Shop Floor Data Collection Software Market Outlook

According to our latest research, the global Shop Floor Data Collection Software market size reached USD 2.41 billion in 2024, driven by the increasing emphasis on real-time production insights and operational efficiency in manufacturing. The market is expected to grow at a robust CAGR of 9.8% from 2025 to 2033, reaching a forecasted value of USD 5.60 billion by 2033. This impressive growth is primarily fueled by the rapid digitalization of manufacturing environments, the escalating need for automation, and the adoption of Industry 4.0 technologies across various industrial sectors.

One of the most significant growth factors for the Shop Floor Data Collection Software market is the heightened demand for operational transparency and process optimization in manufacturing industries. As companies strive to enhance productivity, reduce downtime, and minimize wastage, there is a growing reliance on advanced data collection tools that can provide granular visibility into shop floor activities. These solutions enable manufacturers to monitor equipment performance, track inventory levels, and ensure compliance with quality standards in real time. The integration of data analytics and machine learning capabilities further empowers organizations to make data-driven decisions, optimize resource allocation, and foresee potential bottlenecks before they impact production.

Another key driver propelling the market is the increasing adoption of cloud-based solutions, which offer unparalleled scalability, flexibility, and cost-effectiveness. Cloud-based shop floor data collection software eliminates the need for heavy upfront investments in IT infrastructure and allows seamless updates and integration with other enterprise systems. This shift is particularly beneficial for small and medium-sized enterprises (SMEs) that are keen to modernize their operations without incurring excessive costs. Additionally, the proliferation of IoT devices and smart sensors on the shop floor has enabled real-time data acquisition and remote monitoring, further accelerating the adoption of these software solutions across diverse manufacturing verticals.

The market is also benefiting from the growing focus on compliance and quality assurance, especially in highly regulated industries such as pharmaceuticals, food & beverage, and aerospace. Manufacturers in these sectors are increasingly deploying shop floor data collection software to maintain detailed records, ensure traceability, and adhere to stringent regulatory requirements. The shift towards digital record-keeping and automated data capture reduces the risk of human error and enhances audit readiness, thereby safeguarding brand reputation and customer trust. Moreover, the ongoing trend of workforce digitalization and the need to manage a multi-generational workforce have heightened the demand for intuitive, user-friendly software that can streamline workforce management and improve collaboration on the shop floor.

Regionally, North America continues to dominate the Shop Floor Data Collection Software market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The presence of technologically advanced manufacturing hubs, early adoption of digital solutions, and strong government support for smart manufacturing initiatives have positioned these regions at the forefront of market growth. However, Asia Pacific is expected to witness the fastest growth during the forecast period, driven by rapid industrialization, increasing investments in automation, and the emergence of new manufacturing clusters in China, India, and Southeast Asia. Latin America and the Middle East & Africa are also showing promising potential, albeit from a smaller base, as manufacturers in these regions gradually embrace digital transformation to stay competitive in the global market.

Component Analysis

The Shop Floor Data Collection Software market by component is segmented into software and services, ea

Discover the booming Shop Floor Data Collection Software market! This in-depth analysis reveals a $5 billion market in 2025, growing at a 12% CAGR through 2033. Explore key trends, regional insights, and leading companies shaping this dynamic industry. Learn how real-time data collection is transforming manufacturing efficiency and driving Industry 4.0 adoption.

Shop Floor Data Collection Software Market Outlook

According to our latest research, the global shop floor data collection software market size stood at USD 1.64 billion in 2024, and is expected to reach USD 4.38 billion by 2033, expanding at a robust CAGR of 11.5% over the forecast period. This market’s rapid growth is primarily driven by the increasing need for real-time production visibility, enhanced operational efficiency, and the rising adoption of Industry 4.0 technologies across manufacturing sectors worldwide. As manufacturers strive to streamline processes and reduce downtime, the demand for advanced shop floor data collection solutions continues to surge, making this software a critical component in modern industrial operations.

The growth trajectory of the shop floor data collection software market is significantly influenced by the widespread digital transformation initiatives undertaken by manufacturing enterprises across the globe. As organizations aim to achieve higher levels of automation and integrate their operations with enterprise resource planning (ERP) systems, the need for accurate, real-time data from the production floor has become paramount. This shift towards data-driven decision-making is fueling investments in shop floor data collection tools that enable seamless tracking of production metrics, machine status, inventory levels, and workforce productivity. Additionally, the proliferation of IoT devices and smart sensors on the factory floor is further enhancing the capabilities of these software solutions, enabling predictive maintenance and reducing unplanned downtime. The convergence of these technological advancements is creating a fertile environment for market expansion, with manufacturers increasingly recognizing the value of actionable insights derived from shop floor data.

