Manufacturing Data Analytics Market Outlook

The global manufacturing data analytics market size is estimated to reach USD 7.5 billion in 2023 and is projected to grow to USD 19.3 billion by 2032, at a compound annual growth rate (CAGR) of 11.4% during the forecast period. This growth is driven by the increasing demand for actionable insights to optimize production processes, enhance supply chain efficiency, and improve product quality.

The primary growth factor for the manufacturing data analytics market is the rising adoption of Industry 4.0 and smart manufacturing practices. Manufacturers are increasingly leveraging data analytics to gain real-time insights into their operations, reducing downtimes, and streamlining workflows. The advent of the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) has revolutionized data collection and processing, providing manufacturers with powerful tools to predict machine failures, manage quality, and optimize supply chains. These technologies enable predictive maintenance, leading to significant cost savings and enhanced productivity.

Moreover, the shift toward a data-driven culture in the manufacturing sector is propelling market growth. With the availability of vast amounts of data generated from various sources like sensors, machines, and enterprise systems, manufacturers are increasingly investing in advanced analytics solutions to analyze and interpret this data. This shift is driven by the need to remain competitive in a rapidly evolving market landscape. Data analytics provides manufacturers with a competitive edge by offering insights into customer preferences, production efficiency, and market trends, allowing them to make informed decisions and respond swiftly to market demands.

Another critical factor contributing to the market growth is the increasing focus on regulatory compliance and quality management. Manufacturers are under constant pressure to comply with stringent regulations and standards across various industries, such as automotive, aerospace, and pharmaceuticals. Data analytics solutions help manufacturers monitor and maintain quality standards throughout the production process, ensuring adherence to regulatory requirements. By identifying defects and inconsistencies early in the production cycle, manufacturers can take corrective actions promptly, minimizing the risk of non-compliance and ensuring product quality.

Regionally, the Asia Pacific region is expected to witness substantial growth in the manufacturing data analytics market. The region's rapid industrialization, coupled with the increasing adoption of advanced manufacturing technologies, is driving the demand for data analytics solutions. Countries like China, Japan, and India are at the forefront of this growth, with significant investments in smart manufacturing initiatives. Additionally, the increasing focus on digital transformation and the presence of a large number of manufacturing enterprises in the region further contribute to market expansion.

Component Analysis

In the manufacturing data analytics market, the component segment is broadly categorized into software, hardware, and services. The software segment encompasses various analytics platforms and tools that enable manufacturers to collect, analyze, and visualize data. These software solutions offer capabilities such as predictive analytics, machine learning, and real-time monitoring, which are crucial for optimizing manufacturing processes. The increasing demand for advanced analytics software is driving the growth of this segment, as manufacturers seek to leverage data-driven insights to enhance operational efficiency and productivity.

The hardware segment includes sensors, IoT devices, and other infrastructure required to collect and transmit data in manufacturing environments. These hardware components are essential for capturing real-time data from machines, equipment, and production lines. The proliferation of IoT devices and the need for robust data collection infrastructure are driving the growth of the hardware segment. Manufacturers are investing in advanced hardware solutions to ensure seamless data acquisition, which forms the foundation for effective data analytics.

The services segment comprises consulting, implementation, and support services offered by various vendors to help manufacturers deploy and maintain data analytics solutions. Consulting services play a crucial role in guiding manufacturers through the process of selecting the right analytics tools and developing customized solutio

The Shop Floor Data Collection (SFDC) software market is experiencing robust growth, driven by the increasing need for real-time manufacturing data visibility and process optimization across diverse industries. The market, valued at approximately $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors, including the rising adoption of Industry 4.0 technologies, the imperative to enhance operational efficiency and reduce production costs, and the growing demand for advanced analytics capabilities within manufacturing settings. The cloud-based segment is experiencing particularly rapid growth due to its scalability, accessibility, and cost-effectiveness compared to on-premises solutions. Key industry verticals driving demand include automotive, aerospace, and medical manufacturing, where precise data capture and analysis are crucial for quality control, regulatory compliance, and competitive advantage. However, the market faces challenges such as the high initial investment required for implementation, concerns regarding data security and integration complexities with existing systems, and a potential skills gap in effectively utilizing the collected data. The competitive landscape is characterized by a mix of established players and emerging technology providers. Established vendors like Plantrun, EFI, and ShopFloorConnect offer comprehensive solutions, while newer entrants are focusing on niche applications and innovative technologies such as AI-powered analytics and machine learning for predictive maintenance. Geographical expansion is also a significant trend, with North America currently holding the largest market share followed by Europe and Asia-Pacific. However, developing economies in Asia-Pacific are demonstrating significant growth potential, driven by industrialization and increasing automation adoption. The forecast period will likely see continued market consolidation, with larger players acquiring smaller firms to expand their product portfolios and geographical reach. Furthermore, the increasing integration of SFDC software with other manufacturing execution systems (MES) and enterprise resource planning (ERP) solutions will be a key driver of future market growth.

