Aircraft component manufacturer uses data science to predict defects and reduce cost

Solution: GE Digital’s Data Science team was engaged to identify cost reduction opportunities for the customer, as well as determine root causes of variability in the manufacturing process.  In doing so, the Data Science team leveraged data that had otherwise been confined to silos, including defect data, timestamp data and material cost data, to compute an ideal route for each part, generate heat maps to break down costs and determine root causes of defects.  Results: Use of heat maps allowed the Data Science team to identify specific paint points in the manufacturing life cycle and develop models for leading indicators of possible delinquencies, cost overruns or defects months ahead of predicted occurrence.  Drawing upon Data Science discoveries, the customer was strategically positioned to tactically reduce costs and accurately predict defects,…


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