This dataset provides real-time data for smart logistics operations, capturing various aspects of supply chain management over the past year (2024). It includes information on asset tracking, inventory levels, shipment statuses, environmental conditions, traffic, and user behaviors. The dataset features multiple stakeholders within the logistics network, including asset IDs, timestamps, traffic conditions, waiting times, and reasons for delays. Additionally, the data is enriched with real-time information from IoT sensors, such as temperature, humidity, and asset utilization, to facilitate advanced logistics optimization and decision-making. The target variable, Logistics_Delay, helps in identifying delays in logistics processes, which is essential for enhancing supply chain efficiency through proactive management and optimization techniques. This dataset is designed to be used for research and machine learning applications focused on smart logistics and supply chain performance improvement.

Key Features: Timestamp: Date and time when the data was recorded, representing logistics activity. Asset_ID: Unique identifier for the logistical assets (e.g., trucks). Latitude & Longitude: Geographical coordinates of the asset for tracking and monitoring. Inventory_Level: Current level of inventory associated with the asset or shipment. Shipment_Status: Status of the shipment (e.g., In Transit, Delivered, Delayed). Temperature: Temperature recorded at the time of the shipment or transportation. Humidity: Humidity level at the time of recording. Traffic_Status: Current traffic condition (e.g., Clear, Heavy, Detour). Waiting_Time: Time spent waiting during the logistics process (in minutes). User_Transaction_Amount: Monetary amount associated with user transactions. User_Purchase_Frequency: Frequency of purchases made by the user. Logistics_Delay_Reason: Reason for any delays in the logistics process (e.g., Weather, Mechanical Failure). Asset_Utilization: Percentage of asset utilization, indicating how effectively assets are being used. Demand_Forecast: Predicted demand for the logistics services in the coming period. Logistics_Delay (Target): Binary variable indicating whether a logistics delay occurred (1 for delay, 0 for no delay).

This dataset is designed to simulate supply chain operations in large-scale engineering projects. It integrates realistic data from IoT sensors, digital twins, and blockchain-enabled monitoring systems over the years 2023 to 2024.

It aims to support research in predictive maintenance, resource optimization, secure data exchange, and supply chain transparency through advanced analytics and machine learning.

⭐ Key Features Time-bound IoT Sensor Data: Includes real-time-like sensor outputs such as temperature and vibration across multiple locations and assets.

Digital Twin Sync Fields: Tracks Condition_Score and Last_Maintenance to simulate digital twin feedback loops.

Operational KPIs: Features supply chain metrics like Resource_Utilization, Delivery_Efficiency, and Downtime_Hours.

Blockchain Contextual Fit: Designed to be compatible with blockchain audit trails and smart contract triggers (e.g., anomaly response, automated logistics payments).

Labeled Targets: SupplyChain_Efficiency_Label classifies overall efficiency into 3 tiers (0: Low, 1: Medium, 2: High) based on predefined KPI thresholds.

Location-aware Simulation: Assets and operations are tagged by realistic geographic locations.

Supply Chain Economics: Captures Inventory_Level and Logistics_Cost for resource allocation analysis.

Year-specific Scope: Covers the period from 2023 to 2024, aligning with recent and ongoing digital transformation trends.

Dataset Title: Retail Supply Chain Sales Dataset

Dataset Description: This dataset provides detailed information on retail orders, including sales performance, customer details, and shipment logistics. It is accompanied by a calendar reference table to enable time-series analysis and other temporal insights.

Main Dataset (Sheet: Retails Order Full Dataset):

• Row Count: 9,994 rows
• Column Count: 23 columns

Key Columns:

• Order ID, Order Date, Ship Date, Ship Mode – Details about individual orders and shipment schedules.
• Customer ID, Customer Name, Segment – Information on customers and their purchasing categories.
• Product ID, Category, Sub-Category, Product Name – Information on the products sold.
• Sales, Quantity, Discount, Profit – Key performance metrics for each order.
• Region, State, City, Postal Code – Geographic details.
• Returned – Indicates if the product was returned.
• Retail Sales People – Relevant personnel involved in the sales process.

