Industrial Production in China increased 5.70 percent in July of 2025 over the same month in the previous year. This dataset provides - China Industrial Production - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

Comparison of economic performance over time is a key factor in economic analysis and a fundamental requirement for policy-making. Short-term indicators play an important role in this context by providing such comparison indicators. Among the short-term indicators, the Index of Industrial Production (IIP) has historically been one of the most well-known and well-used indicators. The all India IIP is a composite indicator that measures the short-term changes in the volume of production of a basket of industrial products during a given period with respect to that in a chosen base period. It is compiled and published monthly by the Central Statistics Office (CSO) with a time lag of six weeks from the reference month.

Geographic coverage

Industrial Coverage: Although UNSD guidelines state that IIP is to be compiled for activities in ISIC Rev. 4 Sections B, C, D and E, i.e. (i) Mining and quarrying, (ii) Manufacturing, (iii) Electricity, Gas, Steam and Air-conditioning supply and (iv) Water supply, Sewerage, Waste management and Remediation activities, due to constraints of the data availability and other resources, the index is being compiled with (i) Mining, (ii) Manufacturing and (iii) Electricity as scope of All India IIP. In the current base year (i.e. 2011-12), the index covers 839 items clubbed into 407 item groups under three sectors i.e. Mining (29 items clubbed into 1 item group), Manufacturing (809 items clubbed into 405 item groups) and Electricity (1 item) with weights of 14.37%, 77.63% and 7.99% respectively.

The mining sector covers 29 items under different headings viz. Fuel Minerals, Metallic Minerals and Non-Metallic Minerals. This sector also includes Crude Petroleum, Natural Gas, Coal and Lignite. The manufacturing sector covers 809 items under different groups e.g. Food products, Beverages, Textiles, Chemicals and chemical products etc. The Electricity sector is treated as a single item.

Product Coverage: Within an industry the products are covered on the basis of the concepts of Primary (Main) Product as well as Secondary (By) Product. All those items which represent at least 80% of the output within each industry group, i.e., 3-digit industry of NIC-2008 (based on ISIC 4) have been included in the Item basket. Essential products like tea, coffee, salt and sugar have been included. The over-riding criteria for finalization of item basket have been the regular monthly flow of production data from the source agencies/collection authorities.

Analysis unit

Frame for coverage of units is decided by the source agencies which collect data from the factories. For compilation of IIP both large and medium factories are covered for collection of data by the source agencies.

Mode of data collection

The sample size for data collection is decided by the source agencies. Generally, efforts are made to cover all the major units.

statistical techniques :

Procedures for Non-Response: In India, the Index of Industrial Production is based on the responded production as well as estimated production for non-responding units. The production estimates for the non-responding units are developed using various methods including: repetition of last available data; taking the average production data for the last few months; using previous year's growth rate; etc. The appropriate estimation procedure is decided by the source agencies themselves in consultation with CSO. Treatment of Missing Production: The index is compiled on the basis of the data on a fixed number of items collected from the source agencies which in turn collect the data from different factories and estimate the data on their own, as per the requirements. Selection of Replacement Items: Replacement of items is not done at present. Introducing New Units and Products: New units/ new products are included only at the time of the revision of base year.

Other statistical procedures : The production figures, if not reported by all the units in the current month due to any reason, are estimated for the current month and revised subsequently in the next month, and finally in the third month on the basis of which the final indices for a month are calculated.

Nature of Weights: The weights for the three sectors (mining, manufacturing, and electricity) are based on share of the sector in total domestic production in the base year. The overall weight of the manufacturing sector is apportioned to the industry groups at the 2-digit, 3-digit- and 4-digit level of the National Industrial Classification (NIC) 2008, on the basis of the Gross Value Added (GVA). The weighting diagram for the current series of IIP is prepared on the basis of GVA up to the 2-digit, 3 and 4 digit level of NIC based on the results of ASI 2011- 12. At the final level (i.e. 5 digit level of NIC), weights to items have been distributed on the basis of Gross Value of Output (GVO). The weights of selected items within an industry group are apportioned on the basis of the value of output.