Another critical growth factor for the shop floor data collection software market is the escalating pressure on manufacturers to comply with stringent quality standards and regulatory requirements. Industries such as automotive, aerospace & defense, pharmaceuticals, and food & beverage are subject to rigorous compliance mandates that necessitate meticulous documentation and traceability of production processes. Shop floor data collection software provides the necessary infrastructure to capture, store, and analyze production data in real time, ensuring adherence to quality benchmarks and facilitating swift corrective actions in case of deviations. This capability not only helps organizations maintain product quality and safety but also minimizes the risk of costly recalls and reputational damage. As regulatory landscapes evolve and customer expectations for transparency increase, the adoption of sophisticated data collection solutions is set to intensify, further propelling market growth.

The ongoing shift towards smart manufacturing and the integration of advanced analytics and artificial intelligence (AI) into shop floor operations are also playing a pivotal role in driving market expansion. Manufacturers are leveraging these technologies to gain deeper insights into production performance, identify inefficiencies, and optimize resource allocation. Shop floor data collection software equipped with advanced analytics capabilities enables real-time monitoring of key performance indicators (KPIs), predictive maintenance scheduling, and dynamic workforce management. This not only enhances operational agility but also supports continuous improvement initiatives, fostering a culture of innovation within manufacturing organizations. As the competitive landscape intensifies, companies that harness the power of data-driven insights are better positioned to achieve operational excellence and sustain long-term growth.

From a regional perspective, Asia Pacific is emerging as a dominant force in the shop floor data collection software market, driven by the rapid industrialization and digitalization of manufacturing sectors in countries such as China, India, and Japan. North America and Europe also continue to exhibit strong demand, fueled by the presence of advanced manufacturing ecosystems and early adoption of Industry 4.0 technologies. While Latin America and the Middle East & Africa are still in the nascent stages of market development, increasing investments in industrial automation and smart factory initiatives are expected to unlock significant growth opportunities in these regions over the coming years.

Component Analysis

A survey conducted among manufacturing companies in the fourth quarter of 2020 showed that ** percent implemented plans to use sensors, electronic devices, among others to collect and use real time data, for their regular manufacturing activities. In Industry *** era, adaptation of real time data collection would become increasingly common in all sectors of manufacturing industry.

Abstract

Introduction

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. Industrial sector occupies an important position in the State economy and has a pivotal role to play in the rapid and balanced economic development. 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

Coverage of the Annual Survey of Industries extends to the entire Factory Sector, comprising industrial units (called factories) registered under section 2(m)(i) and 2(m)(ii) of the Factories Act.1948, wherein a "Factory", which is the primary statistical unit of enumeration for the ASI is defined as:- "Any premises" including the precincts thereof:- (i) wherein ten or more workers are working or were working on any day of the preceding twelve months, and in any part of which a manufacturing process is being carried on with the aid of power or is ordinarily so carried on, or (ii) wherein twenty or more workers are working or were working on any day of the preceding twelve months, and in any part of which a manufacturing process is being carried on without the aid of power. In addition to section 2(m)(i) & 2(m)(ii) of the Factories Act, 1948, electricity units registered with the Central Electricity Authority and Bidi & Cigar units, registered under the Bidi & Cigar Workers (Conditions of Employment) Act,1966 are also covered in ASI.

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 same scheme (census or sample) 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. The geographical coverage of the Annual Survey of Industries, 2002-03 has been extended to the entire country except the states of Arunachal Pradesh, Mizoram and Sikkim and Union Territory of Lakshadweep.

Kind of data

Census and Sample survey data [cen/ssd]

Sampling procedure

Sampling Procedure

The sampling design followed in ASI 2002-03 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 industrial units belonging to the six less industrially developed states/ UT's viz. Manipur, Meghalaya, Nagaland, Tripura, Sikkim and Andaman & Nicobar Islands.
b) For the rest of the twenty-six states/ UT's., (i) units having 100 or more workers, and (ii) all factories covered under Joint Returns. c) After excluding the Census Sector units as defined above, all units belonging to the strata (State by 4-digit of NIC-04) having less than or equal to 4 units are also considered as Census Sector units.

Remaining units, excluding those of Census Sector, called the sample sector, are arranged in order of their number of workers and samples are then drawn circular systematically considering sampling fraction of 20% within each stratum (State X Sector X 4-digit NIC) for all the states. An even number of units with a minimum of 4 are selected and evenly distributed in two sub-samples. The sectors considered here are Biri, Manufacturing and Electricity.

Sampling deviation

There was no deviation from sample design in ASI 2002-03

Mode of data collection

Statutory return submitted by factories as well as Face to face

Research instrument

Annual Survey of Industries Questionnaire (in External Resources) is divided into different blocks:

BLOCK A :IDENTIFICATION PARTICULARS BLOCK B : PARTICULARS OF THE FACTORY (TO BE FILLED BY OWNER OF THE FACTORY) BLOCK C : FIXED ASSETS BLOCK D : WORKING CAPITAL & LOANS BLOCK E : EMPLOYMENT AND LABOUR COST BLOCK F : OTHER EXPENSES BLOCK G : OTHER INCOMES BLOCK H : INPUT ITEMS (indigenous items consumed) BLOCK I : INPUT ITEMS – directly imported items only (consumed) BLOCK J : PRODUCTS AND BY-PRODUCTS (manufactured by the unit)

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.

