Embark on a thorough investigation as we navigate the transactional dataset of UCI, a non-store retail UK company, utilizing SQL queries and PowerBI visualization tools. This analysis empowers businesses with strategic insights for precise inventory management and sales planning in the coming year.

We all eagerly wait for Black Friday sales and plan ahead in order to make most out of it. Similar is the objective of a retail outlet on Black Friday. They also aspire to bring the best out of this day. The major objective of a store is to maximize the revenue on this day, by selling off a large proportion of their unsold inventory. The main challenge to achieve this objective is “What optimal prices should the store set to capture demand that maximizes revenue?” The problem we solve would help the business to get the predicted Purchase amount (or Willingness to Pay) for each product for each user. They can use this then to set optimal prices on the product (using Multinomial Model for Price Optimization or others). So, when we find Black Friday Sales Analysis data on Kaggle, it highly motivated our team to work for this interesting real-world problem for ABC Retail Store.

The objective of this project is to design a Stock & Inventory Management Analysis system that helps retail managers make data-driven decisions about inventory, pricing, and promotions.

Medicine Shop Automation (MSA): Perform structured analysis and structured design for the following Medicine Shop Automation (MSA) software: A retail medicine shop deals with a large number of medicines procured from various manufacturers. The shop owner maintains different medicines in wall mounted and numbered racks. The shop owner maintains as few inventory for each item as reasonable, to reduce inventory overheads after being inspired by the just-in-time (JIT) philosophy. Thus, one important problem the shop owner faces is to be able to order items as soon as the number of items in the inventory reduces below a threshold value. The shop owner wants to maintain medicines to be able to sustain selling for about one week. To calculate the threshold value for each item, the software must be able to calculate the average number of medicines sales for one week for each part. At the end of each day, the shop owner would request the computer to generate the items to be ordered. The computer should print out the medicine description, the quantity required, and the address of the vendor supplying the medicine. The shop owner should be able to store the name, address, and the code numbers of the medicines that each vendor deals with. Whenever new supply arrives, the shop owner would enter the item code number, quantity, batch number, expiry date, and the vendor number. The software should print out a cheque favoring the vendor for the items supplied. When the shop owner procures new medicines it had not dealt with earlier, he should be able to enter the details of the medicine such as the medicine trade name, generic name, vendors who can supply this medicine, unit selling and purchasing price. The computer should generate a code number for this medicine which the shop owner would paste the code number in the rack where this medicine would be stored. The shop owner should be able to query about a medicine either using its generic name or the trade name and the software should display its code number and the quantity present. At the end of every day the shop owner would give a command to generate the list of medicines which have expired. It should also prepare a vendor-wise list of the expired items so that the shop owner can ask the vendor to replace these items. Currently, this activity alone takes a tremendous amount of labour on the part of the shop owner and is a major motivator for the automation endeavour. Whenever any sales occurs, the shop owner would enter the code number of each medicine and the corresponding quantity sold. The MSA should print out the cash receipt. The computer should also generate the revenue and profit for any given period. It should also show vendor-wise payments for the period.

Stockify is a cutting-edge demand prediction and product stock analysis system, meticulously designed to empower retail store managers with data-driven inventory decisions.

This project focuses on Exploratory Data Analysis (EDA) for retail store inventory forecasting. The goal is to analyze sales, orders, and inventory levels to understand trends, detect anomalies, and prepare the data for predictive modeling.

This project focuses on analyzing and visualizing the performance of a retail chain to uncover operational inefficiencies and highlight data-driven insights. The analysis was based on real sales and inventory data collected from multiple store branches.

Retail Sales & Inventory Forecasting Capstone Full-spectrum analysis of retail data using Python (Pandas, Seaborn, Matplotlib), covering demand forecasting, performance diagnostics, and deep-dive analytics across BR1–11 and GAR1–11. Features clean, executive-ready visualizations and end-to-end insights—all versioned on GitHub.