Period of Current Index Weights: The current index weights are based on the value of production of the industries during the base year period viz. April, 2011 to March 2012 as reported in the Annual Survey of Industries for the year 2011-12. The same weights are used until the revision of the base year is done.

Frequency of Weight Updates: The weights are revised with every revision of the base year. The base year was revised to 2011-12 from 2004-05 in May 2017. Efforts would be made to revise the base year once in every five years as per UNSD's recommendations (the previous base years of the index were 2004-05, 1993-94, 1980-81, 1970, 1956, 1951 and 1946).

• Computation of lowest level indices: The lowest level, for which an index is prepared, is the item group. It is compiled as the ratio of production quantity in the current month with respect to its average monthly production quantity in the base year.

• Aggregation: The IIP is calculated using the Laspeyres formula as a weighted arithmetic average of production relatives. The index is primarily quantity based, although for some item groups the quantity relatives are obtained by price deflation.

The index at group level/ 2-digit level of NIC is compiled by using the Laspeyeres' formula, i.e. I = Uppercase sigma(Wi*Ri)/ Uppercase sigm(Wi) where Ri is the production relative and Wi is the weight of an item.

The index is prepared for each two-digit level of NIC. Also the index is prepared on the basis of the following use-based classification: Primary Goods, Capital Goods, Intermediate Goods, Infrastructure/ Construction Goods, Durable Consumer Goods and Non-Durable Consumer Goods.

• Alignment of Value of Weights and Base Period: No alignment of the weights is required as the weights as well as the base year production relate to the same reference period viz. April, 2011 to March 2012.

-- Linking of Re-weighted Index to Historical Index: Whenever there is change in the base year, the new series can be linked with the old series by preparing linked series. For the common period, the index series are available with both old weights & new weights for linking the two series.

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Israel Industrial Production Index (IPI): Manufacturing (Mfg): Real data was reported at 141.800 2004=100 in Dec 2012. This records a decrease from the previous number of 143.300 2004=100 for Nov 2012. Israel Industrial Production Index (IPI): Manufacturing (Mfg): Real data is updated monthly, averaging 51.700 2004=100 from Jan 1959 (Median) to Dec 2012, with 648 observations. The data reached an all-time high of 146.400 2004=100 in Mar 2011 and a record low of 6.660 2004=100 in Feb 1959. Israel Industrial Production Index (IPI): Manufacturing (Mfg): Real data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Israel – Table IL.B005: Industrial Production Index: Manufacturing: 2004=100. Rebased from 2004=100 to 2011=100 Replacement series ID: 283651104 Industrial Production Indices are preliminary estimates, computed according to reports of about 50% of the sample establishments in total Manufacturing for the last month.

This table has been discontinued due to a shift in the base year. This table presents information about developments in production and turnover in industry (excl. construction), SIC 2008 sections B - E. The data can be divided by a number of branches according to Statistics Netherlands' Standard Industrial Classification of all Economic Activities 2008 (SIC 2008). Developments are presented as percentage changes compared to a previous period and by means of indices. In this table, the base year is updated to 2015, in previous publications the base year was 2010. Developments in turnover and volume are published in two formats. Firstly, in the form of year-on-year changes relative to the same period in the preceding year. These figures are shown both unadjusted and adjusted for calendar effects. The second format pertains to period-on-period changes, for example quarter-on-quarter. Period-on-period changes are calculated by applying seasonal adjustment. Data available from January 2005 up and until December 2023. Status of the figures: The figures of a calendar year will become definite no later than five months after the end of that calendar year. Until then, the figures in this table will be “provisional” and can still be adjusted as a result of delayed response. Currently, the monthly turnover figures of 2022 are definitive. Once definitive figures have been published, Statistics Netherlands will only revise the results if significant adjustments and/or corrections are necessary. Since this table has been discontinued, the data will not be finalized. Changes as of 14 February 2024: The figures of December 2023 have been added to the table and those of September up to and including November 2023 have been adjusted and this table has been discontinued. Changes as of 9 June 2023: The figures of April 2023 have been added to the table and those of January 2022 up to and including March 2023 have been adjusted. This month the annual update of the seasonal-adjustment models has taken place. All figures of 2022 have been revised for the final time and set to ''definitive'' status. Changes as of 10 June 2021: The figures of April 2021 have been added to the table. The figures of January 2020 up to and including March 2021 have been adjusted. This month the annual update of the seasonal-adjustment models has taken place. Because of additional changes that have been made due to Covid-19 the adjustments are a bit larger than in other years. All figures of 2020 have been revised for the final time and set to ''definitive'' status. The underlying coding of the following classifications used in this table has been adjusted: - Manufacture of capital goods - Manufacture of consumer goods - Manufacture of durable consumer goods - Manufacture of intermediate goods - Manufacture of non-durable consumergoods It is now in line with the standard encoding defined by CBS. The structure and data of the table have not been adjusted. When will new figures be published? No longer applicable. This table is succeeded by "Industry; production and sales, changes and index, 2021=100". See Section 3.

