01. Flow Basic Flow Metrics Basics

Question 1

What are the different types of Averages?

Answer 1

1. Mean
2. Median
3. Mode

Question 2

What is an average

Answer 2

1. It is the values that are most representative/typical of the dataset.

Question 3

How do you calculate the mean

Answer 3

Add all the numbers together, and then divide by how many numbers there are.

Question 4

What are outliers

Answer 4

An extremely high or low values that stands out from the rest of the dataset

Question 5

How do outliers change the representation of the dataset

Answer 5

1. They skew the representation depending on the outliers.
2. Outliers “pull” the data to the left or right

Question 6

How do outliers affect the mean?

Answer 6

Outliers pull the mean higher or lower, skewing the data to the left or right.

Mean won’t give you the best representation of what a typical value is

Question 7

What does it mean if the data is skewed to the right

Answer 7

Data that is skewed to the right has a “tail” of high outliers that trail off to the right.

Question 8

What does it mean if the data is skewed to the left?

Answer 8

Data that is skewed to the left has a “tail” of high outliers that trail off to the left.

Question 9

What does it mean if the data is Symmetric

Answer 9

If the data is symmetric, the Mean, Median and Mode are in the middle.
No outliers pull the mean in either direction, and the data has the same shape on either side of the centre.

Question 10

Which averages are not affected as much by outliers?

Answer 10

1. Median
2. Mode

Question 11

How is the Median found?

Answer 11

1. Line up all the values in ascending order.
2. If there are an odd number of values, the
   median is the one in the middle.
3. If there are an even number of values,
   add the two middle ones together and
   divide by two.

Question 12

Where is the Mean and Median in a right-skewed dataset

Answer 12

If the data is skewed to the right, the mean is to the right of the median (higher).

Question 13

What is the Mode?

Answer 13

1. The mode of a set of data is the most popular value, the value with the highest frequency
2. The mode has to be in the data set.
3. It’s the only average that works with categorical data.

Question 14

Where is the Mean and Median in a Left-skewed dataset

Answer 14

If the data is skewed to the left, the mean is to the left of the median (lower).

Question 15

Can a dataset have more than one Mode?

Answer 15

1. If there is more than one value with the highest frequency, then each one of these values is a mode.
2. If the data appears to represent more than one trend or set of data, we can provide a mode for each set.
3. This would be a bimodal dataset.

Question 16

What are the Three steps for finding the mode?

Answer 16

1. Find all the distinct categories or values in your data set.
2. Write down the frequency of each value or category.
3. Pick the one(s) with the highest frequency to get the mode.

Question 17

Flow metrics

Question 18

What metrics can be used when analysing flow

Answer 18

1. Range
2. Quartiles
3. Percentiles
4. MAD - Median of the absolute

Question 19

Define Range.

Answer 19

1. We can use the range to measure Variability / Spread?
2. The range lets us know how the data varies
3. The more variability, the less predictable the source of the data is

Question 20

What is the Use of range in Flow Analysis?

Answer 20

Shows full Spread / Width of flow times.

Question 21

How do we measure the range?

Answer 21

1. The range measures the spread/width of a dataset.
2. It’s given by Upper bound - Lower bound.
3. Where the upper bound is the highest value, and the lower bound is the lowest value.

Question 22

What are the Advantages of Range

Answer 22

• Simple to calculate
• Highlights extreme variation

Question 23

What are the disadvantages of Range

Answer 23

1. Sensitive to outliers
2. It doesn’t tell whether the values are clustered or spread out. It ignores whether values are bunched near the median or scattered all over.
3. It can be misleading if you assume it reflects “overall variation.”
4. It only uses two data points—the smallest and largest—and doesn’t care what happens in between.
5. It can give a sense of less predictability because of the wide range

Question 24

When is the Range used?

Answer 24

When you want a quick view of a variation.

Question 25

How can we negate the impact of outliers when analysing dispersion?

Answer 25

Use the range within the dataset that does not include the outliers

Question 26

Quartiles

Question 27

Define Quartiles

Answer 27

Quartiles specifically refer to the values that split the data into quarters. The lowest quartile is the lower or first quartile (Q1). The highest quartile is known as the upper quartile or third quartile (Q3). The quartile in the middle (Q2) is the median, as it splits the data in half.

Question 28

How are Quartiles created?

Answer 28

1. First, line up the values in ascending order and then split the data into four equally sized chunks, each containing one-quarter of the data.

Question 29

How is the Interquartile calculated?

Answer 29

Check pic

Question 30

What is the Interquartile range?

Answer 30

Interquartile range = Upper quartile – Lower quartile The interquartile range is much less sensitive to outliers. It’s another way in which we can compare different sets of data.

Question 31

Can Quartiles show variability in your data?

Answer 31

Quartiles also show you how spread out your data is: The Interquartile Range (IQR) = Q3 - Q1 tells you the range of the middle 50% of your data. This is a very robust measure of variation — it ignores outliers.

