Introduction of PROPEL for Advanced Supply Chain Optimization

Researchers at Georgia Tech have developed a new artificial intelligence tool that dramatically improves how companies plan their supply chains, cutting down the time and cost it takes to generate complex production and inventory schedules.

The tool, known as PROPEL, combines machine learning with optimization techniques to help manufacturers make better decisions in less time. It was created by researchers at the NSF AI Institute for Advances in Optimization, or AI4OPT, based at Georgia Tech under Tech AI (the AI Hub at Georgia Tech).

Real-World Validation Using Kinaxis Supply Chain Data

The technology is already being tested on real-world supply chain data provided by Kinaxis; a Canada-based company that provides supply-chain orchestration and planning software to some of the largest global manufacturers in industries ranging from automotive to consumer goods.

Vahid Eghbal Akhlaghi, a senior research scientist at Kinaxis and former postdoctoral fellow at AI4OPT and Georgia Tech's H. Milton Stewart School of Industrial and Systems Engineering, said, “Kinaxis has been instrumental in shaping PROPEL’s capabilities. By validating the approach with real operational data, we ensured it addresses true bottlenecks in supply chain planning.”

Research Advancements in AI-Driven Planning Algorithms

"PROPEL represents a leap forward in how we tackle massive, complex planning problems with millions of integer decision variables," said Pascal Van Hentenryck, lead researcher, the director of Tech AI and the NSF AI4OPT Institute, and the A. Russell Chandler III Chair and Professor at Georgia Tech with appointments in the colleges of engineering and computing. "By combining supervised and reinforcement learning, we can make near-optimal industrial-scale decisions, an order of magnitude faster."

Traditional supply chain planning problems are typically solved using mathematical models that require immense computing power—often too much to meet real-time business needs. PROPEL, short for Predict, Reduce, OPtimize, and EnLarge, reduces this burden by teaching the AI model to first eliminate irrelevant decisions and then fine-tune the solution to meet quality standards.

Reza Zandehshahvar, Research Engineer II at Tech AI, AI4OPT and H. Milton Stewart School of Industrial and Systems Engineering (ISyE) at Georgia Tech said: "This work combines machine learning and optimization to solve real-world and industrial problems at scale."

Performance Benchmarks in Speed and Solution Accuracy

In trials using Kinaxis’ historical industrial data, PROPEL achieved an 88 percent reduction in the time needed to find a high-quality plan and improved solution accuracy by more than 60 percent compared to conventional methods.

While many AI methods in supply chain rely on simulated data or simplified models, PROPEL’s performance has been validated using real-world scenarios, ensuring its reliability in high-stakes operational settings.

Applicability Across Complex Industrial Sectors

The Georgia Tech team says PROPEL could benefit industries that manage large, multi-tiered production networks, including pharmaceuticals, electronics, and heavy manufacturing. The researchers are now exploring partnerships with additional companies to deploy PROPEL in live environments.

Read the full abstract here: https://arxiv.org/abs/2504.07383