Abstract

Introduction

The Annual Survey of Industries (ASI) is one of the large-scale sample survey conducted by Field Operation Division of National Sample Survey Office for more than three decades with the objective of collecting comprehensive information related to registered factories on annual basis. ASI is the primary source of data for facilitating systematic study of the structure of industries, analysis of various factors influencing industries in the country and creating a database for formulation of industrial policy.

The main objectives of the Annual Survey of Industries are briefly as follows:

(a) Estimation of the contribution of manufacturing industries as a whole and of each unit to national income.

(b) Systematic study of the structure of industry as a whole and of each type of industry and each unit.

(c) Casual analysis of the various factors influencing industry in the country: and

(d) Provision of comprehensive, factual and systematic basis for the formulation of policy.

The Annual Survey of Industries (ASI) is the principal source of industrial statistics in India. It provides statistical information to assess changes in the growth, composition and structure of organised manufacturing sector comprising activities related to manufacturing processes, repair services, gas and water supply and cold storage. The Survey is conducted annually under the statutory provisions of the Collection of Statistics Act 1953, and the Rules framed there-under in 1959, except in the State of Jammu & Kashmir where it is conducted under the State Collection of Statistics Act, 1961 and the rules framed there-under in 1964.

Geographic coverage

The ASI is the principal source of industrial statistics in India and extends to the entire country except Arunachal Pradesh, Mizoram & Sikkim and the Union Territory of Lakshadweep. It covers all factories registered under Sections 2m(i) and 2m(ii) of the Factories Act, 1948.

Analysis unit

The primary unit of enumeration in the survey is a factory in the case of manufacturing industries, a workshop in the case of repair services, an undertaking or a licensee in the case of electricity, gas & water supply undertakings and an establishment in the case of bidi & cigar industries. The owner of two or more establishments located in the same State and pertaining to the same industry group and belonging to census scheme is, however, permitted to furnish a single consolidated return. Such consolidated returns are common feature in the case of bidi and cigar establishments, electricity and certain public sector undertakings.

Universe

The survey cover factories registered under the Factory Act 1948.

Establishments under the control of the Defence Ministry,oil storage and distribution units, restaurants and cafes and technical training institutions not producing anything for sale or exchange were kept outside the coverage of the ASI.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Procedure

The sampling design followed in ASI 1998-99 is a Circular Systematic one. All the factories in the updated frame (universe) are divided into two sectors, viz., Census and Sample.

Census Sector: Census Sector is defined as follows:

a) All the complete enumeration States namely, Manipur, Meghalaya, Nagaland, Tripura and Andaman & Nicobar Islands. b) For the rest of the States/ UT's., (i) units having 200 or more workers, and (ii) all factories covered under Joint Returns.

Rest of the factories found in the frame constituted Sample sector on which sampling was done. Factories under Biri & Cigar sector were not considered uniformly under census sector. Factories under this sector were treated for inclusion in census sector as per definition above (i.e., more than 200 workers and/or joint returns). After identifying Census sector factories, rest of the factories were arranged in ascending order of States, NIC-98 (4 digit), number of workers and district and properly numbered. The Sampling was taken within each stratum (State X Sector X 4-digit NIC) with a minimum of 8 samples in each stratum in the form of 2 sub-samples. For the first time, all electricity undertakings other than captive units, Government Departmental undertakings such as Railway Workshops, P & T workshops etc. were kept out of coverage of ASI.

