Supply Chain Analytics Market Size, Share, Growth Analysis, By Component, By Service, By Deployment Mode, By Organization Size, By Region - Industry Forecast 2024-2031

Global Supply Chain Analytics Market size was valued at USD 5.10 billion in 2022 and is poised to grow from USD 6.18 billion in 2023 to USD 28.57 billion by 2031, growing at a CAGR of 21.10% during the forecast period (2024-2031).

Supply chain analytics (SCA) is experiencing growing demand due to heightened awareness of its benefits, including improved forecasting accuracy, enhanced supply chain optimization, reduced waste, and better synthesis of business data. The increasing number of small and medium-sized enterprises (SMEs) investing in analytics to strengthen their market positions and compete more effectively is expected to drive market growth throughout the forecast period. SCA offers organizations a comprehensive view of their supply chain activities, enabling them to manage issues that could affect profitability and sustainability more efficiently. Mobile-based analytics solutions play a crucial role by helping businesses track inefficiencies in supplier networks, address rising warehousing costs, and correct forecasting errors. These solutions are also vital for analyzing business operations, which can lead to improved inventory management and cost reduction, thus fostering business growth. The recent rise in the adoption of mobile-based analytics solutions is anticipated to create new growth opportunities in the market.

Top-down and bottom-up approaches were used to estimate and validate the size of the Global Supply Chain Analytics market and to estimate the size of various other dependent submarkets. The research methodology used to estimate the market size includes the following details: The key players in the market were identified through secondary research, and their market shares in the respective regions were determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews for key insights from industry leaders such as CEOs, VPs, directors, and marketing executives. All percentage shares split, and breakdowns were determined using secondary sources and verified through Primary sources. All possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.

Global Supply Chain Analytics Market Segmental Analysis

Global Supply Chain Analytics Market is segmented by Component, Service, Deployment Mode, Organization Size, Vertical, and region. Based on Component, the market is segmented into Software, Demand Analysis and Forecasting, Demand and Supply Planning, Supplier Performance Analytics, Supplier Performance Metrics Analysis, Spend and Procurement Analytics, Inventory Analytics, and Distribution Analytics. Based on Service, the market is segmented into Managed Services and Professional Services. Based on Deployment Mode, the market is segmented into On-premises and Cloud. Based on Organization Size, the market is segmented into Small and Medium-sized Enterprises, and Large Enterprises. Based on Vertical, the market is segmented into Retail and Consumer Goods, Automotive, Pharmaceutical, Food & Beverage Manufacturing, Machinery and Industrial Equipment Manufacturing, Energy and Utilities, and Government. Based on region, the market is segmented into North America, Europe, Asia Pacific, Latin America and Middle East & and Africa.

Drivers of the Global Supply Chain Analytics Market

The growing volume of data and the ongoing need for unified cost-cutting solutions are expected to increase the demand for advanced analytics solutions across various business sectors. Big data has become essential in shaping corporate growth strategies. Additionally, businesses are focusing on cost reduction and resource optimization, which can be achieved through effective supply chain management. Utilizing data enhances the analytical capabilities of these solutions, providing valuable insights into the root causes of lost productivity and operational inefficiencies. These insights also offer improved visibility into supply chain performance, enabling businesses to manage resource flow more effectively and target their improvement initiatives. By leveraging data, companies can better understand performance issues and optimize their supply chain strategies to achieve greater efficiency and cost savings.

Restraints in the Global Supply Chain Analytics Market

Unethical practices and rising cyber threats could pose significant challenges to the adoption of supply chain analytics (SCA) solutions. Despite the promising prospects of integrating technology into supply chain operations, many industry players remain apprehensive about potential security and data breaches. These security concerns may hinder the growth and widespread implementation of SCA solutions. The potential for breaches and unethical activities raises apprehensions among businesses, which could impact the future expansion of SCA technology. As companies weigh the benefits of advanced analytics against these risks, addressing security issues will be crucial for fostering trust and encouraging broader adoption of these solutions.

Market Trends of the Global Supply Chain Analytics Market

Data generated by machines and humans is expanding at a pace ten times faster than traditional commercial data. By 2027, the global market is expected to feature 41 billion IoT devices, which will be responsible for collecting, analyzing, and sharing this vast amount of data. The increasing need to handle and analyze substantial volumes of both structured and unstructured data has driven many businesses and individuals to adopt advanced big data analytics solutions. This rapid growth in data generation highlights the urgency for effective storage, processing, and analysis capabilities. As the volume of data continues to surge, organizations are increasingly turning to sophisticated analytics to manage and derive insights from these complex datasets.