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Horizontal axis (x): This axis represents the months. Each point or line on this axis corresponds to a given month, and each month displays a quartile for processing time.
Vertical axis (y): This axis indicates the cycle time (Flow Time), or the time needed to process a ticket. Values on this axis represent processing times in days, hours, or another relevant unit of time.
Quartiles for each month:
Q1 (1st quartile): The bottom part of the box or row represents the processing time for which 25% of tickets were processed faster.
Median (2nd quartile): The middle line represents the median processing time for the month.
Q3 (3rd quartile): The upper part of the box or line represents the processing time under which 75% of tickets were processed.
Minimum & Maximum: The highest line of the Quartile identifies the maximum value of flow time identified over a month. The lowest line displays the minimum monthly flow time value.
Patterns to observe:
Trends in the median: If the median (center line) drops over months, this indicates that average processing times are improving (tickets are processed faster). If it increases, it could signal slowdowns in ticket processing.
Dispersion of processing times: If the gap between Q1 and Q3 is small, this means that the majority of tickets are processed within a similar time frame. If the gap is wide, this shows significant variability in processing times (with some tickets being processed much slower or faster than others).
Monthly anomalies: Months with particularly wide gaps or a very high median can indicate anomalies in workflow, such as periods of overload or temporary inefficiencies.
Usefulness in
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Flow Analytics Pro
The Flow Time Evolution with Quartiles in Wiveez Flow Analytics Pro allows users to:
Evaluate monthly process performance: By observing median processing times and quartiles, users can track performance month by month and identify periods where performance is degrading or improving.
Understanding processing time variability: By visualizing processing time dispersion via quartiles, it is possible to identify months where performance variability is greater, which might require adjustments in the process.
Identify anomalies: Unusual gaps between quartiles can signal periods of underperformance, overload, or temporary inefficiency, requiring further analysis.
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Of course ! The concept of Thin-Tailed and Fat-Tailed distributions is often used in risk analysis, statistics and finance to understand and model the impact of rare and extreme events. Here is a description that you could integrate into the Wiveez Flow Analytics Pro user documentation, adapted to explain their principle, their operation and their usefulness in this context.
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Thin-Tailed: A thin-tailed distribution is characterized by a relatively low probability that extreme events (or large deviations from the mean) will occur. In other words, extreme values (very far from the average) are rare. A typical example would be the normal (or Gaussian) distribution, where most of the data concentrates around the mean and extremes are very unlikely.
Fat-Tailed: In contrast, a fat-tailed distribution has a higher probability of extreme events. This means that rare (but very impactful) events are more frequent than would be expected with a thin-tailed distribution. Fat-tailed distributions are used to model phenomena where extreme events have a disproportionate impact, such as stock market crashes or economic crises.
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Detailed ticket analysis
Wiveez Flow Analytics Pro allows the user to analyze the performance of each flow in detail by displaying the list of tickets associated with a column and displaying the details of the Flow Metrics of a ticket.
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Analyze with our AI Alice
Wiveez Flow Analytics Pro provides you with its AI, named Alice, to help you analyze graphs.
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