Code list, State code list, Tabulation program and ASICC code are also may be refered in the External Resources which are used for editing and data processing as well..

Tabulation procedure The tabulation procedure by CSO(ISW) includes both the ASI 2002-03 data and the extracted data from ASI 01-02 for all tabulation purpose. For extracted returns, status of unit (Block A, Item 12) would be in the range 17 to 20. 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. Please note that a separate inflation factor (Multiplier) is available for each unit against records belonging to Block-A ,pos:62-70 (Please refer STRUC03.XLS) for ASI 2002-03 data. The multiplier is calculated for each sub-stratum (i.e. State X NIC'98(4 Digit) X sub-stratum) after adjusting for non-response cases.

Status of unit code 17-20 may always be considered for all processing.

Merging of unit level data As per existing policy to merge unit level data at ultimate digit level of NIC'98 (i.e., 5 digit) for the purpose of dissemination, the data have been merged for industries having less than three units within State, District and NIC'98(5 Digit) with the adjoining industries within district and then to adjoining districts within a state. There may be some NIC'98(5 Digit) ending with '9' which do not figure in the book of NIC '98. These may be treated as 'Others' under the corresponding 4-digit group. To suppress the identity of factories data fields corresponding to PSL number, Industry code as per Frame (4-digit level of NIC-98) and RO/SRO code have been filled with '9' in each record.

It may please be noted that, tables generated from the merged data may not tally with the published results for few industries, since the merging for published data has been done at aggregate-level to minimise the loss of information.

Sampling error estimates

Relative Standard Error (RSE) is calculated in terms of worker, wages to worker and GVA using the formula. Programs developed in Visual Foxpro 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.

Manufacturing Data Platform Market Outlook

According to our latest research, the global Manufacturing Data Platform market size reached USD 4.8 billion in 2024, reflecting a robust momentum within the industrial digital transformation landscape. The market is expected to grow at a CAGR of 16.2% from 2025 to 2033, with the value projected to reach USD 18.1 billion by 2033. This remarkable growth is primarily fueled by the increasing adoption of smart manufacturing initiatives, the integration of Industrial Internet of Things (IIoT), and the accelerating need for real-time visibility and analytics across manufacturing operations. As per our latest research, the market is witnessing substantial investments from both large enterprises and SMEs, aiming to streamline production, enhance quality, and drive predictive maintenance through data-driven insights.

One of the most significant growth drivers for the Manufacturing Data Platform market is the rapid digitalization across manufacturing sectors worldwide. Companies are increasingly recognizing the value of harnessing real-time data from diverse sources, including machines, sensors, and enterprise systems, to optimize their production processes. The proliferation of IIoT devices has enabled seamless data collection and integration, empowering manufacturers to gain actionable insights, improve operational efficiency, and reduce downtime. Furthermore, the growing emphasis on Industry 4.0 and smart factory initiatives is compelling organizations to invest in advanced data platforms that can aggregate, analyze, and visualize data at scale, thus fostering data-driven decision-making across all levels of the enterprise.

Another critical factor propelling the growth of the Manufacturing Data Platform market is the increasing complexity of global supply chains and the heightened demand for quality assurance. Manufacturers are under constant pressure to deliver high-quality products while minimizing costs and adhering to stringent regulatory standards. Data platforms play a pivotal role in enabling real-time monitoring of production lines, tracking the quality of outputs, and identifying potential bottlenecks or defects before they escalate into costly issues. The integration of predictive analytics and machine learning capabilities within these platforms further enhances their value proposition, allowing manufacturers to anticipate equipment failures, optimize maintenance schedules, and ensure continuous improvement throughout the manufacturing lifecycle.

The expanding scope of manufacturing data platforms to support sustainability and energy efficiency initiatives is also contributing to market expansion. As environmental regulations become more stringent and stakeholders demand greater transparency, manufacturers are leveraging data platforms to monitor energy consumption, reduce waste, and track carbon emissions. These platforms enable organizations to implement sustainable manufacturing practices by providing granular visibility into resource utilization and facilitating compliance with environmental standards. The ability to generate comprehensive reports and dashboards not only helps manufacturers meet regulatory requirements but also strengthens their brand reputation and competitiveness in an increasingly eco-conscious market landscape.

Regionally, the Asia Pacific market is emerging as a powerhouse for manufacturing data platform adoption, driven by the rapid industrialization of countries such as China, India, and Japan. North America continues to lead in terms of innovation and early adoption, supported by a strong presence of technology providers and a mature manufacturing base. Europe is also exhibiting substantial growth, particularly in sectors like automotive and aerospace, where precision and quality are paramount. Meanwhile, Latin America and the Middle East & Africa are gradually embracing digital transformation, albeit at a slower pace, due to varying levels of technological maturity and investment capacity. Nevertheless, the global outlook remains highly optimistic, with all regions contributing to the sustained expansion of the manufacturing data platform market.

