Thin Locator Records for Distributed Inventory Queries
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Solution Overview
Problem
Current systems lack efficient methods for selecting alternatives and generating compound price quotes for vehicles, particularly in determining availability and pricing across different geographic regions, which complicates the purchasing process for customers due to the complexity of factors influencing vehicle prices and the difficulty in accessing dealer inventories.
Innovation Solution
A system and method that utilize distributed computing agents to generate thin locator style records for full inventory records, allowing for efficient database updates and query execution, and an automated quoting engine that maintains a database of vehicle specifications, features, and dealer inventory, enabling the selection of alternatives and generation of timely, detailed price quotes based on customer requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If distributed computing agents are used to access dealer inventories, then information availability is improved, but system complexity increases
Solution Approach 1:
The system segments the inventory management functionality by distributing computing agents to multiple dealer systems. Each agent operates semi-autonomously to manage local inventory data, while the central system coordinates across agents. This segmentation allows comprehensive information gathering without requiring the central system to directly manage all dealer inventories, reducing overall system complexity while improving information availability.
Solution Approach 2:
Distributed computing agents serve as intermediaries between the central system and dealer inventory systems. These agents handle data extraction, validation, and initial processing locally, then communicate with the central system. This intermediary layer simplifies the central system's burden while ensuring comprehensive inventory information is captured across the distributed network.
2Measurement precision
If complete inventory records are maintained centrally, then data accuracy is improved, but processing time increases
Solution Approach 1:
The system extracts only essential inventory data elements from complete dealer records and maintains them centrally in a normalized format. By taking out only the critical fields needed for pricing and availability calculations, the system ensures data accuracy for decision-making while avoiding the processing overhead of maintaining complete detailed records centrally. Full detail records remain at the dealer level.
Solution Approach 2:
Distributed computing agents perform preliminary data validation, normalization, and extraction at the source before transmitting to the central system. This preliminary action ensures data accuracy is established early in the process, reducing the need for extensive central processing and validation, thereby decreasing overall processing time while maintaining high data quality.
3Measurement precision
If multiple factors are considered in pricing calculations, then pricing accuracy is improved, but calculation complexity increases
Solution Approach 1:
The system applies different levels of pricing factor consideration to different scenarios and vehicle types. For standard vehicles, a core set of factors is applied, while for specialized cases, additional factors are incorporated. This local quality approach ensures pricing accuracy is tailored to each situation's needs without requiring all possible factors to be processed in every calculation, thereby managing complexity effectively.
Solution Approach 2:
The system dynamically adjusts which pricing factors are applied based on vehicle characteristics, market conditions, and dealer preferences. By changing parameters selectively rather than applying a fixed comprehensive set of factors to all cases, the system achieves high pricing accuracy where needed while reducing calculation complexity for routine transactions. The factor set is adaptive rather than static.
4Loss of information
If real-time inventory updates are implemented, then information freshness is improved, but system load increases
Solution Approach 1:
The system implements periodic inventory updates at strategically determined intervals rather than continuous real-time synchronization. Distributed agents and the central system exchange data at scheduled times based on update priorities and market conditions. This periodic action maintains information freshness for critical data while significantly reducing the continuous system load and energy consumption associated with constant real-time updates.
Solution Approach 2:
Distributed computing agents autonomously manage local inventory data validation and update scheduling without requiring constant central system intervention. Agents can perform self-service updates, conflict resolution, and data normalization locally, then synchronize with the central system only when necessary. This self-service capability maintains information freshness while reducing the overall system load by eliminating redundant centralized processing.
Data Source
AI summary
Systems and methods of database optimization and distributed computing are provided herein. In some embodiments, a method includes distributing remote agents to a plurality of remote computing systems, receiving incoming inventory from a database of available inventory via the remote agents, generating the database of available inventory that retains complete inventory records, generating thin locator style records for the complete inventory records, wherein the thin locator style records include key record identifiers, distributing a query to one or more of the remote agents, selecting the thin locator style records in response to the query, obtaining the complete inventory records corresponding to the thin locator style records of the selected key record identifiers, and providing the complete inventory records to a requestor.