This dataset provides synthetic yet realistic data for analyzing and forecasting retail store inventory demand. It contains over 73000 rows of daily data across multiple stores and products, including attributes like sales, inventory levels, pricing, weather, promotions, and holidays.

The dataset is ideal for practicing machine learning tasks such as demand forecasting, dynamic pricing, and inventory optimization. It allows data scientists to explore time series forecasting techniques, study the impact of external factors like weather and holidays on sales, and build advanced models to optimize supply chain performance.

Challenges for Data Scientists:

Challenge 1: Time Series Demand Forecasting Predict daily product demand across stores using historical sales and inventory data. Can you build an LSTM-based forecasting model that outperforms classical methods like ARIMA?

Challenge 2: Inventory Optimization Optimize inventory levels by analyzing sales trends and minimizing stockouts while reducing overstock situations.

Challenge 3: Dynamic Pricing Develop a pricing strategy based on demand, competitor pricing, and discounts to maximize revenue.

Key Data Features:

Date: Daily records from [start_date] to [end_date]. Store ID & Product ID: Unique identifiers for stores and products. Category: Product categories like Electronics, Clothing, Groceries, etc. Region: Geographic region of the store. Inventory Level: Stock available at the beginning of the day. Units Sold: Units sold during the day. Demand Forecast: Predicted demand based on past trends. Weather Condition: Daily weather impacting sales. Holiday/Promotion: Indicators for holidays or promotions.

Example Notebook Ideas

Exploratory Data Analysis (EDA): Analyze sales trends, visualize data, and identify patterns. Time Series Forecasting: Train models like ARIMA, Prophet, or LSTM to predict future demand. Pricing Analysis: Study how discounts and competitor pricing affect sales.

Grocery Inventory and Sales Dataset

Dataset Overview:

This dataset provides detailed information on various grocery items, including product details, supplier information, stock levels, reorder data, pricing, and sales performance. The data covers 990 products across various categories such as Grains & Pulses, Beverages, Fruits & Vegetables, and more. The dataset is useful for inventory management, sales analysis, and supply chain optimization.

Columns:

• Product_ID: Unique identifier for each product.
• Product_Name: Name of the product.
• Category: The product category (e.g., Grains & Pulses, Beverages, Fruits & Vegetables).
• Supplier_ID: Unique identifier for the product supplier.
• Supplier_Name: Name of the supplier.
• Stock_Quantity: The current stock level of the product in the warehouse.
• Reorder_Level: The stock level at which new stock should be ordered.
• Reorder_Quantity: The quantity of product to order when the stock reaches the reorder level.
• Unit_Price: Price per unit of the product.
• Date_Received: The date the product was received into the warehouse.
• Last_Order_Date: The last date the product was ordered.
• Expiration_Date: The expiration date of the product, if applicable.
• Warehouse_Location: The warehouse address where the product is stored.
• Sales_Volume: The total number of units sold.
• Inventory_Turnover_Rate: The rate at which the product sells and is replenished.
• Status: Current status of the product (e.g., Active, Discontinued, Backordered).

Dataset Usage:

• Inventory Management: Analyze stock levels and reorder strategies to optimize product availability and reduce stockouts or overstock.
• Sales Performance: Track sales volume and inventory turnover rate to understand product demand and profitability.
• Supplier Analysis: Evaluate suppliers based on product availability, pricing, and delivery frequency.
• Product Lifecycle: Identify discontinued or backordered products and analyze expiration dates for perishable goods.

How to Use:

This dataset can be used for various tasks such as: - Predicting reorder quantities using machine learning. - Analyzing inventory turnover to optimize stock levels. - Conducting sales trend analysis to identify popular or slow-moving items. - Improving supply chain efficiency by analyzing supplier performance.

Notes:

• This dataset is fictional and used for educational or demonstration purposes only.
• The expiration dates and last order dates should be considered for time-sensitive or perishable items.
• Some products have been marked as discontinued or backordered, indicating their current status in the inventory system.

License:

This dataset is released under the Creative Commons Attribution 4.0 International License. You are free to share, adapt, and use the data, provided proper attribution is given.

Canadian Sales of goods manufactured (shipments), new orders, unfilled orders, inventories, raw materials, goods or work in process, finished goods, and inventory to sales ratios for durable and non-durable goods by North American Industry Classification System (NAICS) for reference periods January 2002 to the current reference month. Not all combinations are available. Values are in constant dollars.

Dataset

This dataset was created by Dahalia Howell

Contents

Graph and download economic data for Existing Home Sales: Housing Inventory (HOSINVUSM495N) from Oct 2024 to Oct 2025 about inventories, sales, housing, and USA.

In terms of inventory management, the pandemic was a true disruption for U.S. retailers. This graph looks at the amount of inventory compared to the number of fulfilled sales from ************ to *************. In **********, the inventories-to-sales ratio jumped to its annual peak due to the imposed lockdown. Only two months later, it decreased abruptly as stores reopened and consumers could shop with the same frequency. The ratio stood at **** percent as of *************.

United States Existing Home Sales: Inventory data was reported at 1,850,000.000 Unit in Oct 2018. This records a decrease from the previous number of 1,880,000.000 Unit for Sep 2018. United States Existing Home Sales: Inventory data is updated monthly, averaging 2,280,000.000 Unit from Jan 1999 (Median) to Oct 2018, with 238 observations. The data reached an all-time high of 4,040,000.000 Unit in Jul 2007 and a record low of 1,460,000.000 Unit in Dec 2017. United States Existing Home Sales: Inventory data remains active status in CEIC and is reported by National Association of Realtors. The data is categorized under Global Database’s United States – Table US.EB005: Existing Home Sales.

