pm4py.statistics.traces.cycle_time.util package#

PM4Py – A Process Mining Library for Python

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Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

Submodules#

pm4py.statistics.traces.cycle_time.util.compute module#

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

pm4py.statistics.traces.cycle_time.util.compute.cycle_time(events: List[Tuple[float, float]], num_instances: int) → float[source]#

Computes the cycle time given a list of events (having a start and a complete timestamp) and the number of instances of the log

The definition that has been followed is the one proposed in: https://www.presentationeze.com/presentations/lean-manufacturing-just-in-time/lean-manufacturing-just-in-time-full-details/process-cycle-time-analysis/calculate-cycle-time/#:~:text=Cycle%20time%20%3D%20Average%20time%20between,is%2024%20minutes%20on%20average.

So: Cycle time = Average time between completion of units.

Example taken from the website: Consider a manufacturing facility, which is producing 100 units of product per 40 hour week. The average throughput rate is 1 unit per 0.4 hours, which is one unit every 24 minutes. Therefore the cycle time is 24 minutes on average.

Parameters#

events: List of events (each event is a tuple having the start and the complete timestamp)
num_instances: Number of instances of the log

Returns#

cycle_time: Cycle time