Supply Chain Big Data Analytics Market - Global Size, Share, Trend Analysis, Opportunity and Forecast Report, 2019–2029, Segmented By Solution (Logistics Analytics, Manufacturing Analytics, Planning & Procurement, Sales & Operations Analytics, Visualization & Reporting); By Service (Professional, Support & Maintenance); By End User (Retail, Transportation & Logistics, Manufacturing, Healthcare); By Region (North America, Europe, Asia Pacific, Latin America, Middle East and Africa)
Global Supply Chain Big Data Analytics Market Size More Than Trebles to Cross USD 15 Billion by 2029
Global supply chain big data analytics market is flourishing because of an increasing adoption of internet of things (IoT) solutions and a surging demand for advanced analytics solutions.
BlueWeave Consulting, a leading strategic consulting and market research firm, in its recent study, estimated global supply chain big data analytics market size at USD 4.78 billion in 2022. During the forecast period between 2023 and 2029, BlueWeave expects global supply chain big data analytics market size to grow at a significant CAGR of 17.98% reaching a value of USD 15.03 billion by 2029. Major growth factors of global supply chain big data analytics market include increasing adoption of IoT solutions and surging demand for advanced analytics solutions. The retail industry presently occupies a significant share of the supply chain big data analytics market, owing to the adoption of IoT solutions, beacons, and RFID technologies across the supply chain, and it is expected to present vast growth opportunities due to the growing number of data sources being generated. Retailers employ IoT devices and solutions to analyze customer data, track stock levels, and engage with customers. All of these technology improvements not only make it easier to track products along the supply chain, but they also help to gain a better insight of customer behavior. Increased awareness of the benefits of supply chain analytics (SCA) solutions, such as forecasting accuracy, supply chain optimization, waste minimization, and meaningful synthesis of business data, is expected to boost the expansion of overall market during the period in analysis. However, high inventory cost is anticipated to restrain the growth of global supply chain big data analytics market.
Global Supply Chain Big Data Analytics Market – Overview
Supply chain analytics (SCA) refers to the processes that businesses use to gain insight and extract value from large amounts of data associated with the procurement, processing, and delivery of commodities. SCA is an important component of supply chain management (SCM). Big Data is the term used to describe the huge volumes of structured and unstructured data that corporations utilize to find trends and patterns in human behavior and interactions. Because of improvements in information technology, businesses can now access, store, and process massive volumes of data. Organizations are analyzing data sets and gaining valuable insights to apply to their operations, highlighting the value of Big Data in any industry. Analytics is utilized in a wide range of industries, from food and beverage distribution to high technology. Big Data Analytics (BDA) has emerged as a critical business capability for organizations trying to extract value from an ever-increasing volume of data and gain a competitive edge as a result of the widespread adoption of digital technology.
Impact of COVID-19 on Global Supply Chain Big Data Analytics Market
The COVID-19 pandemic had a negative short-term impact on global supply chain big data analytics market. The pandemic has forced numerous manufacturers to temporarily suspend production in order to comply with new government requirements. The epidemic has directly impacted revenue sources, as supply chain and trade interruptions have harmed overall operations. The crisis, on the other hand, is likely to present a huge opportunity for supply chain management system suppliers to enhance their revenue shares by offering advanced technology-based supply chain solutions. Customers around the world must determine how supply chain analytics solutions may better prepare businesses for demand variations, difficult conditions, and macroeconomic volatility following the crisis. However, improved business outcomes and cost-effectiveness of supply chain management as a result of supply chain analytics adoption are predicted to stimulate the adoption of supply chain analytics solutions in a variety of end-use applications. Demand in the retail and consumer products, healthcare, and manufacturing industries is projected to continue strong. Furthermore, the market's ability to provide effective and efficient administration of end-to-end corporate operations is expected to boost its growth over the forecast period.
