Why traditional BI cannot answer "why" and "what next"?
Global trade volatility, overcapacity, and rigid cost increases make the operating environment for logistics and manufacturing more complex than ever. Traditional BI reports remain at the descriptive statistics level, unable to support deep attribution or forward-looking decisions.
Factor Substitution Model: Quantifiable, Drillable Attribution
MTC decomposes target metrics into multiplicative factor forms, using sequential substitution to automatically calculate each factor's contribution to total variance.
The model is fully configurable: substitution order, core algorithm, and analysis scenarios can all be adjusted without code changes.
Predictive Model: From Reactive to Dynamic Projection
MTC's predictive model uses rolling allocation forecasting with multi-source fusion, cross-day distribution, rolling correction mechanisms, and curve fitting assistance.
Prediction granularity drills down to vessel + day dimensions. Authorized users can manually adjust forecast baselines for human-machine collaboration.
Forward Exploration: Lightweight AI Assistance
Building on the solid engineering foundation, MTC explores AI-assisted capabilities such as auto-flagging anomalous dimensions, natural language insight summaries, and multi-scenario comparison simulations.