The Shop Floor Data Collection Software market is experiencing robust growth, driven by increasing automation in manufacturing, the need for real-time production insights, and the rising adoption of Industry 4.0 technologies. This market is projected to reach $5 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors, including the demand for improved operational efficiency, reduced production costs, and enhanced product quality. Businesses across various sectors, including aerospace, automotive, and medical industries, are actively deploying these solutions to optimize their manufacturing processes and gain a competitive edge. The cloud-based segment is expected to dominate the market owing to its scalability, accessibility, and cost-effectiveness. However, concerns regarding data security and integration challenges with legacy systems are acting as restraints. The market's segmentation reflects the diverse applications of shop floor data collection software. While on-premises solutions still hold a significant market share, particularly in industries with stringent data security requirements, cloud-based deployments are rapidly gaining traction, particularly among smaller and medium-sized enterprises. Geographically, North America currently leads the market, followed by Europe and Asia-Pacific. However, the Asia-Pacific region is projected to witness the fastest growth in the coming years, driven by expanding manufacturing bases and increasing government investments in digital transformation initiatives. The competitive landscape is characterized by a mix of established players and emerging startups, indicating a dynamic and innovative market. The ongoing development and adoption of advanced analytics capabilities within these software solutions promise further market expansion in the years to come.

Abstract

The presence of adequate and current statistical data in various economic sectors that are considered essential for development planning, socio-economic policy formulation and economic analysis is vital in promoting the economic development of a country. Based on this general objective, the Central Statistical Authority (CSA) has been conducting surveys of various economic activities of which the annual Large and Medium Scale Manufacturing Industries survey is one.

Manufacturing is defined here according to International Standard Industrial Classification as "the physical or chemical transformation of materials or components into new products, whether the work is performed by power-driven machines or by hand, whether it is done in a factory or in the worker's home, and whether the products are sold at wholesale or retail. The assembly of the component parts of manufactured products is also considered as manufacturing activities."

The survey questionnaire is designed to answer questions about number of establishments, number of persons engaged, wages and salaries paid by industrial group, sex, nationality and occupation, paid-up capital, gross value of production, industrial and non-industrial costs. value added, operating surplus, quantity of production and raw materials conusmed, fixed assets, investment and production capacity and etc..

The main objectives of the annual survey of Large and Medium Scale Manufacturing and Electricity Industries are to: 1.Obtain basic statistical data that are essential for policy makers, planners and researchers by major industrial group. 2.Collect basic quantitative information on employment, volume of quantitative information on employment, volume of production and raw materials, structure and performance of the country's Large and Medium Scale Manufacturing and Electricity Industries. 3.Compile statistical data which will be an input to the System of National Accounts (SNA), on Large and Medium Scale Manufacturing and Electricity establishments as a whole and by major industrial group. 4.Find out the major problems that creates stumbling block for the activities of Large and Medium Scale Manufacturing Industries.

Geographic coverage

National

Analysis unit

Establishment

Universe

The universe of the large and medium scale manufacturing survey is confined to those establishments which engaged 10 persons and above and use power-driven machines and covers both public and private industries.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The survey covers all large and medium manufacturing industries which engage 10 persons or more and use power-driven machines

Mode of data collection

Face-to-face [f2f]

Research instrument

The questinnaire contains the following sections/ items:

Section 1.1. Adress of the establishments: This section has varibles that identify the questionnaire uniquely. The variables are; Killil, Zone, Wereda, Town, Higher, Kebele, House no, Year, ISIC, Establishmnet no, Eelephone no and P.O.Box codes or numbers.

Section 1.2. Address of Head Office if Separated From Factory: In this section information about factory head office is collected (if the factory is separated from the head office). The varibles used to collect the information are; Killil, Zone, Wereda, Town, Higher, Kebele, House no, Telephone no and P.O.Box.

Section 2. Basic Information About The Establishment: This section has questions related to basic information about the establishment.

Section 3.1. Number of Persons Engaged: This section has variables (questions) that used to collect establishment's employees number by employees occupation.

Section 3.2. Wages and Salaries and Other Employee Benefits Paid: This section has variables related to wages and other employees benefits by employee occupation.

