The bike rental business is affected by multiple parameters like.

• Weather
• Day of the week
• Peak timings
• Temperature
• Humidity
• location of bike station,
• Bike quality
• Rental amount

Even casual riders are more over the weekend, during an event or festive time and that’s why predicting bike usage becomes a difficult task. The increase in the demand for shared bikes, maintenance activities, and station availability creates a big impact on business performance. Tracking docks, bike availability per dock as per the demand, and peak hours are important parameters for a business.

Our Primary Objective for Eliminating This Challenge

After understanding all the data points from various sources, the Techknomatic team finalized the scope of work to bring visibility to the decision-making system.

The objective of this project was to simplify the following tasks:

• Track dock usage
• Identify the demand patterns bike availability per dock
• Forecasting demand for keeping bikes ready at the dock

What was the Final Outcome?

Initially, our team collected all the data points captured and moved them into a semantic layer for decision making. After cleaning and transforming the data, Tableau dashboards were created for different business units to track activities.

The newly created dashboards helped our client track bike usage, usage patterns, demand forecast and helped them eliminate the manual efforts of tracking data points from multiple sources for decision making.

Solution Provided by Techknomatic

The Techknomatic team analyzed all the data points captured using sensors, app data, and GPS to make business decisions and came up with a strategy & implementation plan to deliver a series of dashboards on the existing reporting platform.

The provided dashboards enable users to check on every dock performance and see KPIs, such as minimum Bike or Dock availability per day, Overlapping stations, Dock utilization, etc.

We had multiple iterations to finalize the KPIs to track, dashboard designs & utilization by teams, and the effectiveness of analytics created. The provided solution addresses their challenges of bike and dock availability while opening up opportunities to improve their bike availability.

The areas covered in the delivered solution were:

Avg Bikes Per Hour

Peak hours for bike use are 7:00 AM up to 4:00 PM. If any rearrangement is to be done, it should be carried out in the remaining time except for this duration.

At a station, when the avg bikes decrease in number, avg docks remain higher in number which shows the open docks. 12:00 AM up to 6:00 AM can be considered to be a suitable time for the rearrangements.

Minimum Bike or Dock Availability Per Day

Shows the stations and days to understand the docks and bike availability. The stations and days when either bikes or docks were unavailable, can be noticed. With this ability, the failure detection is easy.

Avg. Bikes vs Avg. Docks Per Station

The stations where the avg number of bikes goes higher than the avg number of docks are to be focused to rebalance with other stations or areas.

Overlapping Stations

Shows stations and time concurrency by bike availability. Stations with high concurrency should be focused on uniform distribution of bikes and docks to get the uniform availability of bikes or docks on any day at any time.

Avg. Bikes and Avg. Docks on Weekday/Weekend

Our solution helped our client trace the stations with varying usage statistics on weekends vs weekdays. It may help to reconsider the change in the station capacity for the weekends.

Zero Docks per Area

Stations with darker color shades are the points to be focused on. If the number of avg bikes is higher and min docks are lower in number, it shows that the area has a high demand for bikes but the number of stations is less. So, there is a need to add more stations to the particular area.

Key Solution Benefits

Techknomatic’s solution resulted in the following benefits:

• Simplified tracking of Bikes & Dock availability.
• Tracking Bike usage pattern & impact of the external parameter.
• Improvement in keeping bike availability in crowded areas.
• Identifying timings for bike maintenance and rearrangements.
• Forecasting helped our client anticipate the Bike usage trends.