The Automatic Identification and Data Capture Market size was valued to be USD 68.54 Billion in the year 2023 and it is expected to reach USD 186.45 Billion in 2031, at a CAGR of 14.70 % over the forecast period of 2024 to 2031.

Key Market Drivers E-commerce growth driving demand for efficient logistics: The rapid expansion of e-commerce is fueling the need for AIDC technologies in warehouse management and logistics. According to the U.S. Census Bureau, e-commerce sales in the United States grew from $57 1.2 billion in 2019 to $870.8 billion in 2021, representing a 52.5% increase over just two years. This surge in online shopping has intensified the demand for AIDC solutions to streamline inventory management and order fulfillment processes. Healthcare industry adoption for patient safety and asset tracking: The healthcare sector is increasingly adopting AIDC technologies to enhance patient safety, reduce errors, and improve asset management. The U.S. Food and Drug Administration (FDA) reports that barcode medication administration systems, a form of AIDC, can reduce medication errors by up to 51%. This significant improvement in patient safety is driving healthcare providers to implement AIDC solutions across their operations. Manufacturing sector embracing Industry 4.0 and IoT: The manufacturing industry's shift towards Industry 4.0 and the Internet of Things (IoT) is boosting the adoption of AIDC technologies for real-time tracking and process optimization. According to the International Federation of Robotics (IFR), the number of industrial robots operating in factories around the world increased by 12% to 3.5 million units in 2021. This growth in automation is closely tied to the implementation of AIDC systems for seamless integration and data collection in smart factories.

Abstract

Introduction

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 extends to the entire country except the States of Arunachal Pradesh, Mizoram, and Sikkim and Union Territory of Lakshadweep. It covers all factories registered under Sections 2m(i) and 2m(ii) of the Factories Act, 1948 i.e. those factories employing 10 or more workers using power; and those employing 20 or more workers without using power. The survey also covers bidi and cigar manufacturing establishments registered under the Bidi & Cigar Workers (Conditions of Employment) Act, 1966 with coverage as above. All electricity undertakings engaged in generation, transmission and distribution of electricity registered with the Central Electricity Authority (CEA) were covered under ASI irrespective of their employment size. Certain servicing units and activities like water supply, cold storage, repairing of motor vehicles and other consumer durables like watches etc. are covered under the Survey. Though servicing industries like motion picture production, personal services like laundry services, job dyeing, etc. are covered under the Survey but data are not tabulated, as these industries do not fall under the scope of industrial sector defined by the United Nations.

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 same scheme (census or sample) 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.

Merging of unit level data As per existing policy to merge unit level data at ultimate digit level of NIC'08 (i.e., 5 digit) for the purpose of dissemination, the data have been merged for industries having less than three units within State, District and NIC-08 (5 Digit) with the adjoining industries within district and then to adjoining districts within a state. There may be some NIC-08 (5 Digit) ending with '9' that do not figure in the book of NIC '08. These may be treated as 'Others' under the corresponding 4-digit group. To suppress the identity of factories data fields corresponding to PSL number, Industry code as per Frame (4-digit level of NIC-09) and RO/SRO code have been filled with '9' in each record.

It may please be noted that, tables generated from the merged data may not tally with the published results for few industries, since the merging for published data has been done at aggregate-level to minimise the loss of information.

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. The geographical coverage of the Annual Survey of Industries, 2008-2009 has been extended to the entire country except the states of Arunachal Pradesh, Mizoram and Sikkim and Union Territory of Lakshadweep.

Kind of data

Census and Sample survey data [cen/ssd]

Sampling procedure

Sampling Procedure

The sampling design followed in ASI 2008-09 is a stratified 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 industrial units belonging to the six less industrially developed states/ UT's viz. Manipur, Meghalaya, Nagaland, Tripura, Sikkim and Andaman & Nicobar Islands.
b) For the rest of the twenty-six states/ UT's., (i) units having 100 or more workers, and (ii) all factories covered under Joint Returns. c) After excluding the Census Sector units as defined above, all units belonging to the strata (State by 4-digit of NIC-04) having less than or equal to 4 units are also considered as Census Sector units.

Remaining units, excluding those of Census Sector, called the sample sector, are arranged in order of their number of workers and samples are then drawn circular systematically considering sampling fraction of 20% within each stratum (State X Sector X 4-digit NIC) for all the states. An even number of units with a minimum of 4 are selected and evenly distributed in two sub-samples. The sectors considered here are Biri, Manufacturing and Electricity.

Sampling deviation

There was no deviation from sample design in ASI 2008-09.

