Digital Placement Allocation Using Co-Ordering Models
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Solution Overview
Problem
Current supply chain management systems for retail and e-commerce are inefficient in inventory placement and allocation, leading to increased shipping costs due to the lack of consideration for true customer activity and patterns in item ordering.
Innovation Solution
A software service that generates digital placement and allocation (DPA) plans by receiving digital demand forecasts, determining distribution among shipping locations, and using a model to allocate items based on patterns of co-ordering and ordering speed, thereby optimizing item placement and allocation across warehouses and retail locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If items are placed at default stocking locations based on historical sales levels, then inventory management is simplified, but shipping costs increase due to inefficient item placement
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimal item placement locations using machine learning models that analyze co-ordering patterns and customer activity data. Items are proactively positioned at fulfillment centers or stores where they are most likely to be ordered together, before actual customer orders are placed. This advance preparation eliminates the need for reactive shipping from suboptimal locations.
Solution Approach 2:
The patent replaces traditional mechanical/manual inventory management systems with an automated machine learning-based system. Instead of relying on historical sales data and manual placement decisions, the system uses computational models that process customer activity data, co-ordering patterns, and supply chain constraints to automatically determine optimal item placement and allocation strategies.
2Loss of energy
If items are placed close to intended sale locations to reduce shipping costs, then shipping efficiency improves, but true customer activity patterns are not captured
Solution Approach 1:
The system implements feedback loops where customer activity data from digital orders, mobile app usage, and online browsing behavior continuously informs and updates the machine learning models. These models learn from actual customer purchasing patterns and co-ordering preferences, refining item placement recommendations over time. The feedback mechanism ensures that placement decisions are based on real customer behavior rather than assumptions.
Solution Approach 2:
The patent transforms the approach by changing the input parameters from traditional historical sales data to real-time customer activity data including digital order patterns, mobile app interactions, and online browsing behavior. This parameter change enables the system to capture true customer activity patterns and use them to optimize item placement decisions dynamically.
3Measurement precision
If digital demand forecasts are aggregated by location identifiers, then distribution accuracy improves, but computational complexity increases
Solution Approach 1:
The system segments the aggregation process by applying different aggregation granularities to different types of data and locations. Customer activity data is aggregated at the individual customer level first, then by geographic region, and finally by fulfillment center. This hierarchical segmentation allows the system to maintain high precision in demand forecasting while managing computational complexity through structured data organization and processing at multiple levels.
Data Source
AI summary
Methods, systems, and platforms are described for digital placement and allocation planning. An unconstrained distribution of items in a retail supply chain may be determined from digital demand forecasts by aggregating the digital demand forecasts based on location identifiers. An item allocation ratio between shipping locations may be determined using a model based on items being ordered together and the speed of items being ordered. An unconstrained DPA plan may be generated, with the distribution and the ratio, for placing and allocating a projected total quantity. Constraints relating to the supply chain may be identified. In response to the constraints, a constrained distribution may be generated from an unconstrained distribution. A constrained plan may be generated in response to the constrained distribution. The unconstrained or constrained DPA plan may be sent to a plan executor for initiating movements of items according to the plan within the supply chain.