Supply chain analytics is a valuable part of data-driven decision-making in various industries such as manufacturing, retail, healthcare, and logistics. It is the process of collecting, analyzing and interpreting data related to the movement of products and services from suppliers to customers.

This dataset captures a comprehensive set of logistics and supply chain operations, specifically collected from a logistics network in Southern California. The data spans from January 2021 to January 2024, encompassing various aspects of transportation, warehouse management, route planning, and real-time monitoring. It includes detailed hourly records of logistics activities, reflecting conditions in urban areas and transport corridors known for high traffic and dynamic operational challenges.

The dataset is collected from various sources, such as GPS tracking systems, IoT sensors, warehouse management systems, and external data providers. It covers different transportation modes, including trucks, drones, and rail, providing insights into operational efficiency, risk factors, and service reliability. The data has been anonymized and processed to ensure privacy while preserving the information needed for analysis.

Features Overview The dataset includes a variety of features that represent different aspects of logistics operations:

Timestamp: The date and time when the data was recorded (hourly resolution). Vehicle GPS Latitude: The latitude coordinate indicating the location of the vehicle. Vehicle GPS Longitude: The longitude coordinate indicating the location of the vehicle. Fuel Consumption Rate: The rate of fuel consumption recorded for the vehicle in liters per hour. ETA Variation (hours): The difference between the estimated and actual arrival times. Traffic Congestion Level: The level of traffic congestion affecting the logistics route (scale 0-10). Warehouse Inventory Level: The current inventory levels at the warehouse (units). Loading/Unloading Time: The time taken for loading or unloading operations in hours. Handling Equipment Availability: Availability status of equipment like forklifts (0 = unavailable, 1 = available). Order Fulfillment Status: Status indicating whether the order was fulfilled on time (0 = not fulfilled, 1 = fulfilled). Weather Condition Severity: The severity of weather conditions affecting operations (scale 0-1). Port Congestion Level: The level of congestion at the port (scale 0-10). Shipping Costs: The costs associated with the shipping operations in USD. Supplier Reliability Score: A score indicating the reliability of the supplier (scale 0-1). Lead Time (days): The average time taken for a supplier to deliver materials. Historical Demand: The historical demand for logistics services (units). IoT Temperature: The temperature recorded by IoT sensors in degrees Celsius. Cargo Condition Status: Condition status of the cargo based on IoT monitoring (0 = poor, 1 = good). Route Risk Level: The risk level associated with a particular logistics route (scale 0-10). Customs Clearance Time: The time required to clear customs for shipments. Driver Behavior Score: An indicator of the driver's behavior based on driving patterns (scale 0-1). Fatigue Monitoring Score: A score indicating the level of driver fatigue (scale 0-1). Target Variables (Labels) The dataset also includes several target variables for predictive modeling:

Disruption Likelihood Score: A score predicting the likelihood of a disruption occurring (scale 0-1). Delay Probability: The probability of a shipment being delayed (scale 0-1). Risk Classification: A categorical classification indicating the level of risk (Low Risk, Moderate Risk, High Risk). Delivery Time Deviation: The deviation in hours from the expected delivery time. Use Cases This dataset can be used for various applications in logistics and supply chain management, including:

Predictive modeling for risk assessment and disruption detection. Optimization of routing and scheduling to minimize delays. Predictive maintenance for logistics vehicles. Analysis of the impact of external factors such as traffic and weather on delivery times. Enhancing warehouse and inventory management practices. The dataset provides a real-world scenario to apply machine learning techniques, allowing for improvements in logistics efficiency and risk management strategies.

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

A DataSet of Supply Chains used by the company DataCo Global was used for the analysis. Dataset of Supply Chain , which allows the use of Machine Learning Algorithms and R Software. Areas of important registered activities : Provisioning , Production , Sales , Commercial Distribution.It also allows the correlation of Structured Data with Unstructured Data for knowledge generation.

Type Data : Structured Data : DataCoSupplyChainDataset.csv Unstructured Data : tokenized_access_logs.csv (Clickstream)

Types of Products : Clothing , Sports , and Electronic Supplies

Additionally it is attached in another file called DescriptionDataCoSupplyChain.csv, the description of each of the variables of the DataCoSupplyChainDatasetc.csv.

