The Supply Chain Big Data Analytics Market was valued at USD 3.03 billion in 2019 and is expected to reach USD 7.91 billion by 2025, at a CAGR of 17.31% over the forecast period 2020 - 2025. 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 in Logistics & Supply Chain Management (LSCM) has garnered increasing attention due to its complexity and the prominent role of LSCM in enhancing the overall business performance. According to a survey conducted by Accenture in 2014, more than one-third of the respondents reported being engaged in serious conversations to deploy analytics in LSCM, while three out of ten already have taken an initiative to implement analytics.
-LSCM faces the most significant challenges that can potentially result in inefficiencies and wastage in supply chains, such as delayed shipments, rising fuel costs, inconsistent suppliers, and ever-increasing customer expectations, among others.
-The power of data is becoming evident to businesses of all shapes and sizes, from financial service to automobile manufacturing, healthcare, NGO, and more. It is increasingly becoming essential to make the best use of big data analytics in a supply chain to generate more profound insights. The retail sector streams a massive amount of data across its supply chains, at diverse customer touch points in many omnichannel operations.
-According to a survey by Softweb Solutions, retailers who use predictive analytics have achieved a 73% increase in sales compared to those who did not use it. Therefore, retailers are utilizing big data solutions via customer analytics to multiply profitability and outperform competitors by personalizing their in-store offerings and online product.However, there are few stumbling blocks for supply chain management while executing real-time analytics.
The scope of the Report
Supply chain analytics solutions can aid enterprises achieve growth, enhance profitability, and increase market shares by utilizing derived insights for making strategic decisions. These solutions can also offer a holistic view of supply chain and help in enhancing sustainability, reducing inventory cost, and accelerating time-to-market for products in the long run.
Key Market Trends
Retail is expected to register a Significant Growth
-The retail industry currently holds the largest share of the global data analytics market, and is expected to present vast opportunities of growth, owing to the growing number of data sources being generated, with the adoption of IoT solutions, beacons, and RFID technologies across the supply chain. According to the Global Shopping Survey 2015, 96% of the retailers are ready to adopt IoT solutions and devices to analyze customer data, track stock levels, and strengthen customer relationships. All these technological improvisations not only enable better tracking of the products across the supply chain, but also help in gaining a clear understanding of customer behavior.
-For instance, retailers have also put in a network of RFID readers into the roof space of their sales floors, allowing them to read all of the stock on display and providing more accurate inventory visibility. Augmenting this trend, the American Apparel is leveraging RFID tags and data analytics tools to improve inventory management, while Walmart employed big data analytics itself to enhance its in-store and supply chain management.
-However, massive amounts of this useful information are left to rot, resulting in the overall conversion rates of only 2 to 3%. Thus, the big analytics market has been gaining traction in the retail market, to leverage the data, with its ability to understand, analyze, and generate valuable insights.
The United States is Expected to Hold Major Share
-The United States is rigorously looking to strengthen its manufacturing industry, by enhancing its productivity by laying emphasis on improving activities across the supply chain, within the industrial sector in the country. The e-commerce industry in the United States is proliferating, owing to which, the requirement for efficient supply chain management is on the rise. According to the US Commerce Department, the e-commerce industry in the country rose by over 40% in 2017. As a result, big data is expected to rise significantly, thereby, having a positive impact on the supply chain analytics in the country.
-The e-retailers in the North American retail market are rigorously trying to enhance the customer experience, by incorporating same-day delivery, which can effectively be achieved through effective supply chain management. Notably, according to Auburn University’s Harbert College of Business, in early 2018, the retailers in the United States are expected to foster their investment in the supply chain management, especially in technology upgrade, owing to expansion and rapid growth in the e-commerce industry.
-Additionally, startups are trying to venture into the retail space in the region that are raising funds to boost their operational efficiency through big data analytics and other emerging technologies. For instance, A.S. Watson group (ASW) announced a partnership with Rubikloud, a Toronto-based startup, primarily to invest in developing big data capabilities. The former company invested about USD 70 million to enhance the operational efficiency and customer experience through the integration of visualization and machine learning capabilities. As a result, it is projected to propel the supply chain big data analytics market growth in the country.
