I have a challenge which is perhaps more about statistics than Alteryx per se, but since I'd like to solve it using Alteryx I'm turning to the Community for help.
A company is moving into a new office and needs to decide on the optimal number of desks. They have about 180 employees, but they know not all of them will be at the office every day in the post-covid reality.
What would be the best way in Alteryx to build a model calculating the optimal number of desks, with corresponding confidence intervals (e.g. if the number of desks is sufficient on 95% of days, the company can rent extra meeting rooms on the remaining 5% of days, however if they would have to be renting extra rooms on e.g. 20% of days it would be better to just get more desks from the beginning)?
For input assumptions, I consider two scenarios:
- Simple scenario: assuming each employee spends the same number of days at the office (e.g. 3) and the days are entirely random for each employee
- Advanced scenario: assuming the company runs a survey and finds out for each employee: (1) how many days on average will they spend at the office, (2) whether some days are more or less likely (i.e. for each weekday, whether they will "never be at the office", "always be at the office", "sometimes be at the office")
I saw https://community.alteryx.com/t5/Engine-Works/COVID-19-Return-to-Work-Scheduling-with-Alteryx-MBA-Pr... , which is cool but a little high-level, not showing the actual modelling part.
Many thanks to everyone for thinking along and/or tips!