Sampling deviation

There was no deviation from sample design in ASI 1998-99.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Pre-data entry scrutiny was carried out on the schedules for inter and intra block consistency checks. Such editing was mostly manual, although some editing was automatic. But, for major inconsistencies, the schedules were referred back to NSSO (FOD) for clarifications/modifications.

The final unit level data of ASI 98-99 is available now in electronic media. This document describes additional information regarding ASI 98-99 data from the point of data processing. Users of ASI 98-99 data are requested to read this document carefully before they attempt to process the unit level data for their own purpose. They are also requested to refer to the schedule and the instruction manual for filling up the schedule before interpreting contents of various data fields. A. Contents The CD (or any other media) should contain the following files: ASI99.TXT This file contains unit level detail data of ASI 98-99 as per structure given in ANNEXURE- Total no. of records: 104740 XASI98.TXT (Metadata created from this .TXT file) This file contains unit level detail data of ASI 97-98 for those factories which were found not responding during the survey of ASI 98-99. The record layout is already available with the Computer Centre, New Delhi. Record Length: 135 Total no. of records: 6974 README.DOC This file.

B. Tabulation procedure The tabulation procedure by CSO(ISW) includes both the ASI 98-99 data and the extracted data from ASI 97-98 for all tabulation purpose. To make results comparable, users are requested to follow the same procedure. For calculation of various parameters, users are requested to refer instruction manual/report for the respective years. Please note that a separate inflation factor (Multiplier) is available for each factory against records belonging to Block-A ,pos:38-46 (Please refer ANNEXURE-I) for ASI 98-99 data. Since the data extracted from ASI 97-98 belong to Census Sector no such inflation (Multiplier) factor is required. Industry code as per Return(5-digit level of NIC-98) Industry code as reported by the factories in Block-A, Item 1 has been further codified because of the following two policies practiced at CSO(ISW). Tabulation policy: As per the latest tabulation policy, it has been decided to publish detail information regarding factories belonging to 01 to 37 of industry codes( 2-digit, NIC-98). Factories belonging to other industry groups would be clubbed together and to be published under 'Others'. Accordingly all industry codes other than 01 to 37 were replaced with a 5-digited code 'YYYYY'. Merging and suppression of identity: To suppress the identity of factories, less frequent industry codes were modified accordingly. Example: if a reported industry code is found as 2930Z, this is to be treated as 'other merged industry code under industry group 2930 (4-digit NIC'98)'. Similarly if the reported industry code is found as 293ZZ, the same as to be treated as 'other merged industry code under industry group 293 (3-digit NIC'98)' and so on.

FIXED ASSETS (Block-C) Columnwise relationship (please refer schedule) may not hold true for data in this block. This is because of the lack of information available from the factory owners. E. EMPLOYMENT AND LABOUR COST (Block-E) It has been found that a larger number of factory owners were unable to provide detailed break-up of information regarding provident fund (Block-E, Col.7). Instead they provide total provident fund as a whole for all employees (Block-E, Srl. No. 7, Col.7). Users are requested to use Srl.9, Col.7 for information on provident fund. The total of srl.6 to 8 for Col.7 may not tally with srl.9, col.7. F. ASICC codes in Block H, I & J Because of the proximity of various item's description, it is possible that same ASICC code may appear against multiple records in these blocks. They should not be treated as duplicates. They are clubbed together at the time of tabulation to provide information at ASICC level. G. Record Identification Key Record identification key for each factory is Despatch Serial No. (DSL, pos: 4-8) X Block code (Blk, pos: 3). Please refer ANNEXURE-I for item level identification key for each factory.

Sampling error estimates

Relative Standard Error (RSE) is calculated in terms of worker, wages to worker and GVA using the formula (Pl ease refer to Estimation Procedure document in external resources). Programs developed in Visual Faxpro are used to compute the RSE of estimates.

Data appraisal

To check for consistency and reliability of data the same are compared with the NIC-2digit level growth rate at all India Index of Production (IIP) and the growth rates obtained from the National Accounts Statistics at current and constant prices for the registered manufacturing sector.

