MaaS Shopping Support System Route Optimization
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Solution Overview
Problem
Consumers face challenges in obtaining products while on the move due to inconvenient store locations, lack of transportation, and expensive or restrictive delivery services, leading to inefficient shopping experiences.
Innovation Solution
A MaaS-based system that allows users to request product purchases and deliveries along their scheduled routes, identifying suitable shopper candidates and dynamically updating execution methods based on factors like location, transportation, and time constraints, enabling direct or indirect delivery through public or private transportation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional delivery services are used to deliver products to consumers on the move, then product delivery can be achieved, but delivery costs increase and delivery options become restrictive
Solution Approach 1:
The system enables shoppers to voluntarily perform delivery actions by integrating product acquisition into their existing travel routines. Shoppers receive notifications about products along their route, visit stores independently, and deliver products back to their origin or designated locations, transforming passive consumers into active participants who serve themselves and others simultaneously.
Solution Approach 2:
The system combines multiple functions into a single platform: product discovery, shopper matching, route optimization, payment processing, and delivery coordination. This multi-functional approach eliminates the need for separate specialized services, reducing overall system complexity and cost while improving accessibility.
2Quantity of substance
If consumers make additional trips to stores to purchase products, then product acquisition is possible, but time consumption increases and travel efficiency decreases
Solution Approach 1:
The system performs preliminary actions by identifying products and potential shoppers before the consumer reaches their destination. Product notifications are sent in advance, and compatible shoppers are pre-matched based on their scheduled routes, allowing product acquisition to occur during existing travel rather than requiring separate trips.
Solution Approach 2:
The system dynamically adapts to changing conditions by continuously monitoring shopper availability, product availability, and route information. It adjusts matching algorithms and delivery options in real-time based on current travel patterns and constraints, optimizing product acquisition opportunities as they arise during actual travel sequences.
3Adaptability or versatility
If a unified gateway combines multiple transportation services, then mobility solutions become more accessible, but system complexity increases
Solution Approach 1:
The system introduces an intermediary platform that sits between consumers and the complex network of transportation and retail services. This intermediary handles the complexity of integrating multiple data sources, matching algorithms, and coordination logic, while presenting a simplified interface to users who only need to request product delivery along their route.
Data Source
AI summary
In an approach for generating the most optimal support execution method, a processor receives a shopping support request from a user requesting at least one product be purchased and delivered to the user while the user travels along a scheduled travel route via a public or private transportation method. A processor identifies a plurality of delivery places and a plurality of delivery times. A processor determines at least one shopper candidate who can purchase and deliver the at least one product. A processor presents, to the at least one shopper candidate, a list comprised of optimal support execution methods. Responsive to receiving an acceptance of the shopping support request, a processor sends a support task to at least one shopper. A processor enables the at least one shopper to execute the shopping support request. A processor dynamically updates one or more details of the support task.