Container Demand Forecasting From Web Access Data
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Solution Overview
Problem
Existing inventory management systems struggle to accurately manage and distribute containers with identical specifications across different types of contents due to varying demand and supply dynamics, especially in high-mix low-volume production scenarios.
Innovation Solution
An information processing device that utilizes machine learning to analyze web access information from containers to generate demand-supply information, predicting consumer preferences and optimizing container distribution based on network data and consumer behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If container identification codes are used to manage product distribution, then inventory management is enabled, but demand-supply information for different content types cannot be obtained
Solution Approach 1:
The container ID code system is enhanced to serve multiple functions: it not only identifies the container for inventory management but also stores web access information that links to content-type-specific demand-supply data. This allows the same identification system to provide both inventory tracking and demand prediction capabilities without adding separate complex systems.
Solution Approach 2:
Web access information acts as an intermediary between the container ID code and the demand-supply information. The web access information stored in the container ID code enables retrieval of detailed demand-supply data from external servers, bridging the gap between simple container identification and complex demand prediction without directly embedding all data in the ID code.
2Adaptability or versatility
If identical specification containers are used for multiple content types, then production flexibility is improved, but distribution accuracy deteriorates
Solution Approach 1:
While containers share identical physical specifications for production flexibility, each container is assigned specific web access information tailored to its content type. This allows the system to treat each container individually with content-specific distribution rules, achieving both production flexibility and distribution accuracy by applying local quality differentiation through information assignment.
Solution Approach 2:
Web access information including content-type-specific demand-supply data is pre-assigned to each container ID code before distribution. This preliminary action ensures that when identical containers are distributed, the system already has the necessary information to accurately track and manage them according to their specific content types, maintaining distribution accuracy despite production flexibility.
3Measurement precision
If machine learning models are trained with network information, then demand prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The data processing system is segmented into modular components: an information acquisition unit that collects web access data, a machine learning unit that processes the data, and a demand-supply information generation unit that outputs predictions. This segmentation allows complex machine learning operations to be performed systematically while maintaining manageable system architecture and enabling independent optimization of each module.
Data Source
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AI summary
[Object] To provide an information processing device that can stably distribute containers. [Solution] An information processing device 2 includes: an information acquisition unit 203A that acquires, based on web access information, acquisition information D12 as network information D15 related to a prediction target container; and a generation processing unit 203B that generates the demand-supply information D17 corresponding to the network information D15 of the prediction target container when the network information D15 related to the prediction target container and acquired by the information acquisition unit 203A is input to a learning model D18. The web access information is included in an information storage carrier attached to a container. The container is filled with a content and thus constitutes a product. The acquisition information D12 indicates a type of a content that a consumer of the product desires to acquire among contents with which a different container having a specification identical to a specification of the container can be filled. The learning model D18 is trained by machine learning with a correlation between the network information D15 of a training target container and demand-supply information D16 including, for each of the types of the contents, information related to demand or supply of the product in which the different container having the specification identical to the specification of the container has been filled.