This dataset provides values for INDUSTRIAL PRODUCTION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Industrial Production Machinery Automation Market Outlook

The industrial production machinery automation market is poised to witness significant growth over the coming years, with an expected market size of USD 182 billion by 2023, and projected to reach USD 312 billion by 2032, growing at a CAGR of 6.2%. This surge is primarily driven by the increasing adoption of automation technologies across various industries aimed at enhancing productivity, reducing operational costs, and ensuring higher quality standards. Contributing to this growth is the rapid advancement in technology, leading to the development of more sophisticated and efficient machinery that meets the evolving demands of industries worldwide.

One of the primary growth factors for the industrial production machinery automation market is the rising demand for efficiency and productivity in manufacturing processes. As industries face increasing competition, there is a constant push to optimize production lines, minimize waste, and enhance the overall output. Automation technologies, such as robotics and machine vision systems, are becoming integral in achieving these goals by enabling faster and more precise operations. Moreover, the implementation of automation reduces human error, thereby ensuring consistent product quality, which is a critical factor in maintaining competitiveness in the global market.

Another significant factor propelling growth is the integration of the Internet of Things (IoT) and artificial intelligence (AI) in industrial production processes. IoT-enabled devices and AI algorithms provide real-time data analytics, predictive maintenance, and improved decision-making capabilities. This integration allows manufacturers to foresee potential downtimes, optimize resource allocation, and enhance overall equipment effectiveness. As industries increasingly recognize the value of data-driven operations, the demand for automation systems that incorporate these technologies is expected to rise, further fueling market growth.

The trend towards sustainable and environmentally friendly manufacturing practices also plays a crucial role in driving the adoption of automation in industrial production. Automation can significantly reduce energy consumption and material waste, aligning with the growing emphasis on sustainability. Industries are under pressure to comply with stringent environmental regulations and achieve sustainability goals, making automation an attractive solution for reducing the carbon footprint and promoting eco-friendly manufacturing processes. This shift towards green manufacturing is not only beneficial for the environment but also contributes to cost savings and improved corporate image, driving further adoption of automated machinery.

Regionally, the Asia Pacific is anticipated to dominate the industrial production machinery automation market, attributed to the robust industrial growth in countries like China, India, and Japan. The region's substantial investments in infrastructure and manufacturing, coupled with favorable government initiatives supporting industrial automation, contribute significantly to market expansion. Additionally, the presence of a large number of manufacturing facilities and the increasing adoption of advanced technologies in production processes contribute to the region's leading position in the global market. North America and Europe are also expected to witness substantial growth due to the early adoption of advanced manufacturing technologies and a strong focus on innovation and development.

Component Analysis

In examining the component segment of industrial production machinery automation, it's clear that hardware continues to play a crucial role in the market. Hardware components such as sensors, actuators, and control devices form the backbone of industrial automation systems. As technology advances, these components are becoming more sophisticated, offering enhanced performance, reliability, and functionality. For example, the introduction of advanced sensors and actuators with IoT capabilities allows for real-time monitoring and control of machinery, leading to improved operational efficiency. The ongoing innovation in hardware components is expected to drive further investment and adoption of automation technologies across various industries.

The software segment within industrial machinery automation is equally significant, as it enables the integration and functioning of hardware components. Advanced software solutions facilitate various automation processes, including data collection, analysis, and visualization. The ris

This table presents information about developments in production and turnover in industry (excl. construction), SIC 2008 sections B - E. The data can be divided by a number of branches according to Statistics Netherlands' Standard Industrial Classification of all Economic Activities 2008 (SIC 2008). Developments are presented as percentage changes compared to a previous period and by means of indices.

Developments in turnover and volume are published in two formats. Firstly, in the form of year-on-year changes relative to the same period in the preceding year. These figures are shown both unadjusted and adjusted for calendar effects. The second format pertains to period-on-period changes, for example quarter-on-quarter. Period-on-period changes are calculated by applying seasonal adjustment.

Data available from: January 2005

Status of the figures: The monthly figures of 2025 are provisional, the other figures are definitive.

