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Let's imagine a Flow Time Breakdown in Wiveez Flow Analytics Pro for an IT request management flow:
Close average and median times: If the average and median time curves are close at each stage, this means that the majority of tickets are processed within relatively constant times, without large disparities in processing times.
Large difference at one step: Let's assume that the "Technical Validation" step has a marked difference between the average time and the median. This may indicate that some tickets are stuck in this step much longer than others, which could signal a bottleneck in this part of the process.
Difference by ticket type: If the ticket type columns show that emergency tickets consistently take longer than others in the "Review" stage, this could indicate that this ticket type requires additional resources or a more complex processing, thus slowing down the entire process.
Usefulness in
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Flow Analytics Pro
Flow Time Breakdown in Wiveez Flow Analytics Pro is essential for:
Understand performance by stage: Users can identify the stages where tickets are taking the most time and assess whether those stages require adjustments or optimizations.
Analyze gaps between ticket types: The columns by ticket type allow you to see how different ticket types are handled at each stage and to identify specific areas for improvement for certain ticket types.
Identify inefficiencies: Deviations between the average time and the median reveal anomalies or inefficiencies in ticket processing, allowing users to focus their efforts on the most problematic steps.
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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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