The Eora global supply chain database consists of a multi-region input-output table (MRIO) model that provides a time series of high-resolution IO tables with matching environmental and social satellite accounts for 190 countries.

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.

The Supply Chain Big Data Analytics Market Report is Segmented by Component (Solution, Service), End User Industry (Retail, Transportation and Logistics, Manufacturing, Healthcare, Other End-User Industries), Deployment Model (On-Premise, Cloud), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

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

Snapshot img

Supply Chain Analytics Market Size 2025-2029

The 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 & Insights

North 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 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 389.88 million
Market Future Opportunities: USD 15,514.40 million
CAGR : 22.2%
North America: Largest market in 2023

Market Summary

The 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?

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How 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

  Retail
  Manufacturing
  Healthcare
  Transportation
  Others


Deployment

  On-premises
  Cloud-based


Service

  Professional services
  Managed services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By End-user Insights

The 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.

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The 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 s

This dataset provides comprehensive supply chain risk information on products, companies and industries through continuous harvesting of online articles, websites, and public sources. The data focuses on publicly traded companies providing comprehensive coverage of supply chain vulnerabilities and risk factors across all levels of the supply chain ecosystem. This valuable resource stands out due to its real-time harvesting capabilities and comprehensive multi-source aggregation, offering insights that would be difficult and time-intensive to compile manually.

Building on this foundation, the data covers supply chain risk indicators such as financial stability ratings, foreign ownership analysis, workforce evaluations, litigation tracking, and operational dependencies. The dataset can be delivered as structured datasets or finished reports, with the provider accommodating a wide breadth of customization options to meet specific analytical requirements. What makes this dataset particularly unique is its continuous monitoring approach and the depth of risk analysis across multiple supply chain tiers.

Success.ai’s Supply Chain Data for Business Supplies & Equipment Professionals Worldwide offers a comprehensive dataset designed to help businesses connect with key stakeholders in the global supply chain and business equipment sectors. Covering procurement managers, operations directors, and supply chain professionals, this dataset provides verified contact details, business registration insights, and firmographic data.

With access to over 700 million verified global profiles and data from 70 million businesses, Success.ai ensures your outreach, market analysis, and business development strategies are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution supports your efforts to thrive in the dynamic global supply chain industry.

Why Choose Success.ai’s Supply Chain Data?

1. Verified Contact Data for Supply Chain Professionals

   • Access verified work emails, phone numbers, and LinkedIn profiles of procurement officers, logistics managers, and supply chain strategists.
   • AI-driven validation ensures 99% accuracy, enabling confident communication and effective campaign execution.

2. Comprehensive Global Coverage

   • Includes profiles from global supply chain hubs and business equipment companies in regions such as North America, Europe, Asia-Pacific, and the Middle East.
   • Gain insights into regional supply chain trends, operational challenges, and emerging opportunities.

3. Continuously Updated Datasets

   • Real-time updates reflect changes in leadership, business registrations, and supply chain dynamics.
   • Stay aligned with industry shifts and evolving market conditions.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible data usage and compliance with legal standards.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with decision-makers, operations leaders, and supply chain professionals worldwide.
• 70M Business Profiles: Access firmographic data, including business sizes, locations, and operational scopes.
• Business Registration Insights: Gain visibility into company registrations, certifications, and compliance statuses.
• Leadership Profiles: Engage with CEOs, supply chain directors, and procurement leads driving global supply chain strategies.

Key Features of the Dataset:

1. Decision-Maker Profiles in Supply Chain and Equipment

   • Identify and connect with professionals managing procurement, logistics, and operational efficiency.
   • Target individuals responsible for vendor selection, inventory management, and supply chain optimization.

2. Advanced Filters for Precision Targeting

   • Filter companies by industry focus (logistics, manufacturing, retail), geographic location, revenue size, or operational scope.
   • Tailor outreach campaigns to align with specific supply chain needs and business challenges.

