Robotic Process Automation Market Report 2020-2025
発行: Arcluster Pte. Ltd.
ページ情報: 英文 143 Pages | 55 Tables | 49 Charts | 46 Companies
Arcluster' ‘2020 Robotic Process Automation - RPA Market Report’ covers the market sizes and forecasts of:
The report on the RPA market provides an in-depth analysis of the market size, forecasts and opportunities of RPA Tools and RPA Services targeted at small and medium businesses, and enterprises. The study includes market analysis of RPA solutions that cater to corporate functions such as Finance, Procurement, Human Resources, Customer Services, and Specific Processes. RPA services analyzed in the study include Consulting, Implementation, and Maintenance. Further to this, the report also provides the market data for RPA across 8 verticals across five regions - North America, Europe, Asia-Pacific, Central America, and Latin America (CALA), and Middle-East and Africa (MEA).
Arcluster's RPA market report also includes insights into key market requirements gathered from SMBs, enterprises, commercial entities, IT managers, CIOs, IT decision makers, opinion leaders, users, buyers, and service providers. The study also covers key demand side ratings such as ratings across regions for RPA approaches, sourcing models, hosting types, function ratings, and deployment timelines.
The analysis in this report will help market participants, vendors, suppliers, system integrators, channel players, manufacturers, and value-added resellers (VARs) to develop strategies, marketing goals and business decisions based on the actionable market intelligence from this report. This report has been built on a rigorous period of information gathering from both secondary and primary sources through several interviews with industry participants, business owners, consultants, organizations, and regulators. Data gathered from these interviews and surveys was curated, analyzed, and engineered to understand buying, spending, demand, and supply patterns and to estimate market sizes and forecasts. The resultant data was checked for applicability across value chains through market-data associations and then further validated through multiple check points to check consistencies, error samples, variances.