Supply Chain Analytics

Predictive Analytics for Supply Chain Forecasting

4.1.

Demand Forecasting Fundamentals

4.1.1.

Forecasting Concepts and Terminology

4.1.2.

Forecasting Process Framework

4.1.3.

Forecast Accuracy Measurement

4.1.3.1.

Mean Absolute Error

4.1.3.2.

Mean Absolute Percentage Error

4.1.3.3.

Root Mean Squared Error

4.1.3.4.

Mean Absolute Scaled Error

4.1.3.5.

Forecast Bias

4.1.4.

Forecasting Horizons

4.1.4.1.

Short-Term Forecasting

4.1.4.2.

Medium-Term Forecasting

4.1.4.3.

Long-Term Forecasting

4.1.5.

Demand Patterns and Characteristics

4.1.5.1.

4.1.5.2.

4.1.5.3.

4.1.5.4.

4.2.

Time Series Forecasting Methods

4.2.1.

Simple Forecasting Methods

4.2.1.1.

Naive Forecasting

4.2.1.2.

Simple Moving Average

4.2.1.3.

Weighted Moving Average

4.2.2.

Exponential Smoothing Methods

4.2.2.1.

Simple Exponential Smoothing

4.2.2.2.

Double Exponential Smoothing

4.2.2.3.

Triple Exponential Smoothing

4.2.2.4.

Damped Trend Methods

4.2.3.

Advanced Time Series Models

4.2.3.1.

Autoregressive Models

4.2.3.2.

Moving Average Models

4.2.3.3.

ARIMA Models

4.2.3.4.

Seasonal ARIMA Models

4.2.3.5.

Vector Autoregression Models

4.2.4.

State Space Models

4.2.4.1.

Kalman Filter Applications

4.2.4.2.

Dynamic Linear Models

4.3.

Causal Forecasting Models

4.3.1.

Simple Linear Regression

4.3.2.

Multiple Linear Regression

4.3.3.

Polynomial Regression

4.3.4.

Logistic Regression

4.3.5.

Time Series Regression

4.3.6.

Econometric Models

4.4.

Machine Learning for Demand Forecasting

4.4.1.

Data Preparation for Machine Learning

4.4.1.1.

Feature Engineering

4.4.1.2.

Data Transformation

4.4.1.3.

Feature Selection

4.4.2.

Supervised Learning Algorithms

4.4.2.1.

Decision Trees

4.4.2.2.

Random Forest

4.4.2.3.

Gradient Boosting Methods

4.4.2.4.

Support Vector Machines

4.4.3.

Neural Network Approaches

4.4.3.1.

Multilayer Perceptrons

4.4.3.2.

Recurrent Neural Networks

4.4.3.3.

Long Short-Term Memory Networks

4.4.3.4.

Convolutional Neural Networks

4.4.4.

4.4.4.1.

4.4.4.2.

4.4.4.3.

4.4.5.

Model Selection and Validation

4.4.5.1.

Cross-Validation Techniques

4.4.5.2.

Hyperparameter Tuning

4.4.5.3.

Model Comparison Methods

4.5.

Specialized Forecasting Applications

4.5.1.

New Product Forecasting

4.5.1.1.

Analogical Forecasting

4.5.1.2.

Market Research Integration

4.5.1.3.

Bass Diffusion Model

4.5.2.

Intermittent Demand Forecasting

4.5.2.1.

Croston's Method

4.5.2.2.

Syntetos-Boylan Approximation

4.5.2.3.

Bootstrapping Methods

4.5.3.

Hierarchical Forecasting

4.5.3.1.

Top-Down Approaches

4.5.3.2.

Bottom-Up Approaches

4.5.3.3.

Middle-Out Approaches

4.5.3.4.

Optimal Combination Methods

4.6.

Supply Forecasting and Lead Time Prediction

4.6.1.

Supplier Performance Forecasting

4.6.2.

Lead Time Variability Modeling

4.6.3.

Production Capacity Forecasting

4.6.4.

Transportation Time Prediction

4.7.

Risk and Disruption Forecasting

4.7.1.

Risk Factor Identification

4.7.2.

Probability Modeling

4.7.3.

Impact Assessment

4.7.4.

Scenario-Based Forecasting

4.8.

Forecast Integration and Consensus Building

4.8.1.

Statistical and Judgmental Forecast Combination

4.8.2.

Sales and Operations Planning Integration

4.8.3.

Collaborative Forecasting Processes

4.8.4.

Forecast Value Added Analysis

3. Diagnostic Analytics for Root Cause Analysis

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5. Prescriptive Analytics for Supply Chain Optimization