Automated Object Placement Mode Determination
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Solution Overview
Problem
Conventional methods for placing physical objects in storage spaces are largely dependent on personal experience, are not scientific, prone to errors, time-consuming, and inefficient, leading to improper placement modes that reduce sales volumes.
Innovation Solution
A method and system that determine attraction factors for storage areas and spatial elasticity factors of objects to scientifically and efficiently determine the optimal placement mode, using attraction factor determining, spatial elasticity factor determining, and placement mode determining devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If staff determine placement mode empirically based on personal experience, then the process is simple to implement, but the accuracy and scientificity of placement decisions deteriorates
Solution Approach 1:
The patent replaces the manual empirical decision-making process with an automated computer-based system that uses algorithms and data analysis to determine placement modes. The system substitutes human staff's subjective judgment with objective computational analysis, thereby improving accuracy while maintaining ease of implementation through automated processing.
Solution Approach 2:
The system enables self-service by allowing the computer to automatically analyze sales data, customer behavior patterns, and product characteristics to generate placement recommendations without requiring staff expertise. The system serves itself by using its own computational resources and data processing capabilities to make placement decisions independently.
2Device complexity
If staff determine placement mode manually, then the system complexity is low, but the time consumption and efficiency deteriorates
Solution Approach 1:
The patent replaces manual staff operations with an automated computer-based system that processes placement determination tasks. This substitution dramatically reduces time consumption by using automated data processing and algorithmic analysis, while the system complexity remains manageable through the use of standard computing hardware and software.
Solution Approach 2:
The system introduces a computer-based intermediary that acts as a mediator between raw sales data and placement decisions. This intermediary processes and analyzes data automatically, serving as an efficient bridge between information and actionable insights, thereby improving productivity without requiring complex manual procedures.
3Ease of operation
If improper placement mode is used, then the implementation is simple, but the sales volume and revenue deteriorates
Solution Approach 1:
The system implements feedback mechanisms by analyzing sales data and customer behavior patterns to continuously improve placement recommendations. The computer-based system uses historical sales information and real-time data to adjust placement modes, ensuring that simple placement decisions are based on reliable, data-driven insights that maximize sales volume and revenue.
Solution Approach 2:
The system performs preliminary analysis of sales data, product characteristics, and customer preferences before determining placement modes. By conducting this preliminary work automatically, the system ensures that placement decisions are both simple to implement and reliable in terms of generating sales volume, as the computational analysis is completed in advance to guide placement decisions.
Data Source
AI summary
Machine logic (for example, software) for determining a placement mode of at least one kind of objects in a multiplicity of storage areas are disclosed. A placement method includes the following operations: determining attraction factors of the multiplicity of storage areas, an attraction factor of each storage area indicating a capability that the storage area attracts attention of a customer; determining a spatial elasticity factor of the at least one kind of objects, a spatial elasticity factor of each kind of objects indicating an impact of a change of the storage areas where the kind of objects are placed on an attention degree of the kind of objects; and determining the placement mode of the objects in the multiplicity of storage areas, at least according to the attraction factors of the multiplicity of storage areas and the spatial elasticity factor of the at least one kind of objects.