Supply Chain Big Data Analytics Market Size (2024 - 2029)

The Supply Chain Big Data Analytics market is experiencing significant growth, driven by advancements in information technology that enable organizations to access and analyze vast data sets. This capability is becoming essential across various industries, as companies leverage analytics to enhance operational efficiency and gain a competitive edge. The integration of big data analytics in supply chain management is particularly beneficial, offering insights that optimize processes, reduce costs, and improve decision-making. The COVID-19 pandemic has further underscored the importance of these analytics, as businesses seek to navigate disruptions and maintain economic sustainability.

Market Size of Supply Chain Big Data Analytics Industry

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Supply Chain Big Data Analytics Market Summary
Study Period 2019 - 2029
Base Year For Estimation 2023
CAGR 17.31 %
Fastest Growing Market Asia Pacific
Largest Market North America
Market Concentration Medium

Major Players

Supply Chain Big Data Analytics Market Major Players

*Disclaimer: Major Players sorted in no particular order

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Supply Chain Big Data Analytics Market Analysis

Supply Chain Big Data Analytics Market is expected to register a CAGR of approximately 17.31% over the forecast period. With advancements in information technology, firms are now able to access, store, and process a massive amount of data. Organizations are analyzing data sets and identifying key insights to apply to their operations, making it evident that Big Data has an important role to play in any industry. From food and beverage distribution to high tech, companies are incorporating analytics.

  • The widespread use of digital technologies has led to the emergence of Big Data Analytics (BDA) as a critical business capability to provide companies with better opportunities to obtain value from an increasingly huge amount of data and gain a commanding competitive advantage.
  • Big data analytics aid in the improvement of the supply chain in the manufacturing business. For example, energy-intensive manufacturing runs can be scheduled to take advantage of changing electricity rates. Data on production characteristics, such as assembly forces or size variances between components, can be saved and examined to aid in the root-cause investigation of errors, even if they arise years later. Agricultural seed processors and producers monitor the quality of their products in real-time using various types of cameras to obtain quality assessments for every individual seed.
  • Analytics are already being used by trucking businesses to optimize their operations. For example, they employ fuel usage analytics to increase vehicle economy and GPS technology to cut waiting times by distributing storage spaces in real time. Courier companies have begun real-time scheduling of deliveries to consumers based on geo-location and congestion data from their trucks. UPS, for example, has invested ten years in creating its On-Road Integrated Optimization and Navigation system (Orion) to improve the network's 55,000 paths. According to the company's CEO, David Abney, the new method would save $300 million to $400 million yearly. Big data analytics will also assist logistics operators in delivering goods with fewer delivery efforts by mining their data to estimate when a parcel will be delivered.
  • Big data analytics can help businesses investigate the sales benefits of grouping related goods together. Google has bought Skybox, a source of high-resolution satellite images that can be used to watch automobiles in a parking lot to predict in-store demand. Others have investigated the use of camera-equipped drones to track on-shelf stock levels.
  • The pandemic of COVID-19 has caused disruptions and hazards in global supply systems. Big data analytics (BDA) has recently arisen as a viable solution for providing firms with predicted and pre-emptive information to assist them in planning and reducing the effects of such hazards. The outbreak highlighted the need for solutions for supply chains to ensure long-term economic sustainability. During these difficult times, supply chain analytics helped firms to detect processes that needed immediate adjustment or products/items that were expected to run out soon, helping them to manage the demand-supply gap better. Furthermore, the suppliers are actively developing and delivering solutions to mitigate the detrimental effects of the outbreak on global supply chains.

Supply Chain Big Data Analytics Industry Segmentation

Supply chain analytics solutions can aid enterprises in achieving growth, enhancing profitability, and increasing market shares by utilizing derived insights for making strategic decisions. These solutions can also offer a holistic view of the supply chain and help enhance sustainability, reduce inventory costs, and accelerate time-to-market for products in the long run. The Supply Chain Big Data Analytics Market 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 sizes and forecasts are provided in terms of value (USD million) for all the above segments.

By Type
Solution
Supply Chain Procurement and Planning Tool
Sales and Operations Planning
Manufacturing Analytics
Transportation and Logistics Analytics
Other Solutions (Inventory Planning and Optimization Analytics and Scheduling and Reporting Tools)
Service
Professional Service
Support and Maintenance Service
End User
Retail
Transportation and Logistics
Manufacturing
Healthcare
Other End Users
Geography
North America
United States
Canada
Europe
United Kingdom
Germany
France
Italy
Rest of Europe
Asia-Pacific
China
Japan
South Korea
India
Rest of Asia-Pacific
Latin America
Mexico
Brazil
Argentina
Rest of Latin America
Middle-East & Africa
United Arab Emirates
Saudi Arabia
South Africa
Rest of Middle-East & Africa
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Supply Chain Big Data Analytics Market Size Summary

