Measuring the efficiency of complex and dynamic organizational processes is a problem that remains unresolved in the management science literature. We propose a novel sequence transverse velocity measurement of the sequential decision processes from the itemset transition speed of frequent subsequences. Specifically, sequential decision chains of different source sequences under comparison are modeled as time-stamped itemset sequences and their frequent subsequences are extracted using sequential pattern discovery algorithm. The subsequence transverse velocity is represented as the inverse ranking of the traversing time duration of a common subsequence among the source sequences that share this subsequence. The proposed velocity measurement is then combined into a velocity timeline accompanying each source sequence by weighting the velocity of the constituent subsequences according to the statistical and contextual significance of the subsequences. To demonstrate the usage of this velocity measurement and its related computational tools, the paper also includes two empirical studies: one based on real-world business expansion chains and the other based on healthcare delivery systems.