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.

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

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.

Data Set Bibliometric Green Supply Chain

This dataset, Green Supply Chain Optimization Dataset, is designed to support research and development in supply chain sustainability, green computing, and deep reinforcement learning. It provides 1,000 records covering various supply chain attributes, including resource consumption, transportation emissions, energy usage, cost efficiency, and environmental impact.

Key Features: Product Type: Categorized into Electronics, Apparel, Automotive, Pharmaceutical, and Food. Resource Usage: Raw material consumption, energy consumption, and waste generation. Environmental Impact: CO₂ emissions, renewable energy usage, and sustainability score. Operational Metrics: Transportation distance, manufacturing energy, cost, and delivery time. Target Variable: Sustainability Score, calculated based on emissions, waste, renewable energy, and cost. Use Cases: Optimizing supply chains using AI-driven decision-making. Evaluating green computing strategies in logistics and manufacturing. Applying Deep Reinforcement Learning for dynamic resource allocation. Conducting Lifecycle Assessment (LCA) for sustainability analytics.

This supply chain analysis provides a comprehensive view of the company's order and distribution processes, allowing for in-depth analysis and optimization of various aspects of the supply chain, from procurement and inventory management to sales and customer satisfaction. It empowers the company to make data-driven decisions to improve efficiency, reduce costs, and enhance customer experiences. The provided supply chain analysis dataset contains various columns that capture important information related to the company's order and distribution processes:

• OrderNumber • Sales Channel • WarehouseCode • ProcuredDate • CurrencyCode • OrderDate • ShipDate • DeliveryDate • SalesTeamID • CustomerID • StoreID • ProductID • Order Quantity • Discount Applied • Unit Cost • Unit Price

Global supply chain network design market size was valued at USD 16.5 billion in 2025 and projected to reach at USD 46.5 billion by 2034. (CAGR) of 12.2%

Demographic, psychographic, geographic and brand-affinity data for the Supply chain management audience in United States, sourced from Rascasse's panel of 12+ social and digital signals.

Cloud Supply Chain Management Market Outlook

According to our latest research, the global Cloud Supply Chain Management (SCM) market size in 2024 is valued at USD 9.8 billion, exhibiting robust momentum driven by digital transformation initiatives across industries. The market is experiencing a healthy compound annual growth rate (CAGR) of 11.2% from 2025 to 2033. By the end of 2033, the Cloud Supply Chain Management market is forecasted to reach USD 25.1 billion. This growth trajectory is underpinned by increasing demand for real-time supply chain visibility, enhanced collaboration capabilities, and the scalability offered by cloud-based solutions, as per our latest research findings.

A key growth driver in the Cloud Supply Chain Management market is the accelerating adoption of digital technologies by enterprises aiming to streamline their supply chain operations. Organizations are increasingly shifting from traditional on-premises systems to cloud-based platforms to gain agility, scalability, and cost-efficiency. The proliferation of e-commerce, globalization of supply chains, and the need for rapid responsiveness to market changes have made cloud SCM solutions indispensable. Cloud-based systems enable seamless integration across procurement, inventory, order management, and logistics, allowing businesses to optimize processes and respond swiftly to disruptions. Moreover, the ability to access real-time data and analytics empowers decision-makers to enhance operational efficiency, reduce costs, and improve customer satisfaction, further fueling market expansion.

Another significant factor propelling the Cloud Supply Chain Management market is the growing emphasis on supply chain resilience and risk management. Recent global events, such as the COVID-19 pandemic and geopolitical disruptions, have exposed vulnerabilities in traditional supply chains, prompting organizations to invest in cloud-based SCM solutions for greater transparency and flexibility. Cloud platforms facilitate end-to-end visibility, enabling businesses to monitor supplier performance, track shipments, and anticipate potential bottlenecks. This proactive approach to risk mitigation is critical for maintaining business continuity and meeting regulatory compliance requirements. Additionally, cloud SCM solutions support advanced technologies such as artificial intelligence, machine learning, and IoT, which further enhance predictive analytics and automation capabilities in supply chain operations.

The Cloud Supply Chain Management market is also benefiting from the rapid evolution of cloud infrastructure and services, which are making advanced supply chain capabilities accessible to organizations of all sizes. Cloud vendors are continuously enhancing their offerings with industry-specific solutions, robust security features, and seamless integration with existing enterprise resource planning (ERP) systems. This has lowered the barriers to adoption, particularly for small and medium-sized enterprises (SMEs) that previously lacked the resources to implement sophisticated supply chain management tools. The shift towards hybrid and multi-cloud strategies is further expanding the addressable market, as organizations seek to leverage the strengths of different deployment models to meet their unique operational and compliance needs.

Regionally, North America continues to lead the Cloud Supply Chain Management market due to its advanced IT infrastructure, high adoption of cloud technologies, and presence of major industry players. However, Asia Pacific is emerging as a high-growth region, driven by rapid industrialization, expansion of the e-commerce sector, and increasing investments in digital transformation by businesses in China, India, and Southeast Asia. Europe is also witnessing steady growth, supported by stringent regulatory requirements and a strong focus on supply chain optimization. Meanwhile, Latin America and the Middle East & Africa are gradually catching up as organizations in these regions recognize the strategic advantages of cloud-based supply chain solutions.

