Graph and download economic data for Business Applications: Manufacturing in the United States (BABANAICSMNFNSAUS) from Jul 2004 to Aug 2025 about business applications, business, manufacturing, and USA.

Graph and download economic data for High-Propensity Business Applications: Manufacturing in the United States (BAHBANAICSMNFNSAUS) from Jul 2004 to Aug 2025 about high-propensity, business applications, business, manufacturing, and USA.

The Intelligent Manufacturing Dataset for Predictive Optimization is a dataset designed for research in smart manufacturing, AI-driven process optimization, and predictive maintenance. It simulates real-time sensor data from industrial machines, incorporating 6G network slicing for enhanced communication and resource allocation.

Key Features: ✔ Industrial IoT Sensor Data – Temperature, vibration, power consumption, etc. ✔ 6G Network Performance Metrics – Latency, packet loss, and communication efficiency. ✔ Production Efficiency Indicators – Defect rate, predictive maintenance score, error rate. ✔ Target Column (Efficiency_Status) – Classifies manufacturing efficiency as High, Medium, or Low based on performance metrics.

Applications: 🔹 AI-based predictive maintenance 🔹 Resource allocation optimization in 6G-enabled smart factories 🔹 Real-time anomaly detection in industrial production 🔹 Deep learning model training for intelligent manufacturing systems

This dataset serves as a benchmark for AI and deep learning applications in Industry 4.0 and 6G network-integrated manufacturing systems.

Graph and download economic data for Business Applications with Planned Wages: Manufacturing in the United States (BAWBANAICSMNFSAUS) from Jul 2004 to Aug 2025 about business applications, wages, business, manufacturing, and USA.

In 2019, around half of the industrial manufacturers surveyed were already utilizing Internet of Things (IoT)-related solutions in logistics, while some ** percent were planning to do so in the following two years. The second common application of IoT in manufacturing was in supply chain management in 2019.

Fuzzy based Smart Manufacturing Dataset contains 1,000 samples of sensor readings and process parameters collected for optimizing industrial process control using a Fuzzy-PID Controller. This dataset is designed for research and development in smart manufacturing, adaptive control, and Industry 4.0 applications.

Features: Temperature_C (°C): Temperature readings from industrial sensors. Pressure_Bar (Bar): Pressure levels in the manufacturing process. Speed_RPM (RPM): Rotational speed of motors or actuators. Error: Deviation between setpoint and measured value. Delta_Error: Rate of change of the error signal. Load_Variation: Load factor indicating dynamic variations (0.8 - 1.2). Ambient_Temp_C (°C): External environmental temperature. Energy_Consumption_W (W): Power usage in watts. Use Cases: Tuning and optimizing Fuzzy-PID controllers Energy-efficient control system design Fault detection and predictive maintenance Industrial automation and adaptive process control This dataset is particularly useful for machine learning, optimization algorithms, and MATLAB/Python simulations in smart manufacturing environments. 🚀

This dataset contains all current and active business licenses issued by the Department of Business Affairs and Consumer Protection. This dataset contains a large number of records /rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search.

Data fields requiring description are detailed below.

APPLICATION TYPE: 'ISSUE' is the record associated with the initial license application. 'RENEW' is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. 'C_LOC' is a change of location record. It means the business moved. 'C_CAPA' is a change of capacity record. Only a few license types my file this type of application. 'C_EXPA' only applies to businesses that have liquor licenses. It means the business location expanded.

LICENSE STATUS: 'AAI' means the license was issued.

Business license owners may be accessed at: http://data.cityofchicago.org/Community-Economic-Development/Business-Owners/ezma-pppn To identify the owner of a business, you will need the account number or legal name.

Data Owner: Business Affairs and Consumer Protection

Time Period: Current

Frequency: Data is updated daily

Business licenses issued by the Department of Business Affairs and Consumer Protection in the City of Chicago from 2006 to the present. This dataset contains a large number of records/rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search.

Data fields requiring description are detailed below.

APPLICATION TYPE: ‘ISSUE’ is the record associated with the initial license application. ‘RENEW’ is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. ‘C_LOC’ is a change of location record. It means the business moved. ‘C_CAPA’ is a change of capacity record. Only a few license types may file this type of application. ‘C_EXPA’ only applies to businesses that have liquor licenses. It means the business location expanded. 'C_SBA' is a change of business activity record. It means that a new business activity was added or an existing business activity was marked as expired.

