Transition From Manual Predictive Process To Automated Predictive Analytics For A Retailer


VNB assisted a client to transition from a manual predictive process to a retail dashboard Power BI automated predictive solutions leveraging Azure Analytics and Power BI

Customer Profile

A retailer with hundreds of locations providing both prescription and non-prescription eye ware along with optician services.

Customer Situation

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Customer had a monthly forecasting processes that involved various manual steps using Excel worksheets and models. This process was time consuming and had high rate of deviation from actual targets. Customer kicked off an initiative to build an automated advance analytics solution that would be faster, flexible and have a high rate of accuracy. VNB advance analytics team was chosen by the customer to implement this key predictive analytics solution.

VNB Solution

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After going through customers current manual process, VNB advance analytics team proposed an architecture with automated data ingestion, data preparation, self-learning predictive models and delivery of predictive outputs via PowerBI share and collaborate features. Following were the important activities performed during the project: - Followed Data Science Process Lifecycle (TDSP) for the end to end implementation - Prepared data sets for Sales, Promotion, Store Remodeling, Workforce(Full Time/Part Time) and Products. - As part of the first data collection process all the raw data were stored in Azure Blog Storage. Data were later feed to Azure Machine Learning flow where they were cleaned and fed to actual model for training and prediction. - Output published through Web Service which later utilized in PowerBI and other application - The implementation of the retail dashboard Power BI solution

Customer Benefits

  • Manual Process removed by implementing automation. Development time minimized. Web Service output was utilized by many applications reducing the redundancy in the process.
  • Better accuracy was achieved for predictive models by leveraging Azure Machine Learning based templates.

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