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Demand Forecasting

Demand Forecasting

Introduction

Maintainer: robert_cohen@bmc.com

In the Consumer Goods industry, demand forecasting is crucial for maintaining optimal inventory levels, reducing waste, and meeting customer demands. Control-M can be used to orchestrate the steps of data source retrieval, data collection, data storage, data processing, and data consumption. This orchestration ensures that all data handling processes are automated, monitored, and compliant with audit requirements, while meeting SLAs and providing comprehensive reporting.

Use Case Overview

By integrating data from supply chain systems, sales channels, and market analytics platforms with Control-M, organizations in the consumer goods industry can achieve streamlined, efficient, and accurate demand forecasting. Control-M provides end-to-end visibility and control over data workflows, allowing for seamless scheduling, monitoring, and management of demand forecasting tasks. This unified environment helps organizations respond quickly to market changes and consumer trends while maintaining control, governance, and compliance across the forecasting process.

Control-M supports advanced analytics and predictive modeling, enabling companies to anticipate demand fluctuations and adjust production and inventory levels proactively. By leveraging intelligent automation and customizable reporting standards, organizations can ensure that demand forecasting aligns with business goals and regulatory standards. This not only enhances operational efficiency but also minimizes the risk of stockouts or overproduction, allowing consumer goods companies to optimize performance and maintain a competitive edge in a dynamic market.

The Consensus Recording for this use case can be found here: Watch this video!

Use Case Technical Explanation

Creating a demand forecasting workflow for the consumer goods industry using Control-M involves several steps to ensure accurate data collection, integration, and analysis. Below is an example of how such a workflow might be structured.

  1. Data Source Retrieval

Control-M initiates the retrieval of data from multiple data sources, including:

  • Retrieve sales data from Salesforce CRM.
  • Retrieve inventory data from SAP ERP.
  • Retrieve market data from Nielsen and IRI APIs.
  • Retrieve weather data from Weather API.
  1. Data Collection Control-M schedules and automates the extraction of data from these sources, ensuring data consistency and timeliness.
  • Collect and aggregate all retrieved data into a centralized repository using Control-M for File Transfers.
  1. Data Storage

Control-M manages the storage of collected data by:

  • Store collected data in a structured format in Oracle/Postgres databases or Amazon S3.
  1. Data Processing

Control-M automates data processing tasks, which include:

  • Data cleaning and transformation using Apache Spark.
  • Aggregate and analyze data using Hadoop
  • Additional data processing using AWS Glue for refined insights.
  1. Data Consumption

Finally, Control-M orchestrates the delivery of processed data to end-users and regulatory bodies by:

  • Generate reports based on processed data.
  • Publish reports to Tableau and Power BI.
  • Notify stakeholders (e.g., supply chain managers, sales teams) through email notifications with job results and reports.

Audit, Compliance, and SLA Reporting

Control-M includes comprehensive features for audit compliance and SLA reporting, essential for the Consumer Goods industry:

  • Audit Trails: Maintain detailed logs of all job executions, user actions, and data changes. This meets regulatory requirements for transparency and accountability.
  • Compliance Reporting: Generate reports to demonstrate adherence to regulations and internal policies. SLA Reporting
  • Real-Time Monitoring: Continuously monitor job performance against predefined SLAs to ensure timely execution.
  • Alerts and Notifications: Send alerts and notifications in case of SLA breaches or job failures, enabling quick resolution.
  • Performance Dashboards: Visualize SLA metrics and job performance through customizable dashboards

To view the demo flow code-base, and all artifacts, please navigate to the Demand Forecasting Git Repository

Job Types Included

  • Salesforce
  • SAP ERP
  • API (Nielsen, IRI, Weather)
  • Databases (Oracle, PostgreSQL), AWS S3
  • Apache Spark, Hadoop, AWS Glue
  • Power BI, Tableau

Demo Environment Information

EnvironmentStatusFolder
Helix ProductionComing soon!
Helix Pre-ProductionComing soon!
VSE CTM PRODAvailableDemand_Forecasting_Demo
VSE CTM QAAvailableDemand_Forecasting_Demo
AWS CTM PRODAvailableDemand_Forecasting_Demo