The supply chain big data analytics market is experiencing significant growth, driven by advancements in information technology and the increasing importance of data-driven decision-making across various industries. Companies are leveraging big data analytics to enhance operational efficiency, optimize supply chain processes, and gain a competitive edge. This technology is being applied across diverse sectors, from manufacturing to logistics, where it aids in scheduling energy-intensive runs, monitoring product quality in real-time, and optimizing delivery routes. The COVID-19 pandemic has further underscored the value of big data analytics, as it provided businesses with predictive insights to navigate disruptions and maintain economic sustainability. The retail sector, in particular, is witnessing substantial growth opportunities due to the proliferation of IoT solutions and the rising volume of data generated throughout the supply chain.

In the United States, the retail industry plays a pivotal role in the supply chain big data analytics market, with e-commerce growth driving demand for efficient supply chain management. Retailers are increasingly adopting advanced analytics to improve demand forecasting, inventory management, and customer engagement. The market is highly competitive, with major players like SAP SE, IBM Corporation, and Oracle Corporation leading the charge through strategic collaborations and technological innovations. These companies are continuously enhancing their analytics solutions to provide businesses with real-time visibility and actionable insights, thereby facilitating better decision-making and operational optimization. As the market evolves, the integration of big data analytics into supply chain operations is expected to become more prevalent, offering businesses the tools needed to thrive in a data-driven economy.

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Supply Chain Big Data Analytics Market Size - Table of Contents

  1. 1. MARKET INSIGHTS

    1. 1.1 Market Overview

    2. 1.2 Market Drivers

      1. 1.2.1 Increasing Need of Business Data to Improve Efficiency

    3. 1.3 Market Restraints

      1. 1.3.1 Operational Complexity Coupled with High Maintenance Costs

    4. 1.4 Value Chain / Supply Chain Analysis

    5. 1.5 Industry Attractiveness - Porter Five Forces

      1. 1.5.1 Threat of New Entrants

      2. 1.5.2 Bargaining Power of Buyers/Consumers

      3. 1.5.3 Bargaining Power of Suppliers

      4. 1.5.4 Threat of Substitute Products

      5. 1.5.5 Intensity of Competitive Rivalry

    6. 1.6 Assessment of the Impact of COVID-19 on the Market

  2. 2. MARKET SEGMENTATION

    1. 2.1 By Type

      1. 2.1.1 Solution

        1. 2.1.1.1 Supply Chain Procurement and Planning Tool

        2. 2.1.1.2 Sales and Operations Planning

        3. 2.1.1.3 Manufacturing Analytics

        4. 2.1.1.4 Transportation and Logistics Analytics

        5. 2.1.1.5 Other Solutions (Inventory Planning and Optimization Analytics and Scheduling and Reporting Tools)

      2. 2.1.2 Service

        1. 2.1.2.1 Professional Service

        2. 2.1.2.2 Support and Maintenance Service

    2. 2.2 End User

      1. 2.2.1 Retail

      2. 2.2.2 Transportation and Logistics

      3. 2.2.3 Manufacturing

      4. 2.2.4 Healthcare

      5. 2.2.5 Other End Users

    3. 2.3 Geography

      1. 2.3.1 North America

        1. 2.3.1.1 United States

        2. 2.3.1.2 Canada

      2. 2.3.2 Europe

        1. 2.3.2.1 United Kingdom

        2. 2.3.2.2 Germany

        3. 2.3.2.3 France

        4. 2.3.2.4 Italy

        5. 2.3.2.5 Rest of Europe

      3. 2.3.3 Asia-Pacific

        1. 2.3.3.1 China

        2. 2.3.3.2 Japan

        3. 2.3.3.3 South Korea

        4. 2.3.3.4 India

        5. 2.3.3.5 Rest of Asia-Pacific

      4. 2.3.4 Latin America

        1. 2.3.4.1 Mexico

        2. 2.3.4.2 Brazil

        3. 2.3.4.3 Argentina

        4. 2.3.4.4 Rest of Latin America

      5. 2.3.5 Middle-East & Africa

        1. 2.3.5.1 United Arab Emirates

        2. 2.3.5.2 Saudi Arabia

        3. 2.3.5.3 South Africa

        4. 2.3.5.4 Rest of Middle-East & Africa

Supply Chain Big Data Analytics Market Size FAQs

The Supply Chain Big Data Analytics Market is projected to register a CAGR of 17.31% during the forecast period (2024-2029)

SAP SE, IBM Corporation, Oracle Corporation, MicroStrategy Incorporated and Genpact Limited are the major companies operating in the Supply Chain Big Data Analytics Market.

Supply Chain Big Data Analytics Market Size & Share Analysis - Growth Trends & Forecasts (2024 - 2029)