Global Supply Chain Analytics Market Report and Forecast 2023-2028

Global Supply Chain Analytics Market Report and Forecast 2023-2028

Market Outlook

According to the report by Expert Market Research (EMR), the global supply chain analytics market reached a value of approximately USD 22.5 billion in 2022. Aided by the growing need for efficient supply chain operations and the increasing volume of business data, the market is projected to further grow at a CAGR of 11.4% between 2023 and 2028 to reach a value of USD 43.01 billion by 2028.

Supply chain analytics involves the application of data analytics tools and techniques to enhance the efficiency and effectiveness of supply chain operations. It plays a vital role in forecasting, improving efficiency, reducing costs, and enhancing both the supplier and customer relationships. The adoption of supply chain analytics helps businesses to transform raw supply chain data into useful insights for decision-making, hence improving overall business performance.

The increasing need for efficient supply chain operations is a key factor propelling the global supply chain analytics market growth. With the growing volume of business data and the rising complexities in supply chain processes, there has been a significant shift towards the adoption of advanced analytics solutions. These solutions enable businesses to predict future scenarios, facilitate real-time decision-making, and optimise their supply chain processes, leading to a surge in demand for supply chain analytics.

The expanding application of supply chain analytics across various industries is also contributing significantly to the supply chain analytics market development. In the retail industry, for instance, supply chain analytics is used for demand forecasting, inventory management, and optimising logistics. The manufacturing sector utilises supply chain analytics to streamline production planning and manage procurement activities. Furthermore, the healthcare sector uses these analytics for better inventory management and enhancing patient care logistics.

Moreover, the emergence of AI and machine learning technologies in supply chain operations is further driving the supply chain analytics market expansion. These technologies enable the automation of various supply chain processes, enhance predictive accuracy, and allow for real-time tracking and monitoring of supply chain activities.

Looking ahead, the global supply chain analytics market is projected to continue its robust growth, underpinned by the digital transformation trends, the growing adoption of AI and machine learning in supply chain operations, and the increasing need for improved supply chain visibility. The ongoing shift towards data-driven decision-making and the need to mitigate supply chain risks amidst volatile market conditions also provide lucrative opportunities for the supply chain analytics market growth in the future.

Market Segmentation

The market can be divided based on service, solution, deployment, and region.

Market Breakup by Service

• Professional
• Support and Maintenance

Market Breakup by Solution

• Planning and Procurement
• Manufacturing Analytics
• Sales and Operations Analytics
• Logistics Analytics
• Visualisation and Reporting

Market Breakup by Deployment

• On-Premises
• Cloud

Market Breakup by Region

• North America
• Europe
• Asia Pacific
• Latin America
• Middle East and Africa

Competitive Landscape

The EMR report looks into the market shares, plant turnarounds, capacities, investments, and acquisitions and mergers, among other major developments, of the global supply chain analytics companies. Some of the major key players explored in the report by Expert Market Research are as follows:

• SAP SE
• Salesforce, Inc.
• IBM Corporation
• Capgemini SE
• Oracle Corporation
• SAS Institute Inc.
• Mu Sigma, Inc.
• Genpact Limited
• Kinaxis Inc.
• Sage Clarity Systems
• Others

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*We at Expert Market Research always strive to provide you with the latest information. The numbers in the article are only indicative and may be different from the actual report.