This dataset is designed to support research and development in supply chain inventory management. It simulates real-world operations with daily, SKU-level data capturing sales, inventory levels, supplier lead times, replenishment behavior, regional distribution, and promotional effects.

It is suitable for studying demand forecasting, inventory control strategies, stockout risk analysis, cost minimization, and overall supply chain optimization. The data provides realistic complexity for exploring both traditional analytical approaches and modern data-driven solutions.

Key Features Date: Daily timestamps spanning one year of activity.

SKU-Level Detail: Unique product identifiers with varying demand patterns.

Warehouse and Region: Spatial dimensions representing distribution networks.

Units Sold: Simulated sales data with seasonal trends and random noise.

Inventory Levels: Dynamic on-hand stock that evolves over time.

Supplier Lead Times: Variable delivery delays for replenishment orders.

Reorder Points and Quantities: Inventory policy thresholds and simulated replenishments.

Promotions: Binary indicator of promotional periods influencing demand.

Stockout Events: Flags indicating when demand exceeds available inventory.

Supplier Information: Links products to specific suppliers with unique lead times.

Cost and Price: Realistic unit costs and selling prices with profit margins.

Forecasted Demand: Approximate prediction values reflecting planning estimates.

Potential Uses Demand forecasting and sales prediction.

Inventory policy simulation and evaluation.

Stockout risk modeling and mitigation planning.

Cost optimization and pricing strategy analysis.

Data exploration and feature engineering for supply chain problems.

This dataset provides a flexible and realistic foundation for testing and developing advanced solutions to complex inventory optimization challenges in supply chain networks.

United States - Retailers: Inventories to Sales was 1.29000 Ratio in July of 2025, according to the United States Federal Reserve. Historically, United States - Retailers: Inventories to Sales reached a record high of 1.75000 in April of 1995 and a record low of 1.09000 in June of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Retailers: Inventories to Sales - last updated from the United States Federal Reserve on November of 2025.

United States - Total Business: Inventories to Sales was 1.37000 Ratio in July of 2025, according to the United States Federal Reserve. Historically, United States - Total Business: Inventories to Sales reached a record high of 1.74000 in April of 2020 and a record low of 1.24000 in March of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Total Business: Inventories to Sales - last updated from the United States Federal Reserve on December of 2025.

View monthly updates and historical trends for US Existing Home Inventory. from United States. Source: National Association of Realtors. Track economic da…

The dataset is about a retail sales dataset containing information about store sales for various products over time.

The specific variables include: Store: Unique identifier for the store location Date: Calendar date of the sales data Product: Name of the product being sold Weekly Sales: Total number of units sold for the product in a week Inventory Level: Number of units of the product currently in stock at the store Temperature: Average temperature for the week at the store location Past Promotion of Product (in lac): Total value (in lakhs) of any past promotions for the product during the week (1 lac = 100,000) Demand Forecast: Predicted number of units to be sold for the product in the next week (provided for baseline model comparison)

This dataset can be used for various analytical purposes related to retail sales and inventory management, including:

Demand forecasting: By analyzing historical sales data, temperature, past promotions, and other relevant factors, you can build models to predict future demand for products. This information can be used to optimize inventory levels and prevent stock outs or overstocking. Promotion analysis: You can compare sales data during promotional periods with non-promotional periods to assess the effectiveness of different promotions and identify products that respond well to promotions. Product analysis: By analyzing sales data across different stores and time periods, you can identify which products are most popular and in which locations. This information can be used to inform product placement, marketing strategies, and assortment planning. Store performance analysis: You can compare sales performance across different stores to identify top-performing stores and understand factors contributing to their success. This information can be used to identify areas for improvement in underperforming stores.

By utilizing this dataset for these analytical purposes, retail organizations can gain valuable insights into their sales patterns, customer behavior, and inventory management practices. This information can be used to make data-driven decisions that improve sales performance, profitability, and customer satisfaction.

Graph and download economic data for Retailers: Inventories to Sales Ratio (RETAILIRSA) from Jan 1992 to Aug 2025 about ratio, inventories, sales, retail, and USA.

View monthly updates and historical trends for US Business Inventory/Sales Ratio. from United States. Source: Census Bureau. Track economic data with YCha…

Monthly Canadian manufacturers' sales, new orders, unfilled orders, raw materials, goods or work in process, finished goods, total inventories, inventory to sales ratios and finished goods to sales ratios for durable and non-durable goods by North American Industry Classification System (NAICS), in dollars unless otherwise noted. Unadjusted and seasonally adjusted values available from January 1992 to the current reference month.

This dataset contains a list of sales and movement data by item and department appended monthly. Update Frequency : Monthly

About this dataset

The dataset includes practical information about the sales and inventory data of many retailers in Vietnam in the domain of fashion. The dataset includes sales data (from 2022 - 2023), inventory management data(from 2022 - 2023), and master data, which includes information such as details of prices, products, and distribution channels.

Data explanation

The dataset contains many files and is divided into three areas

Sales data area

Sales data are collected every month from 2022 - 2023, the files follow the format TT

Graph and download economic data for Existing Single-Family Home Sales: Housing Inventory (HSFINVUSM495N) from Oct 2024 to Oct 2025 about 1-unit structures, inventories, family, sales, housing, and USA.

Graph and download economic data for Housing Inventory Estimate: Vacant Housing Units for Sale in the United States (ESALEUSQ176N) from Q2 2000 to Q2 2025 about vacancy, inventories, sales, housing, and USA.

View monthly updates and historical trends for US Wholesale Inventory Sales Ratio. from United States. Source: Census Bureau. Track economic data with YCh…