Mode of data collection

Statutory return submitted by factories as well as Face to face

Research instrument

Annual Survey of Industries Questionnaire (in External Resources) is divided into different blocks:

BLOCK A.IDENTIFICATION PARTICULARS BLOCK B. PARTICULARS OF THE FACTORY (TO BE FILLED BY OWNER OF THE FACTORY) BLOCK C: FIXED ASSETS BLOCK D: WORKING CAPITAL & LOANS BLOCK E : EMPLOYMENT AND LABOUR COST BLOCK F : OTHER EXPENSES BLOCK G : OTHER INCOMES BLOCK H: INPUT ITEMS (indigenous items consumed) BLOCK I: INPUT ITEMS – directly imported items only (consumed) BLOCK J: PRODUCTS AND BY-PRODUCTS (manufactured by the unit)

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.

A list of validation checks carried out on data files is given in External Resources "Validation checks, ASI 2008-09". Code list, State code list, Tabulation program and ASICC code are also may be refered in the External Resources which are used for editing and data processing as well..

Response rate

No. of units to be surveyed No. of units responded No. of units non-responded Response rate (in %)

  58300             52376                  5924             89.84

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 Foxpro 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.

Context

This data was taken from an actual production line near Detroit, Michigan. The goal is to predict certain properties of the line's output from the various input data. The line is a high-speed, continuous manufacturing process with parallel and series stages.

Content

The data comes from one production run spanning several hours. Liveline Technologies has a large quantity of this type of data from multiple production lines in various locations.

Challenge

The data comes from a multi-stage continuous flow manufacturing process. In the first stage, Machines 1, 2, and 3 operate in parallel, and feed their outputs into a step that combines the flows. Output from the combiner is measured in 15 locations surrounding the outer surface of the material exiting the combiner.

Primary Goal: Predict measurements of output from first stage.

Next, the output flows into a second stage, where Machines 4 and 5 process in series. After Machine 5, measurements are made again in the same 15 locations surrounding the outer surface of the material exiting Machine 5.

Secondary Goal: Predict measurements of output from second stage.

Acknowledgements

The Liveline team would like to thank all the technicians and production personnel who assisted with the runs and data collection.

Inspiration

We are always looking for the best predictive modeling approaches to use in real time production environments. Models are employed for several use cases such as development of real time process controllers (use the models in simulation environments) and anomaly detection (compare model predictions to actual outputs in real time).

Join Us

Check us out at Liveline Technologies and help create the future of manufacturing process control!

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 same scheme (census or sample) 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

Census and Sample survey data [cen/ssd]

Sampling procedure

Sampling Procedure

The sampling design followed in ASI 1999-2000 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 100 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 100 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 fraction was taken as 12% within each stratum (State X Sector X 4-digit NIC) with a minimum of 8 samples except for the State of Gujarat where 9.5% sampling fraction was used. For the States of Jammu & Kashmir, Himachal Pradesh, Daman & Diu, Dadra & Nagar Haveli, Goa and Pondicherry, a minimum of 4 samples per stratum was selected. For the States of Bihar and Jharkhand, a minimum of 6 samples per stratum was selected. The entire sample was selected in the form of two independent sub-sample using Circular Systematic Sampling method.

Sampling deviation

There was no deviation from sample design in ASI 1999-2000.

Mode of data collection

Statutory return submitted by factories as well as Face to face

Research instrument

Annual Survey of Industries Questionnaire (in External Resources) is divided into different blocks:

BLOCK A.IDENTIFICATION PARTICULARS BLOCK B. PARTICULARS OF THE FACTORY (TO BE FILLED BY OWNER OF THE FACTORY) BLOCK C: FIXED ASSETS BLOCK D: WORKING CAPITAL & LOANS BLOCK E : EMPLOYMENT AND LABOUR COST BLOCK F : OTHER EXPENSES BLOCK G : OTHER INCOMES BLOCK H: INPUT ITEMS (indigenous items consumed) BLOCK I: INPUT ITEMS – directly imported items only (consumed) BLOCK J: PRODUCTS AND BY-PRODUCTS (manufactured by the unit)

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.

Validation checks are carried out on data files. Code list, State code list, Tabulation program and ASICC code are may be refered in the External Resources which are used for editing and data processing as well..

B. Tabulation procedure The tabulation procedure by CSO(ISW) includes both the ASI 1999-00 data and the extracted data from ASI 98-99 for all tabulation purpose. For extracted returns, status of unit (Block A, Item 12) would be in the range 17 to 20. 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. Please note that a separate inflation factor (Multiplier) is available for each unit against records belonging to Block-A ,pos:54-62 (Please refer STRUC00.XLS) for ASI 99-00 data. The multiplier is calculated for each stratum (i.e. State X NIC'98(4 Digit)) after adjusting for non-response cases.

C. Merging of unit level data As per existing policy to merge unit level data at ultimate digit level of NIC'98 (i.e., 5 digit) for the purpose of dissemination, the data have been merged for industries having less than three units within State, District and NIC'98(5 Digit) with the adjoining industries within district and then to adjoining districts within a state. There may be some NIC'98(5 Digit) ending with '9' which do not figure in the book of NIC '98. These may be treated as 'Others' under the corresponding 4-digit group. To suppress the identity of factories data fields corresponding to PSL number, Industry code as per Frame (4-digit level of NIC-98) and RO/SRO code have been filled with '9' in each record.