Section 3.3. Number of Permanent Employees by Basic Salary Group: This section has variables related to salary groups by sex of employees

Section 4.1. Products and By-products: This section has questions related to product produced, produced quantity and sales.

Section 4.2. Service and Other Receipts: Contains questions related to income from different source other than selling the products.

Section 5. Value of Stocks: Contains questions that related to information about materials in the stock.

Section 6.1. Cost and Quantity of Raw Materials, Parts and Containers Used: This section has questions related to principal raw materials, raw material type, quantity, value and source (local or imported).

Section 6.2. Other Industrial Costs: This sections has questions related to other industrial costs including cost of energy and other expenses.

Section 6.3. Other Non-industrial Expenses: Contains questions related to non-industrial expenses like license fee, advertising, stationary, etc.

Section 6.4. Taxes Paid: This section has questions related to taxes like indirect tax and income tax.

Section 7.1. Type and Value of Fixed Assets: This section has questions related to fixed assets of the establishment.

Section 7.2. Annual Investment by Type and Source: This section has questions related to investment on fixed assets and working capitals.

Section 8.1. Annual Production at Full Capacity: This section has questions about quantity and value of products if the establishment uses its full capacity.

Section 8.2. Estimated Value and Quantity of Raw Materials Needed, at Full Capacity: This section has questions about the estimate of quantity and value of raw materials that needed to function at full capacity.

Section 8.3. The three major problems that prevented the establishment from operating at full capacity.

Section 8.4. The three major problems that are facing the establishment at present.

Cleaning operations

A) Editing, Coding and Verification A number of quality control steps were taken to ensure the quality of data. The first step taken in this direction was, to revise the questionnaire, to make it easier for internal consistency checking or editing, both at field and office level. Furthermore, based on this revised questionnaire, revised instruction manual with field editing procedures were prepared in Amharic for both enumerators and supervisors (field editors). Using this manual, some editing and coding were carried out by field editors during the data collection stage. After the majority of the completed questionnaires were brought back to head office, final editing, coding and verification were performed by 10 statisticans and statistical technicians and 11 editors. Finally, the edited and coded questionnaires were checked and verified by other senior professionals.

B) Data Entry, Cleaning and Tabulation The data were entered and verified on personal computers IMPS (Integrated Microcomputer Processing System) Software. Twelve CSA data entry staff and one data cleaner participated in this activity for fifteen days with close supervision of the activities by two professionals. Then, the data entered were cleaned using personal computers in combination with manual cleaning for some serious errors. Finally, the tabulation of the results was processed using the IMPS (Integrated Microcomputer Processing System) software by one programmer with technical assistance from Industry, Trade and Services Statistics Department staff.

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Big Data In Manufacturing Market Size 2025-2029

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

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

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

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

How is this Big Data In Manufacturing Industry segmented?

The big data in manufacturing industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Services
  Solutions


Deployment

  On-premises
  Cloud-based
  Hybrid


Application

  Operational analytics
  Production management
  Customer analytics
  Supply chain management
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Type Insights

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

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

The MilKey project aims at assessing the environmental, economic, and social sustainability of European dairy production systems, and at identifying ‘win-win’ farming practices for sustainable and greenhouse gas (GHG) optimised milk production. These data collection template were prepared to guide stakeholders wishing to conduct a sustainability assessment of dairy production systems. This template covers all the necessary data to assess the environmental, economic, and social sustainability dimensions of commercial dairy farms. Data requirements gathered in this template were deduced from the list of sustainability indicators presented in the DEXi-Dairy indicator handbook. The template is composed of 5 parts, i.e., Parts I-II-III for the environmental assessment, Part IV for the economic assessment, and Part V for the social assessment. The number of files to fill out depends on the number and nature of additional farming enterprises present on case study dairy farms. 1) Part I concerns the general information that must be collected on all commercial farms. 2) Part II focuses specifically on the dairy enterprise and must thus be completed for all commercial farms. 3) Part III records information about a potential beef enterprise and must thus be filled out for commercial farms that have an additional beef enterprise. 4) Part IV gathers all the economic data and must be filled out for all commercial case study farms. 5) Part V gathers all the social data and must be filled out for all commercial case study farms. Please refer to the guide for the collection of farm environmental and economic data for the detailed description of all variables included in Parts I-II-III-IV.

Industrial Screw Driving Datasets

Overview

This repository contains a collection of real-world industrial screw driving datasets, designed to support research in manufacturing process monitoring, anomaly detection, and quality control. Each dataset represents different aspects and challenges of automated screw driving operations, with a focus on natural process variations and degradation patterns.