Global Supply Chain Big Data Analytics Market – By End User
Based on end user, global supply chain big data analytics market is divided into Retail, Transportation and Logistics, Manufacturing, and Healthcare segments. The retail segment holds the highest market share. The increasing number of data sources generated by the adoption of IoT solutions, beacons, and RFID technologies throughout the supply chain. Merchants also use IoT solutions and devices to analyze customer data, track stock levels, and improve customer interactions. All of these technology advancements not only allow for improved tracking of products across the supply chain, but also aid in acquiring a clear insight of client behavior.
Competitive Landscape
Major players operating in global supply chain big data analytics market include SAP SE (SAP), IBM Corporation, Oracle Corporation, MicroStrategy Incorporated, Genpact Limited, SAS Institute Inc., Sage Clarity Systems, Salesforce.com Inc (Tableau Software Inc.), Birst Inc., Capgemini Group, Kinaxis Inc., Accenture PLC, Aera Technology, JDA Software Group, Inc., Lockheed Martin Corporation, and Maersk Group. To further enhance their market share, these companies employ various strategies, including mergers and acquisitions, partnerships, joint ventures, license agreements, and new product launches.
The in-depth analysis of the report provides information about growth potential, upcoming trends, and statistics of Global Supply Chain Big Data Analytics Market. It also highlights the factors driving forecasts of total market size. The report promises to provide recent technology trends in Global Supply Chain Big Data Analytics Market and industry insights to help decision-makers make sound strategic decisions. Furthermore, the report also analyzes the growth drivers, challenges, and competitive dynamics of the market.
- 1. Research Framework
- 1.1. Research Objective
- 1.2. Product Overview
- 1.3. Market Segmentation
- 2. Executive Summary
- 3. Global Supply Chain Big Data Analytics Market Insights
- 3.1. Industry Value Chain Analysis
- 3.1.1. DROC Analysis
- 3.1.2. Growth Drivers
- 3.1.2.1. Rising Adoption of IOT Solutions
- 3.1.2.2. Demand for Advanced Analytics Solutions
- 3.1.3. Restraints
- 3.1.3.1. High Inventory Cost
- 3.1.4. Opportunities
- 3.1.4.1. Advancement in Technology
- 3.1.5. Challenges
- 3.1.5.1. Security and Privacy Concern
- 3.2. Technology Advancements/Recent Developments
- 3.3. Regulatory Framework
- 3.4. Porter’s Five Forces Analysis
- 3.4.1. Bargaining Power of Suppliers
- 3.4.2. Bargaining Power of Buyers
- 3.4.3. Threat of New Entrants
- 3.4.4. Threat of Substitutes
- 3.4.5. Intensity of Rivalry
- 4. Global Supply Chain Big Data Analytics Market Overview
- 4.1. Market Size & Forecast, 2019–2029
- 4.1.1. By Value (USD Million)
- 4.2. Market Share & Forecast
- 4.2.1. By Solution
- 4.2.1.1. Logistics Analytics
- 4.2.1.2. Manufacturing Analytics
- 4.2.1.3. Planning & Procurement
- 4.2.1.4. Sales & Operations Analytics
- 4.2.1.5. Visualization & Reporting
- 4.2.2. By Service