It may please be noted that, tables generated from the merged data may not tally with the published results for few industries, since the merging for published data has been done at aggregate-level to minimise loss of information.

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 STRUC00.XLS for item level identification key for each unit.

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.

Two Rooks (2022-12-09): Lathe Machining (Aluminium), Robotic Handling, Close To Production Measurement

This dataset has been created by researchers from the Technical University of Munich, Chair for Information Systems and Business Process Management (i17), Boltzmannstraße 3, 85748 Garching b. München. The dataset has been created through https://cpee.org.

The data set contains raw data and refined and aggregated data in the XES SensorStream format https://arxiv.org/abs/2206.11392.

The dataset contains data from the following sources:

1. EMCO MT45 Lathe: standard internal sensors + additional custom power measurement (better quality than internal sensors). All data collected from this service can, to the best of our knowledge, be freely distributed and used for all purposes (e.g., analysis).
2. Keyence LS-7000 High-speed, High-accuracy Optical Digital Micrometer: the part is moved through the measurement beam. On data collection we restricted the accuracy to 0.01 mm.
3. ABB IRB-2600 Industrial Robot: movement coordinates and pneumatic valve states (gripper) are collected.

The data is collected for two manufactured parts: the first part is good, the second part is wrapped in chips from the turning process (see picture).

Abstract

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 extends its coverage to the entire country upto state level.

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 same scheme (census or sample) 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 2000-01 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 100 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 100 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 fraction was taken as 12% within each stratum (State X Sector X 4-digit NIC) with a minimum of 8 samples except for the State of Gujarat where 9.5% sampling fraction was used. For the States of Jammu & Kashmir, Himachal Pradesh, Daman & Diu, Dadra & Nagar Haveli, Goa and Pondicherry, a minimum of 4 samples per stratum was selected. For the States of Bihar and Jharkhand, a minimum of 6 samples per stratum was selected. The entire sample was selected in the form of two independent sub-sample using Circular Systematic Sampling method.

Sampling deviation

There was no deviation from sample design in ASI 2000-01

Mode of data collection

Statutory return submitted by factories as well as Face to face

Research instrument

Annual Survey of Industries Questionnaire (in External Resources) is divided into different blocks:

BLOCK A.IDENTIFICATION PARTICULARS BLOCK B. PARTICULARS OF THE FACTORY (TO BE FILLED BY OWNER OF THE FACTORY) BLOCK C: FIXED ASSETS BLOCK D: WORKING CAPITAL & LOANS BLOCK E : EMPLOYMENT AND LABOUR COST BLOCK F : OTHER EXPENSES BLOCK G : OTHER INCOMES BLOCK H: INPUT ITEMS (indigenous items consumed) BLOCK H1: FUELS, ELECTRICITY AND WATER CONSUMPTION BLOCK I: INPUT ITEMS – directly imported items only (consumed) BLOCK J: PRODUCTS AND BY-PRODUCTS (manufactured by the unit)

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.

Validation checks are carried out on data files. Code list, State code list, Tabulation program and ASICC code are may be refered in the External Resources which are used for editing and data processing as well..

B. Tabulation procedure

The tabulation procedure by CSO(ISW) includes both the ASI 2000-01 data and the extracted data from ASI 99-00 for all tabulation purpose. For extracted returns, status of unit (Block A, Item 12) would be in the range 17 to 20. 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. Please note that a separate inflation factor (Multiplier) is available for each unit against records belonging to Block-A for ASI 2000-01 data. The multiplier is calculated for each stratum (i.e. State X NIC'98(4 Digit)) after adjusting for non-response cases.

.

C. Merging of unit level data

As per existing policy to merge unit level data at ultimate digit level of NIC'98 (i.e., 5 digit) for the purpose of dissemination, the data have been merged for industries having less than three units within State, District and NIC'98(5 Digit) with the adjoining industries within district and then to adjoining districts within a state. There may be some NIC'98(5 Digit) ending with '9' which do not figure in the book of NIC '98. These may be treated as 'Others' under the corresponding 4-digit group. To suppress the identity of factories data fields corresponding to PSL number, Industry code as per Frame (4-digit level of NIC-98) and RO/SRO code have been filled with '9' in each record.

It may please be noted that, tables generated from the merged data may not tally with the published results for few industries, since the merging for published data has been done at aggregate-level to minimise loss of information.

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.

Discover the booming data capture services market! This in-depth analysis reveals a market size of $80 Billion in 2025, projected for significant growth with a CAGR exceeding 18% through 2033. Explore key drivers, trends, and regional insights, including the leading companies and applications across BFSI, manufacturing, healthcare, and more.