Supporting Dataset (Sheet: Calendar Table): - Row Count: 1,826 rows - Column Count: 12 columns

Key Columns: - Date, Year, Quarter, Month, Week of Year, Day of Week – Temporal dimensions for analyzing sales over time. - Quarter (Q), Quarter & Year, Month & Year, Day Name – Additional formatted temporal attributes for convenience.

Usage: This dataset is ideal for:

• Analyzing retail sales performance and profitability.
• Identifying customer trends and product preferences.
• Studying the impact of geographic and temporal factors on sales.
• Developing predictive models for inventory management and demand forecasting.

Data Set Bibliometric Green Supply Chain

This project focuses on optimizing the allocation of resources in a medium-sized hospital by developing a dataset that captures the hospital's inventory management, patient demand, staffing availability, vendor interactions, and financial expenditures. The objective is to analyze resource usage, forecast demand, and ensure efficient supply chain operations that prevent shortages while minimizing costs. The datasets generated can be used to build predictive models for resource optimization, improving hospital efficiency, and supporting better decision-making.

The dataset titled "SCM Dataset with Green Logistics" comprises a comprehensive collection of data from various companies, detailing their supply chain management (SCM) practices, performance metrics, and green logistics initiatives. The dataset includes key information about the companies' SCM practices, such as Agile SCM, Lean Manufacturing, and Cross-Docking, as well as quantitative metrics like Supplier Count, Inventory Turnover Ratio, Lead Time (days), Order Fulfillment Rate (%), and Customer Satisfaction (%).

Additionally, the dataset records the technology utilized by each company, such as ERP, AI, Robotics, and Blockchain, and evaluates their Environmental Impact Score, Supply Chain Agility, Sustainability Practices, and Supply Chain Complexity Index. Financial metrics like Cost of Goods Sold (COGS), Operational Efficiency Score, Revenue Growth Rate, and Supply Chain Risk (%) are also included to provide a holistic view of each company's supply chain performance. Moreover, the dataset measures Supplier Collaboration Level, Supply Chain Resilience Score, Supplier Relationship Score, and Total Implementation Cost, highlighting the collaborative and financial aspects of SCM.

To enhance the relevance and comprehensiveness of the dataset, green logistics-related columns have been added. These new columns include Carbon Emissions (kg CO2e), Recycling Rate (%), Energy Consumption (MWh), Use of Renewable Energy (%), and Green Packaging Usage (%). These columns provide insights into the environmental sustainability practices of the companies, reflecting their efforts in reducing carbon emissions, increasing recycling rates, optimizing energy consumption, utilizing renewable energy sources, and adopting green packaging solutions.

Overall, the "SCM Dataset with Green Logistics" serves as a valuable resource for analyzing the interplay between supply chain management practices and environmental sustainability initiatives across various companies. It enables stakeholders to assess the effectiveness of SCM strategies and their impact on both operational efficiency and environmental performance.

This dataset provides comprehensive, transaction-level records of pharmaceutical inventory supply chains, including order placement, shipment tracking, and storage history at destination facilities. It enables detailed analysis of logistics performance, inventory management, and stockout events, supporting optimization and risk mitigation in pharmaceutical distribution networks.

There are 7 tables in total, the task is, to assign routes to the Orders in the "Order List" Table given the restrictions (e.g. weight restriction). - The order list already contains Historical data of how the orders were assigned in the past.

Please refer to https://brunel.figshare.com/articles/dataset/Supply_Chain_Logistics_Problem_Dataset/7558679 for further clarification.

The other 6 tables describe the restrictions imposed on the system. - some customers can only be serviced by a specific plant - plants and ports have to be physically connected. - plants can only handle specific items

Notes:

• The terms "Warehouse" and "Plant" are used interchangeably, essentially a warehouse is a plant.

• This is a (deterministic) optimization problem, there is only one order date since we are only looking at orders from one specific day and trying to assign them to routes/factories.

• We have to ship all the orders to PORT09

• The goal is to schedule routes while minimizing freight and warehousing costs.

• I am also just working on understanding the Dataset, maybe we can have a discussion in the comment section for clarifications.