Scenario name Number of work pieces Repetitions (screw cylces) per workpiece Individual screws per workpiece Observations Unique classes Purpose

s01_thread-degradation 100 25 2 5.000 1 Investigation of thread degradation through repeated fastening

s02_surface-friction 250 25 2 12.500 8 Surface friction effects on screw driving operations

s03_error-collection-1

1 2

20

s04_error-collection-2 2.500 1 2 5.000 25

s05_injection-molding-manipulations-upper-workpiece 1.200 1 2 2.400 44 Investigation of changes in the injection molding process of the workpieces

Dataset Collection

The datasets were collected from operational industrial environments, specifically from automated screw driving stations used in manufacturing. Each scenario investigates specific mechanical phenomena that can occur during industrial screw driving operations:

Currently Available Datasets:

1. s01_thread-degradation

Focus: Investigation of thread degradation through repeated fastening

Samples: 5,000 screw operations (4,089 normal, 911 faulty)

Features: Natural degradation patterns, no artificial error induction

Equipment: Delta PT 40x12 screws, thermoplastic components

Process: 25 cycles per location, two locations per workpiece

First published in: HICSS 2024 (West & Deuse, 2024)

1. s02_surface-friction

Focus: Surface friction effects on screw driving operations

Samples: 12,500 screw operations (9,512 normal, 2,988 faulty)

Features: Eight distinct surface conditions (baseline to mechanical damage)

Equipment: Delta PT 40x12 screws, thermoplastic components, surface treatment materials

Process: 25 cycles per location, two locations per workpiece

First published in: CIE51 2024 (West & Deuse, 2024)

1. s05_injection-molding-manipulations-upper-workpiece

Manipulations of the injection molding process with no changes during tightening

Samples: 2,400 screw operations (2,397 normal, 3 faulty)

Features: 44 classes in five distinct groups:

Mold temperature

Glass fiber content

Recyclate content

Switching point

Injection velocity

Equipment: Delta PT 40x12 screws, thermoplastic components

Unpublished, work in progress

Upcoming Datasets:

1. s03_screw-error-collection-1 (recorded but unpublished)

Focus: Varius manipulations of the screw driving process

Features: More than 20 different errors recorded

First published in: Publication planned

Status: In preparation

1. s04_screw-error-collection-2 (recorded but unpublished)

Focus: Varius manipulations of the screw driving process

Features: 25 distinct errors recorded over the course of a week

First published in: Publication planned

Status: In preparation

1. s06_injection-molding-manipulations-lower-workpiece (recorded but unpublished)

Manipulations of the injection molding process with no changes during tightening

Additional scenarios may be added to this collection as they become available.

Data Format

Each dataset follows a standardized structure:

JSON files containing individual screw operation data

CSV files with operation metadata and labels

Comprehensive documentation in README files

Example code for data loading and processing is available in the companion library PyScrew

Research Applications

These datasets are suitable for various research purposes:

Machine learning model development and validation

Process monitoring and control systems

Quality assurance methodology development

Manufacturing analytics research

Anomaly detection algorithm benchmarking

Usage Notes

All datasets include both normal operations and process anomalies

Complete time series data for torque, angle, and additional parameters available

Detailed documentation of experimental conditions and setup

Data collection procedures and equipment specifications available

Access and Citation

These datasets are provided under an open-access license to support research and development in manufacturing analytics. When using any of these datasets, please cite the corresponding publication as detailed in each dataset's README file.

Related Tools

We recommend using our library PyScrew to load and prepare the data. However, the the datasets can be processed using standard JSON and CSV processing libraries. Common data analysis and machine learning frameworks may be used for the analysis. The .tar file provided all information required for each scenario.

Contact and Support

For questions, issues, or collaboration interests regarding these datasets, either:

Open an issue in our GitHub repository PyScrew

Contact us directly via email

Acknowledgments

These datasets were collected and prepared by:

RIF Institute for Research and Transfer e.V.

