Dynamic Distance Threshold for Mobile Retail Order Invitations
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Solution Overview
Problem
Mobile retail units face challenges in managing routes and order invitations efficiently, including limited storage space, time constraints, and resource limitations, which affect customer engagement and revenue maximization while maintaining customer loyalty.
Innovation Solution
A method that assigns geographical zones to mobile retail units based on customer segments and inventory, dynamically adjusts distance thresholds for order invitations based on delay probabilities, and optimizes resource allocation to maximize engagement and minimize waiting times and out-of-stock issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the mobile retail unit sends order invitations to all detected customers within the service area, then customer engagement is maximized, but the probability of illegitimate delays increases due to resource constraints
Solution Approach 1:
The system dynamically adjusts the distance threshold parameter based on delay probability. When delay probability exceeds a threshold, the system reduces the distance threshold to limit the number of customers receiving invitations, thereby controlling resource consumption while maintaining acceptable engagement levels
Solution Approach 2:
The system continuously monitors delay probability as feedback from the system state and uses this information to adjust the distance threshold in real-time. This closed-loop control ensures that customer engagement is maximized while keeping delays within acceptable bounds
2Quantity of substance
If the mobile retail unit increases the distance threshold to reach more customers, then the number of potential customers increases, but the resource constraints are violated leading to more delays
Solution Approach 1:
The distance threshold is made dynamic rather than static. It automatically adjusts based on real-time delay probability assessments, allowing the system to expand its customer reach when resources permit and contract when resource constraints would lead to excessive delays
3Productivity
If the mobile retail unit serves more geographical zones, then revenue maximization is achieved, but resource limitations are exceeded
Solution Approach 1:
The service area is segmented into geographical zones that are dynamically assigned to mobile retail units based on user segments and inventory characteristics. This segmentation allows for optimized resource allocation across multiple zones while managing the complexity of serving diverse customer bases
4Loss of time
If the mobile retail unit optimizes for fast service delivery, then customer satisfaction is improved, but the number of customers that can be served is reduced
Solution Approach 1:
The system uses a distance threshold that may be more restrictive than the absolute maximum possible, sacrificing some potential customer reach to ensure that service delivery time constraints are met for the customers who are served
Data Source
AI summary
A method, apparatus and product including: assigning geographical zones to a mobile retail unit based on first user segments of customers from the geographical zones and based on second user segments associated to an inventory of the mobile retail unit; based on the mobile retail unit being located at a location in the geographical zones, detecting a first group of customers within a first distance threshold from the location; sending a first set of order invitations to the first group of customers; determining to adjust the first distance threshold based on a delay probability; based on said determining, increasing the first distance threshold to a second distance threshold, wherein a second group of customers are detected within the second distance threshold; and sending a second set of order invitations to the second group of customers.