Simantha is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines with finite buffers. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Classes for five basic manufacturing objects are included: source, machine, buffer, sink, and maintainer. These objects can be defined by the user and configured in different ways to model various real-world manufacturing systems. The object classes are also designed to be extensible so that they can be used to model more complex processes.In addition to modeling the behavior of existing systems, Simantha is also intended for use with simulation-based optimization and planning applications. For instance, users may be interested in evaluating alternative maintenance policies for a particular system. Estimating the expected system performance under each candidate policy will require a large number of simulation replications when the system is subject to a high degree of stochasticity. Simantha therefore supports parallel simulation replications to make this procedure more efficient.Github repository: https://github.com/usnistgov/simantha
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Manufacturing Production in China increased 7.40 percent in June of 2025 over the same month in the previous year. This dataset provides - China Manufacturing Production- actual values, historical data, forecast, chart, statistics, economic calendar and news.
https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain
Graph and download economic data for Manufacturers Inventories (MNFCTRIMNSA) from Jan 1992 to Mar 2025 about inventories, manufacturing, and USA.
https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain
Graph and download economic data for Manufacturing Sector: Output per Worker for All Workers (PRS30006162) from Q2 1987 to Q1 2025 about productivity, output, sector, per capita, manufacturing, real, rate, and USA.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset shows the projects that have been approved manufacturing license and/or incentives based on factory location.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
energy
https://data.gov.tw/licensehttps://data.gov.tw/license
The number of manufacturing units in the 2016 Industrial and Service Census, by county and detailed industry category.
The Cybersecurity Framework Manufacturing Profile Low Security Level Example Implementations Guide provides example proof-of-concept solutions demonstrating how open-source and commercial off-the-shelf (COTS) products that are currently available today can be implemented in manufacturing environments to satisfy the requirements in the Cybersecurity Framework (CSF) Manufacturing Profile [8] Low Security Level. Example proof-of-concept solutions for a process-based manufacturing environment and a discrete-based manufacturing environment are included in the guide. Depending on factors like size, sophistication, risk tolerance, and threat landscape, manufacturers should make their own determinations about the breadth of the proof-of-concept solutions they may voluntarily implement. The dataset records the Key Performance Indicator (KPI) for the example implementation of the process-based manufacturing system use case.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Manufacturing Production In the Euro Area increased 3.80 percent in May of 2025 over the same month in the previous year. This dataset provides - Euro Area Manufacturing Production - actual values, historical data, forecast, chart, statistics, economic calendar and news.
https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain
Graph and download economic data for Manufacturing Sector: Labor Productivity (Output per Hour) for All Workers (PRS30006091) from Q1 1988 to Q1 2025 about per hour, productivity, output, sector, manufacturing, real, persons, rate, and USA.
Additive Manufacturing Market Size 2025-2029
The additive manufacturing market size is forecast to increase by USD 46.76 billion at a CAGR of 23.9% between 2024 and 2029.
The market is experiencing significant growth, driven primarily by the high demand in the medical device sector for customized and complex components. This trend is further fueled by increasing consumer interest in personalized, 3D-printed products across various industries. However, the market growth is not without challenges. The high initial cost of setting up additive manufacturing facilities remains a significant barrier for entry, limiting the number of players and potentially hindering market penetration. Moreover, the technology's limited material options and the need for specialized expertise pose additional challenges.
To capitalize on the market opportunities and navigate these challenges effectively, companies must focus on collaborations, strategic partnerships, and continuous innovation to reduce costs, expand material offerings, and improve production efficiency. By staying abreast of the latest industry developments and trends, businesses can position themselves to succeed in this dynamic and evolving market.
What will be the Size of the Additive Manufacturing Market during the forecast period?
Request Free Sample
The market continues to experience significant growth and innovation, driven by the increasing adoption of industrial 3d printing technologies in various industries. The market's size is projected to expand at a robust rate, with the automotive and industrial segments leading the charge. Technologies such as fuse deposition modeling, stereolithography, and selective laser sintering are gaining popularity due to their ability to produce complex geometries and reduce production expenses. The market is also witnessing increased regulatory scrutiny, leading to the development of certification standards and quality assurance protocols. The integration of advanced scanning software and design software capabilities is enabling more precise and efficient manufacturing processes.
Mergers & acquisitions and collaboration agreements are common as companies seek to expand their offerings and enhance their competitive positions. Despite the advancements, challenges remain, including the need for installation services, addressing the skills gap, and ensuring compatibility with traditional manufacturing methods. Desktop additive manufacturing and desktop 3d printers are also gaining traction for prototyping and educational purposes. The market's future direction lies in the continued development of more advanced technologies, improved design software, and the expansion of applications beyond prototyping to production. The shift from subtractive manufacturing methods to additive manufacturing is transforming industries, offering new opportunities for innovation and cost savings.
The market's dynamics are shaped by ongoing technological advancements, regulatory developments, and industry 4.0 trends.
How is this Additive Manufacturing Industry segmented?
The additive manufacturing industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.
