In the world of supply chain management, businesses strive to create efficient and effective networks that can meet customer demands while minimizing costs. At its core, every supply chain planning and scheduling problem is an optimization challenge. It involves finding the most effective way to synchronize supply and demand throughout the supply chain network, ultimately enhancing customer satisfaction and driving bottom-line results.

To achieve this, organizations rely on advanced tools and methodologies for supply chain network design and optimization. Two popular approaches in this field are mathematical optimization-based tools and heuristics-based tools. The aim of optimization and heuristic solutions is the same – to provide the best possible solution to a given supply chain problem – but their outcomes are often dramatically different.

Heuristics rely on practical processes, often known as “rules of thumb” or “best practices,” to generate feasible solutions quickly and achieve immediate goals. However, it’s important to note that heuristics may not always yield an optimal solution.

On the other hand, optimization models employ intelligent and automated processes to generate optimal solutions. These models take into account decision variables such as production, inventory, and shipment quantities, as well as constraints and key performance indicators (KPIs). Supply chain optimization solutions aim to provide the most effective path to achieve optimal performance across procurement, production, inventory, and distribution operations.