{% extends "layouts/base.html" %} {% block title %} Delivery analytics {% endblock %} {% block stylesheets %}{% endblock stylesheets %} {% block content %}
Delivery Analytics
Dimension selection
  • Delivery Analytics is the process related to businesses of Logistic Departments. This dashboard gives the general idea of the durations from the date ordered via customers to the date that the customers are received the products. This dashboard will only be filled with real data if the delivery data source is created.
  • There are 3 kind of date can be applied when the delivery data source is created.
  • Delivery Date (Required)
    It is the date when the order is arrived to the customers. It is the timestamp when the delivery person is arrived to the delivery location.
  • Return Date
    It is the date when the delivery person returns back to the first location after the order is delivered.
  • Prepare Date
    It is the date when the orders is ready to deliver.
  • Deliver Duration
    It is total duration between order purchased date and order of delivered date to the customer.
  • Prepare Duration
    It is total duration between order purchased date and ordered prepared date.
  • Ride Duration
    It is total duration between order purchased date and the date which the delivery person returns to the first location after the order is delivered.
  • Return Duration
    It is the total duration between the order delivery date and the date on which the delivery person returns to the first location after the order is delivered.
  • Delivery Location (Latitude - Longitude)
    Each delivery location can be pinned with latitudes - longitudes while the delivery data source is created. Latitudes and Longitudes are not mandatory, but map charts in the dashboard needs locations.
  • What kind of Charts does the dashboard includes?
    There are 2 types of charts. Both types of charts are related to abnormal durations according to delivery - ride -prepare durations. These charts are abnormal durations per weekday per hour on the heatmap and abnormal durations per location on the map.
  • Abnormal Durations per weekday per hour
    Usually, the durations are affected by the day of the hour and the week of the day. Hours between 17:00 pm and 20:00 pm on the weekdays won`t be the similar durations Distributions as Hours in the morning during the weekdays. For another comparison instance, delivery durations at the weekend won`t be similar to durations for the weekdays. This type of chart allows us to see the whole picture weekly and detect which hours are highly possible to assign as abnormal. Numbers are scaled between 0 and 1 on each cell at the charts which do not represent the duration values.
  • Average Durations per Location
    Each location represents delivery locations. It is possible that there are orders more than once for each location. The average duration per location is represented with the map which is centralized with the average of latitude and longitude.
  • Delivery KPIs
    General metrics in order to follow up on how the delivery system is running at the business.
  • Average Delivery Duration (min)
    Average delivery duration of all purchased transactions.
  • Average Prepare Duration (min)
    Average prepare duration of all purchased transactions.
  • Average Ride Duration (min)
    Average ride duration of all purchased transactions.
  • Average Return Duration (min)
    Average return duration of all purchased transactions.
  • Total Number Location (min)
    Total delivered locations of all purchased transactions.
Average Delivery Duration (min) {% if data_type['deliver'] == False %} sample data {% endif %}

{{ kpis.deliver }}

Average Prepare Duration (min) {% if data_type['prepare'] == False %} sample data {% endif %}

{{ kpis.prepare }}

Average Ride Duration (min) {% if data_type['ride'] == False %} sample data {% endif %}

{{ kpis.ride }}

Average Return Duration (min) {% if data_type['returns'] == False %} sample data {% endif %}

{{ kpis.returns }}

Total Number Location (min) {% if data_type['total_locations'] == False %} sample data {% endif %}

{{ kpis.total_locations }}

Abnormal Deliver Duration Breakdown with Hour Weekday {% if data_type['deliver_weekday_hour'] == False %} sample data {% endif %}
Average Deliver Duration per Location {% if data_type['deliver'] == False %} sample data {% endif %}
Abnormal Ride Duration Breakdown with Hour Weekday {% if data_type['ride_weekday_hour'] == False %} sample data {% endif %}
Average Ride Duration per Location (100 Sample) {% if data_type['ride'] == False %} sample data {% endif %}
Abnormal Prepare Duration Breakdown with Hour Weekday {% if data_type['ride_weekday_hour'] == False %} sample data {% endif %}
Average Prepare Duration per Location (100 Sample) {% if data_type['ride'] == False %} sample data {% endif %}
{% endblock content %} {% block javascripts %} {% endblock javascripts %}