniques to drive inventory optimization and supply chain efficiency.

Key Takeaways

✔ Master advanced forecasting techniques such as ARIMA, machine learning, and deep learning for inventory management.
✔ Learn how to improve forecast accuracy using probabilistic methods and scenario planning.
✔ Optimize safety stock, reorder points, and lead times based on forecast accuracy and demand variability.
✔ Gain hands-on experience in integrating forecasting tools with ERP and WMS systems for real-time demand forecasting.
✔ Earn a prestigious certification to advance your career in demand planning and inventory management.