Dataset is divided into 7 tables, one table for all orders that needs to be assigned a route – OrderList table, and 6 additional files specifying the problem and restrictions. For instance, the FreightRates table describes all available couriers, the weight gaps for each individual lane and rates associated. The PlantPorts table describes the allowed links between the warehouses and shipping ports in real world. Furthermore, the ProductsPerPlant table lists all supported warehouse-product combinations. The VmiCustomers lists all special cases, where warehouse is only allowed to support specific customer, while any other non-listed warehouse can supply any customer. Moreover, the WhCapacities lists warehouse capacities measured in number of orders per day and the WhCosts specifies the cost associated in storing the products in given warehouse measured in dollars per unit.Order ID is ID of the order made by the customer, product ID is the specific product ID customer ordered."tpt_day_cnt" in the FrieghtRates table means transportation day count, i.e. estimated shipping time. WhCapacities correspond to the number of orders. For example, let's say Customer 1 requests 10 units of X, Customer 2 requests 20 units of Y. The total number of orders is 2, thus total capacity in "whCapacity" is 2.WhCapacities table is the maximum number of orders that can be processed per each plant, it is not dependant on specific products.The OrderList contains historical records of how the orders were routed and demand satisfied. The whCapacities and rest of the tables are the current state constraints of the network. Thus, we can calculate the costs of historical network and also optimize for the new constraints. In order to build Linear Programming (LP) model, you would take the following from the OrderList: the product ID that needs to be shipped, the destination port, unit quantity (for cost) and unit weight (for weight constraints). And then use the limits of those constraints from other tables.Questions: There is a Carrier V44_3 in OrderList table, but it is missing in the FreightRates table? V44_3 is a carrier that was historically used for supplying given demand, but since it has been discontinued and therefore do not appear in the Freight Rates List. Also, all of the V44_3 instances are CRF - i.e. customer arranges their own shipping and hence cost is not calculated either way.

Sample purchasing data containing information on suppliers, the products they provide, and the projects those products are used for. Data created or adapted from publicly available sources.

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).

The dataset consists of features such as transaction hash, block number, timestamp, sender address, receiver address, transaction fee, gas limit, product name, product ID, product description, quantity, price, and a binary target feature indicating fraudulent (1) or non-fraudulent (0) transactions.

harisss/Supplychain dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

Data Set Bibliometric Green Supply Chain

Guide to approaches and secondary resources for researching supply chains.

1.Introduction

Sales data collection is a crucial aspect of any manufacturing industry as it provides valuable insights about the performance of products, customer behaviour, and market trends. By gathering and analysing this data, manufacturers can make informed decisions about product development, pricing, and marketing strategies in Internet of Things (IoT) business environments like the dairy supply chain.

One of the most important benefits of the sales data collection process is that it allows manufacturers to identify their most successful products and target their efforts towards those areas. For example, if a manufacturer could notice that a particular product is selling well in a certain region, this information could be utilised to develop new products, optimise the supply chain or improve existing ones to meet the changing needs of customers.

This dataset includes information about 7 of MEVGAL’s products [1]. According to the above information the data published will help researchers to understand the dynamics of the dairy market and its consumption patterns, which is creating the fertile ground for synergies between academia and industry and eventually help the industry in making informed decisions regarding product development, pricing and market strategies in the IoT playground. The use of this dataset could also aim to understand the impact of various external factors on the dairy market such as the economic, environmental, and technological factors. It could help in understanding the current state of the dairy industry and identifying potential opportunities for growth and development.

2. Citation

Please cite the following papers when using this dataset:

1. I. Siniosoglou, K. Xouveroudis, V. Argyriou, T. Lagkas, S. K. Goudos, K. E. Psannis and P. Sarigiannidis, "Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory," in the 12th International Conference on Circuits and Systems Technologies (MOCAST 2023), April 2023, Accepted

3. Dataset Modalities

The dataset includes data regarding the daily sales of a series of dairy product codes offered by MEVGAL. In particular, the dataset includes information gathered by the logistics division and agencies within the industrial infrastructures overseeing the production of each product code. The products included in this dataset represent the daily sales and logistics of a variety of yogurt-based stock. Each of the different files include the logistics for that product on a daily basis for three years, from 2020 to 2022.

3.1 Data Collection

The process of building this dataset involves several steps to ensure that the data is accurate, comprehensive and relevant.

The first step is to determine the specific data that is needed to support the business objectives of the industry, i.e., in this publication’s case the daily sales data.

Once the data requirements have been identified, the next step is to implement an effective sales data collection method. In MEVGAL’s case this is conducted through direct communication and reports generated each day by representatives & selling points.

It is also important for MEVGAL to ensure that the data collection process conducted is in an ethical and compliant manner, adhering to data privacy laws and regulation. The industry also has a data management plan in place to ensure that the data is securely stored and protected from unauthorised access.

The published dataset is consisted of 13 features providing information about the date and the number of products that have been sold. Finally, the dataset was anonymised in consideration to the privacy requirement of the data owner (MEVGAL).

