Container Picking Workstation to Eliminate Offline Bagging

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Solution Overview

Problem

Conventional order fulfillment systems are inefficient in terms of packing density and resource utilization, as they rely on conservative container capacity and offline bagging of totes, leading to increased labor time and unnecessary mobile robot cycles.

Innovation Solution

Implementing a container-based picking workstation that allows operators to fill containers to capacity at the workstation, utilizing sensors and controllers to optimize the picking process, reduce labor time, and eliminate unnecessary mobile robot cycles.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional systems transfer totes using mobile robots between storage and picking workstations, then inventory can be moved between locations, but packing density is suboptimal and labor time per bag is excessive (15-20 seconds)

Engineering Contradiction:

Improvelabor time per bagVSAvoidtime spent on offline bagging operations

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system pre-positions containers at the picking workstation before the picker needs them. The mobile robot delivers containers to the workstation in advance, and the system pre-fills containers with eaches based on order predictions, so that when the picker needs a container, it is already ready and positioned for immediate use, eliminating the need for offline bagging operations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The picking workstation acts as an intermediary between storage and final order fulfillment. It receives containers from mobile robots, pre-fills them with eaches, and prepares them for pickup. This intermediary function consolidates bagging operations at a centralized location with optimal resource utilization, reducing the time pickers spend on bagging tasks.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If operators manually bag totes offline, then containers can be prepared, but resource utilization is inefficient and packing density is suboptimal

Engineering Contradiction:

Improveresource utilizationVSAvoidcontainer capacity utilization

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The system uses sensors and controllers to monitor container filling levels, weight thresholds, and order requirements in real-time. This feedback mechanism allows the system to optimize container capacity utilization by knowing exactly when to stop filling, when to transfer containers to the workstation, and how to allocate eaches efficiently, maximizing packing density without exceeding weight limits.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system dynamically adjusts container allocation and filling parameters based on order predictions, container capacity, and weight thresholds. By changing parameters such as the number of containers pre-positioned, the filling rate, and the transfer timing, the system optimizes resource utilization and achieves higher packing density compared to static manual bagging processes.

Inventive Principle:

Principle #35Parameter changes

3Extent of automation

If mobile robots are used for tote transfer, then inventory movement is automated, but unnecessary robot cycles increase system complexity and reduce efficiency

Engineering Contradiction:

Improveautomated inventory transferVSAvoidmobile robot cycle coordination

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The system extracts the bagging and container preparation functions from the mobile robot's task list and assigns them to a dedicated picking workstation. The mobile robot's role is simplified to only transporting containers and totes between storage and the workstation, eliminating complex coordination requirements for robot cycles involved in bagging operations and reducing overall system complexity.

Inventive Principle:

Principle #2Taking out (Extraction)

4Quantity of substance

If containers are not filled to capacity, then weight thresholds are not exceeded, but packing density and resource efficiency are reduced

Engineering Contradiction:

Improvecontainer capacityVSAvoidcontainer weight

Core Design Contradiction:

Quantity of substanceVSWeight of moving object

Solution Approach 1:

The system replaces manual judgment and estimation in container filling with automated sensors and controllers that precisely measure filling levels and weight. This substitution of mechanical/manual processes with automated sensing and control enables the system to fill containers to optimal capacity while accurately monitoring and adhering to weight thresholds, achieving both high packing density and weight compliance.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20260109541A1Automatic storage and retrieval system having container based picking workstation

Publication Date: 2026.04.23 WALMART APOLLO LLC

AI summary

An order fulfillment system for fulfilling orders for goods includes a storage structure configured to store totes, mobile robots configured to transport the totes, and a workstation. The workstation includes a first station configured to receive product totes storing the goods for fulfilling the orders and a second station configured to receive order totes storing the goods of fulfilled orders. The order fulfillment system may also include containers configured to receive the goods from the product totes or the order totes. The containers may be stored at a third station of the workstation and/or in an order tote located at the second station of the workstation and/or in at least one shopping cart located at the workstation.