Now we are moving towards ensuring that JIT (Just In Time) works everywhere - a mechanism for minimizing the time that a courier spends in a restaurant. To do this, the guys from the team of auto-assigning couriers to orders use our forecasts for coming to the restaurant and picking up the order. They try to find the most suitable courier depending on the restaurant's cooking time. By this we want to minimize the waiting time for the courier in the restaurant.
How to control all this?
If we talk about metrics, then we had several tasks:
- Improve the forecast accuracy metric. It shows how accurate the forecast is relative to the fact.
- Improve the delays share metric. This is the percentage of orders with early and late arrivals of couriers.
- Reduce forecast error. This is a metric that correlates with forecast accuracy.
- Reduce time to market. Many of you probably know this metric — it’s the time to deliver features to production.
Of course, we would not be able to control all this without metrics in Grafana. We love various graphs and metrics very much and decided to build some graphs that show how the forecast behaves in real time.