The Supply Chain Big Data Analytics Market is highly competitive and consists of several major players. In terms of market share, few of the major players currently dominate the market. These major players with prominent share in the market are focusing on expanding their customer base across foreign countries. These companies are leveraging on strategic collaborative initiatives to increase their market share and increase their profitability. The companies operating in the market are also acquiring start-ups working on Supply Chain Big Data Analytics technologies to strengthen their product capabilities. In July 2018, Deloitte and SAS entered into an agreement to address the complex risk and regulatory calculations at scale, and turn compliance into an opportunity.
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1.1 Study Deliverables
1.2 Study Assumptions
1.3 Scope of the Study
2 RESEARCH METHODOLOGY
3 EXECUTIVE SUMMARY
4 MARKET DYNAMICS
4.1 Market Overview
4.2 Introduction to Market Drivers and Restraints
4.3 Market Drivers
4.3.1 Increasing Need Of Business Data To Improve Efficiency
4.4 Market Restraints
4.4.1 Operational Complexity Coupled With High Maintenance Costs
4.4.2 Increasing Concerns About Security Regarding Big Data Analytics
4.5 Value Chain / Supply Chain Analysis
4.6 Industry Attractiveness Porters Five Force Analysis
4.6.1 Threat of New Entrants
4.6.2 Bargaining Power of Buyers/Consumers
4.6.3 Bargaining Power of Suppliers
4.6.4 Threat of Substitute Products
4.6.5 Intensity of Competitive Rivalry
5 MARKET SEGMENTATION
5.1 By Type
5.1.1 By Solution
188.8.131.52 Supply Chain Procurement and Planning Tool
184.108.40.206 Sales and Operations Planning
220.127.116.11 Manufacturing Analytics
18.104.22.168 Transportation and Logistics Analytics
22.214.171.124 Other Solutions (Inventory Planning and Optimization Analytics and Scheduling and Reporting Tools)
5.1.2 By Service
126.96.36.199 Professional Services
188.8.131.52 Support and Maintenance Services
5.2 By Deployment
5.3 By End-user Industry
5.3.2 Transportation and Logistics
5.3.5 Other End-user Industries
5.4.1 North America
5.4.4 Latin America
5.4.5 Middle East & Africa
6 COMPETITIVE LANDSCAPE
6.1 Company Profiles
6.1.1 SAP SE (SAP)
6.1.2 IBM Corporation
6.1.3 Oracle Corporation
6.1.4 MicroStrategy Incorporated
6.1.5 Genpact Limited
6.1.6 SAS Institute Inc.
6.1.7 Sage Clarity Systems
6.1.8 Salesforce.com Inc (Tableau Software Inc.)
6.1.9 Birst Inc.
6.1.10 Capgemini Group
6.1.11 Kinaxis Inc.
7 INVESTMENT ANALYSIS
8 MARKET OPPORTUNITIES AND FUTURE TRENDS
Secondary Research Information is collected from a number of publicly available as well as paid databases. Public sources involve publications by different associations and governments, annual reports and statements of companies, white papers and research publications by recognized industry experts and renowned academia etc. Paid data sources include third party authentic industry databases.
Once data collection is done through secondary research, primary interviews are conducted with different stakeholders across the value chain like manufacturers, distributors, ingredient/input suppliers, end customers and other key opinion leaders of the industry. Primary research is used both to validate the data points obtained from secondary research and to fill in the data gaps after secondary research.
The market engineering phase involves analyzing the data collected, market breakdown and forecasting. Macroeconomic indicators and bottom-up and top-down approaches are used to arrive at a complete set of data points that give way to valuable qualitative and quantitative insights. Each data point is verified by the process of data triangulation to validate the numbers and arrive at close estimates.
The market engineered data is verified and validated by a number of experts, both in-house and external.
REPORT WRITING/ PRESENTATION
After the data is curated by the mentioned highly sophisticated process, the analysts begin to write the report. Garnering insights from data and forecasts, insights are drawn to visualize the entire ecosystem in a single report.