Flow Efficiency

Owned by Couthaïer Farfra
Dec 17, 2024
7 min read

Flow Efficiency allows you to measure and visualize the efficiency rate of tickets processed in the flow, that is to say the percentage of time during which tickets are actually being processed (time productive) compared to the total time they spend in the flow (total time). It indicates how efficient the flow is in avoiding waiting or blocking times.

The graph has two axes:

  • Horizontal axis (abscissa): Represents the efficiency rate as a percentage. The efficiency rate measures the proportion of productive time in relation to total time. An efficiency rate of 100% means that a ticket was processed without any waiting period, while a lower rate indicates that there were phases of non-processing or blocking.

  • Vertical axis (ordinate): Represents the number of tickets with a certain efficiency rate, with a distribution by type of ticket. Each bar in the graph shows how many tickets were processed at a specific efficiency rate.

The chart also includes four vertical lines indicating key efficiency thresholds:

  • The maximum rate at which 50% of tickets were processed.

  • The rate at which 75% of tickets were processed.

  • The rate at which 85% of tickets were processed.

  • The rate at which 95% of tickets were processed.

These lines serve as benchmarks to understand how tickets are distributed in terms of processing efficiency and to identify room for improvement.

Operation and Usefulness

Flow Efficiency is particularly useful for:

  1. Measure the overall efficiency of the process: It helps to understand to what extent ticket processing time is actually productive. A highly efficient flow should show a large number of tickets with high efficiency rates.

  2. Compare efficiency between ticket types: The graph allows you to observe whether certain ticket types have higher or lower efficiency rates, which can reveal differences in the complexity or handling of these request types.

  3. Spot inefficiencies: Tickets with low efficiency ratings can flag areas of inefficiency, such as bottlenecks, steps that take too long, or unproductive wait times.

How to read this chart?

Flow Efficiency is represented as stacked vertical bars, where each bar corresponds to a specific efficiency rate, and the height of each bar represents the number of tickets with that efficiency rate. Here are the elements to observe:

  1. Horizontal axis (x): This axis represents the ticket efficiency rate, from 0% (very little efficiency) to 100% (maximum efficiency, with no waiting time). Each segment of the axis corresponds to an interval of efficiency rates.

  2. Vertical axis (y): This axis indicates the number of tickets for each efficiency rate interval. The higher a bar is, the more tickets there are with this efficiency rate.

  3. Bars segmented by ticket type:

    1. Each bar is broken down by ticket type, which allows you to see how the different ticket types break down in terms of efficiency. For example, one type of ticket might show high efficiency (high efficiency rate), while another type might have lower effectiveness (many tickets having low efficiency rates).

  4. Vertical lines:

    1. 50% line: Indicates the maximum efficiency rate at which 50% of tickets have been processed. This allows you to see if half of the tickets have relatively high efficiency or if they are processed with moderate efficiency.

    2. 75% line: Indicates the rate at which 75% of tickets have been processed. This line shows where the performance of the majority of tickets lies.

    3. 85% line: Indicates the rate corresponding to 85% of tickets. It shows the extent to which ticket efficiency begins to concentrate in the highest efficiency rates.

    4. 95% Line: Marks the efficiency rate for 95% of tickets, providing a benchmark for the best performing tickets in terms of processing efficiency.

Patterns to observe:

  1. Concentration of tickets with high efficiency rate:

    1. If the majority of tickets are concentrated around high efficiency rates (around 80% or more), this means that the overall process is well optimized, with little waiting time or blockages in the flow.

    2. If, on the other hand, many tickets have lower efficiency rates, this indicates significant inefficiencies, where tickets spend a lot of time waiting or late in the flow.

  2. Comparison by ticket type:

    • If a certain type of ticket has bars predominantly located in low efficiency rates, this may indicate that these tickets require more waiting time or processing, or that they encounter more obstacles in the flow.

    • A ticket type that has high efficiency rates means that it is processed smoothly and quickly in the flow.

  3. Position of vertical lines:

    • The 50%, 75%, 85% and 95% lines allow you to evaluate the overall performance of the flow. If these lines are concentrated towards high efficiency rates (80% or more), this shows that the majority of tickets are processed efficiently. If these lines are toward lower rates, this may indicate a need for optimization in the feed.

Example:

Let's imagine Flow Efficiency in Wiveez for a technical support ticket processing flow.

  • Efficiency rate concentrated around 70-80%: Most tickets are found in bars corresponding to efficiency rates of 70-80%, which shows that the flow is overall quite efficient, with processing times moderate wait.

  • Urgent tickets with low efficiency: Let's assume that urgent tickets have a lower overall efficiency rate (bars around 40-50%). This could indicate that although these tickets are urgent, they are encountering obstacles in the flow, requiring adjustments to reduce wait times.

  • 75% to 85% line: If the line representing 75% of tickets is at 85% efficiency, this means that three-quarters of tickets are processed very efficiently, with little waiting time or holdups in the process.

Usefulness in Wiveez:

Flow Efficiency in Wiveez allows users to:

  • Measure overall process efficiency: By visualizing the distribution of efficiency rates, users can assess how productive the flow is and identify bottlenecks or periods of non-processing.

  • Optimize processes: Tickets with low efficiency rates can indicate inefficiencies, and analyzing these tickets can help identify areas for improvement, such as reducing wait times or prioritizing tasks.

  • Compare ticket types: The breakdown by ticket type allows you to see if certain ticket types are more efficient in terms of efficiency, or if other types require adjustments to improve their processing.

However, it is essential to note that an efficiency rate of 0% or 100% is not necessarily synonymous with good or bad performance:

  • 0% efficiency: This means that tickets have spent their entire time in the flow without being actively processed. This could be a sign of a bottleneck, a lack of resources, poor priority management, or poor compliance with how the Flow works, significantly delaying ticket processing.

  • 100% efficiency: Conversely, a perfect efficiency rate (100%) might seem ideal at first glance, but it may also signal that tickets were processed so quickly that they did not receive the attention needed, or that the workload is poorly distributed. For example, this could indicate a poor allocation of resources or tickets with undervalued complexity or also a poor respect for the operation of the Flow, which considerably delays the processing of tickets.

Thus, extreme rates should be interpreted with caution, as they may mask quality problems in flow management.

The chart

Les filtres

Predictability Analysis

The Predictability analysis makes it possible to measure the quality of the flow and the level of confidence in its use for projections.

The analysis is based on the Thin-Tailed - Fat-Tailed principle

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 user documentation, adapted to explain their principle, their operation and their usefulness in this context.

Thin-Tailed and Fat-Tailed: Principle and Usefulness

Principle:

In data modeling, particularly in finance and risk management, we often talk about probability distributions to describe how events or values ​​are distributed. Two types of distributions are particularly important: Thin-Tailed and Fat-Tailed distributions.

  • 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.

Detailed ticket analysis

Wiveez 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.

Analyze with our AI Alice

Wiveez provides you with its AI, named Alice, to help you analyze graphs.

  1. Click on the Alice icon to start analyzing your graph;

  2. A page is displayed containing an analysis of the health of your graph and tips for improvement;

  3. You can save this analysis in a PDF file;

  4. You can copy/paste the analysis into another type of document.

As long as no modification has been made to the chart filters or no data refresh has been initiated, your analysis remains accessible.