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Holistic production control introduces a concept for production process optimisation, which is based on detailed analysis of historical process data. Selection of the most influential manipulative variables represents an important HPC design step. Therefore, the selected set of variables should be additionally validated with controllability analysis. Appropriate space-based controllability measure for HPC is presented, where achievable output space is examined. Although controllability measure is based on the non-optimal regression model, it is assumed that such examination of the model knowledge still gives us some additional insight into process and eases our input selection procedure. The HPC controllability approach is illustrated on the Tennessee Eastman benchmark process.