Retail Forecasting

Introduction

Maintainer: robert_cohen@bmc.com

In the consumer goods sector, accurate retail forecasting is essential to align inventory levels with consumer demand, ensure product availability, and minimize stockouts or excess stock. With changing market trends and consumer preferences, retail forecasting demands precise, timely data from various sources, including sales, promotions, market analytics, and seasonal patterns. However, managing these diverse data workflows— from extraction to integration and analysis—can be complex, especially when data needs to be processed in real-time to keep pace with shifting market dynamics.

Control-M simplifies this complexity by orchestrating and automating the entire data pipeline for retail forecasting. By integrating data from multiple systems and providing end-to-end visibility and control, Control-M ensures that critical forecasting data is accurately processed and made available when needed. With features for real-time monitoring, SLA management, and comprehensive reporting, Control-M allows consumer goods organizations to generate reliable forecasts, optimize inventory management, and meet business and regulatory requirements. This not only enhances operational efficiency but also ensures a responsive and agile approach to market demands, supporting more strategic decision-making and improved customer satisfaction.

Use Case Overview

This workflow orchestrates the automated data pipeline for Point-of-Sale (POS) and inventory data collection, processing, and forecasting for retail demand prediction. It ensures data from POS and inventory systems are ingested, validated, transformed, and finally modeled for demand forecasting using machine learning models. The outputs aid in inventory management and customer demand fulfillment, leading to optimized stock levels and improved customer satisfaction.

Use Case Technical Explanation

Data Ingestion and Preparation Jobs:

zzz-Inventory-Data: SQL job that collects inventory data from the ERP system via the Control-M database connection.
zzz-POS-API-Data: File transfer job retrieving POS data from an external API.
zzz-POS-File-Data: Embedded script job that queries POS transaction data stored in flat files.
zzz-POS-RAW-Data: File transfer job for collecting raw POS data from the primary database.

Control-M Value Positioning : Automation: Control-M automates the complex task of ingesting data from multiple sources (API, databases, and files) in a seamless workflow, reducing manual errors and enabling real-time data availability. Event-based triggers: Control-M schedules these jobs based on specific triggers and conditions, ensuring data dependencies are respected. For instance, data ingestion can be rerun if an error occurs, ensuring data quality and reliability.

Data Synchronization and Validation Job:

zzz-Sync-POS-Inventory-Data: Validates and synchronizes both POS and inventory data to ensure completeness and accuracy before loading into a central repository.

Control-M Value Positioning: Data Validation: Automates consistency checks for both data sources, identifying missing data or discrepancies that could impact forecasts. Data Quality Management: By synchronizing data into a central repository, Control-M provides a standardized dataset ready for analysis, reducing data silos.

Machine Learning Model Execution Jobs:

zzz-normalize-Inventory-Data & zzz-normalize-POS-Data: Azure Machine Learning jobs that prepare and normalize POS and inventory data.
zzz-regressor-Modeling: Databricks job that uses machine learning models like random forest and linear regression to predict sales and inventory levels.

Control-M Value Positioning: ML Workflow Orchestration: Orchestrates the machine learning pipeline by triggering Azure ML and Databricks processes, ensuring that data is transformed and ready for model application. Interdependency Management: Control-M monitors interdependencies across processes, ensuring ML jobs execute only once data preparation steps are complete, minimizing failure risks in complex workflows.

Data Visualization Jobs:

zzz-POS-Sale-Results: Tableau job that refreshes the datasource to visualize predicted sales data.
zzz-POS-Inventory-Forecast: Power BI job refreshing inventory forecast datasets, showing critical features affecting predictions.

Control-M Value Positioning: Integration with BI Tools: By integrating with visualization tools, Control-M enables real-time updates to visual dashboards, providing actionable insights to decision-makers. Enhanced Forecast Accuracy: Visualization refreshes based on forecast results provide stakeholders with accurate predictions and feature insights, aiding in operational planning.

SLA Management Job:

zzz-retail-forecasting-Service: Control-M manages the Service Level Agreement (SLA) for the forecasting service, tracking job completion times and deviations. Control-M Value Positioning:

SLA Monitoring: Ensures the retail forecasting process completes within specified timelines, which is crucial for maintaining operational efficiency. Alerting and Reporting: Any deviation from the SLA triggers alerts, allowing quick resolution of issues to maintain forecast reliability.

Key Technical Benefits

Reduced Manual Oversight: Control-M automates every step of this forecasting pipeline, from data ingestion to model execution and visualization, reducing the need for constant monitoring.
Scalability: The modular approach allows retail businesses to scale operations by integrating additional data sources or predictive models without disrupting the existing workflow.
Efficiency and Resilience: Ensures resilience by allowing retries and error handling, which helps prevent data quality issues and increases trust in forecasts.

Business Impact With Control-M, retailers benefit from timely, accurate demand forecasts, leading to optimized inventory levels, reduced stockouts, and improved customer satisfaction.

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

Job Types Included

Control-M Databases
Control-M Managed File Transfer
Control-M OS
Control-M for Azure Machine Learning
Control-M for Azure Databricks
Control-M for Microsoft Power BI
Control-M for Tableau
Control-M SLA Maangement

Demo Environment Information

Environment	Status	Folder
Helix Production	Available	zzz-retail-forecasting
Helix Pre-Production	Available	zzz-retail-forecasting
VSE CTM PROD	Available	zzz-retail-forecasting
VSE CTM QA	Available	zzz-retail-forecasting