University of Kassel, Institute of Material Engineering

• University of Kassel, Institute of Materials Engineering (IfW)

Technical University Dortmund, Institute for Production Systems

The preparation and provision of the research was supported by:

German Ministry of Education and Research (BMBF)

European Union's "NextGenerationEU" program

The research is part of this funding program

More information regarding the research project is available here

Change Log

Version Date Features

v1.1.3 18.02.2025

• Upload of s05 with injection molding manipulations in 44 classes

v1.1.2 12.02.2025

• Change to default names label.csv and README.md in all scenarios

v1.1.1 12.02.2025

• Reupload of both s01 and s02 as zip (smaller size) and tar (faster extraction) files

• Change to the data structure (now organized as subdirectories per class in json/)

v1.1.0 30.01.2025

• Initial uplload of the second scenario s02_surface-friction

v1.0.0 24.01.2025

• Initial upload of the first scenario s01_thread-degradation

The manufacturing analytics market is segmented into a number of different product categories, including:

Data collection and storage: This category includes technologies that collect and store manufacturing data. Data analysis and reporting: This category includes technologies that analyze manufacturing data and generate reports. Predictive analytics: This category includes technologies that use manufacturing data to predict future events. Prescriptive analytics: This category includes technologies that use manufacturing data to prescribe actions that can improve performance.

This data set is captured from a robot workcell that is performing activities representative of several manufacturing operations. The workcell contains two, 6-degree-of-freedom robot manipulators where one robot is performing material handling operations (e.g., transport parts into and out of a specific work space) while the other robot is performing a simulated precision operation (e.g., the robot touching the center of a part with a tool tip that leaves a mark on the part). This precision operation is intended to represent a precise manufacturing operation (e.g., welding, machining). The goal of this data set is to provide robot level and process level measurements of the workcell operating in nominal parameters. There are no known equipment or process degradations in the workcell. The material handling robot will perform pick and place operations, including moving simulated parts from an input area to in-process work fixtures. Once parts are placed in/on the work fixtures, the second robot will interact with the part in a specified precise manner. In this specific instance, the second robot has a pen mounted to its tool flange and is drawing the NIST logo on a surface of the part. When the precision operation is completed, the material handling robot will then move the completed part to an output. This suite of data includes process data and performance data, including timestamps. Timestamps are recorded at predefined state changes and events on the PLC and robot controllers, respectively. Each robot controller and the PLC have their own internal clocks and, due to hardware limitations, the timestamps recorded on each device are relative to their own internal clocks. All timestamp data collected on the PLC is available for real-time calculations and is recorded. The timestamps collected on the robots are only available as recorded data for post-processing and analysis. The timestamps collected on the PLC correspond to 14 part state changes throughout the processing of a part. Timestamps are recorded when PLC-monitored triggers are activated by internal processing (PLC trigger origin) or after the PLC receives an input from a robot controller (robot trigger origin). Records generated from PLC-originated triggers include parts entering the work cell, assignment of robot tasks, and parts leaving the work cell. PLC-originating triggers are activated by either internal algorithms or sensors which are monitored directly in the PLC Inputs/Outputs (I/O). Records generated from a robot-originated trigger include when a robot begins operating on a part, when the task operation is complete, and when the robot has physically cleared the fixture area and is ready for a new task assignment. Robot-originating triggers are activated by PLC I/O. Process data collected in the workcell are the variable pieces of process information. This includes the input location (single option in the initial configuration presented in this paper), the output location (single option in the initial configuration presented in this paper), the work fixture location, the part number counted from startup, and the part type (task number for drawing robot). Additional information on the context of the workcell operations and the captured data can be found in the attached files, which includes a README.txt, along with several noted publications. Disclaimer: Certain commercial entities, equipment, or materials may be identified or referenced in this data, or its supporting materials, in order to illustrate a point or concept. Such identification or reference is not intended to imply recommendation or endorsement by NIST; nor does it imply that the entities, materials, equipment or data are necessarily the best available for the purpose. The user assumes any and all risk arising from use of this dataset.

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 manufacturing data analytics market size was valued at USD 12.01 billion in 2023, and it is expected to expand at a compound annual growth rate (CAGR) of 19.8% from 2023 to 2033. The increasing need to improve operational efficiency, optimize supply chains, and enhance product quality by leveraging data analytics capabilities is driving the market growth. The adoption of Industry 4.0 technologies, such as the Internet of Things (IoT) and cloud computing, is also contributing to the market's expansion as it enables real-time data collection and analysis. The market is segmented into various types, including predictive maintenance, inventory management, supply chain optimization, and others. Among these types, predictive maintenance holds a significant market share owing to its ability to reduce downtime, improve equipment reliability, and optimize maintenance schedules. Key industry segments include semiconductor, chemical, energy production, and biopharmaceutical industries, which leverage data analytics to enhance operational efficiency, reduce costs, and optimize production processes. Geographically, North America holds a large market share due to the presence of a strong manufacturing industry and early adoption of advanced technologies.

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).

