Omnichannel Inventory Valuation via MIP Linearization
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Solution Overview
Problem
Current systems fail to efficiently and cost-effectively extract real-time inventory valuation data from omnichannel retail operations, leading to resource-intensive and time-consuming processes, and lack integration with dynamic real-time operating environments, resulting in margin leaks and lost sales.
Innovation Solution
A computer-implemented method and system that generates an omnichannel non-linear demand-driven inventory pricing model, reformulates it into a mixed-integer program, solves it numerically to eliminate non-convexity, and applies linear programming to recover initial valuations, updating inventory values in real-time across all channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large scale nonlinear nonconvex planning models are used for omnichannel revenue management, then pricing and inventory optimization is improved, but computational complexity and time consumption increase significantly
Solution Approach 1:
The patent segments the omnichannel revenue management problem into multiple independent channel-level linear programming problems. Each channel (e.g., online, offline, mobile) is solved separately using its own demand forecast and pricing parameters, which significantly reduces computational complexity while maintaining overall optimization effectiveness.
Solution Approach 2:
The patent transforms the original nonlinear nonconvex planning model into linear programming formulations by changing the mathematical parameters and assumptions. This involves linearizing demand functions, using piecewise linear approximations, and reformulating constraints to enable efficient computation while preserving the essential optimization objectives.
2Speed
If real-time inventory valuation is extracted from omnichannel revenue management systems, then real-time decision making is improved, but resource consumption and processing time increase
Solution Approach 1:
The patent performs preliminary computations by pre-calculating channel-level pricing and inventory recommendations during off-peak times or using historical data patterns. This allows the system to make real-time decisions based on pre-prepared frameworks rather than performing complex optimizations at the moment of decision, significantly reducing real-time resource consumption.
Solution Approach 2:
The patent creates simplified copies or representations of the full omnichannel revenue management model at the channel level. These reduced-scale models capture the essential dynamics and can be solved quickly to provide real-time valuations, serving as approximations of the full system without requiring its complete computational power.
3Adaptability or versatility
If inventory is shared across multiple sales channels through ship-from-store, then fulfillment flexibility is improved, but inventory management complexity increases
Solution Approach 1:
The patent segments inventory management by channel, treating each sales channel (online, offline, mobile) as a separate optimization unit. This allows the system to independently manage inventory allocations for each channel while respecting overall availability constraints, simplifying the management of shared inventory across multiple platforms.
Solution Approach 2:
The patent implements dynamic inventory valuation and allocation that automatically adjusts based on real-time demand patterns, pricing changes, and inventory levels. The system dynamically reoptimizes channel-level inventory assignments in response to changing conditions, providing flexibility without requiring manual intervention or complex static planning.
Data Source
AI summary
Embodiments are directed to a computer implemented method of generating inventory valuation data for an omnichannel (OC) retail operation. The method starts with an unsolvable OC nonlinear nonconvex problem, applies transformations to generate a mixed-integer program (MIP) that is a tractable linear nonconvex form, solves the MIP, fixes prices at optimal values to achieve dimensionality reduction and eliminate all non-convexity by eliminating the pricing dimension. The method further obtains inventory flow linear programming (LP) that is linear convex, and solves the LP to recover a dual solution as initial inventory valuations.