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Process Details

  • Inputs: Bank statements, General ledger transaction data
  • Outputs: Bank reconciliation report, List of outstanding items and discrepancies, Suggested journal entries for bank fees/interest
  • Systems: ERP System (SAP, Oracle NetSuite, Microsoft Dynamics 365),Supplier Relationship Management (SRM) Systems (SAP Ariba, Coupa)

Supplier Performance Data Collection and Initial Analysis

Agnostic

Use Case Overview

An AI agent that automates the collection of supplier performance data from diverse internal and external sources, standardizes the information, calculates key performance indicators (KPIs), and provides an initial analysis, flagging underperforming suppliers or emerging risks for further review by procurement professionals.

Challenges

  • High potential for costly errors from manual data handling.
  • Significant time and resources are spent on repetitive, low-value work
  • The manual process is difficult to scale without increasing headcount.
  • Process bottlenecks lead to delays and missed deadlines.

Solution

This use case solution follows these general steps at a high level:

  1. Data AcquisitionIt retrieves bank statements (Excel) and transaction data from the ERP System's General Ledger.
  2. Transaction Matching Applies pre-defined rules to match bank transactions with GL entries based on amount, date, reference numbers, and descriptions.
  3. Exception Identification & Categorization Identifies and categorizes unmatched items, outstanding checks, deposits in transit, bank fees, and interest.
  4. Automated Journal Entry SuggestionFor identified bank fees or interest, the AI agent can suggest journal entries to be posted in the ERP System.
  5. Reporting & ReviewGenerates a reconciliation report highlighting matched items, outstanding items, and exceptions, then routes it for review and approval.

Primary Benefits

  • Increase EfficiencyDramatically reduce the time and manual effort required to complete the process.
  • Enhance AccuracyEliminate human error to ensure data integrity and reduce financial risk.
  • Empower EmployeesFree your team from monotonous tasks, allowing them to focus on strategic work that requires their expertise.
  • Improve ScalabilityHandle growing volumes of work without a proportional increase in operational costs.
  • Ensure TransparencyMaintain a complete, auditable trail of every action the AI agent takes, described in plain English.

Related Use Cases

FAQ

How does the agent collect and standardize diverse metrics like On-Time Delivery from our ERP, defect rates from our QMS, and service levels from our ticketing system? +

The agent is designed to be a central data aggregator. It uses a variety of methods to connect to your source systems:
Direct API Connections: It securely connects to your ERP, Quality Management System (QMS), Warehouse Management System (WMS), and other structured databases to pull raw transactional data.
File Ingestion: It can automatically process reports (e.g., CSV, Excel, PDF) exported from systems that do not have APIs.

How customizable are the performance scorecards and KPIs? +

The agent is highly flexible. You can create different scorecards with different KPIs and weightings for various supplier segments. For example:
Direct Material Suppliers: Might be heavily weighted on On-Time Delivery and Quality (defect rates).
Indirect/Service Providers: Might be weighted more on service level agreement (SLA) compliance, responsiveness, and cost savings.
Strategic Partners: Might include additional qualitative metrics around innovation and collaboration.

What is involved in the initial implementation and configuration of the agent? +

Customization and configuration is a structured process to align the agent with your performance management framework, typically taking 4-6 weeks. It involves:
KPI Definition: Working with your procurement team to define the key metrics you want to track for each supplier segment.
Data Source Mapping: Identifying the source systems where the raw data for each KPI resides.
Integration & Configuration: Setting up the secure data connections and configuring the business rules for calculations, scorecards, and alerts.
Validation & Go-Live: Running the agent on historical data to validate the results before activating it for ongoing monitoring.

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