{"id":2983,"date":"2026-01-15T09:00:00","date_gmt":"2026-01-15T08:00:00","guid":{"rendered":"https:\/\/getnave.com\/blog\/?p=2983"},"modified":"2026-01-22T16:39:06","modified_gmt":"2026-01-22T15:39:06","slug":"using-averages-to-make-delivery-predictions","status":"publish","type":"post","link":"https:\/\/getnave.com\/blog\/using-averages-to-make-delivery-predictions\/","title":{"rendered":"Why Using Averages to Make Delivery Predictions Will Land You in Hot Water"},"content":{"rendered":"
<\/div>\n

Often, managers use average delivery times to make future predictions. Even though this method is intuitive and widely spread, making delivery commitments based on averages is not reliable. Let\u2019s explore the challenges that come with this approach.<\/p>\n

What Are Delivery Times Averages?<\/h2>\n

When we talk about using averages to make delivery predictions there are three main values we refer to – the mean, the median and the mode.<\/p>\n

The Mode<\/strong> is the easiest average to calculate \u2013 this is the number that appears most often. Since that\u2019s the most commonly occurring delivery time, if you ask a team how much time they usually need to complete a task, this would be the answer.<\/p>\n

The Median<\/strong> shows the middle number of a data set. It denotes that half of the tasks completed so far have taken less<\/strong><\/em> than the median value to be finished. However, the other half have taken longer to complete.<\/p>\n

The Mean<\/strong> is the average calculation that you are most likely to be familiar with. This involves adding up all of the values and dividing them by the number of instances in the data set.<\/p>\n

Most often, the average we use to make a delivery prediction is either the mode (the most common value) or the mean (the arithmetic average).<\/p>\n

Why Using Averages to Make Delivery Predictions Will Land You in Hot Water<\/h2>\n

Making predictions based on an average is highly likely to land you in hot water. Delivery forecasts based on averages only <\/strong><\/em>make sense if you know something about the shape of the underlying distribution of your delivery times.<\/p>\n

Let\u2019s explore a couple of examples.<\/p>\n

\"Using<\/a><\/p>\n

This is a Cycle Time Histogram<\/a> exposing a high-variability delivery workflow. The Cycle Time Histogram shows the frequency distribution of the completion times of the tasks in your workflow. The vertical axis displays a frequency and the horizontal axis shows your cycle times<\/a>.<\/p>\n

A Cycle Time Histogram with a big hump on the left and a very long tail to the right indicates that your cycle times vary significantly. This means that your process is inconsistent and you\u2019re maintaining an unpredictable system.<\/p>\n

Here, the mode points to 1 day, the median is 9 days, and the mean –\u00a0 21 days. The most popular delivery time is 1 day, and the tail extends all the way up to 130 days. If you were managing this team and you were about to commit to the average value (the mean), you may end up with a delivery time that is 6 times higher than what you\u2019ve promised to achieve.<\/strong><\/p>\n

If you don\u2019t know the distribution of your delivery times, there is no way that you can give a probability of where the average falls. If you don\u2019t know the probability, then you cannot make a reliable delivery prediction. There could be a 20% or 50% or 80% chance of meeting your commitment.<\/p>\n

Would you commit to a delivery date if you know that there is only a 20% chance to keep that promise?<\/strong> Probably not, right? It certainly wouldn\u2019t be something that we\u2019d recommend doing.<\/p>\n

<\/div>\n

Using the Frequency Distribution of Your Delivery Times Effectively<\/h2>\n

Now, assume you know the frequency distribution of your delivery times. Let\u2019s analyze the following example.<\/p>\n

\"Using<\/a><\/p>\n

Here is the cycle time frequency distribution of a mature team that maintains a stable system.<\/p>\n

A key point to note here! The more stable your system is, the more predictable it becomes. And predictable systems produce more accurate delivery predictions. If you\u2019re interested in adopting the practices and the proven strategies to establish a stable system using a step-by-step roadmap, we couldn\u2019t be more thrilled to welcome you to our Sustainable Predictability<\/a> program.<\/p>\n

In the example above, all the averages are very close to each other – the mean is 7.20 days, the median (50% of the cases) points to 7 days and the mode is 9 days.<\/p>\n

Using their frequency distribution, this team can say that there is a 50% chance of finishing any work item in less<\/strong><\/em> than 7 days.<\/p>\n

By committing to that delivery prediction, what they say is there\u2019s an equal likelihood that they\u2019ll either make it on time or not. There is a 50\/50 chance of making that happen. The risk is considerable.<\/p>\n

In order to provide a reliable commitment, what you need to do is to come up with a set of delivery times and the probabilities that come with each of them.<\/p>\n

Using the example above, what this team should do is to deliver a probability forecast<\/a> that looks like this:<\/p>\n

\"Using<\/a><\/p>\n

Now, it\u2019s up to your customers to decide the level of risk that they are willing to take, and which probability they feel most comfortable with. When you\u2019re maintaining a stable system, that decision will be quite an easy one, as the values that come with each probability will be very close to each other.<\/p>\n

It\u2019s important to remember that your forecast is a living thing – it will change as you discover new information, so it\u2019s crucial to reevaluate it on a regular basis. You will need to adjust your course accordingly to be able to hit your goals<\/a>.<\/p>\n

<\/div>\n

Switch to Probabilistic Forecasting As Soon As You Can<\/h2>\n

Let\u2019s make this clear, what we are saying is that using averages to make delivery predictions is not a reliable approach, not that it\u2019s not applicable at all. If you\u2019ve been relying on estimating and guesswork so far, this method is something you can transition to as a starting point. However, strive to switch to probabilistic forecasting as soon as you can.<\/p>\n

Using averages to estimate your work can land you in hot water, as this method communicates one single certain commitment. To produce a reliable forecast, you need to provide a range of delivery times and the probabilities that come with meeting each of them. That\u2019s the most reliable way to establish realistic expectations and deliver on time!<\/p>\n

Alright my friend, I hope you found this piece of content valuable! I\u2019ll see you next week for more managerial goodness. Bye for now.<\/p>\n

<\/div>\n
<\/div>","protected":false},"excerpt":{"rendered":"

Often, managers use average delivery times to make future predictions. Even though this method is intuitive and widely spread, making delivery commitments based on averages is not reliable. Let\u2019s explore the challenges that come with this approach. What Are Delivery Times Averages? 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