The facility handles processing, filling, and sealing of numerous bottles monthly across various products. Operations require precise coordination from material tank arrivals to final shipments. Minor disruptions can cascade into scheduling delays, elevated costs, and delivery setbacks. Such complexities often surpass the limits of traditional optimization methods for swift, large-scale responses.
Collaborating closely, BASF and D-Wave developed a hybrid-quantum tool aimed at cutting product switchover setup times, accelerating tank unloading, and lowering overall product tardiness against due dates. Tested in an actual production environment, the application assigned tasks and tanks while adhering to operational and quality standards. It outperformed the existing classical solution by decreasing lateness by 14 percent, setup durations by 9 percent, and tank unloading periods by up to 18 percent.
“This project showcases how hybrid-quantum computing can help address manufacturing and supply-chain operational bottlenecks and begin delivering measurable value where classical computing falls short,” said Dr. Alan Baratz, CEO of D-Wave. “We are thrilled to work with BASF and see these impressive, industry-defining results.”
“D-Wave’s hybrid quantum technology demonstrated in this proof-of-concept that it has the potential to significantly improve optimization tasks, delivering faster decisions and better outcomes than classical-only solutions,” said Ionel Rusu, quantum computing innovation management, BASF.
“We anticipated such results when we started working with D-Wave, but the project outcome has surpassed our expectations,” said Abhishek Awasthi, quantum computing scientist, BASF.
D-Wave views this collaboration as a key step toward integrating hybrid-quantum solutions into live manufacturing and logistics settings. The outcomes illustrate practical resolution of intricate issues in scheduling, resource distribution, and workflow coordination.
D-Wave leads in quantum computing systems, software, and services development.
This initiative highlights the transformative potential of combining quantum and classical approaches in industrial contexts. By modeling multifaceted constraints—such as machine availability, product sequences, and volume requirements—the application generates near-optimal plans rapidly, enabling agile adjustments during disruptions.
The liquid-filling line's variability, driven by batch sizes and formulation differences, traditionally demands extensive manual recalibration. Quantum-inspired algorithms process vast possibility spaces efficiently, identifying configurations that minimize downtime and maintain throughput.
BASF's operational data validated the model's accuracy, with simulated scenarios mirroring real shifts in tank levels and line speeds. Performance gains translate directly to cost savings through reduced labor for planning and fewer expedited shipments.
Scalability remains a focus, with frameworks designed for cloud deployment across multiple sites. Integration with existing enterprise systems ensures seamless data flow, from sensor inputs to execution outputs.
Future applications may extend to inventory routing, maintenance forecasting, and energy management within chemical production chains. Early adoption in controlled environments builds confidence for broader rollout.
The partnership underscores collaborative innovation, merging domain expertise with computational advancements to enhance industrial resilience and efficiency. As hybrid-quantum tools mature, they promise sustained competitive advantages in dynamic manufacturing landscapes.
