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Horizontal axis (x): This axis represents the date each ticket reached the final stage of the observed flow interval. This allows you to view tickets processed at different points in time.
Vertical axis (y): This axis shows the processing time (or flow time) for each ticket, which is how long it took to move from one step to another in the process.
Patterns to observe:
Vertical dispersion of points: If points are widely dispersed vertically, this means that ticket processing times vary greatly, and it may be necessary to examine why some tickets take longer than others.
Horizontal trend: If a trend appears on the x-axis (date), this may indicate a change in performance over time. For example, if flow times increase over time, this could signal system overload or process slowdown.
Outliers: Tickets that have unusually long or short processing times compared to others may be exceptions or indicators of specific issues to investigate.
Usefulness in
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Flow Analytics Pro
The Flow Time Scatterplot in Wiveez Flow Analytics Pro allows users to:
Track flow performance: By viewing each ticket processed and its dwell time in between, users can quickly spot inefficiencies, delays, or performance fluctuations.
Identify problem tickets: Outliers (tickets with exceptionally long or short processing times) can be identified and analyzed to determine if there are specific issues that need to be resolved.
Evaluate performance trends: By tracking ticket processing times over time, users can evaluate whether the system or process is getting faster, slower, or remaining consistent in performance.
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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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