Case Study
Automating Quality Control Workflows with AI Agents
Validated ML/AI use-cases, automated routing, and time savings
Manufacturer desired to reducing QC inconsistencies and workload
- Manufacturer was going through a Quality Control, Assurance, and Remediation modernization effort across core tools used by QC Engineers within the Early Detection / Early Resolution workflow, specifically focused on Market Impact. The client wanted to better understand use-cases and readiness for applying ML, GenAI, and other data-centric tactics to various processes within the system to standardize, expedite, and prioritize engineer workflow while reducing administrative load.
- Excessively long engineer training cycles still resulted tribal-knowledge biases; Experience- and background-based inconsistencies in classification, prioritization, and department routing of reports.
- Highly manual assignment of reports into quality control cases led to redundant case creation and reduction of scope clarity on many quality issues while not providing an alerting/notification system which identified potential issues or changes and aided in engineer solutioning.
Identified pain points, applied AI/ML for optimized QC workflows
- Worked with client leadership to understand pain points and identify high-value scope of work, building out documentation to show current and future states while delineating scope of project through a solution architecture roadmap.
- Generative AI: Leveraged OpenAI's Assistant platform to create structured JSON data from unstructured open text fields, identifying crucial keywords and data points like part numbers, forcing a coding and routing standard into the result set based on clients current coding and routing standards documentation.
- Classic ML: Leveraged clustering and sorting algorithm across derived data set to assess ability to group incoming field reports into cases representing the same issue.
- Results were aggregated and reviewed with client to identify future application, understand confidence levels of models, and ideate on a potential roadmap for implementing a series of ML solutions.
Validated ML/AI use-cases, automated routing, and time savings
- Proved out use-cases and data readiness for ML/AI based solutions
- Proved out standardizing codes for routing documents to departments, engineers, and research cases
- Determined time savings across several manual processes
- Delivered future-state architecture and roadmap
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