Acknowledgements:

This dataset was taken from the Brunel University of London Website

The Supply Chain Analytics Market is estimated to reach USD 44.4 Billion by 2033, riding on a strong 19.0% CAGR throughout forecast period.

Complete dataset included in the full report. Detailed tables, regional splits, forecasts, and methodologies are available with purchase.

This dataset provides comprehensive tracking of supply chain process exceptions, detailing their types, root causes, resolution actions, and impact levels. It enables organizations to identify patterns, assign responsibility, and implement preventive measures, supporting logistics risk mitigation and continuous process improvement. The dataset is ideal for operational analytics, root cause analysis, and supply chain optimization.

Amid global supply chain disruptions, seaports are vital for maintaining smooth goods flow. However, there is still a significant gap in understanding and addressing factors causing operational delays and inefficiencies in port supply chains. This study aims to systematically identify and rank the factors contributing to these delays and inefficiencies. Employing a hybrid methodology that combines the Decision-Making Trial and Evaluation Laboratory (DEMATEL), Ordinal priority approach (OPA), and Dynamic Grey Relational Analysis (DGRA), this research addresses inefficiencies in port supply chains. The approach involves identifying key factors through expert consultation and literature review, mapping interdependencies, prioritizing factors, and analyzing dynamic relations to validate their influence. Operational delays in ports are classified into five main criteria: port management, shipping and navigation, port terminal operator, customs, and cargo owners, with each criterion broken down into specific sub-criteria. The study yielded significant results, identifying 'Poor management of goods flow' and 'Customs-related issues' within port management as the most critical factors contributing to operational delays. The analysis also highlighted 'Ship structure' and 'Late arrival of ships to port' under shipping and navigation as significant influencers of inefficiency. Notably, 'Operational cost optimization' and 'Expertise of operational staff' were pivotal in the port terminal operator category, reflecting their substantial impact on port operational efficiency. This study identified interdependencies among delay factors and essential intervention points, enhancing port functionality. The findings suggest strategies to improve global trade efficiency and economic stability in coastal regions through better supply chain management. Optimizing operational delay factors in port supply chains enhances port resilience by enabling more robust and reliable operations during disruptions.

964 supply chain management providers headquartered in Germany with size bands, HQ metro, and verified-contact coverage. Compiled by RevenueBase from its company graph.

36 supply chain data providers headquartered in Chile with size bands, HQ metro, and verified-contact coverage. Compiled by RevenueBase from its company graph.

A 2018 survey found that the biggest challenge for global supply chain executives was visibility, with 21.8 percent of respondents selecting this response. Fluctuating customer demand was second, with 19.7 percent, while data management was lowest with 1.3 percent. Visibility The nature of the challenges generated by visibility differs depending on whether a company is a producer or a supplier of goods. Producers were most concerned with having oversight on how materials were provisioned to their production facilities, while suppliers were concerned with visibility over the quality and availability of the products they intend to sell. Both producers and suppliers though were concerned with being able to trace the flow of materials and/or goods through their supply chain process. Supply chain management Given the concerns producers and suppliers have over visibility, supply chain management (SCM) software has been a growing industry over the last decade. One sub-segment of this industry expected to see very strong growth is supply chain analytics, whereby the data captured in a SCM system is used in more sophisticated ways (for example, to identifying the main causes and predict the risk of supply chain disruptions). A 2016 survey found that advanced analytics was the technology manufacturing executives expected to impact their supply chain the most, while some analysts expect the size of the supply chain analytic market to almost double between 2018 and 2023.

Dataset of publication details in IoT and AI in supply chain operations

Sharetronic Data Technology Co Supply Chain: - (2026). Historical annual data from 2006 to 2026 with forecasts.

Complete dataset included in the full report. Detailed tables, regional splits, forecasts, and methodologies are available with purchase.

To create more resilient supply chains, 15.9 percent of logistics industry professionals were likely to adopt operational technologies. In addition, 14.5 percent of respondents were likely to shift their business model towards more digital and direct sales approach.

ABM Industries Supply Chain: - (2026). Historical annual data from 2006 to 2026 with forecasts.