Predictive Maintenance For Manufacturing Industry Market size was valued at USD 8.26 Billion in 2024 and is projected to reach USD 47.64 Billion by 2032, growing at a CAGR of 24.49% from 2026 to 2032.

Key Market Drivers: Advancements in IoT and Sensor Technology: IoT and sensor technology have transformed data collection and analysis in manufacturing. These technologies provide real-time monitoring of equipment health, including vital factors like temperature, vibration, and pressure. The capacity to collect continuous, high-resolution data enables more accurate predictive maintenance models, which reduces unplanned downtime and optimizes the maintenance schedule. Increasing Adoption of Big Data and Analytics: Manufacturers may now evaluate large amounts of data generated by their machines thanks to the growing adoption of big data analytics. Advanced analytics tools and machine learning algorithms can detect patterns and predict equipment failures with great accuracy.

Market Outlook: The Production Data Acquisition System market is projected to witness robust growth, with a CAGR of XX% over the forecast period of 2025-2033. Valued at XXX million in 2025, the market is driven by the increasing adoption of automation in manufacturing industries to enhance productivity, reduce costs, and improve quality control. Other key drivers include the rise of predictive maintenance technologies, the need for real-time data monitoring and analysis, and the increasing adoption of IoT-enabled devices. Key Trends and Opportunities: The market is segmented into various application areas, including automobile, food and beverage, chemical, pharmacy, and others. The cloud deployment segment is expected to gain significant traction during the forecast period as cloud platforms provide scalability, flexibility, and cost-effective solutions. Key trends in the market include the integration of data analytics, the use of artificial intelligence (AI) for predictive analysis, and the emergence of Advanced Production Data Acquisition Systems (APDAs) that offer advanced data management and visualization capabilities. The market is dominated by established players such as Fastec, Böhme & Weihs, and ProLeiT, but new entrants are emerging, providing innovative solutions and driving market competition.

Data is collected from the Manufacturing Lab using the MTConnect standard. That data is aggregated and published internally and externally of NIST via web services. Three channels of data dissemination are available or becoming available from the SMS Test Bed: (1) a volatile data stream using an MTConnect agent, (2) a query-able data repository using the NIST Material Data Curation System (MDCS), and (3) pre-compiled data packages that include a collection of CAx Lab data and associated Manufacturing Lab data.

The Industrial Internet of Things (IIoT) Data Collection and Device Management Platform market is experiencing robust growth, driven by the increasing adoption of smart manufacturing, predictive maintenance, and remote asset monitoring across various industries. The market's expansion is fueled by the convergence of several factors: a proliferation of connected devices generating massive amounts of data, the need for real-time insights to optimize operations, and the rising demand for enhanced security and scalability in industrial environments. We estimate the market size in 2025 to be approximately $15 billion, based on typical growth trajectories in comparable technology sectors and factoring in the accelerating digital transformation within manufacturing and other industrial sectors. A compound annual growth rate (CAGR) of 15% is projected from 2025 to 2033, indicating significant market expansion in the coming years. Key segments driving growth include cloud-based platforms, due to their scalability and accessibility, and applications in manufacturing, energy, and transportation, reflecting the high concentration of connected devices and data generation in these sectors. Challenges include ensuring data security and interoperability across diverse industrial systems, as well as addressing the skills gap in managing and analyzing the vast amounts of data produced. The competitive landscape is highly dynamic, with established technology companies and specialized IIoT solution providers vying for market share. Future growth will hinge on innovation in areas like AI-driven analytics for predictive maintenance, edge computing to reduce latency, and cybersecurity solutions tailored for the industrial environment. The North American region currently holds a substantial market share, driven by early adoption of IIoT technologies and strong investments in digital infrastructure. However, the Asia-Pacific region, particularly China and India, is projected to experience the fastest growth in the coming years, owing to rapid industrialization and government initiatives promoting digital transformation. Europe is also a significant market, with strong presence of industrial automation and a growing emphasis on smart factories. Success in this market necessitates a strong focus on delivering secure, reliable, and scalable solutions tailored to the specific needs of different industries and geographical regions. Companies are increasingly focusing on developing comprehensive platforms that seamlessly integrate data collection, device management, analytics, and security features to cater to this evolving market demand.

Global Automatic Identification and Data Capture Market size was $60.76 Billion in 2023 and is predicted to $168.50 Billion by 2032 at a CAGR of 12%.

Data Acquisition (DAQ) System Market size was valued at USD 1.92 Billion in 2024 and is projected to reach USD 2.86 Billion by 2031, growing at a CAGR of 5.10% from 2024 to 2031.

