Industrial Production in China increased 5.80 percent in May 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.

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.

Purpose and brief description The industrial production index makes it possible to monitor the evolution in volume of the value added at factor cost over a given reference period. Value added at basic prices can be calculated as follows: turnover (excluding VAT and other similar deductible taxes directly linked to turnover) plus capitalised production, plus other operating income, plus or minus the changes in stocks, minus the purchases of goods and services, minus other taxes on products which are linked to turnover but not deductible, and the subsidies received on the products. However, the data to produce this index are not available on a monthly basis. In practice, the adequate representative values to produce the indices are: gross production values (deflated); volumes; turnover (deflated); working hours; raw materials; energy... Reference year From January 2024, the indices are expressed with reference year 2021=100. Therefore, we have created a new downloadable file, expressing the full series with reference year 2021=100. The downloadable files with reference year 2015=100 remain available but will no longer be updated. These files end consequently in December 2023. Data collection method and sample size Prodcom is the monthly survey on industrial production. Cooperation among the EU countries seeks to improve the comparability of statistical data. The Statistical Office of the European Union has therefore taken the initiative to collect data on industrial production in all member states with the same product list, in the same sectors, etc. This initiative was named “Prodcom”: “PRODucts of the European COMmunity”. This survey is compulsory. The legal framework laid down in EC Regulation 3924/91, the Royal Decree of 28 January 1994, published in the Belgian Official Journal of 15 February 1994 and the Royal Decree of 20 February 2008, published in the Belgian Official Journal of 10 March 2008. The language of the form (part 1 and part 2) is determined by the place of business. The Royal Decree of 18 July 1966 applies here, published in the Belgian Official Journal on 2 August 1966, which coordinates the laws on the use of languages in administrative matters. A form must be completed for each local unit. The local unit is an enterprise or part thereof (e.g. workplace, factory, shop, office, mine or warehouse) located in a geographically defined place. At or from that place, economic activities are carried out for which - barring exceptions - one or more persons work (possibly part-time) on behalf of the same enterprise. Population The survey covers the activities of sections B and C of the Statistical Classification of Economic Activities in the European Community NACE Rev. 2 with the exception of sections 5, 6 and 19. In summary, two groups are concerned: any industrial enterprise or establishment employing 20 persons or more according to one of its quarterly NSSO declarations of the previous year or whose annual turnover in the previous year was at least 4,200,000 euros; any new industrial enterprise or establishment employing 20 persons or more according to one of its quarterly NSSO declarations for the current year or whose cumulative turnover in the course of the year amounted to at least 4,200,000 euros. This means that a declarant who has employed 20 persons at least once in a given year must answer in the following year (calendar year). Periodicity Monthly. Release calendar Results available 1 month + 10 days after the reference period. Metadata Prodcom manual.pdf Prodcom conversion table 2022.xlsx Industrial production (Prodcom).pdf Prodcom survey.pdf Data collection in shops and in private or public organisations (PPP).pdf Nomenclature The Prodcom list.xls

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

DIPQ01 - Domestic Industrial Production Indices (Base 2021=100). Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Domestic Industrial Production Indices (Base 2021=100)...

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Big Data In Manufacturing Market Size 2025-2029

The big data in manufacturing market size is forecast to increase by USD 21.44 billion at a CAGR of 26.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of Industry 4.0 and the emergence of artificial intelligence (AI) and machine learning (ML) technologies. The integration of these advanced technologies is enabling manufacturers to collect, process, and analyze vast amounts of data in real-time, leading to improved operational efficiency, enhanced product quality, and increased competitiveness. Cost optimization is achieved through root cause analysis and preventive maintenance, and AI algorithms and deep learning are employed for capacity planning and predictive modeling.
To capitalize on the opportunities presented by the market and navigate these challenges effectively, manufacturers must invest in building strong data analytics capabilities and collaborating with technology partners and industry experts. By leveraging these resources, they can transform raw data into actionable insights, optimize their operations, and stay ahead of the competition. The sheer volume, velocity, and variety of data being generated require sophisticated tools and expertise to extract meaningful insights. Additionally, ensuring data security and privacy, particularly in the context of increasing digitalization, is a critical concern.

