Batch Picking System for Expedited Order Fulfillment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Brick and mortar stores face challenges in efficiently fulfilling expedited sales orders with short and reliable lead times, as they need to balance the availability of pickers, batch processing, and conveyance types to meet customer expectations for instant or same-day delivery.
Innovation Solution
A system utilizing processors and computer-readable storage devices to estimate fulfillment time intervals, cluster orders for batch picking, and assign pickers using k-means clustering and Pareto optimization to minimize pick walks and maximize tote fills, while determining a receivable clock time for promising goods to customers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If brick and mortar stores process expedited orders individually to meet short lead times, then customer service level improves, but operational complexity and processing time increase
Solution Approach 1:
The patent combines multiple expedited orders into batch groups that are processed together. Orders are clustered based on item similarity and fulfillment time windows, allowing the system to achieve economies of scale while still meeting individual customer lead time requirements. This merging approach reduces operational complexity by processing multiple orders through a unified batch fulfillment process.
Solution Approach 2:
The patent segments the fulfillment process into distinct phases: order reception, batch grouping, picker assignment, and fulfillment execution. By dividing the overall process into manageable segments with clear decision points, the system reduces operational complexity while maintaining reliability. Each segment can be optimized independently and managed by different system components.
2Productivity
If stores use batch processing for expedited orders to improve efficiency, then productivity increases, but fulfillment time reliability may deteriorate
Solution Approach 1:
The patent implements dynamic batch formation where order grouping is not fixed but adapts based on real-time factors such as item availability, picker workload, and estimated fulfillment times. The system continuously evaluates and reconfigures batches to ensure that lead time requirements are met while maximizing processing efficiency. This dynamic approach allows the system to maintain reliability even as conditions change.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor fulfillment progress and adjust batch processing parameters in real-time. If a batch is projected to miss lead time targets, the system can redistribute orders, reassign pickers, or adjust priorities. This feedback loop ensures that productivity gains from batch processing do not compromise fulfillment reliability.
3Productivity
If stores increase picker availability to handle more expedited orders, then service capacity improves, but operational costs increase
Solution Approach 1:
The patent creates a flexible picker assignment system where pickers can be dynamically allocated to different order batches based on current workload and expertise. Pickers are not dedicated to specific order types or time periods but can serve multiple functions across different batches. This universality allows the system to maximize the utilization of existing picker resources without needing to increase headcount, thereby improving capacity while controlling labor costs.
Data Source
AI summary
Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving a request for an order comprising one or more items; determining when the order is eligible for batch picking by finding one or more other orders comprising at least one of the one or more items; and when the order is determined to be eligible for batch picking with the one or more other orders: creating one or more clusters by clustering together (1) the at least one of the one or more items of the order and (2) the at least one of the one or more items of the one or more other orders; estimating, for each respective cluster of the one or more clusters, an estimated fulfillment time interval to make ready the order; and assigning each respective cluster of the one or more clusters to at least one respective picker of one or more respective pickers. Other embodiments are disclosed herein.