Invoice Reconciliation via UPC Hierarchy Normalization
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Solution Overview
Problem
The existing systems face difficulties in reconciling distributor and retailer invoices for alcohol products sold under multiple Universal Price Codes (UPCs) and in various quantity units, leading to inefficiencies and costs due to discrepancies in invoiced amounts and units, which are often not accurately matched.
Innovation Solution
A computer-based system that creates normalized records for both distributor and retailer invoices by determining hierarchical UPC families, normalizing unit prices and quantities, and flagging discrepancies to facilitate accurate reconciliation and credit determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional invoice reconciliation methods are used for alcohol products with multiple UPCs and quantity units, then the process becomes complex and error-prone, but implementing a normalization system requires additional processing steps and computational resources
Solution Approach 1:
The system transforms invoice data by normalizing multiple UPCs and quantity units into a standard format. It changes the parameters of UPC codes and quantity measurements to a common reference frame, enabling accurate comparison and reconciliation between distributor and retailer invoices despite different representation formats.
Solution Approach 2:
The system introduces a normalization layer as an intermediary between the distributor invoice and retailer receiving invoice. This intermediary process standardizes the data from both sources using a common reference UPC and quantity unit, facilitating accurate matching and discrepancy identification without direct complex comparison of disparate formats.
2Productivity
If tolerance levels are set to avoid detailed reconciliation, then processing time is reduced, but financial discrepancies and billing errors increase
Solution Approach 1:
The system performs automatic self-reconciliation by comparing normalized invoice data and identifying discrepancies without human intervention. It autonomously matches line items, detects differences in quantity or pricing, and flags issues for review, eliminating the need for manual tolerance-based approximations while maintaining high processing speed.
Solution Approach 2:
The system implements a feedback mechanism where reconciliation results are automatically analyzed and discrepancies are flagged for corrective action. This closed-loop approach ensures that billing errors are detected and addressed, improving reliability while maintaining efficient processing through automated exception handling.
3Measurement precision
If manual reconciliation of each line item is performed, then accuracy is improved, but the time and labor costs increase significantly
Solution Approach 1:
The system replaces manual mechanical reconciliation processes with automated computational methods. It uses computer-based algorithms to perform normalization, comparison, and discrepancy detection that would be impractical to execute manually, achieving high accuracy while dramatically reducing the time and labor required for invoice reconciliation.
4Adaptability or versatility
If multiple UPCs and quantity units are supported without normalization, then product versatility is maintained, but invoice matching becomes extremely difficult
Solution Approach 1:
The system creates a universal normalization framework that can handle multiple UPCs and quantity units through a single standardization process. This multi-functional approach maintains the ability to represent diverse alcohol products in various units while providing a common reference format that simplifies matching and reconciliation across different invoice formats.
Data Source
AI summary
The present disclosure describes a system, method, and computer program for dynamically reconciling a retailer receiving invoice with a distributor invoice for products sold and invoiced under multiple UPC codes and in multiple units of quantity. A retailer receiving invoice is matched to to a distributor invoice. For each line item in both invoices, the UPC, quantity, and unit-price are normalized. Invoiced UPCs are normalized to a hierarchy level in a product family a master product database. Also, invoiced unit prices are compared to upper and lower price limits for invoiced UPCs to determine whether an invoiced UPC correlates to an invoiced unit price. Quantity and unit-price are normalized by converting price-correlated units to base-level units in a product hierarchy. Normalized UPCs, quantities, and unit price are compared to match line items across invoices and discover any discrepancies. Credits are matched to discrepancies.