What will be the Size of the Big Data In Manufacturing Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic manufacturing market, Business Intelligence (BI) plays a pivotal role in driving operational efficiency and competitiveness. Blockchain technology and industrial automation are key trends, enhancing transparency and security in supply chain operations. Real-time monitoring systems, Data Integration Tools, and Data Analytics Dashboards enable manufacturers to gain insights from vast amounts of data. Lifecycle analysis, Smart Manufacturing, and Cloud-based Data Analytics facilitate predictive maintenance and optimize production.
PLC programming, Edge AI, KPI tracking, and Automated Reporting facilitate data-driven decision making. Manufacturing Simulation Software and Circular Economy principles foster innovation and sustainability. The market is transforming towards Digital Transformation, incorporating Predictive Maintenance Software and Digital Thread for enhanced visibility and agility. SCADA systems, Carbon Footprint, and Digital Thread promote sustainable manufacturing practices. AI-powered Quality Control, Performance Measurement, and Sensor Networks ensure product excellence.

How is this Big Data In Manufacturing Industry segmented?

The big data in 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.

Type

  Services
  Solutions


Deployment

  On-premises
  Cloud-based
  Hybrid


Application

  Operational analytics
  Production management
  Customer analytics
  Supply chain management
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Type Insights

The services segment is estimated to witness significant growth during the forecast period. In the realm of manufacturing, the rise of data from sensors, machines, and operations presents a significant opportunity for analytics and insights. Big data services play a pivotal role in this landscape, empowering manufacturers to optimize resource allocation, minimize operational inefficiencies, and discover cost-saving opportunities. Real-time analytics enable predictive maintenance, reducing unplanned downtime and repair costs. Data visualization tools offer human-machine interfaces (HMIs) for seamless interaction, while machine learning and predictive modeling uncover hidden patterns and trends. Data security is paramount, with robust access control, encryption, and disaster recovery solutions ensuring data integrity. Supply chain management and demand forecasting are streamlined through data integration and real-time analytics.

Quality control is enhanced with digital twins and anomaly detection, minimizing defects and rework. Capacity planning and production monitoring are optimized through time series analysis and neural networks. IoT sensors and data acquisition systems feed data warehouses and data lakes, fueling statistical analysis and regression modeling. Energy efficiency is improved through data-driven insights, while inventory management

DIPM01 - Domestic Industrial Production Indices (Base 2021=100). Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Domestic Industrial Production Indices (Base 2021=100)...

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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)...

Abstract

In Nepal, the industrialization process is stagnant; the contribution of Manufacturing Sector to the national economy is minor and most of the factories are of small scales. The fifth decennial survey of small manufacturing establishments (SSME) 2008-2009 is one of the primary source of industrial statistics which covers all functioned manufacturing activities of the nation with a formal registration that engage less than 10 persons. The sampling survey undertaken by the Central Bureau of Statistics in establishment approach has provided complementary information of the Census of Manufacturing Establishments (CME) which is summarized in a set of 101 tables. This information is based on the responses of 614 questions interviewed by permanent staffs in 3737 randomly selected sample establishments. The survey report has quantified the size, composition, and distribution of the small manufacturing sector including ten principal indicators by internationally comparable and standard industrial classification at Development Region level.

There were 32326 small manufacturing establishments found active during mid July 2008 to mid-July 2009, the reference period of the survey when the listing operation had conducted in all districts. In this period, they had altogether contributed Rs. 11.5 Arab value added in the national economy and generated employment for 122 thousand persons. Moreover, there were 75 types of industries which produced 154 items using 186 types of raw materials in the reference period.

The survey has disclosed that grain milling, wearing apparel sewing, furniture making, and jewellery designing are the top four small manufacturing activities in Nepal. Share of which are nearly 39, 17, 10, and 7 percent of total in number; and they contribute about 20, 14, 14 and 7 percent of total value added in the small manufacturing sector.

Geographically, the Central Development Region has the largest number of small factories. It belongs to 45 percent establishments and contributes 50 percent value added to the small manufacturing sector in Nepal which follows by Eastern Development Region with 27 percent establishments and 24 percent value added.

Finally, the survey has told about the capacity utilization status and problem faced by the small industries. Only 32 percent of the industries utilized 60 percent or more than their installed capacity during the reference period and their major problem is the lack of electricity followed by capital dearth in national level. The ratio of capital utilization and problem faced by those establishments are found vary from industrial activity to activity and region to region.

