可视化zy6

Multiomics Analysis with created data in R Studio 4.2.1

coba arima

This plotly residual plot, revealing an alarming vertical clustering around zero quantity for China-to-US/Europe shipments, immediately highlights a critical finding in the Define phase of DMAIC: the problem of residuality is far more severe than initially assumed, likely stemming from fundamental data or plotting issues. During the Measure phase, the hover example of "Molds/Tooling" with a residual of 30325.53 vividly quantifies this risk, demonstrating a catastrophic failure of the current predictive model for high-value items. This necessitates a thorough Analyze phase, where root cause analysis, potentially using tools like a fishbone diagram or 5 Whys, must investigate why the data is clustered and why such extreme prediction errors occur. Without addressing these anomalies, the Improve phase cannot develop effective solutions for accurate international sales forecasts, and the Control phase would merely perpetuate an unreliable model, jeopardizing supply chain efficiency and profitability for your critical US and European markets.