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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 Systems (SAP S/4HANA, Oracle Cloud ERP, Microsoft Dynamics 365),CRM Systems (Salesforce)

Demand Forecasting and Planning

Supply Chain

Use Case Overview

An AI agent that ingests historical sales data, market trends, promotional information, and external factors (e.g., weather, economic indicators, social sentiment) to generate more accurate and granular demand forecasts. It supports the consensus forecasting process by highlighting discrepancies, providing forecast explanations, and facilitating scenario analysis.

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 Acquisition It retrieves bank statements (Excel) and transaction data from the ERP System's General Ledger.
  2. Transaction MatchingApplies 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

What does a typical implementation process involve? +

The customization and configuration typically takes 4-8 weeks.
Data Integration: We establish secure connections to your source systems (ERP, CRM, etc.) to create a unified historical dataset.
Customization and Configuration: We work with your team to select the relevant causal factors and configure the initial forecasting process.
Initial Forecast & Validation: The agent generates its first forecast based on your historical data, and your team validates the results against their experience.
Go-Live & Training: The agent is activated for the next planning cycle, and your team is trained on how to interpret the results and manage exceptions.

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