Supply Chain Analytics

Prescriptive Analytics for Supply Chain Optimization

5.1.

Optimization Fundamentals

5.1.1.

Mathematical Optimization Concepts

5.1.2.

Objective Functions and Constraints

5.1.3.

Feasible Solutions and Optimal Solutions

5.1.4.

Optimization Problem Classification

5.2.

Linear Programming Applications

5.2.1.

Linear Programming Formulation

5.2.2.

Simplex Method

5.2.3.

Sensitivity Analysis

5.2.4.

Duality Theory

5.2.5.

Transportation Problems

5.2.6.

Assignment Problems

5.3.

Integer and Mixed-Integer Programming

5.3.1.

Integer Programming Formulation

5.3.2.

Branch and Bound Methods

5.3.3.

Cutting Plane Methods

5.3.4.

Heuristic Solution Approaches

5.4.

Inventory Optimization Models

5.4.1.

Single-Item Inventory Models

5.4.1.1.

Economic Order Quantity Model

5.4.1.2.

Economic Production Quantity Model

5.4.1.3.

Quantity Discount Models

5.4.2.

Multi-Item Inventory Models

5.4.2.1.

Joint Replenishment Models

5.4.2.2.

Resource Constrained Models

5.4.3.

Stochastic Inventory Models

5.4.3.1.

Newsvendor Model

5.4.3.2.

Base Stock Models

5.4.3.3.

Order-Up-To Models

5.4.4.

Multi-Echelon Inventory Optimization

5.4.4.1.

Serial Systems

5.4.4.2.

Distribution Systems

5.4.4.3.

Assembly Systems

5.4.5.

Inventory Classification and Segmentation

5.4.5.1.

ABC Analysis

5.4.5.2.

XYZ Analysis

5.4.5.3.

Multi-Criteria Classification

5.5.

Network Design Optimization

5.5.1.

Facility Location Models

5.5.1.1.

Uncapacitated Facility Location

5.5.1.2.

Capacitated Facility Location

5.5.1.3.

Multi-Commodity Facility Location

5.5.2.

Network Flow Optimization

5.5.2.1.

Minimum Cost Flow Problems

5.5.2.2.

Maximum Flow Problems

5.5.2.3.

Multi-Commodity Flow Problems

5.5.3.

Supply Chain Network Design

5.5.3.1.

Strategic Network Planning

5.5.3.2.

Tactical Network Planning

5.5.3.3.

Network Reconfiguration

5.5.4.

Capacity Planning Optimization

5.5.4.1.

Capacity Expansion Models

5.5.4.2.

Capacity Allocation Models

5.5.4.3.

Dynamic Capacity Planning

5.6.

Transportation and Routing Optimization

5.6.1.

Vehicle Routing Problems

5.6.1.1.

Capacitated Vehicle Routing Problem

5.6.1.2.

Vehicle Routing with Time Windows

5.6.1.3.

Multi-Depot Vehicle Routing

5.6.1.4.

Dynamic Vehicle Routing

5.6.2.

Traveling Salesman Problems

5.6.2.1.

Symmetric Traveling Salesman Problem

5.6.2.2.

Asymmetric Traveling Salesman Problem

5.6.2.3.

Multiple Traveling Salesman Problem

5.6.3.

Load Planning and Consolidation

5.6.3.1.

Bin Packing Problems

5.6.3.2.

Container Loading Problems

5.6.3.3.

Pallet Loading Optimization

5.6.4.

Mode Selection and Routing

5.6.4.1.

Intermodal Transportation

5.6.4.2.

Multi-Modal Optimization

5.6.4.3.

Service Network Design

5.7.

Production Planning and Scheduling Optimization

5.7.1.

Aggregate Production Planning

5.7.2.

Master Production Scheduling

5.7.3.

Material Requirements Planning Optimization

5.7.4.

Capacity Requirements Planning

5.7.5.

Job Shop Scheduling

5.7.6.

Flow Shop Scheduling

5.7.7.

Flexible Manufacturing Systems Scheduling

5.8.

Procurement and Sourcing Optimization

5.8.1.

Supplier Selection Models

5.8.2.

Sourcing Strategy Optimization

5.8.3.

Contract Optimization

5.8.4.

Auction Mechanisms

5.8.5.

Supply Risk Optimization

5.9.

Revenue Management and Pricing Optimization

5.9.1.

Dynamic Pricing Models

5.9.2.

Yield Management

5.9.3.

Markdown Optimization

5.9.4.

Promotional Planning Optimization

5.10.

Robust and Stochastic Optimization

5.10.1.

Robust Optimization Approaches

5.10.2.

Stochastic Programming Models

5.10.3.

Chance-Constrained Programming

5.10.4.

Scenario-Based Optimization

5.11.

Multi-Objective Optimization

5.11.1.

Pareto Optimality

5.11.2.

Weighted Sum Methods

5.11.3.

Goal Programming

5.11.4.

Evolutionary Multi-Objective Optimization

4. Predictive Analytics for Supply Chain Forecasting

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6. Advanced Analytics and Emerging Technologies