File	Period	Number of Samples (days)
product 1 2020.xlsx	01/01/2020–31/12/2020	363
product 1 2021.xlsx	01/01/2021–31/12/2021	364
product 1 2022.xlsx	01/01/2022–31/12/2022	365
product 2 2020.xlsx	01/01/2020–31/12/2020	363
product 2 2021.xlsx	01/01/2021–31/12/2021	364
product 2 2022.xlsx	01/01/2022–31/12/2022	365
product 3 2020.xlsx	01/01/2020–31/12/2020	363
product 3 2021.xlsx	01/01/2021–31/12/2021	364
product 3 2022.xlsx	01/01/2022–31/12/2022	365
product 4 2020.xlsx	01/01/2020–31/12/2020	363
product 4 2021.xlsx	01/01/2021–31/12/2021	364
product 4 2022.xlsx	01/01/2022–31/12/2022	364
product 5 2020.xlsx	01/01/2020–31/12/2020	363
product 5 2021.xlsx	01/01/2021–31/12/2021	364
product 5 2022.xlsx	01/01/2022–31/12/2022	365
product 6 2020.xlsx	01/01/2020–31/12/2020	362
product 6 2021.xlsx	01/01/2021–31/12/2021	364
product 6 2022.xlsx	01/01/2022–31/12/2022	365
product 7 2020.xlsx	01/01/2020–31/12/2020	362
product 7 2021.xlsx	01/01/2021–31/12/2021	364
product 7 2022.xlsx	01/01/2022–31/12/2022	365

3.2 Dataset Overview

The following table enumerates and explains the features included across all of the included files.

Feature	Description	Unit
Day	day of the month	-
Month	Month	-
Year	Year	-
daily_unit_sales	Daily sales - the amount of products, measured in units, that during that specific day were sold	units
previous_year_daily_unit_sales	Previous Year’s sales - the amount of products, measured in units, that during that specific day were sold the previous year	units
percentage_difference_daily_unit_sales	The percentage difference between the two above values	%
daily_unit_sales_kg	The amount of products, measured in kilograms, that during that specific day were sold	kg
previous_year_daily_unit_sales_kg	Previous Year’s sales - the amount of products, measured in kilograms, that during that specific day were sold, the previous year	kg
percentage_difference_daily_unit_sales_kg	The percentage difference between the two above values	kg
daily_unit_returns_kg	The percentage of the products that were shipped to selling points and were returned	%
previous_year_daily_unit_returns_kg	The percentage of the products that were shipped to

snapshot-tab-pane Supply Chain Analytics Market Size 2025-2029The supply chain analytics market size is valued to increase USD 15.51 billion, at a CAGR of 22.2% from 2024 to 2029. Increased need to improve business processes will drive the supply chain analytics market.Major Market Trends & InsightsNorth America dominated the market and accounted for a 38% growth during the forecast period.By End-user - Retail segment was valued at USD 1.45 billion in 2023By Deployment - On-premises segment accounted for the largest market revenue share in 2023Market Size & ForecastMarket Opportunities: USD 389.88 millionMarket Future Opportunities: USD 15,514.40 millionCAGR : 22.2%North America: Largest market in 2023Market SummaryThe market is a dynamic and ever-evolving domain, driven by the increasing need to optimize business processes and enhance operational efficiency. A key trend fueling market growth is the rising adoption of predictive analytics in supply chain management. However, the implementation of supply chain analytics comes with challenges, including data security concerns and the need for significant IT investment.Core technologies, such as machine learning and artificial intelligence, are at the forefront of innovation, enabling real-time insights and forecasting capabilities. In the application space, logistics and inventory management are leading sectors, while regulatory compliance and regional variations add complexity to the market landscape.What will be the Size of the Supply Chain Analytics Market during the forecast period?Get Key Insights on Market Forecast (PDF) Request Free SampleHow is the Supply Chain Analytics Market Segmented and what are the key trends of market segmentation?The supply chain analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.End-user RetailManufacturingHealthcareTransportationOthersDeployment On-premisesCloud-basedService Professional servicesManaged servicesGeography North America USCanadaEurope FranceGermanyItalyUKAPAC ChinaIndiaJapanSouth KoreaRest of World (ROW) By End-user InsightsThe retail segment is estimated to witness significant growth during the forecast period.In today's dynamic business landscape, retailers are leveraging advanced analytics solutions to gain valuable insights from their supply chain operations. These insights enable retailers to make data-driven decisions, optimize inventory levels, and enhance supplier performance. According to recent studies, the adoption of supply chain analytics in retail has seen a significant increase, with approximately 30% of retailers reporting an improvement in inventory management and a 25% reduction in stockouts. Looking forward, industry experts anticipate that this trend will continue, with up to 40% of retailers planning to invest in advanced analytics tools in the next three years. Predictive modeling and data visualization are essential components of these solutions.They help retailers analyze historical sales data, market trends, and external factors to accurately forecast demand. Furthermore, AI-powered insights, risk management, and digital twin technology facilitate supply chain design and optimization, while optimization algorithms, network optimization, and performance dashboards ensure efficient operations. Demand planning and forecasting are critical aspects of supply chain analytics. By analyzing demand patterns and lead times, retailers can optimize inventory levels, minimize carrying costs, and maintain stockouts. Moreover, retailers can evaluate their suppliers' performance using statistical modeling and cloud computing solutions. This evaluation includes analyzing delivery times, product quality, and overall reliability, enabling retailers to collaborate more effectively and negotiate better terms.Request Free SampleThe Retail segment was valued at USD 1.45 billion in 2019 and showed a gradual increase during the forecast period.In the realm of logistics optimization, real-time tracking using IoT sensors and blockchain technology ensures transparency and security. Additionally, linear programming, inventory optimization, production scheduling, warehouse management, order fulfillment, and machine learning algorithms facilitate efficient operations and minimize costs. In conclusion, the application of supply chain analytics in retail is a continuous and evolving process. By utilizing advanced tools and techniques, retailers can optimize inventory levels, enhance supplier performance, and improve overall supply chain efficiency. With the increasing adoption of these solutions and the anticipated growth in the market, retailers are poised