3. Business Registration and Compliance Insights

   • Access verified company registration data, certifications, and compliance statuses to ensure reliable business partnerships.
   • Leverage these insights to evaluate potential partners and streamline risk management.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and improve engagement outcomes with supply chain stakeholders.

Strategic Use Cases:

1. Sales and Vendor Development

   • Offer business supplies, equipment, or technology solutions to procurement teams and supply chain managers.
   • Build relationships with companies seeking innovative tools to improve efficiency, reduce costs, or enhance sustainability.

2. Market Research and Competitive Analysis

   • Analyze global trends in supply chain operations and equipment usage to guide product development and market strategies.
   • Benchmark against competitors to identify growth opportunities, market gaps, and new industry niches.

3. Supply Chain Optimization and Risk Mitigation

   • Connect with logistics managers and operations leaders looking to streamline processes or mitigate supply chain risks.
   • Provide solutions for inventory management, logistics optimization, or compliance reporting.

4. Recruitment and Workforce Solutions

   • Engage HR professionals and hiring managers seeking skilled professionals in supply chain management or business operations.
   • Offer workforce optimization platforms, training solutions, or staffing services tailored to the supply chain industry.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality supply chain data at competitive prices, ensuring strong ROI for your marketing, sales, and strategic initiatives.

2. Seamless Integration

   • Integrate verified supply chain data into CRM systems, marketing platforms, or analytics tools via APIs o...

This graph presents the result of a worldwide survey of senior executives, conducted by Accenture, into the impact of big data analytics on company supply chains in 2014. In 2014, ** percent of respondents stated that their company had achieved an improvement in customer service and demand fulfillment of ** percent or greater using big data analytics.

This dataset provides values for SUPPLY CHAIN PRESSURE INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Graph Database Platforms for Supply Chain Market Outlook

According to our latest research, the global Graph Database Platforms for Supply Chain market size reached USD 1.84 billion in 2024, reflecting robust demand across multiple industries. The market is projected to register a compelling CAGR of 19.7% from 2025 to 2033, with the total market value expected to reach USD 9.16 billion by 2033. This impressive growth is primarily driven by the increasing complexity of global supply chains, the need for real-time data analytics, and the rapid adoption of digital transformation initiatives in logistics, manufacturing, and retail sectors. As per our most recent analysis, organizations are increasingly leveraging graph database platforms to enhance visibility, optimize operations, and address supply chain disruptions more effectively.

The primary growth factor fueling the expansion of the Graph Database Platforms for Supply Chain market is the escalating demand for advanced data management solutions capable of handling the intricate relationships and dependencies inherent in modern supply chains. Traditional relational databases often struggle with the dynamic and interconnected nature of supply chain data, which includes suppliers, manufacturers, logistics partners, and end customers. Graph databases, by contrast, are designed to efficiently map and analyze these complex networks, enabling organizations to gain actionable insights, identify bottlenecks, and mitigate risks. The ability to visualize and traverse vast data sets in real time is particularly valuable in scenarios involving multi-tier suppliers, global logistics, and compliance requirements, thus propelling the adoption of graph database platforms across industries.

Another significant driver is the growing emphasis on supply chain resilience and risk management, especially in the wake of global disruptions such as pandemics, geopolitical tensions, and natural disasters. Organizations are increasingly recognizing the importance of end-to-end supply chain visibility to anticipate and respond to potential threats. Graph database platforms facilitate real-time monitoring and predictive analytics, empowering businesses to proactively manage risks and ensure business continuity. Enhanced traceability and compliance capabilities also support industries with stringent regulatory requirements, such as healthcare, automotive, and food & beverage, further accelerating market growth. Additionally, the integration of artificial intelligence and machine learning with graph databases amplifies their value, allowing for advanced scenario modeling, anomaly detection, and optimization.

