This dataset provides supply chain health commodity shipment and pricing data. Specifically, the data set identifies Antiretroviral (ARV) and HIV lab shipments to supported countries. In addition, the data set provides the commodity pricing and associated supply chain expenses necessary to move the commodities to countries for use. The dataset has similar fields to the Global Fund's Price, Quality and Reporting (PQR) data. PEPFAR and the Global Fund represent the two largest procurers of HIV health commodities. This dataset, when analyzed in conjunction with the PQR data, provides a more complete picture of global spending on specific health commodities. The data are particularly valuable for understanding ranges and trends in pricing as well as volumes delivered by country. The US Government believes this data will help stakeholders make better, data-driven decisions. Care should be taken to consider contextual factors when using the database. Conclusions related to costs associated with moving specific line items or products to specific countries and lead times by product/country will not be accurate.

Supply Chain Management Market Outlook

According to our latest research, the global supply chain management market size reached $28.7 billion in 2024, demonstrating robust momentum driven by digital transformation and increasing complexity in global trade. The market is projected to grow at a CAGR of 11.6% from 2025 to 2033, reaching a forecasted value of $77.2 billion by 2033. This growth is primarily fueled by the rapid adoption of advanced technologies, such as artificial intelligence, blockchain, and IoT, which are revolutionizing supply chain operations and enhancing transparency, efficiency, and resilience across multiple industries.

A primary growth factor for the supply chain management market is the accelerating pace of globalization, which has significantly increased the complexity of supply chains. Businesses are now required to manage vast networks of suppliers, manufacturers, distributors, and retailers across multiple geographies. This complexity necessitates robust supply chain management solutions that can provide real-time visibility, optimize logistics, and ensure seamless coordination among all stakeholders. The ongoing shift towards e-commerce and omnichannel retailing has further intensified the need for agile and responsive supply chain systems, driving organizations to invest heavily in advanced software and automation tools to maintain competitive advantage and meet evolving customer expectations.

Another significant driver is the increasing emphasis on risk management and supply chain resilience in the wake of global disruptions, such as the COVID-19 pandemic, geopolitical tensions, and natural disasters. Organizations have recognized the critical importance of having resilient and flexible supply chains that can quickly adapt to unforeseen events. This has led to a surge in demand for supply chain management solutions equipped with predictive analytics, scenario planning, and end-to-end visibility features. The ability to proactively identify risks, assess their impact, and implement mitigation strategies has become a top priority for companies across all sectors, fueling the growth of the supply chain management market.

Furthermore, the integration of emerging technologies, such as artificial intelligence, machine learning, and blockchain, is transforming traditional supply chain processes. These technologies enable automation of routine tasks, enhance decision-making through data-driven insights, and improve traceability and transparency across the supply chain. For instance, AI-powered demand forecasting and inventory optimization tools are helping businesses minimize stockouts and reduce excess inventory, while blockchain technology is facilitating secure and transparent transactions. The continuous innovation in supply chain management software and hardware is expected to drive market expansion over the forecast period.

From a regional perspective, North America currently dominates the supply chain management market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The strong presence of leading technology providers, early adoption of digital solutions, and high concentration of large enterprises in these regions contribute to their market leadership. However, Asia Pacific is anticipated to witness the fastest growth during the forecast period, driven by rapid industrialization, expanding manufacturing sectors, and increasing investments in digital infrastructure. The region's growing focus on supply chain optimization, particularly in China and India, is expected to create significant opportunities for market players in the coming years.

Data Integration for Supply Chain Execution plays a pivotal role in the seamless operation of modern supply chains. As businesses strive to enhance efficiency and responsiveness, integrating data from various sources becomes crucial. This integration allows for real-time visibility and coordination across different supply chain functions, from procurement and manufacturing to logistics and distribution. By leveraging advanced data integration techniques, organizations can break down silos, streamline processes, and ensure that all stakeholders have access to accurate and timely information. This not only improves decision-making but also enhances the overall agility and resilience of the s

The supply chain management market size is projected to grow from USD 31.27 billion in 2024 to USD 94.71 billion by 2035, representing a CAGR of 10.60%, during the forecast period till 2035.