The figures of a calendar year will become definite no later than six months after the end of that calendar year. Until then, the figures in this table will be “provisional” and can still be adjusted as a result of delayed response. Once definitive figures have been published, Statistics Netherlands will only revise the results if significant adjustments and/or corrections are necessary.

Changes as of 10 July 2025 The figures for May 2025 have been added. The figures of the 2 most recent present months in the data may have been adjusted.

Changes as of 10 June 2025 As an exception, the unadjusted figures for SBI21 and SBI24 have been revised for the entire year 2024. The calendar and seasonally adjusted figures for the most recent 16 months may have changed due to the annual update of the seasonal adjustment models.

Changes as of 13 May 2025: In the previous version, a small amount of data was incorrectly displayed. In this version, that has been corrected.

When will new figures be published? As a rule, monthly statistics are published six to eight weeks after the end of the reporting month. Quarterly statistics are published on the last working day of the second month after the quarter. For production figures see link in section 3.

The data set records the number of production units and total industrial output value of all industrial enterprises at or above the township level in Qinghai Province. The data is divided by the number of production units and total industrial output value of all industrial enterprises at or above the township level. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains one data table, which is: the number of production units and total industrial output value of all industrial enterprises at or above the township level, 1998.xls. For example, there are five fields in the 1998 data sheet of the number of production units and gross industrial output value of all industrial enterprises at or above the township level Field 1: Indicators Field 2: (new regulation on constant price) Field 3: (new regulations on current year price) Field 4: (new regulations on current year price) Field 5: (new regulations on current year price)

Within the manufacturing industry, most respondents (59 percent) state that quality control is the most important use case for artificial intelligence. Generally, quality control refers to establishing controls which standardize production. For example, artificial intelligence can help improve overall quality control by using smart cameras to improve inspection processes which leads to reduced costs. The manufacturing industry encompasses companies that manufacture goods of raw materials and components into finished merchandise.

Abstract

The Survey of Industrial Production was conducted in 2013.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

MIA05 - Industrial Production Volume and Turnover Indices (Base 2021=100). Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Industrial Production Volume and Turnover Indices (Base 2021=100)...

Graph and download economic data for Producer Price Index by Industry: Total Manufacturing Industries (PCUOMFGOMFG) from Dec 1984 to Jul 2025 about manufacturing, PPI, industry, inflation, price index, indexes, price, and USA.

Abstract

There was a growing concern about the slow growth of Ghana's industrial sector, particularly with respect to the manufacturing sub-sector which has stagnated in comparison with countries like Cote d'Ivoire and Malaysia where manufacturing levels were lower than Ghana 30 years ago. The state of Ghana's industry has been recognized by the Government of Ghana and a number of initiatives have been taken over the years to help accelerate industrial growth. Despite laudable interventions, such as the New Industrial Reform, the Accelerated Growth Programme and the Ghana Poverty Reduction Strategy, manufacturing value added in Ghana grew at a low rate between 1990 and 2001. The small size of the Ghanaian market and the failure of manufacturers to break into export markets are frequently cited among the causes of the present predicament of the manufacturing sub-sector. The Mining sub sector has overcome some of these obstacles with increases in output over the years, which is mostly exported. Production of Electricity and Water on the other hand has not grown enough to support higher rates of growth of industry.

The Ghana Statistical Service in collaboration with Ministry of Trade and Industry and Presidential Special Initiative and supported by UNIDO conducted the 2003 National Industrial Census. The objectives of the census was to obtain benchmark data on the structure of industry, establish an industrial database and regularly update the register, obtain data on production and employment for government and business analysis and decision making. Other objectives was to measure the contribution of each industry and region to Ghana's employment and production. In addition to the above mentioned objectives, the obtained data should be internationally comparable on the structure and activity of each industrial sub sector.

There were two phases of the National Industrial Census with phase I listing all industrial establishment in the country that are primarily engaged in mining and quarrying, manufacturing, construction and the production of electricity and water. Household industries were excluded unless there is a clear indication of the industry by way of a sign board. The phase II covered all establishments primarily engaged in mining and quarrying, construction, the production of electricity and water, all manufacturing establishment engaging 10 or more persons and a representative sample of manufacturing establishements engaging less than 10 persons.

