The Hotelling T² chart relies on Phase 1 parameters estimated from in-control data. When the calibration sample contains outlying observations or anomalous batches, the classical covariance is inflated, and this masking effect hides later mean shifts. We propose MFA-MCD, a robust T² chart for batch processes. It estimates the covariance of each batch with the minimum covariance determinant (MCD), weights each batch by the inverse of its first eigenvalue, as in multiple factor analysis (MFA), and obtains the reference center with a second MCD on the batch centers. The control limit is a heuristic F-type approximation, not an exact distributional result, based on the size of the MCD subset. With six of thirty Phase 1 batches contaminated and a one-standard-deviation shift in all variables, it detects 88.5% of the shifted batches, against 27.0% for the classical chart and 73.0% for the MCD alone; its advantage stays above 0.49 across nine configurations. With a clean Phase 1 and shifts that do not move all variables together, it detects between 5.3 and 5.8 points less than the classical chart. On the Tennessee Eastman process it detects all twenty faulty batches without false alarms; the classical chart detects two. The method targets mean shifts and is implemented in the R package robustT2AFM.

Implements a robust multivariate control-chart methodology for batch-based industrial processes with multiple correlated variables using the Dual STATIS (Structuration des Tableaux A Trois Indices de la Statistique) framework. A robust compromise covariance matrix is constructed from Phase I batches with the Minimum Covariance Determinant (MCD) estimator, and a Hotelling-type T² statistic is applied for anomaly detection in Phase II. The package includes functions to simulate clean and contaminated batches, to compute both robust and classical Hotelling T² control charts, to visualize results via robust biplots, and to launch an interactive 'shiny' dashboard. An internal dataset (pharma_data) is provided for reproducibility. See Lavit, Escoufier, Sabatier and Traissac (1994) for the original STATIS methodology, and Rousseeuw and Van Driessen (1999) for the MCD estimator.