Retail Inventory Analysis is a Python-based inventory transfer decision system that identifies SKU-level decay risk and generates actionable store-to-store transfer recommendations. It analyzes inventory aging and sales velocity to reduce stock obsolescence and working capital lock-in.

Strategic sales analysis for a retail e-commerce store. Translating 24 months of raw transactional data into actionable insights for marketing and inventory optimization.

Exploratory Data Analysis of retail store sales dataset with insights on sales trends, seasonality, promotions, store and product performance. Includes descriptive statistics and visualizations to support forecasting and inventory planning.

This project analyzes retail store sales data to uncover insights into sales performance, customer behavior, and inventory management. The analysis aims to optimize sales strategies and enhance operational efficiency.

This project uses ML & data analysis for retail store optimization and demand forecasting. It features sales anomaly detection, demand prediction, customer segmentation, and integrates external economic factors to boost customer experience and inventory management.

SQL Music Store Project: Contains a complete relational database schema and analysis for a music retail store, including an SQL script and PowerPoint summary. Features data modeling, complex queries, and business insights to help analyze sales, customers, and inventory for data-driven decision making.

Predicts weekly retail sales using ML models (Extra Trees, RF, NN), historical data, store/environmental factors, economics, markdowns. Helps businesses forecast demand, optimize inventory/staffing, and make data-driven decisions for profitability. Includes preprocessing, training, evaluation (RMSE, R-squared), and feature analysis.

Maven Market is a fictional retail grocery chain used for business intelligence learning. This analysis focuses on evaluating sales performance, customer behavior, product profitability, and store operations. The goal is to generate insights that help optimize pricing, promotions, inventory management, and regional sales strategies.

DataSpark is a retail analytics project for Global Electronics leveraging Python, SQL, and Power BI. It uncovers customer insights, sales trends, and store performance to optimize marketing, inventory, and operations. Features include clean datasets, SQL-driven analysis, and interactive dashboards, driving data-driven growth and decision-making.

This repository offers a robust analysis of bike store sales, utilizing an MS Access database, multiple Excel files, and a Power BI dashboard. With these components, it provides a comprehensive overview of sales performance, inventory management, and customer insights, making it a valuable resource for decision-making within the bike retail sector.

Retail store inventory data analysis

Retail Store Inventory Analysis project using Excel to track stock levels, inventory value, reorder status, and supplier performance through interactive dashboards and data analytics.

An end-to-end retail data analysis project featuring an interactive Excel dashboard and statistical regression modeling to optimize inventory efficiency and sales forecasting.

End-to-end retail store inventory analysis using Python for EDA, machine learning for insights, and Power BI for interactive dashboards.

No description available

The Retail Store Inventory Management Analysis project provides a comprehensive solution for managing and analyzing inventory data in a retail environment. This project is designed to help retailers track their inventory efficiently, understand sales trends, and make informed decisions to optimize stock levels.

Proyek analisis data end-to-end untuk optimasi penjualan dan persediaan retail. Mencakup pipeline ETL dengan Airflow, validasi data menggunakan Great Expectations, serta dashboard interaktif di Kibana untuk menganalisis tren penjualan, promosi, dan performa stok.

Retail store and inventory level analysis plays a crucial role in understanding business performance, customer preferences, and operational efficiency. It involves assessing sales trends, stock levels, and store productivity to derive actionable insights that can improve revenue, reduce costs, and enhance customer satisfaction.

Developed an interactive dashboard to track KPIs like revenue, units sold, demand, and pricing. Applied DAX and Power Query for dynamic data modeling, inventory insights, and seasonal trends, optimizing for performance and stakeholder usability.

No description available

This project analyzes seasonal demand patterns, sales performance, and inventory efficiency using SQL. It identifies each product’s peak season, evaluates order-to-sales conversion, measures forecast accuracy, and classifies inventory health. These insights support optimized forecasting, procurement, and inventory planning.

SQL-based analysis of retail store inventory data to extract meaningful business insights, track stock performance, and improve inventory decision-making.