The company which provided the dataset is the world leader in manufacturing of construction and mining equipment, diesel and natural gas engines, industrial gas turbines and diesel-electric locomotives. The current revenue of the company is estimated to be on the order of tens of billions and they sell products and parts via a worldwide dealer network. The company sells more than 3 million products and 700,000 parts in more than 20 countries around the world every year. They operate with more than 3,000 suppliers and 3,000 dealerships and their logistics operations alone are worth more than 60 million dollars per year. The dataset provided is one example of supply chain problem for one product of the company - a medium size excavator.

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 Office of Manufacturing and Energy Supply Chains is responsible for strengthening and securing manufacturing and energy supply chains needed to modernize the nation’s energy infrastructure and support a clean and equitable energy transition. The office is catalyzing the development of an energy sector industrial base through targeted investments that establish and secure domestic clean energy supply chains and manufacturing, and by engaging with private-sector companies, other Federal agencies, and key stakeholders to collect, analyze, respond to, and share data about energy supply chains to inform future decision making and investment. The office manages programs that develop clean domestic manufacturing and workforce capabilities, with an emphasis on opportunities for small and medium enterprises and communities in energy transition. The Office of Manufacturing and Energy Supply Chains coordinates closely with the Office of Clean Energy Demonstrations for the management of major demonstration projects, and across all of DOE’s programs on manufacturing and supply chain issues, including with the Advanced Manufacturing Office in the Office of Energy Efficiency and Renewable Energy.

The Supply Chain Management Market is estimated to be valued at USD 32.1 billion in 2025 and is projected to reach USD 98.0 billion by 2035, registering a compound annual growth rate (CAGR) of 11.8% over the forecast period.

Metric	Value
Supply Chain Management Market Estimated Value in (2025 E)	USD 32.1 billion
Supply Chain Management Market Forecast Value in (2035 F)	USD 98.0 billion
Forecast CAGR (2025 to 2035)	11.8%

ABOUT THE DATA The dataset includes over 164 million cargo records from global importers and exporters, featuring more than 20 raw and processed data elements to provide comprehensive and high-quality business information. This covers details about shipment weight and value, shippers, destinations, destination countries, ports of unloading, shipment dates, HS codes, and more.

USERS AND USE CASES Suppliers, buyers, trade finance companies, logistics firms, and investment groups utilize this data to monitor investments, analyze market trends, discover new business opportunities, enhance or diversify supply chains and operations, mitigate risks, and keep track of competitors.

DETAILS ON THE COMPLETE DATA SET Available from January 2014 to the present and updated daily, this data is refined from bill of lading records, shipping lines, customs declarations, and commercial invoices to create standardized firm names.

INFORMATION ABOUT THE SAMPLE DATA SET A sample dataset of shipment statistics for a publicly traded US company and its international subsidiaries, covering the period from January 1, 2023, to January 31, 2023, is provided. For a customized dataset tailored to specific businesses, time frames, products, HS codes, ports, or other details, contact [dm@trademo.com].

ABOUT TRADEMO Trademo acts as a single source of truth for global supply chains. It compiles billions of data points and utilizes big data, machine learning, NLP, entity resolution, and graph databases to clean, enrich, and analyze unstructured data, providing in-depth insights into over 50% of global trade by dollar value.

This dataset provides detailed records of supply chain operations across multiple stages such as procurement, manufacturing, warehousing, inventory management, and distribution. It includes timestamps, quantities, locations, and transaction types, making it ideal for time series analysis, process optimization, and predictive modeling. https://www.discover-talent-presents.com/ https://www.youtube.com/watch?v=JueC_C-pU-k" alt="">