{"id":5752,"date":"2023-09-14T09:00:00","date_gmt":"2023-09-14T07:00:00","guid":{"rendered":"https:\/\/getnave.com\/blog\/?p=5752"},"modified":"2023-09-14T15:20:00","modified_gmt":"2023-09-14T13:20:00","slug":"strategies-for-risk-mitigation","status":"publish","type":"post","link":"https:\/\/getnave.com\/blog\/strategies-for-risk-mitigation\/","title":{"rendered":"Strategies for Risk Mitigation: How to Make Reliable Delivery Commitments"},"content":{"rendered":"
<\/div>\n

How can we make reliable delivery commitments when there is so much uncertainty that we have to deal with? How can we mitigate the risk of failure while managing realistic expectations?<\/p>\n

The secret lies in accepting that there is more than one possible result that may happen in the future and managing that uncertainty effectively. <\/p>\n

Here is the thing.<\/p>\n

To provide a reliable forecast, you need to communicate the range of the delivery dates you can hit, together with the probabilities that come with it. <\/p>\n

Your commitment should look something like, \u201cWe expect to deliver this project before the 5th of March and we are 85% certain that we can achieve this goal\u201d.<\/p>\n

Now, are we saying that we will deliver exactly on the 5th of March? No, we aren\u2019t. What we are saying is that we\u2019ll probably finish earlier, but it won\u2019t be later than the 5th of March and there is an 85% probability that we\u2019ll keep our promise.<\/p>\n

Pay attention to the language we use here. It may seem to be a simple wording change but it will have significant implications.<\/strong><\/p>\n

Every time you provide a delivery commitment, ask yourself, \u201cWhat\u2019s the chance of meeting that goal\u201d? And if the approach you use to make delivery predictions doesn\u2019t enable you to quantify that probability, it\u2019s worth considering an alternative. <\/p>\n

Remember, the main purpose of estimating is to manage risks effectively. There\u2019s always risk involved and the most effective approach to managing realistic expectations is to quantify that risk by assessing the probability of achieving your goals. <\/p>\n

Every delivery prediction should communicate the risk associated with it. <\/p>\n

You Don\u2019t Need a Crystal Ball<\/h2>\n

So, how can we quantify that risk? The best predictor of your future behavior is your past behavior. More often than not, your past is going to reflect the future, especially if you\u2019re in control of your management practices.<\/p>\n

There is a tool that will enable you to come up with an accurate delivery commitment without spending any extra time and effort. <\/p>\n

That tool is called Monte Carlo<\/a>.<\/p>\n

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

When determining our delivery dates, we use Monte Carlo simulations to come up with a range of commitments<\/strong> and the probabilities that come with each of them<\/strong>.<\/p>\n

The simulation relies on a large number of random trials based on your historical performance data to predict your throughput for a future time frame. <\/p>\n

Throughput<\/a> is the number of items completed in a certain time period. It represents your historical data and it accounts for all the variability in your system, including the effort time plus the waiting time in your workflow.<\/p>\n

In the Monte Carlo simulation, you define the start date and number of items and the simulation will provide a range of possible outcomes and the probability that comes with each of them. <\/p>\n

It will use the throughput of a random day in the past to simulate how many work items are likely to get done on any day in the future.<\/p>\n

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

Now, in this simulation, on Sep 10th, you had a throughput of 2 tasks. The simulation takes this number and assumes that this is how many work items will be completed on Nov 1st. Then, to project the probable throughput of Nov 2nd, it takes the throughput of another random day in the past, and so on.<\/p>\n

The simulation is repeated tens of thousands of times before the results are presented in the form of a probability distribution with percentiles increasing from left to right. <\/p>\n

Here, we set the scope to 44 tasks and stated that we wanted to start working on it on Nov 1st. The simulation tells us that there is an 85% probability of finishing this scope before the 19th of April. The further you go in time, the greater the certainty of completing the projected outcome.<\/p>\n

<\/div>\n

Strategic Risk Mitigation<\/h2>\n

The main benefit of using Monte Carlo simulations is that the method clearly defines the risk associated with certain outcomes. <\/p>\n

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

The vertical percentile lines on the distribution communicate the risk you\u2019re managing!<\/strong> The probabilities that the simulation produces (50%, 70%, 85%, 95%, 98%) quantify that risk in terms of percentages.<\/p>\n

The question is no longer \u201cWhen you will deliver?\u201d, the question is now \u201cHow much risk are you willing to take?\u201d<\/p>\n

If you plan with less confidence \u2013 let\u2019s say you commit to delivering before the 27th of March on the 50th percentile (which is, by the way, the same confidence level that comes with flipping a coin!) \u2013 you really ought to ask yourself whether you want to manage that level of risk. <\/p>\n

We want to mitigate the risk of failure as much as we can. 50% certainty is very low. Ideally, you should be working with 70%, 85%, or even 95% certainty when you\u2019re making your delivery commitments. <\/p>\n

Here is how I want you to think about it:<\/p>\n

If you commit to the delivery date on the 50th percentile, you will be wrong 1 out of every 2 times. If you commit to the date on the 85th percentile, you will be wrong 1 out of every 7 times. If you go for the 95th percentile, the risk of failure drops down to 1 out of 20 times.<\/p>\n

That\u2019s how you manage realistic expectations and plan in the most effective manner.<\/p>\n

<\/div>\n

There is no model that provides 100% certainty that we can achieve something. We can only commit with high confidence. The higher the confidence, the smaller the chance of failure.<\/p>\n

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

How can we make reliable delivery commitments when there is so much uncertainty that we have to deal with? How can we mitigate the risk of failure while managing realistic expectations? The secret lies in accepting that there is more than one possible result that may happen in the future and managing that uncertainty effectively. 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