Leveraging Artificial Intelligence in Configure-Price-Quote (CPQ) Systems for Healthcare Manufacturing

Abstract

Healthcare manufacturing operates under stringent regulatory requirements, high product complexity, and increasing demand for customer-specific configurations. Configure-Price-Quote (CPQ) systems translate customer intent into manufacturable and compliant offerings, yet they often struggle at scale when relying solely on static rules and manually maintained knowledge bases. This paper presents an Artificial Intelligence (AI) enabled CPQ approach for healthcare manufacturing that combines knowledge-based configuration with governed, data-driven intelligence. We propose a validation-aware reference architecture and a digital-thread integration pattern that connects CPQ outputs with downstream PLM/ERP/quality systems to preserve traceability. Practical use cases are discussed: configuration recommendation, pricing and contract guidance, conversational access to catalog and release content, and compliance-oriented explainability, along with governance controls required for regulated environments.