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This repository contains the implementation of a facings identifier using YOLOv8 and image embeddings. The goal of this project is to count the number of facings (product instances) of each product present on shelves in a retail store using computer vision techniques.
This was a Challenge put up by a famous YouTuber Codebasics and he provided all the datasets. This time no mock up was provided. Instead we got a conversation between the stakeholders of the company with their Data Analyst. From that conversation we had to figure out the requirements needed to solve the supply chain problem.
This project is a data analysis and visualization effort aimed at generating insights to address supply chain issues in the Fast-Moving Consumer Goods (FMCG) domain. Leveraging synthetic data provided by the Codebasics Resume Project Challenge, the analysis delves into key metrics such as On-time Delivery %, In-full Delivery %, OTIF%I
🧠 FMCG Lead Intelligence Engine — n8n + Claude AI. Automates distributor outreach: scrape → score → draft WhatsApp/email → send. Built by Milan · SNTL84 · Surat. Automate What's Costing You Money.
Browser-based LPO automation tool for UAE retail vendors—Carrefour, Lulu, Union Coop, Delivery Hero, and custom vendors. Enables efficient processing, validation, and automation of LPO workflows in a single platform.
I Automate What's Costing You Money. · Milan · SNTL 84 · AI Workflow Professional · Surat, India · AI Systems | Full-Stack Builds | Supply Chain Business Intelligence
An end-to-end data analytics platform analyzing nano-influencer performance across 40+ markets. Built with Python and Streamlit, it features automated ETL pipelines, sentiment-weighted engagement tracking, and strategic budget-allocation modeling
Analyzes promotional campaign performance for an FMCG client across products, stores, and cities using sales and events data. Delivers business insights on revenue uplift, sales impact, campaign effectiveness, and promotion performance through clear visualizations.
Dealer Clustering for FMCG companies to enable cluster and rank dealers based on Sales, forecast accuracy and payment parameters. Model uses SciKit learn packages