Digital transformation initiatives, particularly the adoption of cloud computing and the Internet of Things (IoT), are further catalyzing the growth of the Graph Database Platforms for Supply Chain market. Cloud-based deployment models offer scalability, flexibility, and cost-effectiveness, making graph database solutions accessible to organizations of all sizes, including small and medium enterprises. The proliferation of IoT devices throughout supply chains generates massive volumes of interconnected data, which graph databases are uniquely equipped to manage and analyze. This convergence of technologies is fostering innovative applications in inventory management, logistics optimization, and supplier collaboration, thereby expanding the addressable market and driving sustained investment in graph database platforms.

From a regional perspective, North America currently dominates the Graph Database Platforms for Supply Chain market, accounting for the largest revenue share in 2024. This leadership position can be attributed to the region’s advanced IT infrastructure, high levels of digitalization, and strong presence of major technology providers. However, Asia Pacific is projected to exhibit the highest CAGR over the forecast period, fueled by rapid industrialization, expanding e-commerce, and significant investments in supply chain modernization. Europe is also witnessing robust growth, driven by regulatory requirements for traceability and sustainability, particularly in manufacturing and automotive sectors. Latin America and the Middle East & Africa are emerging markets with increasing adoption of graph database technologies, supported by growing awareness and digital transformation initiatives.

The dataset contains contact and description information for local supply chain organizations, offshore wind developers, and original equipment manufacturers that provide goods and services to support New York State’s offshore wind industry. To request placement in this database, or to update your company’s information, please visit NYSERDA’s Supply Chain Database webpage at https://www.nyserda.ny.gov/All-Programs/Offshore-Wind/Focus-Areas/Supply-Chain-Economic-Development/Supply-Chain-Database to submit a request form. How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on Twitter, Facebook, YouTube, or Instagram.

This statistic shows the results of a global logistics survey conducted between spring and summer of 2016, asking shippers about the most important factors in big data management in the supply chain. According to some ** percent of the respondents, big data is valuable in improving process quality and performance.

Graph Database Platforms for Supply Chain Market Outlook

According to our latest research, the global market size for Graph Database Platforms for Supply Chain reached USD 1.54 billion in 2024, driven by the increasing complexity of supply chain operations and the need for advanced data analytics. The market is expected to grow at a robust CAGR of 22.1% from 2025 to 2033, reaching USD 8.03 billion by 2033. This significant growth is primarily fueled by the rising adoption of digital transformation initiatives across industries, coupled with the demand for real-time supply chain visibility and risk management capabilities.

One of the primary growth drivers for the Graph Database Platforms for Supply Chain market is the rapidly increasing complexity of global supply chains. As organizations expand their operations across borders and deal with a multitude of suppliers, logistics partners, and regulatory environments, traditional relational databases often fall short in capturing the intricate relationships and dependencies inherent in modern supply chains. Graph database platforms excel in visualizing and analyzing these connections, enabling companies to identify bottlenecks, mitigate risks, and optimize workflows. The ability to map out entire supply chain networks in real time allows businesses to make faster, more informed decisions, which is crucial in today’s volatile market environment where disruptions are frequent and costly.

Another significant factor propelling market growth is the surging demand for enhanced supply chain transparency and traceability. With increasing consumer expectations, stricter regulatory requirements, and the ongoing need to combat fraud and counterfeiting, companies are investing heavily in technologies that provide end-to-end visibility. Graph database platforms allow organizations to track the journey of goods and materials from origin to destination, facilitating compliance with industry standards and improving accountability. This capability is especially vital in industries such as food & beverage, pharmaceuticals, and automotive, where product recalls and quality issues can have severe financial and reputational consequences. The integration of graph database platforms with IoT devices and blockchain further amplifies their value, offering real-time insights and immutable records for every transaction and movement within the supply chain.

