Demand Density Modeling for Order Batching Efficiency
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Solution Overview
Problem
Current methods lack a robust and scalable approach to model demand indicators, particularly in transaction platforms, which are crucial for operational efficiency and fulfillment processes.
Innovation Solution
A method and system for calculating a demand indicator (DD) using efficiency measures and vector angles between order pairs, expressed as DD = w1*first efficiency measure + w2*second efficiency measure + w3*cosine of the angle, where w1, w2, and w3 are weights summing to 1, to identify inflection points for batching efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional demand modeling approaches are used, then implementation is simple, but the robustness and scalability of the demand indicator is insufficient
Solution Approach 1:
The patent transforms demand modeling from qualitative to quantitative by introducing mathematical parameters including efficiency measures (time/distance), vector representations of order pairs, and angular calculations. The demand indicator is computed through a standardized formula combining these parameters, enabling rigorous and scalable measurement that resolves the contradiction between robustness and complexity.
2Measurement precision
If demand density is quantified without a rigorous model, then the process is faster, but the measurement precision is insufficient for optimal batching
Solution Approach 1:
The patent replaces manual or heuristic demand assessment with an automated mathematical system. By substituting mechanical/manual processes with computational algorithms that calculate efficiency measures, vectors, and angles, the system achieves high measurement precision while minimizing time through automated processing of order data.
3Productivity
If operational parameters are adjusted based on demand indicators, then batching efficiency improves, but the system complexity increases
Solution Approach 1:
The patent establishes a feedback mechanism where the demand indicator, calculated from order pair efficiency measures and angular relationships, feeds back into operational decision-making. This feedback loop enables continuous optimization of batching efficiency by adjusting operational parameters based on quantified demand density, resolving the contradiction between productivity improvement and system complexity.
Data Source
AI summary
Aspects concern a method for providing an indicator of demand density comprising the steps of: determining a first efficiency measure between a first source and a second source, each source associated with at least one good; determining a second efficiency measure between a first destination and a second destination; associating the first destination with the first source to form a first order pair and the second destination with the second source to form a second order pair; determining a first vector associated with the first order pair, and a second vector associated with the second order pair; measuring an angle between the first vector and the second vector; and calculating the indicator of demand density based on a function of the first efficiency measure, the second efficiency measure, and the angle.