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Process Details

  • Inputs: Historical sales data (quantity, value, SKU, location, customer). Promotional plans and calendars. Pricing information. New Product Introduction (NPI) schedules.
  • Outputs: Demand forecasts incorporating causal factors Forecast accuracy reports
  • 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 Ingestion & AggregationCollects historical sales data, promotions, new product introductions (NPIs), and pricing changes from ERP, CRM and other systems.
  2. Causal Factor Analysis & Impact QuantificationQuantifies the impact of various causal factors (promotions, price changes, NPIs, external events) on demand, allowing for more accurate "what-if" scenario planning.
  3. Forecast Enrichment & Collaboration SupportAnalyzes the accuracy of past overrides and provide feedback or adjust weighting for future inputs.
  4. Consensus Forecast FacilitationHighlights discrepancies between the statistical forecast, sales forecasts, and marketing inputs. Facilitates the creation of a consensus demand plan by tracking changes and assumptions.
  5. Report Generation & AlertingGenerates a report to facilitate review.

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

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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