Store Picking Routes Using Mobile Scanning Sequence Guidance
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Solution Overview
Problem
Existing systems for filling customer orders in large retail environments are inefficient and lack effective methods for guiding order fulfillment using mobile devices, particularly in diverse store layouts and product arrangements.
Innovation Solution
A system comprising a central computing system and mobile scanning devices that generate optimized routes for picking ordered items based on their locations within a store, using location indicators and item association tables to guide users in efficiently gathering and packing customer orders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If customers manually navigate through large stores to locate and select products, then they can purchase items, but the time and effort required for order fulfillment increases significantly
Solution Approach 1:
The system pre-calculates and determines the optimal picking route sequence before the picker enters the store. The central computing system receives the customer order, identifies all required items and their locations, computes the optimized route sequence in advance, and transmits it to the mobile scanning device. This preliminary route planning eliminates the need for real-time navigation decisions during picking, significantly reducing the time required to locate and collect products throughout the store.
Solution Approach 2:
The mobile scanning device serves as an intermediary between the central computing system and the picker. It receives the pre-calculated route sequence, displays it to the picker through its interface, and guides the picker through the optimized path. This intermediary device translates complex routing algorithms into simple, actionable directions for the picker, enabling efficient navigation through the store without requiring the picker to manually calculate or remember the optimal path.
2Ease of operation
If pickers navigate without guidance through diverse store layouts, then they can reach items, but the complexity of finding optimal paths increases
Solution Approach 1:
The system segments the complex task of navigating through a large store with diverse layouts into manageable components. The central computing system divides the store into zones or sections based on item locations, calculates optimal paths through each segment, and sequences these segments into a complete picking route. The mobile scanning device presents this segmented route information to the picker in discrete, sequential steps, making the complex navigation task easier to follow and execute.
Solution Approach 2:
The mobile scanning device acts as an intermediary that handles the complexity of route optimization algorithms while presenting simplified directions to the picker. The device receives complex routing calculations from the central computing system, processes this information, and displays it in an user-friendly format such as sequential item lists or turn-by-turn directions. This intermediary role shields the picker from the underlying computational complexity while providing ease of operation.
3Productivity
If multiple orders are fulfilled simultaneously without routing optimization, then order volume increases, but the time and resources required for fulfillment increase
Solution Approach 1:
The central computing system performs preliminary route optimization for multiple orders simultaneously. It receives batch orders, identifies all required items across all orders, determines their locations, and calculates optimized picking routes that consider the spatial relationships between items. This pre-computation of routing paths for multiple orders enables efficient batch fulfillment by minimizing redundant travel and optimizing resource allocation before pickers enter the store.
Solution Approach 2:
The system merges multiple order fulfillments into a coordinated routing process. When multiple orders are received, the central computing system combines the item lists, identifies overlapping items or items located near each other across different orders, and creates integrated picking routes that efficiently combine multiple order fulfillments. This merging approach allows pickers to fulfill multiple orders in a single trip through the store, reducing redundant travel and optimizing the use of picking resources.
Data Source
AI summary
A system includes a plurality of stocked items arranged throughout a store for picking, a central computing system, and a mobile scanning device. The central computing system is configured to receive a customer order that includes a plurality of ordered items indicating which of the stocked items are to be picked. Each of the ordered items is associated with a location in the store. The central computing system is configured to generate a route for the customer order. The route indicates a sequence in which one or more of the ordered items should be picked. The mobile scanning device includes a display and is configured to wirelessly receive the route from the central computing system and arrange items included in the route on the display based on the sequence indicated in the route.