Manufacturing Data Platform Market Outlook

According to our latest research, the global manufacturing data platform market size reached USD 5.3 billion in 2024, reflecting a robust expansion fueled by increased digitalization across manufacturing sectors. The market is projected to grow at a CAGR of 18.7% from 2025 to 2033, reaching an estimated USD 25.6 billion by 2033. This strong growth trajectory is primarily driven by the rising adoption of Industry 4.0 initiatives, the proliferation of IoT devices, and the urgent need for real-time data analytics to optimize manufacturing operations.

One of the most significant growth factors for the manufacturing data platform market is the accelerated digital transformation occurring within manufacturing enterprises worldwide. Manufacturers are increasingly leveraging advanced data analytics, artificial intelligence, and machine learning to streamline production processes, reduce downtime, and enhance product quality. As the complexity of manufacturing ecosystems grows, the need for unified platforms that can aggregate, process, and analyze vast volumes of data from disparate sources becomes paramount. These platforms enable manufacturers to transition from reactive to predictive and prescriptive decision-making, resulting in improved operational efficiency and significant cost savings. The integration of edge computing and cloud technologies further amplifies the value proposition of manufacturing data platforms by enabling real-time insights and facilitating seamless data flow across the value chain.

Another critical driver propelling the expansion of the manufacturing data platform market is the increasing focus on sustainability and regulatory compliance. Stringent environmental regulations and growing consumer demand for sustainable products are compelling manufacturers to adopt data-driven strategies for energy management, waste reduction, and resource optimization. Manufacturing data platforms play a pivotal role in collecting and analyzing environmental, social, and governance (ESG) metrics, allowing companies to monitor their sustainability initiatives and ensure compliance with global standards. Moreover, the ability to track and trace materials throughout the supply chain enhances transparency and accountability, which are vital for building trust with stakeholders and achieving long-term business resilience.

The proliferation of connected devices and the Industrial Internet of Things (IIoT) is also fueling the demand for advanced data platforms in manufacturing. With the exponential growth of machine-generated data, manufacturers are seeking scalable solutions that can handle high-velocity, high-volume, and high-variety data streams. Manufacturing data platforms equipped with robust data integration, storage, and analytics capabilities enable organizations to harness the full potential of IIoT, unlocking new opportunities for process automation, predictive maintenance, and intelligent asset management. As manufacturers increasingly adopt smart factory initiatives, the role of data platforms in driving innovation and competitive differentiation becomes even more pronounced.

From a regional perspective, Asia Pacific is emerging as the fastest-growing market for manufacturing data platforms, supported by rapid industrialization, strong government support for digital manufacturing, and the presence of a large base of manufacturing enterprises. North America and Europe continue to dominate in terms of early adoption and technological innovation, driven by established industrial players and a mature digital infrastructure. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth as manufacturers in these regions increase their investments in digital transformation to improve productivity and global competitiveness.

Component Analysis

The component segment of the manufacturing data platform market is categorized into software, hardware, and services. Software solutions constitute the largest share, propelled by the demand for advanced analytics, data integration, and visualization tools. These software platforms are essential for aggregating data from various sources, performing complex analyses, and generating actionable insights that support decision-making at all levels of the organization. As manufacturing processes become more digitized and interconnected, the need for robust, scalable, and user-friendly software solutions is expected

This collection presents information from the census of manufacturing in states and the District of Columbia. It was constructed from the STATE SAMPLES FROM THE CENSUS OF MANUFACTURING: 1850, 1860, AND 1870 (ICPSR 4071). The data were originally collected to paint a quantitative picture of industrialization in the United States without the need to weight the results. The data describe states and counties in terms of amount of capital invested and numbers of male, female, and child workers employed. Additional information includes daily wages for men, women, and children, annual wage bill, number of waterwheels and steam engines, and horsepower by water or steam.

The global Production Tracking Software market is poised for substantial growth, projected to reach approximately $12,000 million by 2025, with a Compound Annual Growth Rate (CAGR) of around 10% through 2033. This expansion is largely fueled by the increasing demand for enhanced operational efficiency, real-time visibility into manufacturing processes, and improved inventory management across various industries. The "drivers" of this market are multifaceted, including the growing adoption of Industry 4.0 technologies such as IoT, AI, and Big Data analytics, which enable sophisticated data collection and analysis for production optimization. Furthermore, stringent regulatory compliance requirements and the need for robust quality control mechanisms are compelling businesses to invest in advanced tracking solutions. The shift towards digital transformation within manufacturing sectors, coupled with the competitive imperative to reduce costs and lead times, will continue to be significant catalysts for market expansion. The market is segmented by application, with Large Enterprises representing a dominant share due to their complex operational needs and significant investment capacity in sophisticated software solutions. Small and Medium Enterprises (SMEs) are also emerging as a key growth segment, driven by the increasing availability of affordable and scalable cloud-based solutions that democratize access to advanced production tracking capabilities. In terms of deployment types, Cloud-Based solutions are anticipated to witness the fastest growth, offering flexibility, scalability, and cost-effectiveness compared to traditional on-premise systems. Key restraints for the market include the initial implementation costs and the potential resistance to change within organizations. However, the overarching trend towards smart manufacturing and the continuous innovation in software features, such as predictive maintenance and advanced reporting, are expected to outweigh these challenges, ensuring a dynamic and robust market trajectory. This report offers a comprehensive analysis of the global Production Tracking Software market, charting its trajectory from a nascent stage in 2019 to its projected substantial growth through 2033. Our study, encompassing the historical period of 2019-2024, a base year of 2025, and a forecast period extending to 2033, reveals a dynamic landscape driven by the relentless pursuit of operational excellence and data-driven decision-making across industries. With an estimated market size in the tens of millions of units in the base year of 2025, the software's adoption is poised for significant expansion.

