BOPIS Order Prioritization via Pickup Time Prediction
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Solution Overview
Problem
Retailers face challenges in efficiently managing and prioritizing buy-online pickup-in-store (BOPIS) orders, leading to stressful work environments and inefficiencies due to high demand and associate churn, especially with the surge in online shopping.
Innovation Solution
A method that prioritizes BOPIS orders using a cognitive system trained on historical data to estimate pickup times based on user profiles and order details, including product properties and store characteristics, allowing for dynamic ranking and urgent order notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If BOPIS orders are processed in traditional order without prioritization, then all orders are treated equally, but fulfillment efficiency decreases and associate stress increases
Solution Approach 1:
The system dynamically prioritizes BOPIS orders based on real-time factors such as estimated pickup time, customer history, and order characteristics. Instead of static first-come-first-served processing, the system continuously adjusts order priority rankings to optimize fulfillment efficiency and reduce associate stress.
Solution Approach 2:
The system changes the parameter of order processing from equal treatment to differentiated treatment based on multiple parameters including estimated pickup time, customer pickup history, order velocity, and product characteristics. This enables efficient resource allocation while managing complexity through automated classification.
2Productivity
If a cognitive system is implemented to prioritize orders, then fulfillment optimization improves, but system complexity increases
Solution Approach 1:
The cognitive system automatically analyzes order data, estimates pickup times, and generates priority rankings without requiring manual intervention. The system serves itself by using historical data and machine learning to make prioritization decisions, reducing the need for complex manual management processes.
Solution Approach 2:
The patent replaces manual order prioritization mechanics with a cognitive system that uses machine learning and data analysis. This substitution automates the complex decision-making process, improving efficiency while managing system complexity through intelligent algorithms rather than manual procedures.
3Measurement precision
If orders are prioritized based on multiple factors, then fulfillment accuracy improves, but processing complexity increases
Solution Approach 1:
The system performs preliminary analysis of customer history, order characteristics, and product properties before final prioritization. By pre-processing and categorizing data in advance, the system achieves accurate pickup time estimates while managing processing complexity through staged analysis rather than simultaneous complex computation.
Data Source
AI summary
Buy-online pickup-in-store (BOPIS) order fulfillment is optimized by prioritizing BOPIS orders according to estimated pickup times. The pickup time for a given order is based on a customer profile and order details which can include product properties for a product in the order, characteristics of the physical store, and an order velocity indicative of an urgency of the customer in placing the order. The pickup times are estimated by a cognitive system trained with input samples comprised of historical BOPIS orders associated with actual pickup delays. The customer profiles include historical information regarding previous BOPIS orders from the customer such as a percentage of previous BOPIS orders actually picked up, and an average pickup delay for the previous BOPIS orders that were picked up. If any order pickup time is less than a predefined threshold, the system notifies the store associate with a warning that it is urgent.