Retail Sales Forecasting on Azure

The Retail Sales Forecasting on Azure is a fixed-scope implementation designed to help retailers improve demand planning through accurate, scalable, and automated sales forecasting.

This service delivers a production-ready forecasting solution using Azure-based analytics and machine learning capabilities, enabling organisations to generate forward-looking sales predictions across key business dimensions.

The result is improved decision-making across inventory, merchandising, promotions, and operations - supported by consistent, reliable forecasts.

BUSINESS CHALLENGES ADDRESSED

Many retailers face challenges including:

• Difficulty forecasting sales at granular levels due to sparse or intermittent data
• Demand variability caused by seasonality, promotions, and external factors
• Limited ability to incorporate calendar events (holidays, closures) into forecasts
• Lack of automated pipelines for continuous and scalable forecast generation
• Inconsistent visibility across product, store, and regional performance
• Reliance on manual forecasting or static models with limited accuracy

SOLUTION OVERVIEW

This service implements an Azure-based forecasting solution that:

• Generates accurate sales forecasts across multiple business dimensions
• Incorporates key demand drivers such as seasonality, events, and external factors
• Automates forecast generation through a scalable pipeline
• Provides structured outputs and visualizations for business users

The solution is deployed within the customer’s Azure environment, ensuring scalability, security, and seamless integration with existing systems and reporting tools.

WHAT THIS SERVICE DELIVERS

At the end of the engagement, the customer will have:

• A production-ready forecasting model configured for their retail data
• Forecasts generated across multiple dimensions (e.g. store, product, category)
• A fully automated pipeline for recurring forecast generation
• Integration-ready outputs (API or data storage)
• Interactive dashboards for forecast visualisation and analysis
• Documentation and guidance for ongoing use and optimisation

SCOPE OF WORK

1. Discovery and Data Assessment

• Review of historical sales data and business context
• Identification of key forecasting requirements and dimensions

2. Solution Design

• Definition of forecasting approach and model structure
• Selection of Azure services and architecture

3. Model Development and Configuration

• Development or enhancement of forecasting models
• Inclusion of seasonality, calendar effects, and business rules

4. Pipeline Implementation

• Setup of automated data ingestion and forecast generation pipelines
• Scheduling and orchestration of recurring forecasts

5. Integration and Visualisation

• Delivery of structured forecast outputs
• Integration with dashboards for business consumption

6. Testing and Validation

• Validation of forecast accuracy and consistency
• Refinement based on business feedback

7. Deployment and Handover

• Deployment to staging environment
• Documentation and knowledge transfer

KEY BENEFITS

• Improved Forecast Accuracy: Better alignment with real demand patterns
• Optimised Inventory Planning: Reduce stockouts and overstock situations
• Faster Decision-Making: Access to forward-looking insights
• Operational Efficiency: Automated forecasting reduces manual effort
• Scalability: Handles large volumes of data across multiple locations and products
• Consistency: Standardised forecasting approach across the organisation

TARGET CUSTOMER PROFILE

This offer is suited for organisations that:

• Operate in retail or consumer goods sectors
• Require improved demand forecasting and planning capabilities
• Manage multiple stores, products, or regions
• Want to leverage Azure for scalable analytics solutions

SUPPORTED TECHNOLOGIES

• Azure Machine Learning
• Azure Data Factory
• Azure Data Lake Storage
• Azure Synapse Analytics or Fabric
• Power BI
• Azure Monitor

ENGAGEMENT DURATION

Typical delivery: 10–13 weeks (depending on data complexity and scope)

CUSTOMER RESPONSIBILITIES

• Provide historical sales data and relevant business context
• Share calendar data (promotions, holidays, closures)
• Provide access to Azure environment
• Participate in validation and feedback sessions

OPTIONAL SUCCESS METRICS

• Forecast accuracy improvement (%)
• Reduction in stockouts and excess inventory
• Forecast coverage across SKUs and locations
• Reduction in manual forecasting effort
• Adoption of forecasting insights by business users

APPROACH AND METHODOLOGY

• The implementation focuses on structured optimisation to establish a new, robust production system or enhance an existing baseline. The approach involves systematically evaluating different model configurations to identify the most effective setup for the specific retail context. All experiments are tracked and benchmarked.