Question 32

Quartiles can help you see what in Flow Analysis

Answer 32

Quartiles include the median (Q2) 1. What's in the middle of your data 2. A more robust centre than the mean if your data is skewed Quartiles help you see the centre — especially in skewed distributions where the mean is misleading.

Question 33

When using Quartiles, what are the signs of variability

Answer 33

1. If Q1 and Q3 (Interquartile Range (IQR)) are close together → low spread (consistent process) 2. If they’re far apart → high spread (variable process)

Question 34

How are the quartiles used in Flow Analysis?

Answer 34

Quartiles help understand central tendency and spread. 1. Q2 (median) gives a strong view of the middle/typical value. 2. Q1 and Q3 show how tightly or loosely the values are packed around the centre. Together, they give a powerful, outlier-resistant summary of your data.

Question 35

What are the Advantages of quartiles

Answer 35

1. Resistant to outliers 2. Good for skewed data

Question 36

What are the disadvantages of quartiles?

Answer 36

IQR (from quartiles) gives you a summary of the middle, but it: Doesn’t measure how far values deviate from the median (like MAD or SD) Ignores the edges of your data, where rare but critical events happen

Question 37

What question do quartiles not address

Answer 37

1. How far do values deviate from the centre - MAD or standard deviation 2. How bad the worst-case flow delays can get - Percentiles (90th, 95th, max) 3. Are there rare but extreme cases - Look at the tails explicitly or use box plots with outliers

Question 39

What are Percentiles

Answer 39

A percentile indicates that: "X% of the data points are less than or equal to this value." Thus: 1. The 95th percentile represents the threshold at which 95% of the dataset is below. 2. The top five percent surpass that value, typically representing delays, exceptions, or outliers. 3. Percentiles give you a target you can manage and evidence to show stakeholders.

Question 40

What are Percentiles used for?

Answer 40

1. Setting realistic service level targets (SLA). 2. Setting expectations you can communicate to customers or stakeholders. 3. Setting performance goals your team can aim for. 4. Giving visibility into the worst-case (or best-case) scenarios — especially the ends or “tails” of the distribution

Question 41

What are the different methods for calculating percentiles

Answer 41

1. Linear Interpolation Between Ranks 2. Nearest Rank Method

Question 42

What is the difference between Linear Interpolation & Nearest Rank percentiles?

Answer 42

1. Interpolation is more statistically smooth and useful in continuous data analysis. 2. Nearest-rank is simpler and easy to explain manually,

Question 43

How would you calculate the Nearest Rank Method for Percentiles?

Answer 43

See Pic

Question 44

If you have 125 numbers and want to find the 10th percentile.

Answer 44

1. start by calculating 10 × 125 ÷ 100. This gives you a value of 12.5. 2. Rounding this number up gives you 13, which means that the 10th percentile is the number at position 13.

Question 45

Flow example is 95% of items done in under 10 days

Answer 45

see pic

Question 46

What are the advantages of percentiles?

Answer 46

1. Great for setting expectations 2. Can capture tail behaviour

Question 47

Why are percentiles great for setting expectations?

Answer 47

Percentiles are great tools for clearly stating what results to expect, even when the data is noisy, messy, or not normally distributed.

Question 48

What does it mean when we say that a Service Level Agreement (SLA) specifies a 90th percentile cycle time of 10 days?

Answer 48

90% of requests are resolved in 10 days or less.

Question 49

Why do percentiles matter

Answer 49

1. Percentiles reflect actual performance for most people 2. You can base contracts, dashboards, or SLOs on them 3. You don't get tricked by a few extreme cases (like averages do)

Question 50

What is MAD?

Answer 50

1. MAD = Median of the Absolute Deviations from the Median 2. Indicates How tightly the values cluster around the median. 2. Good for: Processes where you monitor consistency or "typical" deviations from the norm. 3. One number gives you a sense of core variation, regardless of shape. MAD = median(lead_time_i - median(lead_times))

Question 51

How to Use MAD in Practice

Answer 51

1. Calculate the Median of your flow metric (lead time, cycle time, etc.) 2. Compute the absolute deviations from the median 3. Find the median of those absolute deviations → That’s your MAD

Question 52

Why Use MAD in Flow Analysis?

Answer 52

1. Robust: Not affected by outliers (unlike standard deviation) 2. Great for skewed or non-normal data (typical in lead times, cycle times) 3. Highlights consistency in process performance 4. One number gives you a sense of core variation, regardless of shape.

Question 53

Find the MAD for the following cycle time. Cycle times (days): [5, 6, 7, 8, 9, 12, 100]

Answer 53

Cycle times (days): [5, 6, 7, 8, 9, 12, 100] 1. Median = 8 2. Absolute deviations = [3, 2, 1, 0, 1, 4, 92] 3. MAD = Median of deviations = 2 MAD = 2 tells us that most values deviate from the median by just 2 days,

Question 54

Can Mad be used with control charts

Answer 54

See Pic