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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
In the heart of Gujarat, AtliQ Mart, a rising star among FMCG manufacturers, had its sights set on a grand horizon of growth. With a firm foothold in Surat, Ahmedabad, and Vadodara, the company harbored ambitions to spread its reach across new metropolises and Tier 1 cities within the next two years. Now i analyzed this dataset using Python.
Through this FMCG Sales Exploratory Data Analysis (EDA) project, we aim to provide actionable insights that can drive business decision-making and enhance performance within the FMCG industry
AtliQ Mart is a growing FMCG manufacturer headquartered in Gujarat, India. It is currently operational in three cities Surat, Ahmedabad and Vadodara. They want to expand to other metros/Tier 1 cities in the next 2 years.AtliQ Mart is currently facing a problem where a few key customers did not extend their annual contracts due to service issues.
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.
Dealer Clustering for FMCG companies to enable cluster and rank dealers based on Sales, forecast accuracy and payment parameters. Model uses SciKit learn packages
An adaptive selector for short-term forecasting of multiple time series. For each time series, it finds the best method from a pool of candidates based on their past performance.
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.