The Gross Domestic Product (GDP) in India was worth 3567.55 billion US dollars in 2023, according to official data from the World Bank. The GDP value of India represents 3.38 percent of the world economy. This dataset provides the latest reported value for - India GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Dataset

This dataset was created by Madhu Boyapalli

Contents

Introduction

Welcome to the Indian English Call Center Speech Dataset for the Travel domain designed to enhance the development of call center speech recognition models specifically for the Travel industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data:

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Travel domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Travel domain call center conversational AI and ASR models for the Indian English language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

Introduction

Welcome to the Indian English Call Center Speech Dataset for the Real Estate domain designed to enhance the development of call center speech recognition models specifically for the Real Estate industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data:

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Real Estate domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Real Estate domain call center conversational AI and ASR models for the Indian English language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Indian English call center speech recognition models.

Key information about India Investment: % of GDP

• India Investment accounted for 34.7 % of its Nominal GDP in Sep 2024, compared with a ratio of 32.8 % in the previous quarter.
• India investment share of Nominal GDP data is updated quarterly, available from Jun 2004 to Sep 2024, with an average ratio of 33.6 %.
• The data reached an all-time high of 41.2 % in Sep 2011 and a record low of 21.8 % in Jun 2020.

• In the latest reports, India GDP expanded 6.1 % YoY in Mar 2023.
• India Nominal GDP reached 873.7 USD bn in Mar 2023.
• Its GDP deflator (implicit price deflator) increased 4.1 % in Mar 2023.
• India GDP Per Capita reached 2,452.2 USD in Mar 2023.
• Its Gross Savings Rate was measured at 30.2 % in Mar 2023.

Analysis of ‘ET Top 500 Indian Companies (2009-2021)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ramjasmaurya/et-top-500-indian-companies-since-2009 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

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The Economic Times is an Indian English-language business-focused daily newspaper. It is owned by The Times Group. The Economic Times began publication in 1961. As of 2012, it is the world's second-most widely read English-language business newspaper, after The Wall Street Journal, with a readership of over 800,000. It is published simultaneously from 14 cities: Mumbai, Bangalore, Delhi, Chennai, Kolkata, Lucknow, Hyderabad, Jaipur, Ahmedabad, Nagpur, Chandigarh, Pune, Indore, and Bhopal. Its main content is based on the Indian economy, international finance, share prices, prices of commodities as well as other matters related to finance. This newspaper is published by Bennett, Coleman & Co. Ltd. The founding editor of the paper when it was launched in 1961 was P. S. Hariharan. The current editor of The Economic Times is Bodhisattva Ganguli.

The Economic Times is sold in all major cities in India. In June 2009, it launched a television channel called ET Now.

--- Original source retains full ownership of the source dataset ---

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 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.

With 19.7 Million Businesses in India , Techsalerator has access to the highest B2B count of Data/Business Data in the country. .

Thanks to our unique tools and large data specialist team, we can select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

We cover all cities and regions in India ( example ) :

