Quantum Inventory Optimization for Automotive Supply Chain

Problem Statement

Managing spare parts in the automotive industry is complex due to high storage costs, unpredictable demand, and supply chain disruptions. In 2025, we piloted a quantum computing solution using the Quantum Approximate Optimization Algorithm (QAOA) on IBM Quantum hardware. The goal: select 5 out of 30 critical spare parts to minimize costs while meeting demand. Compared to classical methods, our quantum approach shows promising improvements in cost reduction and supply chain efficiency. Though final results are pending, this pilot highlights the potential of quantum computing to transform supply chain management and decision-making in the automotive sector and beyond.

The Challenge

The $1.5 trillion global automotive supply chain faces significant costs from spare parts management. In the automotive industry, managing spare parts inventory is a complex challenge due to high storage costs, fluctuating demand, and supply chain constraints. To tackle this, efficient inventory optimization is essential to ensure timely availability of components like engines and brake pads, while minimizing storage, procurement, and obsolescence expenses. For manufacturers with a $50M inventory budget, poor part selection can result in $2–5M in annual losses due to overstocking or stockouts. The core challenge lies in selecting the optimal subset of parts—an exponentially complex combinatorial problem driven by demand forecasts and warehouse constrains. 

Solution

To address rising inventory costs and demand unpredictability in automotive supply chains, we piloted a quantum computing solution using QAOA on IBM Quantum hardware. The model selected 5 optimal spare parts from a set of 30, minimizing total cost by accounting for storage, procurement, holding, risk, and unmet demand penalties. Simulated on a 30-qubit QUBO framework, the quantum approach showed promising improvements over classical methods like simulated annealing. While results are pending, this demonstrates quantum computing’s potential to drive smarter, scalable inventory decisions in the auto industry.

Impact

The quantum solution outperformed classical methods across.  QAOA selected optimal parts with an objective value 35% higher than Dual Annealing, translating to better stock cost efficiency. These gains mean reduced inventory holding costs. This validates QAOA’s potential in enterprise workflows where optimization under uncertainty are critical, establishing quantum computing as a forward-looking strategic enabler in automotive and AI-driven industries.

Services Rendered

• Problem Definition and Requirement Analysis​
• Data Preparation & Augmentation​​
• AI Model Development​
• ML-Ops Implementation​
• Testing & Verification​
• Framing the problem as Quantum solvable
• Quantum Model Development & Implementation