Abstract

Introduction

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. Industrial sector occupies an important position in the State economy and has a pivotal role to play in the rapid and balanced economic development. 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

Coverage of the Annual Survey of Industries extends to the entire Factory Sector, comprising industrial units (called factories) registered under section 2(m)(i) and 2(m)(ii) of the Factories Act.1948, wherein a "Factory", which is the primary statistical unit of enumeration for the ASI is defined as:- "Any premises" including the precincts thereof:- (i) wherein ten or more workers are working or were working on any day of the preceding twelve months, and in any part of which a manufacturing process is being carried on with the aid of power or is ordinarily so carried on, or (ii) wherein twenty or more workers are working or were working on any day of the preceding twelve months, and in any part of which a manufacturing process is being carried on without the aid of power. In addition to section 2(m)(i) & 2(m)(ii) of the Factories Act, 1948, electricity units registered with the Central Electricity Authority and Bidi & Cigar units, registered under the Bidi & Cigar Workers (Conditions of Employment) Act,1966 are also covered in ASI.

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 same scheme (census or sample) 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. The geographical coverage of the Annual Survey of Industries, 1994-95 has been extended to the entire country except the states of Arunachal Pradesh, Mizoram and Sikkim and Union Territory of Lakshadweep.

Kind of data

Census and Sample survey data [cen/ssd]

Sampling procedure

Sampling Procedure

The sampling design followed in ASI 1994-95 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 industrial units belonging to the 12 less industrially developed states/ UT's viz. Manipur, Meghalaya, Nagaland, Tripura, Sikkim and Andaman & Nicobar Islands etc.
b) For the rest of the states/ UT's., (i) units having 100 or more workers irrespective of their operation with or without power and all electricity undertakings and (ii) all factories covered under Joint Returns. c) After excluding the Census Sector units as defined above, all units belonging to the strata (State by 4-digit of NIC-04) having less than or equal to 4 units are also considered as Census Sector units.

Remaining units, excluding those of Census Sector, called the sample sector, are covered on sampling basis through an efficient sampling design adopting State X 3 digit industry group as stratum so as to cover all the units in a span of three years. In any stratum, if the number of units was less than 20 , then the entire stratum was enumerated completely along with census factories. In any stratum if the units is between 21 and 60, a minimum sample of size 20 was selected by Circular Systematic Sampling. For all other units a uniform sampling fraction of 1/3 was adopted.

Sampling deviation

There was no deviation from sample design in ASI 1994-95

Mode of data collection

Statutory return submitted by factories as well as Face to face

Research instrument

Annual Survey of Industries 1994-95 Questionnaire is divided into different blocks : (However only Summarised data is available for processing and analysis). The Summary Results are based on the information provided in the Summary block pf ASI survey schedule. Therefore, there is only on data file in ASI Summary 1994-95

BLOCK 1-Identification Particulars Block 2-Classificatory characteristics Block 3-Particulars of field operation Block 4-Schedule of fixed assets Block 4A – Details of Plant and Machinery Block 5 – Schedule of Working Capital and Loans Block 6 – Working days and shifts Block 7 – Employment BLOCK 8-Labour Cost (including for contract labour) Block 9 – Fuels, electricity and water consumed (excl. intermediate products) Block 10-Other expenditure Block 11 – Other Output/Receipts Block 12 – Electricity Block 13-Materials consumed (excluding intermediate products) during the accounting year Block 13-A-Quantity and value of indigenous and imported industrial components, accessories and imported raw materials consumed during the accounting year Block 13-A- Continued : Quantity and value of indigenous and imported industrial components, accessories and imported raw materials consumed during the accounting year Block 14 – Products and by-products including fixed assets (excluding intermediate products) manufactured and sold during the year ……………………………… Block 14 A – details of distributive expenses on sale during the accounting year

Block 15-Identification and summary information

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.

Code list, State code list, NIC 70, NIC 87, Concordance Table and ASICC code may be refered in the External Resources which are used for editing and data processing as well..

Sampling error estimates

Relative Standard Error (RSE) is calculated in terms of worker, wages to worker and GVA using the formula. Programs developed in Visual Foxpro 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.