Mumbai Maharashtra Delhi Delhi Bangalore Karnataka Hyderabad Telangana Ahmedabad Gujarat Chennai Tamil Nadu Kolkata West Bengal Surat Gujarat Pune Maharashtra Jaipur Rajasthan Lucknow Uttar Pradesh Kanpur Uttar Pradesh Nagpur Maharashtra Indore Madhya Pradesh Thane Maharashtra Bhopal Madhya Pradesh Visakhapatnam[4] Andhra Pradesh Pimpri-Chinchwad Maharashtra Patna Bihar Vadodara Gujarat Ghaziabad Uttar Pradesh Ludhiana Punjab Agra Uttar Pradesh Nashik Maharashtra Ranchi Jharkhand Faridabad Haryana Meerut Uttar Pradesh Rajkot Gujarat Kalyan-Dombivli Maharashtra Vasai-Virar Maharashtra Varanasi Uttar Pradesh Srinagar Jammu and Kashmir Aurangabad Maharashtra Dhanbad Jharkhand Gurgaon[5] Haryana Amritsar Punjab Navi Mumbai Maharashtra Allahabad Uttar Pradesh[6] Howrah West Bengal Gwalior Madhya Pradesh Jabalpur Madhya Pradesh Coimbatore Tamil Nadu Vijayawada Andhra Pradesh Jodhpur Rajasthan Madurai Tamil Nadu Raipur Chhattisgarh Kota[8] Rajasthan Chandigarh Chandigarh Guwahati Assam Solapur Maharashtra Hubli–Dharwad Karnataka Mysore[9][10][11] Karnataka Tiruchirappalli[12] Tamil Nadu Bareilly Uttar Pradesh Aligarh Uttar Pradesh Tiruppur Tamil Nadu Moradabad Uttar Pradesh Jalandhar Punjab Bhubaneswar Odisha Salem Tamil Nadu Warangal[13][14] Telangana Mira-Bhayandar Maharashtra Jalgaon Maharashtra Guntur[15] Andhra Pradesh Thiruvananthapuram Kerala Bhiwandi Maharashtra Tirupati Andhra Pradesh Saharanpur Uttar Pradesh Gorakhpur Uttar Pradesh Bikaner Rajasthan Amravati Maharashtra Noida Uttar Pradesh Jamshedpur Jharkhand Bhilai Chhattisgarh Cuttack Odisha Firozabad Uttar Pradesh Kochi Kerala Nellore[16][17] Andhra Pradesh Bhavnagar Gujarat Dehradun Uttarakhand Durgapur West Bengal Asansol West Bengal Rourkela Odisha Nanded Maharashtra Kolhapur Maharashtra Ajmer Rajasthan Akola Maharashtra Gulbarga Karnataka Jamnagar Gujarat Ujjain Madhya Pradesh Loni Uttar Pradesh Siliguri West Bengal Jhansi Uttar Pradesh Ulhasnagar Maharashtra Jammu[18] Jammu and Kashmir Sangli-Miraj & Kupwad Maharashtra Mangalore Karnataka Erode[19] Tamil Nadu Belgaum Karnataka Kurnool[20] Andhra Pradesh Ambattur Tamil Nadu Rajahmundry[21][22] Andhra Pradesh Tirunelveli Tamil Nadu Malegaon Maharashtra Gaya Bihar Udaipur Rajasthan Karur Tamilnadu Kakinada Andhra Pradesh Davanagere Karnataka Kozhikode Kerala Maheshtala West Bengal Rajpur Sonarpur West Bengal Bokaro Jharkhand South Dumdum West Bengal Bellary Karnataka Patiala Punjab Gopalpur West Bengal Agartala Tripura Bhagalpur Bihar Muzaffarnagar Uttar Pradesh Bhatpara West Bengal Panihati West Bengal Latur Maharashtra Dhule Maharashtra Rohtak Haryana Sagar Madhya Pradesh Korba Chhattisgarh Bhilwara Rajasthan Berhampur Odisha Muzaffarpur Bihar Ahmednagar Maharashtra Mathura Uttar Pradesh Kollam Kerala Avadi Tamil Nadu Kadapa[23] Andhra Pradesh Anantapuram[24] Andhra Pradesh Kamarhati West Bengal Bilaspur Odisha Sambalpur Odisha Shahjahanpur Uttar Pradesh Satara Maharashtra Bijapur Karnataka Rampur Uttar Pradesh Shimoga Karnataka Chandrapur Maharashtra Junagadh Gujarat Thrissur Kerala Alwar Rajasthan Bardhaman West Bengal Kulti West Bengal Nizamabad Telangana Parbhani Maharashtra Tumkur Karnataka Khammam Telangana Uzhavarkarai Puducherry Bihar Sharif Bihar Panipat Haryana Darbhanga Bihar Bally West Bengal Aizawl Mizoram Dewas Madhya Pradesh Ichalkaranji Maharashtra Karnal Haryana Bathinda Punjab Jalna Maharashtra Eluru[25] Andhra Pradesh Barasat West Bengal Kirari Suleman Nagar Delhi Purnia[26] Bihar Satna Madhya Pradesh Mau Uttar Pradesh Sonipat Haryana Farrukhabad Uttar Pradesh Durg Chhattisgarh Imphal Manipur Ratlam Madhya Pradesh Hapur Uttar Pradesh Arrah Bihar Anantapur Andhra Pradesh Karimnagar Telangana Etawah Uttar Pradesh Ambarnath Maharashtra North Dumdum West Bengal Bharatpur Rajasthan Begusarai Bihar New Delhi Delhi Gandhidham Gujarat Baranagar West Bengal Tiruvottiyur Tamil Nadu Pondicherry Puducherry Sikar Rajasthan Thoothukudi Tamil Nadu Rewa Madhya Pradesh Mirzapur Uttar Pradesh Raichur Karnataka Pali Rajasthan Ramagundam[27] Telangana Silchar Assam Haridwar Uttarakhand Vijayanagaram Andhra Pradesh Tenali Andhra Pradesh Nagercoil Tamil Nadu Sri Ganganagar Rajasthan ...