Component Analysis

The Cloud Supply Chain Management market by component is primarily segmented into software and services. The software segment dominates the market, accounting for a significant share due to the increasing demand for integrated platforms that offer end-to-end supply chain visibility, automation, and analytics. Cloud SCM software encompasses m

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

The global Digital Healthcare Supply Chain Management market size was valued at USD 4.6 billion in 2025 and is projected to reach USD 22.9 billion by 2033, growing at a CAGR of 21.60% from 2026 to 2033

The datasets are comprised of greenhouse gas (GHG) emission factors (Factors) for 1,016 U.S. commodities as defined by the 2017 version of the North American Industry Classification System (NAICS). The Factors are based on GHG data representing 2019. Factors are given for all NAICS-defined commodities at the 6-digit level except for electricity, government, and households. Each record consists of three factor types as in the previous releases: Supply Chain Emissions without Margins (SEF), Margins of Supply Chain Emissions (MEF), and Supply Chain Emissions with Margins (SEF+MEF). One set of Factors (SupplyChainGHGEmissionFactors_v1.2_NAICS_CO2e_USD2021.csv) provides kg carbon dioxide equivalents (CO2e) per USD for all GHGs combined using 100 yr global warming potentials from the 4th IPPC Assessment report to calculate the equivalents. In this dataset there is one SEF, MEF and SEF+MEF per commodity. The other dataset of Factors (SupplyChainGHGEmissionFactors_v1.2_NAICS_byGHG_USD2021.csv) provides kg of each unique GHG emitted per dollar per commodity without the CO2e calculation. The dollar (USD) in the denominator of all factors uses purchaser prices in 2021 USD. See the supporting file 'Aboutthe2019v1.2SupplyChainGHGEmissionFactors.pdf' for complete documentation of this dataset.

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

According to a global survey conducted in 2024, most respondents worldwide insisted on having data encryption in place to protect their supply chain. Other key security measures for supply chains included security awareness and multi-factor authentication to develop and/or build systems.

Global healthcare supply chain management market size was USD 2984.37 million in 2023 and is expected to rise to USD 8,309.20 million by 2032 at a CAGR of 12.05%.

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

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.

The Supply Chain Big Data Analytics market is booming, projected to reach $55 billion by 2033 with an 18% CAGR. Learn about key market drivers, trends, and leading companies leveraging big data for supply chain optimization, efficiency, and predictive capabilities.

Brazil soy - Supply chain data is a publicly available dataset that maps the connections between commodity production regions and the companies or markets that consume those goods.

snapshot-tab-pane Supply Chain Management (SCM) Software Market Size 2026-2030The supply chain management (scm) software market size is valued to increase by USD 29.63 billion, at a CAGR of 15.2% from 2025 to 2030. Strategic mainstreaming of agentic AI and autonomous decision-support systems will drive the supply chain management (scm) software market.Major Market Trends & InsightsNorth America dominated the market and accounted for a 35.8% growth during the forecast period.By Application - SCP segment was valued at USD 9.26 billion in 2024By Deployment - On-premises segment accounted for the largest market revenue share in 2024Market Size & ForecastMarket Opportunities: USD 40.55 billionMarket Future Opportunities: USD 29.63 billionCAGR from 2025 to 2030 : 15.2%Market SummaryThe supply chain management (SCM) software market is undergoing a significant transformation, moving beyond traditional data reporting to embrace autonomous orchestration and agentic intelligence. This evolution is driven by the need for enterprises to build resilient, self-healing supply chains capable of navigating persistent global trade volatility.Core to this shift is the adoption of unified, cloud-native platforms that integrate advanced technologies like artificial intelligence, digital twins, and generative AI. For instance, a global manufacturer can now leverage a digital twin of its entire network to simulate the impact of a potential port strike, allowing it to proactively reroute shipments and adjust inventory levels before disruptions occur.The market is also heavily influenced by the mandatory digitalization of sustainability and ESG compliance, compelling firms to invest in solutions that provide end-to-end visibility into multi-tier supplier ethics and Scope 3 emissions.However, this progress is tempered by challenges such as the technical complexity of integrating modern software with legacy systems and the escalating threat of cybersecurity risks in highly interconnected digital ecosystems. The democratization of technology through low-code/no-code platforms is a key trend, empowering business users to develop custom applications and accelerate digital adoption across the organization.What will be the Size of the Supply Chain Management (SCM) Software Market during the forecast period? Get Key Insights on Market Forecast (PDF) Get Free SampleHow is the Supply Chain Management (SCM) Software Market Segmented?The supply chain management (scm) software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2026-2030, as well as historical data from 2020-2024 for the following segments.ApplicationSCPProcurementWMSTMSDeploymentOn-premisesCloud-basedComponentSoftwareServicesGeographyNorth AmericaUSCanadaMexicoEuropeUKGermanyFranceAPACChinaJapanSouth KoreaMiddle East and AfricaSaudi ArabiaSouth AfricaTurkeySouth AmericaBrazilArgentinaRest of World (ROW)By Application InsightsThe scp segment is estimated to witness significant growth during the forecast period.The supply chain planning (SCP) segment is shifting from periodic planning cycles toward continuous, event-driven models powered by predictive demand sensing and autonomous orchestration. Modern platforms now use digital twin technology for real-time disruption simulation, allowing for proactive strategy testing.A key development is the integration of agentic AI, with software agents now capable of independently identifying supply-demand imbalances and executing corrective actions, a transition that has improved forecast accuracy by over 15% in early adoption cases.This move toward self-healing networks, which also incorporate prescriptive analytics and ethical sourcing metrics, minimizes reliance on manual forecasting. The evolution of low-code/no-code platforms and cloud-native orchestration further democratizes access to advanced optimization tools, enhancing overall system resilience and performance. Get Free SampleThe SCP segment was valued at USD 9.26 billion in 2024 and showed a gradual increase during the forecast period. Get Free SampleRegional AnalysisNorth America is estimated to contribute 35.8% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. See How Supply Chain Management (SCM) Software Market Demand is Rising in North America Get Free SampleThe global supply chain management (SCM) software market is geographically led by North America, which accounts for over 35% of the incremental growth, driven by its sophisticated technological ecosystem and rapid adoption of predictive demand sensing and warehouse automation.Europe follows c