Geographic coverage

National

Analysis unit

Establishments

Universe

The Census included all establishments in the following sectors: mining and quarrying, electricity & water production and construction. With regard to the manufacturing sub sector, all establishments engaging 10 persons or more were included whereas a sample was taken from establishments engaging less than 10 persons.

Kind of data

Census/enumeration data [cen]

Sampling procedure

A one-stage sample design was used for the survey with the primary sampling units (PSU) being the individual establishment. The sampling frame of establishments had two levels of stratification in addition to implicit stratification from ordering the establishments within each stratum. The first level of stratification was by the 4-digit ISIC activities, which represented the smallest domains of analysis. Within each of these individual activities, the establishments were further stratified by the number of persons engaged, which is also correlated with industrial production, revenue, expenditures and other aggregates to be measured in the survey.

Sampling deviation

All of the establishments in the Construction, Electricity and Water, Mining and Quarrying sub-sectors were included in the survey regardless of size. Given that reliable estimates are required for each 4-digit ISIC group, activities with few establishments were also identified as certainty strata.

Mode of data collection

Face-to-face [f2f]

Research instrument

There were five types of questionnaires used in the 2003 National industrial census. The phase I had one scannable questionnaire which collected information on all establishments engaged in mining and quarrying, manufacturing, construction and the production of electricity and water. The location of these establishments were also taken as well as the persons engaged.

During the phase II there were four types of questionnaire. The national industrial census 2003 (manufacturing) labeled 3A which was used for establishments engaging 10+ persons.

The national industrial census 2003 (manufacturing) labeled 3B which was used for establishments engaging less than 10 persons.

The national industrial census 2003 (mining and quarrying) labeled 2 which was used for establishments engaged in mining and quarrying irrespective of number of persons engaged.

The national industrial census 2003 (electricity and water) labeled 4 which was used for establishments engaged in the production of electricity and water irrespective of number of persons engaged.

The national industrial census 2003 (construction) labeled 5 which was used for establishments engaged in construction irrespective of number of persons engaged.

Cleaning operations

All questionnaires were edited for completeness, scope and internal consistency in the office before sending them for data capture. Questionnaires that did not meet the test were set aside for determination. Each questionnaire goes through the hands of a chain of editors i.e. lead editor then by the team leader in charge. All editors are confined to a particular place. This promotes consultations.

During data capture, there was double entry i.e. main and verification with comparison and necessary corrections effected. Structural checks and completeness is also done during data capture.

Response rate

Establishments are classified into mining and quarrying, manufacturing, electricity and production and construction sectors. Under manufacturing we considered establishments which engaged less than 10 persons and that which engaged 10 or more persons.

The frame for establishments engaging 1 - 9 persons is 22,404 The frame for establishments engaging 10+ persons is 4,230

The number of establishments sampled: 1 - 9 =1,263 10+ =4,233

Number of establishment completed: 1 - 9 = 895 10+ = 2,918

Number of establishment out of scope + Association
1 - 9 = 53 10+ = 765

Number of establishment not completed 1 - 9 = 304 10+ = 484

Number of establishment closed down
1 - 9 = 43 10+ = 81

Response rate for : 1 - 9 = 0.77 10+ = 0.86

Response rate is equal to the ratio of number of completed to the adjusted number of not completed establishment

Adjusted number of establishments excludes out of scopes and association, groups and cooperative, closed down and not located establishments

• the difference in the sample and the frame for 10+ persons engaged establishment was due to the migration of 1-9 persons engaged establishment into the 10+ category

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically. Sample errors was calculated for survey estimates of total number of persons engaged by the 4-digit ISIC to ensure that all ISIC groups had a coefficient of variation (CV) lower than 10 percent

Details of the sampling errors are presented in appendix-3.

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Home to 22,000 manufacturers and more than a million industrial workers, California is the largest manufacturing state in the U.S. The state's abundance of skilled labor and access to capital has drawn some key innovative enterprises to its borders, particularly in the aerospace and electronics industries. Today, we're focusing on the latest trends in California manufacturing, including the state's top industries and cities, and we'll also explore its ten largest manufacturing companies.

