The concept

This tool helps you decide whether spending time to optimise a recurring task is worth it. It is the return-on-time-investment question, made concrete: if I spend an hour automating something, how long until I have earned that hour back, and what's the most I should spend before the optimisation stops being worth doing?

The mathematics

The calculation has three inputs and one output:

  • T, current time per task
  • F, how often the task happens (converted to a daily frequency)
  • P, the period over which you care about the savings, in days

Efficiency improvement

Efficiency is a percentage, E. A 60% improvement means the optimised task takes 40% of its original time.

Break-even formula

The maximum time you can defensibly spend on the optimisation is:

Mopt = T × F × P × E / 100

Where T × F × P is the total time the task consumes over the period, and E / 100 is the share of that time the improvement returns to you.

The matrix

The grid shows the maximum optimisation time across two axes:

  • Rows, how much time the task takes today
  • Columns, how often you perform the task

Cells fade out when the optimisation would take longer than the savings it returns. A faded cell means the answer is not worth it.

Tech stack

Backend

  • Python with Flask, served by gunicorn behind nginx
  • Just three routes, the home page, the about page, and a small /healthz probe used by the deploy script

Frontend

  • HTML, CSS, and vanilla JavaScript modules (engine.js and scripts.js)
  • All calculation happens client-side, the matrix updates as you move the sliders
  • The visual design borrows from kipjordan.com, the same palette and typography family

About me

I'm Kip, a data and analytics consultant in financial services. I built this tool because the xkcd 1205 comic is one of my favourite pieces of practical advice, and I wanted an interactive version I could point colleagues at when they asked whether some bit of automation was actually worth the time.

I'm studying a Master of Data Science at RMIT alongside the day job. More work lives at kipjordan.com, and you can reach me on LinkedIn.

Inspired by xkcd 1205.

Kip Jordan