Geographic coverage

National 5 Development Regions Urban-Rural

Analysis unit

Establishment

Universe

The survey covered all active manufacturing establishments in Nepal that engage less than 10 persons and have a legal registration.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design of SSME 2008-2009 comprised of complete as well as sample enumeration. Actually, there were three modes of selecting samples and the probability of selection of a sample is unequal. The sampling frame for the design was obtained by conducting one year listing operation throughout the country. In that operation, there were 32326 small industries found active. Out of them, 3737 had been selected for the survey.

At first, the sampling frame was sorted and split by NSIC and development regions. Then, a cut-off point (<5 establishments) was determined and in turn, all establishments under a NSIC with frequency less than 5 in a development region are selected. The total number of such establishments was 152. In fact, they were chosen for complete enumeration. This was done to achieve more reliable and better representative sampling distribution. The probability of selection of these establishments was considered one.

Secondly, the remaining establishments of the sampling frame had been further divided into two groups. The first group contained all establishments within NSIC 1531, 1810, 2811, 3610 and 3691; and the next group belonged to remaining NSICs. The first group again classified into 3 strata by number of persons engaged. There were altogether 24837 establishments within this group. Out of them, 2485 simple random samples were selected with probability 0.10 proportional among the strata.

Thirdly, the next group had 7337 establishments. It was also divided into 3 strata by number of persons engaged. Among them, 1100 simple random samples had been selected with probability 0.15 proportionally.

Finally, about 12 percent of total industries were selected. Out of total about 45 percent belong to Central Development region. Eastern and Western Development Region contains nearly 27 percent and 16 percent samples respectively.

Mode of data collection

Face-to-face [f2f]

Research instrument

In CBS, it is a general procedure to consult with a Technical Committee composed of subject matter specialists, prominent users and data processing experts in order to give original shape to the questionnaire and tabulation plan before the survey or census starts. In place of such committee, during the questionnaire design stage of the SSME 2008-2009, a consultative committee was composed under the chairmanship of the Deputy Director General of Economic Statistics Division of CBS. This committee consisted of all six directors of each section under the division. At first, they discussed on the purposed first draft of the questionnaire prepared by the Section. Later many additional improvements were incorporated, in particular, related to the format, sequential flow or logical arrangement of data items in accordance with the recommendation provided by the committee.

In fact, the questionnaire for the SSME 2008-2009 was a kind of structured questionnaire especially based on the CME 2006-2007 questionnaire with some modifications and additions. An establishment questionnaire was administered to each establishment which collected various information on employment, input-output and capital formation including legal status, major activity, capacity utilization, indirect taxes paid and problem faced by the establishment.

It contains 15 sections as stated below: 1. Employment 2. Purchase of Fuels 3. Purchase of Raw Materials 4. Production and Sale 5. Service Industries Only 6. Cost of and Receipt from Industrial and Other Services 7. Stocks 8. Indirect Tax and Fees 9. Loan Transaction 10. Cost of Non-industrial Services 11. Receipts from Non-industrial Services 12. Utilization of Production Capacity 13. Fixed Assets 14. Environment 15. Major problems faced by the establishments during the reference period

Aside from these 15 sections, there was an introduction part at the first page of the questionnaire. It was a large questionnaire. There were altogether 614 questions to be filled or answered.

The questionnaire was first developed in Nepali language and translated into English version for report writing purpose. The translation was undertaken by Mr.Lok Bahadur Khatri, the Statistics Officer of ECSS of CBS. The translation was then reviewed finally by Mrs Ganga Dabadi, the Director of the section. The Nepali questionnaire was tested in some establishments at Kathmandu Valley.

Cleaning operations

For quality control and to make successive steps easier the usual practice was to have manual editing in the field of some key items which generally cannot be corrected in the center. Such edit instructions were mainly of the following types: i. To check if all cells are properly filled in according to the instruction manual; ii. To ensure whether all entries are consistent with one another; iii. To check if all skipping instructions have been correctly followed; iv. To work out some ratios or rates and see if they are reasonable.

For examples of such calculations are: a. Percentage of output quantity on principal raw material consumed, and b. Average wage per employees etc.

In spite of these strict and clear cut rules, many forms would be found with blank cells and incorrect entries. There was always a heavy load at the time of detailed editing. As far as possible, correction works including imputations were done by developing suitable procedures. But, sometimes, there was no way out except to return to the field for the correct information. It is still a general tendency of respondents to report more expenditures and less outputs or receipts. In most cases, the value added figures turn out to be very low or sometimes negative. This situation had created a lot of headache at the processing stage. Since it was not possible to go back to the field for a majority of the forms, the only meaningful alternative was to correct the wrong reporting by adopting some plausible assumptions.

