Niamh Broderick

Imagine we have 4 tasks that normally take 1 hour each. In the baseline, team A does all of the work, taking 4 hours. They have an occupancy of 80% meaning that translates to 5 hours of Team A’s scheduled time, costing £128. In the ideal scenario for moving the work, team B do it all with the same handling time and occupancy, but cost £72 leading to a £56 saving. However, what if team B can only complete two of the tasks but have to send the others back to team A (after spending half an hour identifying that they can’t finish them). In this case, if both teams have 80% occupancy, we need 3.75 team B hours but also 2 team A hours, leading to a total cost of £105 - still a saving of £23, but only 41% of the total opportunity. And that’s not the only thing that can affect cost. For example, if team B are twice as slow as team A at doing these tasks, then we will need 10 team B hours costing £144 - a £16 loss vs the baseline. Or if team B have much lower occupancy (e.g. if they are scheduled less efficiently) - if team B have an occupancy of 50% instead of 80%, we need 8 team B hours to do the 4 1-hour tasks, and this costs us £115. We only save £13 vs the baseline, 23% of the amount we save if team B have the same occupancy as team A.

18 May 2023

Sensitivity analysis

This blog explains sensitivity analysis, which is a useful data science technique for assessing the impact of different variables on an outcome metric.

Data

Technology

4 February 2022

How we validated our handling time data

We make lots of decisions based on data from customer support. We need to make sure we can trust this data. In this post, Niamh explains how her team validated the data.

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