Inventory Availability Prediction for Faster Warehouse Picking
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Solution Overview
Problem
Current delivery systems face challenges in predicting inventory availability, leading to pickers wasting time searching for unavailable items and customer frustration due to fluctuating item stock levels.
Innovation Solution
A machine-learned model is trained using historical delivery order data to predict item availability, providing probabilistic instructions to pickers and customers, reducing search time and improving satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pickers search for items at warehouse locations without availability prediction, then they can fulfill orders, but they waste time looking for unavailable items
Solution Approach 1:
The system performs preliminary actions by training a machine-learned model on historical delivery order data to predict item availability before pickers arrive at warehouse locations. The model processes characteristics such as item type, warehouse location, time of day, and historical availability to generate availability probabilities in advance, enabling pickers to receive targeted instructions without wasting time searching for unavailable items.
2Reliability
If real-time inventory tracking is implemented, then item availability can be monitored, but system complexity increases
Solution Approach 1:
The patent introduces a machine-learned model as an intermediary between historical delivery order data and real-time inventory status. Instead of implementing complex real-time tracking infrastructure, the model processes historical characteristics (item type, warehouse, time, delivery patterns) to predict current availability, serving as a computational mediator that simplifies the system while maintaining reliability.
3Productivity
If pickers are sent to warehouses without availability information, then orders can be processed, but customer satisfaction decreases due to unavailable items
Solution Approach 1:
The system implements feedback by using historical delivery order outcomes (whether items were successfully picked or not) to train the machine-learned model. This feedback loop continuously improves prediction accuracy, allowing the system to provide reliable availability information to pickers and customers, reducing frustration from unavailable items while maintaining order processing capability.
Data Source
AI summary
A method for predicting inventory availability, involving receiving a delivery order including a plurality of items and a delivery location, and identifying a warehouse for picking the plurality of items. The method retrieves a machine-learned model that predicts a probability that an item is available at the warehouse. The machine-learned model is trained, using machine learning, based in part on a plurality of datasets. The plurality of datasets include data describing items included in previous delivery orders, whether each item in each previous delivery order was picked, a warehouse associated with each previous delivery order, and a plurality of characteristics associated with each of the items. The method predicts the probability that one of the plurality of items in the delivery order is available at the warehouse, and generates an instruction to a picker based on the probability. An instruction is transmitted to a mobile device of the picker.