Machine Learning in Sales & Operations Planning Process: 4-Month Implementation


Gain machine driven sales predictions, incorporate price sensitivity for sales predictions, establish a Data Science & ML Platform using the Azure ecosystem, Improve accuracy of planning process.

Machine Learning – Improve sales predictions, increase efficiency in planning & budgeting process and save up-to 2-weeks of manual effort every month using machine-driven insights for Sales & Operations Planning (S&OP)

Problem Statement:

Traditional Sales & Operations Process involves manual activities such as preparation of data, manual forecasting and aggregation of organization wide forecasted v/s actual view. This results in the baseline forecast for the upcoming months. This process may take up to 2 weeks and is entirely manual which may result in lower accuracies of forecast & delays in meeting business requirements. The manual predictions/forecasts by sales teams are always with 1 dimension & generally tend to be less accurate which skews monthly sales forecasts at a broader level.

Our Solution:

We have helped several companies create machine-based algorithms using Azure ML to save time & increase accuracy of sales budgeting and planning process by establishing a data platform on Azure creating a single source of truth. We help you extract data across multiple sources using Azure Data Factory & Azure Databricks, engineer the data and store on Azure Data Lake (Blob Storage) and design and create the data warehouse on Azure Synapse. We apply machine-based rules using Azure ML and help Sales & Operations Leaders visualize the forecasts at various granularities like Customer-SKU & Business Unit Category level on Power BI. This helps eliminate manual effort and automate a majority of the planning process. The machine-driven process will also take into consideration price sensitivity which will enable sales teams to visualize forecasts at various price points helping them decide on discounting campaigns.


  • Data Engineering – Ingestion of Sales & related data sets into Azure Blob Storage using Azure Data Factory
  • Machine learning & predictions – Monthly SKU-Category level price sensitive predictions
  • Data Analytics – Using Azure Synapse build a data warehouse showcasing prediction v/s actual analysis
  • Data Visualization – Power BI based dashboards to showcase six months forecasts along with comparison of last six month forecast v/s actuals

Azure Components Delivered:

  • Azure Databricks
  • Azure Data Factory
  • Blob Storage
  • Azure Synapse
  • Azure ML
  • Power BI.