Executive Summary

A retail company faced challenges in managing inventory and meeting customer demands across its diverse product portfolio. With operations spanning multiple regions, they struggled with frequent overstock and stockouts, resulting in lost revenue and increased holding costs. To address this, we implemented an AI-powered demand forecasting solution, leveraging cloud-based tools and advanced machine learning models.

About the Client

A mid-sized retail company operating across multiple regions with a diverse product portfolio. The client faced challenges in managing inventory, addressing frequent overstock and stockouts, and improving supply chain efficiency. With fragmented sales and inventory data, they sought an AI-driven demand forecasting solution to optimize operations, reduce costs, and enhance customer satisfaction.

Problem Statement

The retail company encountered several operational challenges:

Inventory Imbalances

Overstock led to increased holding costs, while stockouts resulted in missed sales opportunities.

Supply Chain Inefficiencies

Lack of forecasting accuracy disrupted procurement and logistics planning.

Data Siloes

Fragmented sales and inventory data from multiple sources hindered centralized decision-making.

Solution Overview

We developed and deployed an AI/ML-driven demand forecasting system designed to:

Consolidate Data

Integrate and centralize sales, inventory, and external data (e.g., promotions, holidays) into a unified data warehouse.

Automate Forecasting

Build machine learning models to generate demand predictions for each region.

Enable Insights

Provide actionable insights through real-time dashboards, facilitating informed decision-making for procurement and inventory management.

Key Components of the Solution

#1 Data Integration & Centralization

Tools Used: Azure Data Factory (ADF) for ingesting data from POS systems, ERP, and marketing platforms into Snowflake, which served as the centralized data warehouse.
Process: Automated pipelines were set up in ADF to collect and clean data, ensuring accuracy and consistency.

#2 Model Development

Tools Used: Databricks with MLlib for developing time-series and regression models.
Process: Features like historical sales, seasonality, and regional trends were engineered in Databricks. MLlib algorithms (e.g., ARIMA and Random Forest) were used to train forecasting models.

#3 Model Deployment

Tools Used: Databricks MLflow for model deployment and real-time predictions.
Process: Forecasting models were deployed to generate SKU-level predictions for each store and region.

#4 Visualization & Insights Delivery

Tools Used: Power BI for creating interactive dashboards.
Process: Dashboards showcased demand forecasts, inventory levels, and procurement recommendations, accessible to the supply chain and sales teams.

Results

96% Improved Forecast Accuracy

Achieved 96% accuracy in demand predictions, reducing forecasting errors by 25% compared to the previous model, resulting in better alignment of supply with demand.

$1.2M Reduction in Inventory Costs

Lowered annual holding costs by $1.2 million, optimizing procurement and reducing overstock by 18%, leading to a leaner inventory.

22% Faster Supply Chain Efficiency

Streamlined procurement processes, reducing lead times by 22% (from 14 days to 11 days), ensuring faster replenishment cycles and improved supplier coordination.

$3.5M Revenue Growth

Avoided stockouts during peak periods, leading to an 8% increase in annual revenue, equating to an additional $3.5 million in sales for the year.

20% Boost in Operational Efficiency Through Actionable Insights

Real-time dashboards empowered stakeholders with over 150 key performance indicators (KPIs), enabling quicker decision-making and improving operational efficiency by 20% across the supply chain.

Tools & Tech Stack

Data Integration

Azure Data Factory.

Data Storage

Snowflake.

Model Development

Databricks (MLlib).

Model Deployment

Databricks MLflow.

Visualization

Power BI.

Team

Data Engineer : 1
Data Scientist : 1
ML Engineer : 1
Business Analyst : 1
Project Manager: 1

Testimonials

Our Slam Book

Tony Lehtimaki

DIRECTOR - AMEOS

Spain

Very professional, accurate and efficient team despite all the changes I had them do. I look forward to working with them again.

Antoine de Bausset

CEO - BEESPOKE

France

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Vivek Singh

MARKETING & SALES HEAD - VARMORA

Gujarat

I really liked their attention to detail and their sheer will to do the job at hand as good as possible beyond professional boundaries.

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DIRECTOR - COVERTEK CERAMICA

Gujarat

Excellent work, and on time with all goals. Communication was very easy, and knowledge of work was excellent. Will be working with them on upcoming projects. I highly recommend.

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CO-FOUNDER - ESR

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