Department: Quality - Production - Engineering. Profile sought: Industrial/Manufacturing/Data student with focus on AI & process optimization.
Objective: identify and predict scrap rate variations across workstations/teams and propose operational settings to reduce scrap.
Tasks & Deliverables
Create a consolidated database including production data, operators, machine parameters, timestamps, and defect records; ensure data cleaning, labeling and time alignment.
Develop AI/ML models (classification/regression/time-series) to predict scrap risk based on production conditions and operator/machine contexts; produce model evaluation and validation reports.
Propose optimal machine settings or operating conditions derived from model insights; deliver concrete recommendations and implementation guidelines.
Required Skills & Tools
Manufacturing and quality control knowledge to interpret process parameters and defect modes.
Data analysis and AI/ML experience (Python or R), including preprocessing, feature engineering, model training and evaluation.
Familiarity with databases (SQL), data visualization/dashboard tools and basic experimental design for validating recommended settings.
Expected Outcomes & Impact
Quantifiable reduction in scrap rate through targeted adjustments and data-driven operating recommendations.
Improved understanding of hidden root causes of defects and operator/machine interactions contributing to scrap.
Deliverables: cleaned database, trained models, evaluation results, recommended parameter ranges, and a short implementation plan for the production team.