Global Data Acquisition (DAQ) System Market Drivers

The market drivers for the Data Acquisition (DAQ) System Market can be influenced by various factors. These may include:

Growing Need for Industrial Automation: The need for data collection systems is being driven by the growing trend of automation in a number of industries, including manufacturing, automotive, aerospace, and healthcare. These systems are essential for gathering and evaluating data from sensors and other devices in order to enhance decision-making, quality assurance, and operational effectiveness.

Improvements in Internet of Things and Big Data Analytics: The demand for effective data acquisition solutions is being driven by the widespread use of Internet of Things (IoT) devices and the rapidly increasing volume of data they create. In contexts powered by the Internet of Things, DAQ systems provide real-time data collecting and analysis, facilitating predictive maintenance, asset optimization, and process optimization.

Growing Adoption of Wireless Data collection Systems: The need for wireless data collection systems is being driven by the uptake of wireless communication technologies like Bluetooth, Wi-Fi, and Zigbee. Compared to conventional wired solutions, these systems are more flexible, scalable, and affordable—especially in applications where wired communication is difficult or impracticable.

Growing Priority for Industry 4.0 and Intelligent Manufacturing: The integration of modern technologies like robotics, machine learning, and artificial intelligence into industrial processes is being driven by the concept of smart manufacturing and Industry 4.0. Real-time monitoring, control, and optimization of industrial processes are made possible by data acquisition systems, which operate as the foundation for gathering, processing, and transmitting data from linked devices and equipment.

Extending Research and Development (R&D) Applications: Data acquisition systems are extensively employed in academic institutions and research laboratories for a variety of R&D projects in fields including engineering, physics, chemistry, and biology. Stronger emphasis on innovation, product development, and scientific research is fueling the need for high-performance DAQ systems that can reliably and precisely capture and analyze large, complex data sets.

Strict Regulations for Safety and Compliance: There are strict regulations for safety, quality, and compliance in a number of industries, including food and beverage, pharmaceutical, and healthcare. Due to its ability to provide precise data monitoring, recording, and reporting for compliance needs, data acquisition systems are essential in guaranteeing compliance with these standards.

Growing Need for Control and Monitoring in Real-Time: Adoption of data collection solutions is being driven by the requirement for real-time control and monitoring of essential processes and systems in a variety of industries. DAQ systems offer the infrastructure required for real-time data collecting, analysis, and reaction, whether it is for monitoring environmental conditions, managing production parameters, or guaranteeing equipment reliability.

Production Monitoring Market Outlook

The global production monitoring market size is witnessing substantial growth, with an estimated value of USD 4.5 billion in 2023, projected to reach USD 9.8 billion by 2032, growing at a CAGR of approximately 8.5% during the forecast period. This growth is fueled by the increasing demand for real-time data analytics in production environments, aiming to enhance operational efficiency and productivity. As industries across the globe continue to embrace digital transformation, the need for robust production monitoring solutions has become paramount, driving the market’s upward trajectory.

One of the primary growth factors behind the surge in the production monitoring market is the escalating need for operational efficiency across various industry verticals. With the advent of Industry 4.0, there is a significant push towards automation and data exchange in manufacturing technologies. This transformation is largely driven by the need to optimize production processes, reduce downtime, and improve resource allocation. Production monitoring systems enable businesses to achieve these objectives by providing real-time insights into manufacturing workflows, equipment status, and potential bottlenecks, thereby facilitating informed decision-making and strategic planning.

Another pivotal driver for the production monitoring market is the growing emphasis on quality control and compliance across industries. As regulatory standards become more stringent, particularly in sectors like automotive, aerospace, and food and beverages, companies are compelled to adopt advanced monitoring solutions to ensure adherence to quality benchmarks. Production monitoring tools play a crucial role in maintaining product quality by tracking production parameters and identifying deviations in real-time. This capability not only helps in minimizing the risk of non-compliance but also enhances customer satisfaction by ensuring the delivery of high-quality products.

Moreover, the integration of the Internet of Things (IoT) and artificial intelligence (AI) into production monitoring systems is significantly propelling market growth. IoT-enabled devices and sensors provide a seamless flow of data across production lines, offering insights that were previously inaccessible. When combined with AI algorithms, these systems can predict equipment failures, suggest maintenance schedules, and optimize production cycles, leading to cost reductions and increased productivity. This technological synergy is expected to further accelerate the adoption of production monitoring solutions across various industrial domains.