The growing emphasis on supply chain resilience and agility in response to global disruptions, such as the COVID-19 pandemic and geopolitical tensions, has also accelerated the adoption of graph database platforms. Organizations are increasingly recognizing the need to proactively identify vulnerabilities and simulate various scenarios to ensure business continuity. Graph databases facilitate advanced risk modeling and predictive analytics, empowering supply chain leaders to anticipate disruptions, evaluate alternative sourcing strategies, and maintain optimal inventory levels. As the frequency and impact of supply chain shocks continue to rise, the demand for intelligent platforms that can quickly adapt to changing conditions is expected to sustain the market’s momentum over the next decade.

From a regional perspective, North America currently dominates the Graph Database Platforms for Supply Chain market, accounting for the largest share in 2024. This dominance is attributed to the early adoption of advanced technologies, strong presence of key market players, and a mature supply chain ecosystem. However, Asia Pacific is projected to exhibit the fastest growth rate during the forecast period, driven by the rapid expansion of manufacturing and e-commerce sectors, increasing investments in digital infrastructure, and a growing focus on supply chain optimization. Europe also remains a significant market, supported by stringent regulatory standards and a strong emphasis on sustainability and risk management. The Middle East & Africa and Latin America are gradually emerging as promising markets, buoyed by rising industrialization and efforts to modernize supply chain operations.

Component Analysis

The Graph Database Platforms for Supply Chain market is segmented by component into software and services. The software segment currently holds the largest share of the market, as organizations increasingly invest in robust platforms that can handle vast and complex datasets. These software solutions are designed to provide advanced analytics, visuali

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

This statistic represents the results of a global logistics survey conducted between spring and summer of 2016, asking third party providers about what services their customers find more important in big data management in the supply chain. According to some ** percent of the respondents, improving logistics optimization was among the most important factors for ***** customers during that time period.

Datasets produced by CDP Worldwide. Provided to CDP by companies (suppliers) responding publicly to an information request from their clients. More information at https://www.cdp.net/en. ACCESS LIMITED TO CURRENT HARVARD UNIVERSITY COMMUNITY MEMBERS ONLY

Global Value Chains (GVC's) data from World Bank's WDR 2020 data

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.

1. Citation

Please cite the following papers when using this dataset:

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

1. 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 selling points and were returned the previous year

%

points_of_distribution

The amount of sales representatives through which the product was sold to the market for this year

previous_year_points_of_distribution

The amount of sales representatives through which the product was sold to the market for the same day for the previous year

Table 1 – Dataset Feature Description

1. Structure and Format

4.1 Dataset Structure

The provided dataset has the following structure:

Where:

Name

Type

Property

Readme.docx

Report

A File that contains the documentation of the Dataset.

product X

Folder

A folder containing the data of a product X.

product X YYYY.xlsx

Data file

An excel file containing the sales data of product X for year YYYY.

Table 2 - Dataset File Description

1. Acknowledgement

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957406 (TERMINET).

References

[1] MEVGAL is a Greek dairy production company

The global supply chain big data analytics market is projected to reach USD 15.76 billion by 2033, exhibiting a CAGR of 10.5% during the forecast period. The rapid surge in data volumes generated by the multifaceted supply chain processes, such as inventory management, demand forecasting, and logistics optimization, is propelling market growth. Moreover, the increasing adoption of Industry 4.0 technologies and the need for real-time visibility and predictive capabilities in supply chains are further contributing to the expansion of the market. The market is segmented based on application into retail, healthcare, transportation and logistics, manufacturing, and others. Among these, the retail segment currently holds the largest share of the market due to the growing need for personalized customer experiences, optimized inventory management, and enhanced supply chain efficiency in the retail industry. The healthcare vertical is expected to witness substantial growth in the coming years driven by the increasing adoption of supply chain big data analytics for patient care optimization, drug development, and inventory management. Key players operating in the market include Accenture, IBM, Google Inc., Hewlett-Packard Company, SAP SE, and Intel Corp.

Context

This data set 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.

Content

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.

Acknowledgements

Source : https://www.usaid.gov/data/dataset/0162a542-4f2e-4fe2-ad5d-8f6ed2344056