After editing process, the next step was to perform coding work. In order to avoid clerical errors, a complete recheck process or, in some cases, on sampling basis was the general practice to correct previous mistakes as far as possible. As in the previous CME and SSME, in SSME 2008-2009, the coding scheme of NSIC and CPC 1.0 was used to made data comparable nationally and internationally.

Moreover, there were many inconsistencies found especially in the following area of the filled questionnaire: - Monthly wage and salary of a paid employee - Per unit cost of fuels - Per unit cost of raw materials - Per unit cost of sold items - Monthly house rent - Printing expenses - Travelling expenses - Rate of indirect taxes - Relation between the answer of capacity utilization and production

To overcome such variations, certain

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.

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 June 2025 The figures for April 2025 have been added. The figures of the 4 most recent present months in the data may have been adjusted. 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.

Snapshot img

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

INSPIRE dataset for Production and Industrial Facilities theme represents information about industrial facilities and related buildings in Lithuania.

According to Cognitive Market Research, The Global Blockchain in Manufacturing market size will be USD 0.5 billion in 2022 and will grow at a compound annual growth rate (CAGR) of 72.30% from 2023 to 2030. Increased Adoption of Advanced Blockchain Technology In The Manufacturing Industry Will Drive Market Growth

The rise of the industrial internet of things, in conjunction with industry 4.0, has influenced the adoption rate of the manufacturing industry, which is already on the cutting edge of technological progress. The advancement of automation, from basic machinery or electromechanical to mechatronics, is beneficial to the industry.

For example, according to Microsoft's manufacturing trends study, the industrial internet of things may have a big impact on the manufacturing industry and the world economy in 2019; it is anticipated that the global GDP will reach $15 trillion by 2030. In addition, IoT spending is estimated to exceed $1 trillion by 2020.As a result, the increase in technical advancement in the sector aids in the penetration of Blockchain in Manufacturing in the market.

(Source: info.microsoft.com/rs/157-GQE-382/images/EN-US-CNTNT-Report-2019-Manufacturing-Trends.pdf)

Market Dynamics of Blockchain in Manufacturing

Lack of Awareness Limits Market Growth

Manufacturers' ignorance of blockchain's potential is a major barrier to the growth of the sector. Many consumers are unaware about blockchain applications in manufacturing, which may impede market growth over the projection period. Furthermore, the lack of a unified set of standards and an ambiguous regulatory environment are important impediments to market expansion throughout the projection period.

Impact of COVID-19 on the Blockchain in Manufacturing market

The COVID-19 pandemic impacted supply chain systems since a lot of critical equipment and commodities are heavily reliant on imports from other nations. Additionally, the enterprises had to operate with a minimum of workers due to the strict mandate of the governing authorities to adhere to social distancing requirements, which decreased the output rate of the industry. As a result, each of these limitations has a negative impact on the growth of blockchain in the manufacturing industry. Introduction of Blockchain in Manufacturing

In the manufacturing industry, blockchain technology not only allows clients to track and trace incoming parts along the supply chain, but it also provides immutable quality control documentation and production procedures for data. The blockchain database in manufacturing uniquely identifies each product and records every transaction, alteration, or quality check in the blockchain. The advantages of blockchain in the manufacturing business are that it eliminates the need for an intermediary and enhances the overall security and data management, making it market-reliable.

According to PwC, 24% of industrial manufacturing CEOs are researching or using blockchain technology, which allows them to optimize operations, get better visibility into supply networks, and manage assets with unparalleled precision.

(Source: www.netsuite.com/portal/resource/articles/inventory-management/blockchain-in-manufacturing.shtml)

For instance, Hindalco Industries, an Indian manufacturer, used blockchain to coordinate work-order progress throughout its network of contract vendors. Hindalco can now receive real-time insight into vendor inventories, enforce service-level agreements (SLAs) using smart contracts, certify and track the origin and validity of items, execute continuous audits, and facilitate invoice financing thanks to the technology.

(Source: www.netsuite.com/portal/resource/articles/inventory-management/blockchain-in-manufacturing.shtml)

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