Cloud-Based Shipping Transaction Data Quality Management
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Solution Overview
Problem
Current supply chain transaction systems face complexity and inefficiency in managing data quality across multiple partners, requiring extensive resources for data normalization, error detection, and correction, with limited visibility and diagnostic capabilities for senders, leading to prolonged resolution times for message integration issues.
Innovation Solution
A cloud-based supply chain transaction service that normalizes messages into a common format, provides visibility into the message integration pipeline, allows collaborative access, and enables participants to identify and correct errors directly within the system, reducing the need for IT intervention and streamlining the data management process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data normalization approaches are used with mapping tables or additional capabilities in sending/receiving systems, then data quality and consistency are improved, but system complexity increases significantly as the number of partners grows
Solution Approach 1:
The patent introduces a cloud-based intermediary service that sits between sending and receiving systems. This intermediary handles data normalization, format translation, and quality validation centrally, eliminating the need for each partner system to maintain complex mapping tables and normalization capabilities. The intermediary acts as a mediator that standardizes data exchange protocols across all partners.
Solution Approach 2:
The cloud-based service provides universal data normalization and validation capabilities that serve all partners through a single platform. Instead of each partner implementing their own normalization logic, the universal service handles all data types and formats through standardized interfaces, reducing overall system complexity while maintaining data quality consistency.
2Reliability
If IT personnel manually reconstruct and resend messages after errors are detected, then data accuracy is maintained, but resolution time increases to days or weeks
Solution Approach 1:
The patent enables business users to self-service error correction directly in the cloud interface. When errors are detected in data exchange, users can view error details, edit the problematic data fields, and resend corrected messages without requiring IT personnel intervention. This self-service capability maintains data accuracy while dramatically reducing resolution time from weeks to minutes.
Solution Approach 2:
The system provides real-time feedback to users about data quality issues and error locations. The cloud-based service sends notifications and error reports back to users, enabling them to identify and correct problems quickly without waiting for manual IT investigation. This feedback mechanism accelerates the error resolution cycle while maintaining data integrity.
3Difficulty of detecting and measuring
If senders have limited visibility into receiver integration systems and data pipelines, then system security is maintained, but diagnostic capability and error detection improve when senders can access logging information
Solution Approach 1:
The cloud-based intermediary service provides a centralized logging and diagnostic platform that all partners can access. This intermediary collects error information, system status, and diagnostic data from all data exchange transactions, making it available to users through standardized interfaces. Users gain visibility into the data pipeline without direct access to other partners' internal systems, maintaining security while improving diagnostic capability.
4Productivity
If cloud-based managed services are used for message integration, then scalability and maintenance are improved, but visibility into multi-stage processing and error location becomes limited
Solution Approach 1:
The patent segments the cloud-based processing into distinct, visible stages with clear error tracking. The multi-stage message processing is divided into identifiable steps (validation, transformation, routing, delivery), each with its own error detection and logging mechanisms. This segmentation allows users to locate errors at specific stages while maintaining the scalability and maintenance benefits of cloud-based managed services.
Data Source
AI summary
Files related to shipping transactions are received from participants. Each of the files are processed in a first stage of a multi-stage process that includes attempting to translate the files to generate a translated file and storing an error status for each file in which the translation experienced an error. For those files that are successfully translated, they are processed in subsequent stage(s) including applying application rule(s) to those files and storing an error status for those translated files resulting in an error. A participant may view a summary of those translated files that are associated with an error status. The participant may further view details of an error to be displayed and retrieve the underlying file. The participant may edit the file to correct the issue and republish the modified file to be reprocessed in the stage of the multi-stage process that identified the error.