A method for improving a bottleneck detection system comprising the steps of: a step of receiving in a processor a plurality of data items related to an industrial process, each of the data item being time stamped so that each of the data item includes time stamp and industrial process data regarding an industrial process occurring at a time of the time stamp; a step of identifying in the processor corrupt or outlier industrial process data or corrupt time stamp data of the data items; the method characterized by: the identifying step including first identifying a type of data distribution being received and using a cumulative density function with a cutoff to identify the outlier industrial process data; the identifying step in the processor further identifying all null values and negative values for the industrial process data as corrupt industrial process data and all null values and negative values of the time stamp data as corrupt time stamp data of the data items; wherein the method further comprises: a step of providing the plurality of data items excluding data items having the identified corrupt and outlier industrial process data and corrupt time stamp data to permit performing in the processor at least one statistical calculation; a step of discarding the data items with corrupt industrial process data or time stamp data; and a step of providing the plurality of data items including items with the outlier industrial process data but without the discarded data items to be able to be used to identify a bottleneck based on the result of the statistical calculation. a step of receiving in a processor a plurality of data items related to an industrial process, each of the data item being time stamped so that each of the data item includes time stamp and industrial process data regarding an industrial process occurring at a time of the time stamp; a step of identifying in the processor corrupt or outlier industrial process data or corrupt time stamp data of the data items; the method characterized by: the identifying step including first identifying a type of data distribution being received and using a cumulative density function with a cutoff to identify the outlier industrial process data; the identifying step in the processor further identifying all null values and negative values for the industrial process data as corrupt industrial process data and all null values and negative values of the time stamp data as corrupt time stamp data of the data items; wherein the method further comprises: a step of providing the plurality of data items excluding data items having the identified corrupt and outlier industrial process data and corrupt time stamp data to permit performing in the processor at least one statistical calculation; a step of discarding the data items with corrupt industrial process data or time stamp data; and a step of providing the plurality of data items including items with the outlier industrial process data but without the discarded data items to be able to be used to identify a bottleneck based on the result of the statistical calculation. the identifying step including first identifying a type of data distribution being received and using a cumulative density function with a cutoff to identify the outlier industrial process data; the identifying step in the processor further identifying all null values and negative values for the industrial process data as corrupt industrial process data and all null values and negative values of the time stamp data as corrupt time stamp data of the data items; wherein the method further comprises: a step of providing the plurality of data items excluding data items having the identified corrupt and outlier industrial process data and corrupt time stamp data to permit performing in the processor at least one statistical calculation; a step of discarding the data items with corrupt industrial process data or time stamp data; and a step of providing the plurality of data items including items with the outlier industrial process data but without the discarded data items to be able to be used to identify a bottleneck based on the result of the statistical calculation.