- 4.2.2.1. Professional
- 4.2.2.2. Support & Maintenance
- 4.2.3. By End User
- 4.2.3.1. Retail
- 4.2.3.2. Transportation & Logistics
- 4.2.3.3. Manufacturing
- 4.2.3.4. Healthcare
- 4.2.3.5. Others
- 4.2.4. By Region
- 4.2.4.1. North America
- 4.2.4.2. Europe
- 4.2.4.3. Asia Pacific (APAC)
- 4.2.4.4. Latin America (LATAM)
- 4.2.4.5. Middle East and Africa (MEA)
- 5. North America Supply Chain Big Data Analytics Market
- 5.1. Market Size & Forecast, 2019–2029
- 5.1.1. By Value (USD Million)
- 5.2. Market Share & Forecast
- 5.2.1. By Solution
- 5.2.2. By Service
- 5.2.3. By End User
- 5.2.4. By Country
- 5.2.4.1. US
- 5.2.4.1.1. By Solution
- 5.2.4.1.2. By Service
- 5.2.4.1.3. By End User
- 5.2.4.2. Canada
- 5.2.4.2.1. By Solution
- 5.2.4.2.2. By Service
- 5.2.4.2.3. By End User
- 6. Europe Supply Chain Big Data Analytics Market
- 6.1. Market Size & Forecast, 2019–2029
- 6.1.1. By Value (USD Million)
- 6.2. Market Share & Forecast
- 6.2.1. By Solution
- 6.2.2. By Service
- 6.2.3. By End User
- 6.2.4. By Country
- 6.2.4.1. Germany
- 6.2.4.1.1. By Solution
- 6.2.4.1.2. By Service
- 6.2.4.1.3. By End User
- 6.2.4.2. UK
- 6.2.4.2.1. By Solution
- 6.2.4.2.2. By Service
- 6.2.4.2.3. By End User
- 6.2.4.3. Italy
- 6.2.4.3.1. By Solution
- 6.2.4.3.2. By Service
- 6.2.4.3.3. By End User
- 6.2.4.4. France
- 6.2.4.4.1. By Solution
- 6.2.4.4.2. By Service
- 6.2.4.4.3. By End User
- 6.2.4.5. Spain
- 6.2.4.5.1. By Solution
- 6.2.4.5.2. By Service
- 6.2.4.5.3. By End User
- 6.2.4.6. The Netherlands
- 6.2.4.6.1. By Solution
- 6.2.4.6.2. By Service
- 6.2.4.6.3. By End User
- 6.2.4.7. Rest of Europe
- 6.2.4.7.1. By Solution
- 6.2.4.7.2. By Service
- 6.2.4.7.3. By End User
- 7. Asia-Pacific Supply Chain Big Data Analytics Market
- 7.1. Market Size & Forecast, 2019–2029
- 7.1.1. By Value (USD Million)
- 7.2. Market Share & Forecast
- 7.2.1. By Solution
- 7.2.2. By Service
- 7.2.3. By End User
- 7.2.4. By Country
- 7.2.4.1. China
- 7.2.4.1.1. By Solution
- 7.2.4.1.2. By Service
- 7.2.4.1.3. By End User
- 7.2.4.2. India
- 7.2.4.2.1. By Solution
- 7.2.4.2.2. By Service
- 7.2.4.2.3. By End User
- 7.2.4.3. Japan
- 7.2.4.3.1. By Solution
- 7.2.4.3.2. By Service
- 7.2.4.3.3. By End User
- 7.2.4.4. South Korea
- 7.2.4.4.1. By Solution
- 7.2.4.4.2. By Service
- 7.2.4.4.3. By End User
- 7.2.4.5. Australia & New Zealand
- 7.2.4.5.1. By Solution
- 7.2.4.5.2. By Service
- 7.2.4.5.3. By End User
- 7.2.4.6. Indonesia
- 7.2.4.6.1. By Solution
- 7.2.4.6.2. By Service
- 7.2.4.6.3. By End User
- 7.2.4.7. Malaysia
- 7.2.4.7.1. By Solution
- 7.2.4.7.2. By Service
- 7.2.4.7.3. By End User
- 7.2.4.8. Singapore
- 7.2.4.8.1. By Solution
- 7.2.4.8.2. By Service
- 7.2.4.8.3. By End User
- 7.2.4.9. Philippines
- 7.2.4.9.1. By Solution
- 7.2.4.9.2. By Service
- 7.2.4.9.3. By End User
- 7.2.4.10. Vietnam
- 7.2.4.10.1. By Solution
- 7.2.4.10.2. By Service
- 7.2.4.10.3. By End User
- 7.2.4.11. Rest of APAC
- 7.2.4.11.1. By Solution
- 7.2.4.11.2. By Service
- 7.2.4.11.3. By End User
- 8. Latin America Supply Chain Big Data Analytics Market