The report covers Global Supply Chain Big Data Analytics Market Size and it is segmented by Type (Solution, Service), End User (Retail, Manufacturing, Transportation and Logistics, Healthcare, Other End Users), and Geography (North America, Europe, Asia Pacific, Latin America, and Middle East and Africa). The market size and forecasts are provided in terms of value (USD) for all the above segments.

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. In the current dataset, the number of dealers, production facilities and shipping ports is the same as in the original problem; it is only the demand figures, the production capacities, the transportation times and costs and the sale prices that have been randomly generated. The figures have been generated according to a normal distribution with the same mean and standard deviation as in the original dataset (e.g. the demand figures have the same mean and standard deviation as those found in the original problem).The dataset has been extended with 9 more years of demand distributions. The additional 9 years have been created with the same random problem generator. The purpose of the dataset is to provide more instances of the problem. The dataset may be interpreted as containing 10 years of demand for one product or the demand figures of 10 similar products. For instance, we adopted this dataset in a machine learning context to have a larger and more comprehensive training set.

The historical sales dataset for this research is obtained from a Bangladeshi retailer. The dataset covers a period of 1826 days and includes daily sales data for a particular product from 01 January 2013 to 31 December 2017. The raw sales data has 2 columns: the first column contains timestamps, while the remaining column reflects the quantity sold.

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.

Supply Chain Analytics Market is Segmented by Component (Software [Demand Planning and Forecasting, and More], by Deployment (On-Premise, Cloud, and More), by Analytics Type (Descriptive, and More), by Application (Inventory Management, and More), by End-Use Industry (Retail and E-Commerce, and More), by Enterprise Size (Large Enterprises, and More), and Geography.

In 2019, the global next-gen supply chain market generated roughly ** billion U.S. dollars. By 2030, the size of this market is forecasted to more than ******. Next-gen supply chain market refers to the high utilization of the digital revolution by logistics companies or start-ups to enhance the economic and environmental sustainability of supply chain services.

In a 2021 survey, over **** of surveyed supply chain professionals stated that they found supply chain disruptions and shortages extremely or very challenging. During the survey, demand-side challenges, such as faster response time were cited among the most difficult hurdles supply chain companies face.

In 2023, the Supply Chain Management Market reached a value of USD 29.3 billion, and it is projected to surge to USD 58.7 billion by 2030.

IBM reports a significant increase, or ** percent, in business efficiency in supply chains for companies adapting artificial intelligence (AI). Companies that adopted AI reported improvements in efficiency, coupled with reduction in structural costs.

The global cloud supply chain management market is projected to register a CAGR of 11.1 % by 2034, to reach USD 16.02 Billion in 2034 from USD 6.93 Billion in 2020

The asset contains commodity shipment data describing all anti-retroviral and rapid test kit shipments into PEPFAR countries. Included are SCMS’ transactions, not all purchases made under PEPFAR. The data are not intended to determine lead times or total landed cost because a single order may have different freight costs, points of origin, or delivery dates (e.g., emergency orders). Not all orders include cost of freight and/or insurance. None of the data include costs of customs clearance, security, or in-country distribution costs. See more information in the “Data Dictionary”.

The Supply Chain Analytics Market size is expected to reach a valuation of USD 43.0 billion in 2033 growing at a CAGR of 18.00%. The Supply Chain Analytics Market research report classifies Market by share, trend, demand, forecast and based on segmentation.

Data Set Bibliometric Green Supply Chain

The Global IoT for Supply Chain Management Market Size Was Worth USD 21.38 Billion in 2023 and Is Expected To Reach USD 57.6 Billion by 2032, CAGR of 13%.

The supply chain analytics market is likely to record a strong CAGR of 15.1% during the forecast period. As of 2024, the market is valued at USD 7.8 billion. It is slated to surpass USD 31.7 billion by the end of 2034.

The global supply chain analytics market size surpassed USD 9.62 billion in 2025 and is projected to grow at a CAGR of over 16.5%, reaching USD 44.3 billion revenue by 2035, attributed to AI and predictive analytics for demand forecasting.