Regionally, the Asia Pacific region is expected to dominate the production monitoring market during the forecast period. This can be attributed to the rapid industrialization in countries like China and India, where there is a growing need for efficient manufacturing processes to meet the increasing consumer demand. Additionally, government initiatives promoting smart manufacturing and investments in infrastructure development are likely to bolster market growth in this region. Other regions such as North America and Europe are also witnessing significant adoption of production monitoring systems, driven by technological advancements and established industrial bases, albeit at a slower growth rate compared to the Asia Pacific.

Component Analysis

In the production monitoring market, the component segment is primarily divided into software, hardware, and services, each playing a distinct role in the ecosystem. The software component is a critical aspect that underpins the entire monitoring framework. It includes the application platforms that collect, analyze, and report data from various production processes. Advanced analytics, predictive maintenance, and real-time monitoring software are seeing increased adoption as they enable businesses to harness the power of big data to drive operational efficiencies. The robust functionality offered by software solutions makes them indispensable in modern production environments, contributing significantly to the overall market revenue.

On the hardware front, the market comprises various sensors, devices, and equipment necessary for gathering data from production lines. This includes a wide range of components such as controllers, actuators, and data acquisition systems that are integral to facilitating the smooth operation of production monitoring systems. As manufacturing processes become more complex and automated, the demand for sophisticated hardware solutions is expected to grow. The hardware segment, while cruci

Artificial Intelligence in Manufacturing Market Outlook

The global artificial intelligence (AI) in manufacturing market size was estimated at USD 3.8 billion in 2023 and is projected to reach USD 30.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 25.3% during the forecast period. This impressive growth is driven by the increasing adoption of AI technologies across various manufacturing processes, which aim to enhance efficiency, reduce operational costs, and improve product quality.

The primary growth factor for the AI in manufacturing market is the increasing need for automation to cope with complex manufacturing processes. Industries are progressively recognizing the potential of AI to streamline operations, optimize resource utilization, and facilitate real-time decision-making. By integrating AI, manufacturers can address challenges such as labor shortages, fluctuating demand, and stringent quality standards. Moreover, the ability of AI to predict equipment failures and maintenance requirements before they occur significantly enhances the operational lifespan of machinery, thus reducing downtime and maintenance costs.

Another significant growth driver is the rising adoption of smart manufacturing practices. The concept of Industry 4.0, which envisions the smart factory where cyber-physical systems monitor physical processes, has been instrumental in promoting the use of AI. Advanced AI technologies, such as machine learning and computer vision, are being employed to achieve higher levels of automation and data exchange, resulting in improved productivity and operational efficiency. Additionally, the integration of AI with the Internet of Things (IoT) further amplifies the benefits by enabling real-time data collection, analysis, and actionable insights.

Investment in AI research and development by key players is also propelling market growth. Companies are increasingly allocating budgets to develop AI-powered solutions tailored for the manufacturing sector. These solutions range from predictive maintenance systems and quality control algorithms to autonomous robots and production planning tools. The ongoing innovation and continuous improvement of AI technologies ensure that manufacturers have access to cutting-edge tools that can transform their production processes.

Manufacturing Predictive Analytics is becoming an integral part of the AI in manufacturing landscape. By leveraging vast amounts of data collected from various manufacturing processes, predictive analytics enables manufacturers to foresee potential issues and optimize operations proactively. This approach not only helps in anticipating equipment failures but also in predicting demand fluctuations and optimizing inventory levels. The integration of predictive analytics with AI technologies enhances decision-making capabilities, allowing manufacturers to respond swiftly to changing market conditions and customer demands. As a result, predictive analytics is playing a crucial role in driving efficiency and competitiveness in the manufacturing sector.

In terms of regional outlook, North America is expected to dominate the AI in manufacturing market, owing to the early adoption of advanced technologies and the presence of major technology vendors. The U.S., in particular, is a key contributor to market growth due to its robust manufacturing sector and extensive R&D activities. Europe is also anticipated to witness significant growth, driven by the increasing focus on smart manufacturing and government initiatives to support digital transformation in industries. Meanwhile, the Asia Pacific region is poised to register the highest CAGR, fueled by rapid industrialization, the expansion of manufacturing activities, and supportive government policies in countries like China and India.

Component Analysis

The AI in manufacturing market is segmented by component into hardware, software, and services. The hardware segment includes AI chips, sensors, and other essential devices that enable the functioning of AI systems. The increasing demand for powerful and efficient hardware solutions, capable of handling complex algorithms and large volumes of data, is a significant factor driving the growth of this segment. Additionally, continuous advancements in AI-specific hardware, such as GPUs and TPUs, are expected to further boost the adoption of AI in manufacturing.

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