The problem Door2dooR solves

Problems with current logistic management:

• The sad truth of the scenario is that a package stays put at just one place for longer than moving.
• Unhappy customers: A customer is less likely to buy from a merchant who cannot keep to the promised delivery time. Thus reduces Customer Lifetime Value (CLV).
• Cash flow trouble: If you don’t deliver your goods on time, you don’t get paid on time. This creates cash flow problems that affect your day-to-day operations.
• Manufacturing hold-up: If the delayed cargo is raw material you imported for an important project, you would be forced to halt production.
• Loss of goods value: An extended transit time can be disastrous for perishable goods. By the time the shipment reaches its destination, it might be worth nothing.

Some statistics to ponder upon

• 68% of consumers “are much less or less likely to shop with a retailer in the future if an item they purchased is not delivered within two days of the date promised.” (https://www.websitemagazine.com/blog/the-impact-of-late-and-inaccurate-deliveries-on-customer-loyalty)
• 17% of respondents will stop shopping with a retailer after receiving a late delivery one time.

Proposed Solution

• Conventionally, a package is transferred from src to dest by just one company it creates a lot of delay.
• We can reform this system by introducing the concept of hotspots and multiple party collaboration.
• In this system we would divide the journey of a package into several sub-journeys. These sub-journeys will then be covered by various existing resources like local courier companies/ road/rail/air transportation systems thus reducing the time consumed.
• These sub-journeys are chosen by considering cost, time and quality.
• Several routes are proposed among which client chooses one route suitable for him.

Challenges we ran into

1. Unavailability of data of railways and airways. Had to create one based on Indian railways site data. For this, we had to learn and use selenium.

2. Less number of hotspots results in less accurate prediction. More hotstops resulted in more data entry for trains and flights.