Component
Hardware
Software
Services
End-user
Automotive
Aerospace
Industrial
Healthcare
Defense
Consumer Goods
Education/Research
Others
Material
Plastics
Metals
Ceramics
Others
Technology
Stereolithography
Polyjet printing
Binder jetting
Laser sintering
Fused Deposition Modeling (FDM)
Direct Metal Laser Sintering (DMLS)
Electron Beam Melting (EBM)
Directed Energy Deposition (DED)
Others
Binder jetting
Geography
North America
US
Canada
Europe
France
Germany
Spain
UK
APAC
China
India
Japan
South America
Brazil
Middle East and Africa
UAE
Rest of World
By Component Insights
The hardware segment is estimated to witness significant growth during the forecast period.
Additive manufacturing, also known as 3D printing, is revolutionizing industrial production by enabling the creation of complex parts layer-by-layer. The market for this technology is in a high-growth stage, driven by the increasing adoption in industries such as aerospace, automotive, healthcare, and manufacturing. Industrial 3D printers, which use technologies like Fused Deposition Modeling (FDM), Stereolithography, Selective Laser Sintering (SLS), and Digital Light Processing (DLP), are at the heart of this process. These printers offer advantages such as enhanced material usage, functional parts precision, and reduced production expenses. The dental industry and education sector are witnessing significant growth in the utiliz
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
<ul style='margin-top:20px;'>
<li>Comoros manufacturing output for was <strong>$0.00</strong>, a <strong>0% increase</strong> from .</li>
<li>Comoros manufacturing output for was <strong>$0.00</strong>, a <strong>0% increase</strong> from .</li>
<li>Comoros manufacturing output for was <strong>$0.00</strong>, a <strong>0% increase</strong> from .</li>
</ul>Manufacturing refers to industries belonging to ISIC divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Data are in current U.S. dollars.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Manufacturing Production in Austria increased 1 percent in May of 2025 over the same month in the previous year. This dataset provides - Austria Manufacturing Production - actual values, historical data, forecast, chart, statistics, economic calendar and news.
https://fred.stlouisfed.org/legal/#copyright-citation-requiredhttps://fred.stlouisfed.org/legal/#copyright-citation-required
Graph and download economic data for Nonfarm Private Payroll Employment for Manufacturing (ADPMINDMANNERSA) from Jan 2010 to May 2025 about payrolls, nonfarm, private, manufacturing, employment, and USA.
https://www.ibisworld.com/about/termsofuse/https://www.ibisworld.com/about/termsofuse/
Employment statistics on the Truck & Bus Manufacturing industry in the US
https://www.mordorintelligence.com/privacy-policyhttps://www.mordorintelligence.com/privacy-policy
The APAC Smart Manufacturing Market report segments the industry into Enabling Technologies (Industrial Control Systems, Industrial Robotics, Machine Vision Systems, and more), End-User Industry (Automotive, Semiconductor, Oil and Gas, and more), and Country (China, India, and more).
In the third quarter of 2024, in Southeast Asia, the industrial production index (IIP) score of manufacturing in Vietnam was *****. In comparison, the IIP of manufacturing in Thailand in the third quarter of 2024 was ****.
In this study, we have undertaken a robust analysis of the global supply chain and manufacturing costs for components of Organic Rankine Cycle (ORC) Turboexpander and steam turbines used in geothermal power plants. We collected a range of market data influencing manufacturing from various data sources and determined the main international manufacturers in the industry. The data includes the manufacturing cost model to identify requirements for equipment, facilities, raw materials, and labor. We analyzed three different cases; 1) 1 MW geothermal ORC Turboexpander 2) 5 MW ORC Turboexpander 3) 20 MW geothermal Steam Turbine
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Cloud manufacturing and logistics service composition
Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
License information was derived automatically
Contained within the 3rd Edition (1957) of the Atlas of Canada is a map that shows two condensed maps illustrating the distribution of the labour force engaged in manufacturing circa early 1950s. The map has a dot representing every 100 people in the manufacturing labour force, with places of 1000 or more people in manufacturing being shown as proportional circles, instead. There are additional data for the 18 census metropolitan areas. This consists of a pie graph for each of these places showing the breakdown of the manufacturing labour force into each of 16 manufacturing industry types. The total manufacturing labour force in each of the census metropolitan areas is also given.
Simantha is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines with finite buffers. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Classes for five basic manufacturing objects are included: source, machine, buffer, sink, and maintainer. These objects can be defined by the user and configured in different ways to model various real-world manufacturing systems. The object classes are also designed to be extensible so that they can be used to model more complex processes.In addition to modeling the behavior of existing systems, Simantha is also intended for use with simulation-based optimization and planning applications. For instance, users may be interested in evaluating alternative maintenance policies for a particular system. Estimating the expected system performance under each candidate policy will require a large number of simulation replications when the system is subject to a high degree of stochasticity. Simantha therefore supports parallel simulation replications to make this procedure more efficient.Github repository: https://github.com/usnistgov/simantha