Sales Order Data Collection System for ERP Integration
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Solution Overview
Problem
Retail businesses face inefficiencies in managing sales order data across multiple sources due to manual entry processes, which are labor-intensive and prone to errors, and existing systems lack comprehensive automation for integrating diverse transaction sources and formats.
Innovation Solution
A computer-implemented sales order data collection and management system (SODCMS) that automates the collection, categorization, validation, and transmission of sales order data from various sources into an enterprise resource planning (ERP) system, using a cloud-based platform with email parsing and API integration to minimize human intervention and streamline data importation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual entry processes are used for sales order data, then flexibility in handling diverse transaction sources is maintained, but labor costs increase and error rates rise
Solution Approach 1:
The system segments data collection from multiple sources (email, web forms, CSV files, APIs) into separate collection modules, each handling specific data formats. This segmentation allows automated processing of diverse transaction sources while maintaining modular system architecture that manages complexity.
Solution Approach 2:
The patent introduces an intermediary data collection system that acts as a buffer between diverse transaction sources and the ERP system. This intermediary layer standardizes data from various formats (emails, web forms, CSV, APIs) into a unified structure, enabling automated processing without direct integration complexity between each source and ERP.
2Adaptability or versatility
If comprehensive customized system integration is implemented to handle diverse transaction sources, then data collection completeness improves, but system complexity and implementation difficulty increase
Solution Approach 1:
The data collection system is designed with universal multi-functionality to handle diverse transaction sources including email messages, web forms, CSV files, and API integrations through a single unified platform. This universal approach allows the system to adapt to multiple data formats and sources without requiring separate customized integrations for each source, thereby reducing overall system complexity while maintaining versatility.
3Productivity
If automated data collection is implemented across multiple sources, then productivity increases, but data validation and error handling complexity increase
Solution Approach 1:
The system implements feedback mechanisms through automated validation rules that check collected data against predefined criteria (e.g., email format validation, required field checks, data type verification). When validation failures occur, the system generates error reports and alerts users for correction, ensuring data accuracy while maintaining high processing speed through automated rather than manual verification.
Solution Approach 2:
The patent applies preliminary action by performing data validation and formatting operations immediately upon data collection, before data is transmitted to the ERP system. This preliminary processing includes validating email formats, checking required fields, and standardizing data structures, thereby ensuring data reliability is established early in the workflow rather than requiring complex post-processing verification.
4Loss of time
If manual transaction entry is performed daily, then data accuracy can be monitored closely, but time consumption and labor costs increase
Solution Approach 1:
The system implements self-service functionality where the automated data collection and validation processes operate independently without requiring manual intervention for each transaction entry. The system automatically collects data from multiple sources, validates it against predefined rules, and prepares it for ERP import, thereby dramatically reducing time consumption while maintaining operational simplicity through a user-friendly interface for monitoring and configuration.
Data Source
AI summary
A computer implemented method and a sales order data collection and management system (SODCMS) collect and manage sales order data including data from a web based sales order, a manual sales order, a sales order over a communication device, a third party consumer to consumer web based sales order, etc. The SODCMS receives sales order data from one or more sources and categorizes the sales order data based on a source type. The SODCMS parses the categorized sales order data based on filtering criteria, stores the parsed sales order data in one or more databases, and validates the stored sales order data against validation criteria. The SODCMS processes the validated sales order data and transmits the processed sales order data to a resource management platform. The SODCMS renders messages on modifications and discrepancies associated with the sales order data to consumers based on the validation of the sales order data.