Introduction

Welcome to the Indian English Call Center Speech Dataset for the Telecom domain designed to enhance the development of call center speech recognition models specifically for the Telecom industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Telecom domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Telecom domain call center conversational AI and ASR models for the Indian English language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

Key information about India Labour Productivity Growth

• India Labour Productivity dropped by 2.80 % YoY in Dec 2022, compared with a growth of 3.99 % in the previous year
• India Labour Productivity Growth data is updated yearly, available from Dec 1992 to Dec 2022, averaging at 4.31 %
• The data reached an all-time high of 9.15 % in Dec 2016 and a record low of 0.74 % in Dec 2000

The AI market size in India was around 6.3 billion U.S. dollars in 2024. Among all the segments, machine learning had the largest share at 2.8 billion dollars. Artificial intelligence has been responsible for drastic changes in the technology sector where it can greatly improve productivity through process simplification and automation. It is also an integral part and one of the fundamental bases of Industry 4.0. IT industry in India The IT industry in India is a huge industry which consists of information technology services, consulting, and outsourcing. India’s IT services industry was born in Mumbai in 1967 when Tata Consultancy Services was established. India made up to more than 19 percent of the global IT spending in financial year 2021. Within the global IT industry, India is renowned for its IT outsourcing services, and with governmental support and foreign investments, the industry is also developing technologies relative to AI and IoT. AI technologies The main branches of an AI ecosystem are machine learning, robotics, artificial neural networks, and Natural Language Processing (NLP). In machine learning, software programs run through existing data, and apply the learned knowledge to new data or to predict data. In the field of robotics, it develops and trains robots for various applications. A prominent example is autonomous vehicles, though the level of autonomy varies, it was estimated that between 2024 and 2025, fully autonomous cars could be seen in the market.

Techsalerator’s Import/Export Trade Data for Asia

Techsalerator’s Import/Export Trade Data for Asia offers a comprehensive and detailed examination of trade activities across the Asian continent. This extensive dataset provides deep insights into import and export transactions involving companies across various sectors throughout Asia.

Coverage Across All Asian Countries

The dataset encompasses a broad range of countries within Asia, including:

Central Asia:

Kazakhstan Kyrgyzstan Tajikistan Turkmenistan Uzbekistan East Asia:

China Hong Kong Japan Mongolia North Korea South Korea Taiwan Southeast Asia:

Brunei Cambodia East Timor (Timor-Leste) Indonesia Laos Malaysia Myanmar (Burma) Philippines Singapore Thailand Vietnam South Asia:

Afghanistan Bangladesh Bhutan India Maldives Nepal Pakistan Sri Lanka West Asia (Middle East):

Armenia Azerbaijan Bahrain Cyprus Georgia Iran Iraq Israel Jordan Kuwait Lebanon Oman Palestine Qatar Saudi Arabia Syria Turkey United Arab Emirates Yemen Comprehensive Data Features

Transaction Details: The dataset includes detailed information on individual trade transactions, such as product descriptions, quantities, values, and dates. This level of detail allows for accurate tracking and analysis of trade patterns across Asia.

Company Information: It provides insights into the companies involved in trade, including their names, locations, and industry sectors. This information supports targeted market analysis and competitive intelligence.

Categorization: Transactions are categorized by industry sectors, product types, and trade partners, helping users understand market dynamics and sector-specific trends across diverse Asian economies.

Trade Trends: Historical data is available to analyze trade trends, identify emerging markets, and assess the impact of economic or geopolitical events on trade flows within the region.

Geographical Insights: Users can explore regional trade flows and cross-border dynamics between Asian countries and their global trade partners, including major trading nations outside the continent.