- 8.1. Market Size & Forecast, 2019–2029
- 8.1.1. By Value (USD Million)
- 8.2. Market Share & Forecast
- 8.2.1. By Solution
- 8.2.2. By Service
- 8.2.3. By End User
- 8.2.4. By Country
- 8.2.4.1. Brazil
- 8.2.4.1.1. By Solution
- 8.2.4.1.2. By Service
- 8.2.4.1.3. By End User
- 8.2.4.2. Mexico
- 8.2.4.2.1. By Solution
- 8.2.4.2.2. By Service
- 8.2.4.2.3. By End User
- 8.2.4.3. Argentina
- 8.2.4.3.1. By Solution
- 8.2.4.3.2. By Service
- 8.2.4.3.3. By End User
- 8.2.4.4. Peru
- 8.2.4.4.1. By Solution
- 8.2.4.4.2. By Service
- 8.2.4.4.3. By End User
- 8.2.4.5. Rest of LATAM
- 8.2.4.5.1. By Solution
- 8.2.4.5.2. By Service
- 8.2.4.5.3. By End User
- 9. Middle East & Africa Supply Chain Big Data Analytics Market
- 9.1. Market Size & Forecast, 2019–2029
- 9.1.1. By Value (USD Million)
- 9.2. Market Share & Forecast
- 9.2.1. By Solution
- 9.2.2. By Service
- 9.2.3. By End User
- 9.2.4. By Country
- 9.2.4.1. Saudi Arabia
- 9.2.4.1.1. By Solution
- 9.2.4.1.2. By Service
- 9.2.4.1.3. By End User
- 9.2.4.2. UAE
- 9.2.4.2.1. By Solution
- 9.2.4.2.2. By Service
- 9.2.4.2.3. By End User
- 9.2.4.3. Qatar
- 9.2.4.3.1. By Solution
- 9.2.4.3.2. By Service
- 9.2.4.3.3. By End User
- 9.2.4.4. Kuwait
- 9.2.4.4.1. By Solution
- 9.2.4.4.2. By Service
- 9.2.4.4.3. By End User
- 9.2.4.5. South Africa
- 9.2.4.5.1. By Solution
- 9.2.4.5.2. By Service
- 9.2.4.5.3. By End User
- 9.2.4.6. Nigeria
- 9.2.4.6.1. By Solution
- 9.2.4.6.2. By Service
- 9.2.4.6.3. By End User
- 9.2.4.7. Algeria
- 9.2.4.7.1. By Solution
- 9.2.4.7.2. By Service
- 9.2.4.7.3. By End User
- 9.2.4.8. Rest of MEA
- 9.2.4.8.1. By Solution
- 9.2.4.8.2. By Service
- 9.2.4.8.3. By End User
- 10. Competitive Landscape
- 10.1. List of Key Players and Their Offerings
- 10.2. Global Supply Chain Big Data Analytics Company Market Share Analysis, 2022
- 10.3. Competitive Benchmarking, By Operating Parameters
- 10.4. Key Strategic Developments (Mergers, Acquisitions, Partnerships, etc.)
- 11. Impact of Covid-19 on Global Supply Chain Big Data Analytics Market
- 12. Company Profile (Company Overview, Financial Matrix, Competitive Landscape, Key Personnel, Key Competitors, Contact Address, Strategic Outlook, SWOT Analysis)
- 12.1. SAP SE (SAP)
- 12.2. IBM Corporation
- 12.3. Oracle Corporation
- 12.4. MicroStrategy Incorporated
- 12.5. Genpact Limited
- 12.6. SAS Institute Inc.
- 12.7. Sage Clarity Systems
- 12.8. Salesforce.com Inc (Tableau Software Inc.)
- 12.9. Birst Inc.
- 12.10. Capgemini Group
- 12.11. Kinaxis Inc.
- 12.12. Accenture PLC
- 12.13. Aera Technology
- 12.14. JDA Software Group, Inc.
- 12.15. Lockheed Martin Corporation
- 12.16. Maersk Group.
- 12.17. Other Prominent Players
- 13. Key Strategic Recommendations
- 14. Research Methodology
- 14.1. Qualitative Research
- 14.1.1. Primary & Secondary Research
- 14.2. Quantitative Research
- 14.3. Market Breakdown & Data Triangulation
- 14.3.1. Secondary Research
- 14.3.2. Primary Research
- 14.4. Breakdown of Primary Research Respondents, By Region
- 14.5. Assumptions & Limitations
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