Global Supply Chain Analytics Market

Global Supply Chain Analytics Market Size, Share, Growth Analysis, By Software(Supplier Performance Analytics, Dem), By Service(Professional, Support & Maintenance), By Deployment Model(Cloud, On-premises) - Industry Forecast 2024-2031

Report ID: SQMIG45C2024 | Region: Global | Published Date: February, 2024
Pages: 160 | Tables: 89 | Figures: 76

Global Supply Chain Analytics Market News

  • In July 2020, Blue Yonder collaborated with Uber Freight to assist businesses in developing efficient and transparent global supply chains. Uber Freight worked with Blue Yonder'sLuminateTM Platform as part of this cooperation to power the dynamic pricing discovery solution for quoting real-time market-based prices and quick booking capabilities.
  • In January 2020, Infosys Limited joined forces with GEFCO, a European supplier of multimodal supply-chain solutions, to help the latter with digital transformation over the next five years. The agreement envisions Infosys Limited assisting GEFCO in implementing new supply chain capabilities such as real-time data extraction and processing, obtaining valuable insights with extremely low latency, and responding intelligently and effectively across the value chain.
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Global Supply Chain Analytics Market size was valued at USD 5.18 billion in 2019 and is poised to grow from USD 6.12 billion in 2023 to USD 22.46 billion by 2031, growing at a CAGR of 17.8% in the forecast period (2024-2031).

SAP SE, Oracle Corporation, Accenture PLC, and Aera Technology are just a few of the prominent providers providing unified software solutions that address the diverse demands of a supply chain. The trend of improving the delivery mechanism from point of production to point of consumption has encouraged businesses to use unified advanced analytics software. The advancement of supply chain solutions using big data and predictive analytics, as well as the use of interactive software solutions and functional dashboards, stimulates market participants to create sophisticated supply chain analytics solutions. 'IBM', 'SAP SE', 'Oracle Corporation', 'Kinaxis', 'MicroStrategy', 'Tableau Software', 'Sisense', 'Qlik Technologies Inc.', 'TIBCO Software Inc.', 'SAS Institute Inc.', 'Teradata Corporation', 'Manhattan Associates Inc.', 'JDA Software Group Inc.', 'Logility Inc.', 'GEP Worldwide', 'E2open LLC', 'The AnyLogic Company', 'AIMMS', 'Axway Inc.', 'INFORM GmbH'

Rising data volumes, along with the ongoing need for a unified cost-cutting solution, are projected to drive demand for advanced analytics solutions across business verticals. Big data has emerged as a critical component in developing corporate growth plans. Furthermore, one of the key concerns of businesses is cost-cutting and resource-saving, which may be accomplished through appropriate supply chain optimization. As a result, the utilisation of data improves the analytical capacity of such analytical solutions and offers businesses with useful insights into the core causes of lost productivity and velocity. These data also provide improved visibility into supply chain performance, allowing businesses to govern the flow of their resources and target their improvement projects.

Machine-generated and human-generated data are growing at a rate that is ten times faster than traditional commercial data. Furthermore, by 2027, the worldwide industry is predicted to have 41 billion IoT devices that will gather, analyse, and exchange this data. The growing need to store, process, and analyse huge volumes of structured and unstructured datasets has prompted many businesses and people to adopt advanced and big data analytics.

With a market share of more than 38% in 2021, North America dominated the global supply chain analytics market. Owing to the complexity of corporate processes, North American industrial organizations are putting a greater emphasis on the visual display of operational data. This enables businesses to quickly assess the current data health of their supply chain, allowing for better strategic decision-making.

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