Regulatory and Compliance Data: Information on trade regulations, tariffs, and compliance requirements is included, assisting businesses in navigating the complex regulatory environments across different Asian countries.

Applications and Benefits

Market Research: Businesses can use the data to identify new market opportunities, assess competitive landscapes, and understand consumer demand across various Asian countries.

Strategic Planning: Companies can leverage insights from the data to refine trade strategies, optimize supply chains, and manage risks associated with international trade in Asia.

Economic Analysis: Analysts and policymakers can monitor economic performance, evaluate trade balances, and make informed decisions on trade policies and economic development initiatives.

Investment Decisions: Investors can assess trade trends and market potentials to make informed decisions about investments in Asia’s diverse and rapidly evolving markets.

Techsalerator’s Import/Export Trade Data for Asia provides a vital resource for organizations involved in international trade, offering a detailed, reliable, and expansive view of trade activities across the Asian continent.

Introduction

Welcome to the Indian English Call Center Speech Dataset for the Healthcare domain designed to enhance the development of call center speech recognition models specifically for the Healthcare industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Healthcare domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Healthcare domain call center conversational AI and ASR models for the Indian English language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Indian English call center speech recognition models.

Usage and Applications

This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Healthcare domain. Potential use cases include:

Snapshot img

India Bearings Market Size 2025-2029

The india bearings market size is forecast to increase by USD 853.9 million million at a CAGR of 3.2% between 2024 and 2029.

The market is experiencing significant growth driven by the increasing focus on automation across various industries, leading to a higher demand for reliable and efficient bearing solutions. Additionally, the adoption of additive manufacturing technologies in the production of bearings is gaining momentum, offering opportunities for enhanced customization and reduced lead times. However, the market is not without challenges, as fluctuations in raw material prices, particularly for steel and other metals used in bearing production, continue to impact profitability and competitiveness. Companies seeking to capitalize on these opportunities and navigate these challenges effectively should consider strategic partnerships, technology investments, and supply chain optimization to ensure long-term success in the Indian bearing market.

What will be the size of the India Bearings Market during the forecast period?

Request Free Sample

The Indian bearing market encompasses a diverse range of applications, including automobile parts, farm equipment, household appliances, defense equipment, aerospace equipment, and renewable energy sectors. With a significant focus on maintenance requirements and specialized bearing solutions, India's bearing industry caters to various industries' unique needs. Inner rings and outer rings are essential components of high-performance bearings used in automotive sectors, wind turbines, and turbine performance enhancement. Chrome steel and anti-friction bearings are commonly used in automotive applications, while magnetic bearings and smart bearings gain traction in automation focus and high-precision industries. The market's sizeable OEM sales and aftermarket demand are driven by the increasing vehicle fleet and the growing importance of bearing efficiency in various industries. Roller bearings and ball bearings are among the most widely used bearing types, catering to the diverse needs of the Indian market.

How is this market segmented?

The market 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. End-userAutomotive industryHeavy industryARS industryOthersProductAnti-friction bearingsMagnetic bearingsOthersProduct TypeBall bearingsRoller bearingsPlain bearingsOthersGeographyIndia

By End-user Insights

The automotive industry segment is estimated to witness significant growth during the forecast period.

The market is primarily driven by the automotive industry, which utilizes bearings extensively in various automotive components to minimize friction, prolong vehicle life, and boost operational efficiency. A typical passenger car incorporates over 100 bearings. The expanding automotive sector in India, fueled by rising disposable income, improving living standards, and economic growth, is a significant contributor to the market's expansion. Key automotive OEMs, including BMW AG and Ford Motor Co., are major consumers of bearings. Additionally, the bearings market caters to other sectors like agriculture, household appliances, defense, aerospace, and manufacturing, where specialized bearing solutions are essential for wind turbines, turbine performance, energy production, and machinery maintenance. Technological advancements, such as lightweight materials, electro-mechanical features, sensor units, digital monitoring, and predictive maintenance services, further boost market growth.

Get a glance at the market share of various segments Request Free Sample

The Automotive industry segment was valued at USD 1396.70 million in 2019 and showed a gradual increase during the forecast period.

Market Dynamics

Our researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in adoption of India Bearings Market?