Special Equipment Manufacturing Market Outlook

The global special equipment manufacturing market size was valued at approximately $460 billion in 2023 and is projected to reach around $750 billion by 2032, growing at a compound annual growth rate (CAGR) of 6%. This market is poised for substantial growth driven by advancements in technology and increasing demand across various industries such as healthcare, construction, and aerospace and defense. The high CAGR reflects a dynamic sector characterized by ongoing innovation and diversification in product offerings and applications.

Several factors are propelling the growth of the special equipment manufacturing market. One primary growth factor is the rapid technological advancements, particularly in automation, robotics, and 3D printing. These technologies are revolutionizing manufacturing processes by enhancing precision, reducing production times, and lowering operational costs. For instance, automation and robotics are increasingly being adopted in industrial settings to improve efficiency and safety, while 3D printing is enabling the creation of complex components that were previously difficult or impossible to manufacture using traditional methods. As these technologies continue to evolve, their integration into special equipment manufacturing is expected to drive significant market growth.

Another critical growth factor is the increasing demand for specialized equipment in various end-use industries. The healthcare industry, for example, is experiencing a surge in demand for advanced medical equipment due to the growing global population, rising prevalence of chronic diseases, and an aging population. Similarly, the aerospace and defense sector is investing heavily in advanced machinery and equipment to enhance capabilities and maintain a competitive edge. Additionally, the construction industry is witnessing a shift towards more efficient and innovative machinery to meet the rising demand for infrastructure development and urbanization. These industry-specific demands are fueling the expansion of the special equipment manufacturing market.

Moreover, government initiatives and investments in infrastructure development and modernization projects are significantly contributing to market growth. Governments worldwide are implementing policies and providing financial support to promote industrial development and technological innovation. For example, initiatives such as Industry 4.0 and smart manufacturing are encouraging the adoption of advanced technologies in manufacturing processes. These initiatives not only enhance productivity and efficiency but also foster the development of new and specialized equipment. The supportive regulatory environment and financial incentives provided by governments are expected to further drive market expansion.

From a regional perspective, Asia Pacific is expected to dominate the special equipment manufacturing market over the forecast period. The region's rapid industrialization, growing population, and increasing investments in infrastructure development are key drivers of market growth. North America and Europe are also significant markets due to their established industrial base and continuous technological advancements. The Middle East & Africa and Latin America regions are anticipated to witness moderate growth, driven by increasing investments in infrastructure and industrial development. Each region's specific economic conditions, industrial policies, and technological advancements play a crucial role in shaping the market dynamics.

Product Type Analysis

Industrial Machinery

The industrial machinery segment encompasses a wide range of equipment used in manufacturing processes across various industries. This segment is expected to witness substantial growth due to increasing automation and technological advancements. Industrial machinery is essential for efficient production, and its demand is driven by the need for precision, speed, and cost-effectiveness. The adoption of Industry 4.0 technologies, such as IoT and AI, is further enhancing the capabilities of industrial machinery, making it more intelligent and connected. These advancements are expected to drive the growth of the industrial machinery segment significantly.

Moreover, the rise of smart manufacturing and the integration of advanced control systems are revolutionizing the industrial machinery landsc

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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?

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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

Defect sampling is used in industrial settings to determine the types and amounts of defects in manufactured items. Items at various stages of production are removed from the process and inspected for defects. Sustained testing allows operations managers to discover whether some part of the manufacturing process is failing to meet performance criteria and product standards. To minimize manufacturing defects, early detection and problem resolution are critical.

In the current sampling plan, one component from the production line is randomly selected every 15 minutes. Each component is inspected and tested for major and minor defects. Major defects, which affect component performance, must be addressed immediately. Fortunately, major defects are rare and are generally contained and corrected early in the process. Minor defects, such as nicks and scratches, are those that affect the appearance of a component but not its functionality.

The data set contains ten days of data on minor defects. Each day, one item is tested every fifteen minutes during an eight-hour shift. The variables in the data set are: - Day - Day of the test: 1 – 10 - Sample - Time of the day that sample was taken in military time (e.g., 13:00 is 1pm) - Defects - Number of minor defects detected on the sampled item