Increase in focus on automation is the key driver of the market. The Indian bearings market is experiencing growth due to the rising adoption of automation in manufacturing processes. Automation brings numerous benefits to the manufacturing process, including increased productivity, reduced lead time, efficient use of resources, consistent product quality, enhanced safety, and decreased workload for factory workers. Global end-users are integrating automation solutions to execute intricate manufacturing processes, as these solutions boost machine efficiency and predictive maintenance, ensure greater safety, and lead to increased profitability.

T

The India Full Service Restaurants Market is segmented by Cuisine (Asian, European, Latin American, Middle Eastern, North American), by Outlet (Chained Outlets, Independent Outlets) and by Location (Leisure, Lodging, Retail, Standalone, Travel). Market Value in USD is presented. Key data points observed include the number of outlets for each foodservice channel; and, average order value in USD by foodservice channel.

Goa Budget 2018-19: Expenditure for a Demand - Major Heads summary - INDUSTRIES TRADE AND COMMERCE

Introduction

Welcome to the Hindi Call Center Speech Dataset for the Delivery and Logistics domain designed to enhance the development of call center speech recognition models specifically for the Delivery and Logistics industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data

This training dataset comprises 30 Hours of call center audio recordings covering various topics and xscenarios related to the Delivery and Logistics domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Delivery and Logistics domain call center conversational AI and ASR models for the Hindi language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

Key information about India Industrial Production Index Growth

• India Industrial production rose 5.0 % YoY in Jan 2025, following an increase of 3.5 % YoY in the previous month.
• India Industrial production index growth rate YoY data is updated monthly, available from Apr 2006 to Jan 2025, with an average rate of 4.4 %.
• The data reached an all-time high of 133.5 % in Apr 2021 and a record low of -57.3 % in Apr 2020.

Goa Budget 2017-18: Expenditure for a Demand - Major Heads summary - INDUSTRIES TRADE AND COMMERCE

The Data shows the sectoral deployment of bank credit collected from 40 select scheduled commercial banks, accounting for about 93 per cent of the total non-food credit deployed by all scheduled commercial banks. Note: 1. Data for the period April 2007 - December 2018 is in the Old Format and the data for January 2019 - September 2022 is in the New Format.

1. Report 1 a. Data are provisional and relate to select 41 scheduled commercial banks. From September 2017, data account for 90 per cent of total non-food credit extended by all scheduled commercial banks. b. Export credit under priority sector relates to foreign banks only. c. Micro and small under industry include credit to micro and small industries in manufacturing sector. d. Micro and small enterprises under Priority Sector include credit to micro and small enterprises in manufacturing as well as services sector. e. Priority Sector is as per the old definition and does not conform to FIDD Circular FIDD.CO.Plan.BC.54/04.09.01/2014-15 dated April 23, 2015. f. A sharp adjustment of Rs.17300 Crore in consumer durables credit in August 2018 was due to rectification of an error, as one bank had previously wrongly classified housing loans as consumer durable loans.

2. New Format a. Data are provisional. Non-food credit data are based on Section - 42 return, which covers all scheduled commercial banks (SCBs), while sectoral non-food credit data are based on sector-wise and industry-wise bank credit (SIBC) return, which covers select banks accounting for about 93 per cent of total non-food credit extended by all SCBs. b. With effect from January 2021, sectoral credit data are based on a revised format due to which values and growth rates of some of the existing components published earlier have undergone some changes. c. Non-food credit given for the periods December 18, 2020 and December 20, 2019 pertains to the periods January 1, 2021 and January 3, 2020 respectively. d. Credit data are adjusted for past reporting errors by select SCBs from December 2021 onwards. e. Micro and Small include credit to micro and small industries in the manufacturing sector. f. NBFCs include HFCs, PFIs, Microfinance Institutions (MFIs), NBFCs engaged in gold loans and others. g. Other Services include Mutual Funds (MFs), Banking and Finance other than NBFCs and MFs and other services which are not indicated elsewhere under services. h. Agriculture and Allied Activities also include priority sector lending certificates (PSLCs). i. Micro and Small Enterprises include credit to micro and small enterprises in manufacturing and services sectors and also include PSLCs. j. Medium Enterprises include credit to medium enterprises in the manufacturing and services sector.