LICENSE STATUS: ‘AAI’ means the license was issued. ‘AAC’ means the license was cancelled during its term. ‘REV’ means the license was revoked. 'REA' means the license revocation has been appealed.

LICENSE STATUS CHANGE DATE: This date corresponds to the date a license was cancelled (AAC), revoked (REV) or appealed (REA).

Business License Owner information may be accessed at: https://data.cityofchicago.org/dataset/Business-Owners/ezma-pppn. To identify the owner of a business, you will need the account number or legal name, which may be obtained from this Business Licenses dataset.

Data Owner: Business Affairs and Consumer Protection. Time Period: January 1, 2006 to present. Frequency: Data is updated daily.

Graph and download economic data for Average Duration (in Quarters) from Business Application to Formation Within Four Quarters: Manufacturing in the United States (BFDUR4QNAICSMNFNSAUS) from Jul 2004 to Dec 2021 about duration, business applications, average, business, manufacturing, and USA.

Manufacturing Data Report

I. Introduction

This report presents an analysis of a manufacturing dataset, which simulates real-world data collected from a manufacturing process. The dataset is designed to explore the relationships between various process parameters and product quality. It contains both feature variables that represent process conditions and a target variable that represents the quality rating of the manufactured items.

II. Dataset Description

The manufacturing dataset consists of the following columns:

1. Temperature (°C): This column represents the temperature during the manufacturing process, measured in degrees Celsius. Temperature plays a critical role in many manufacturing processes, influencing material properties and product quality.

2. Pressure (kPa): The pressure applied during the manufacturing process, measured in kilopascals (kPa). Pressure can affect the material transformation and the overall outcome of the manufacturing process.

3. Temperature x Pressure: This feature is an interaction term between temperature and pressure, which captures the combined effect of these two process parameters.

4. Material Fusion Metric: A derived metric calculated as the sum of the square of temperature and the cube of pressure. It represents a material fusion-related measurement during the manufacturing process.

5. Material Transformation Metric: Another derived metric calculated as the cube of temperature minus the square of pressure. It provides insight into material transformation dynamics.

6. Quality Rating: The target variable, 'Quality Rating,' represents the overall quality rating of the produced items. Quality is a crucial aspect of manufacturing, and this rating serves as a measure of the final product's quality.

III. Data Analysis

3.1. Polynomial Relationships

In this dataset, we have explored polynomial relationships between the features and the 'Quality Rating.' Polynomial regression was employed to assess the impact of different polynomial degrees (ranging from 1 to 9) on the predictive performance. The results revealed optimal polynomial degrees for each feature, highlighting the complexity of the relationships.

3.2. Data Visualization

To visualize these relationships, we created graphs for each feature, showing how the Mean Squared Error (MSE) varies with polynomial degree. These visualizations provide insights into the choice of polynomial degree that best fits each feature.

IV. Potential Applications

The manufacturing dataset can find applications in various fields, including:

1. Manufacturing Process Optimization: By understanding the relationships between process parameters (temperature, pressure) and product quality, manufacturers can optimize their processes for higher-quality output.

2. Quality Control: The 'Quality Rating' can serve as a quality control metric, helping manufacturers identify potential issues in real-time and take corrective actions.

3. Predictive Modeling: Machine learning models can be trained using this data to predict product quality based on process conditions, enabling proactive quality assurance.

V. Conclusion

In conclusion, the manufacturing dataset provides valuable insights into the complex relationships between process parameters and product quality. It serves as a valuable resource for process optimization, quality control, and predictive modeling in the manufacturing industry.

Enterprise Mobile Application Development Market is Segmented by Platform Type (Business Applications, Consumer-Facing Applications, and More), Deployment Model (Cloud and On-Premise), Enterprise Size (Small and Medium Enterprises and Large Enterprises), Industry Vertical (BFSI, Healthcare, Manufacturing, Retail and E-Commerce, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

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The artificial intelligence (AI) market share in manufacturing industry is expected to increase by USD 7.87 billion from 2020 to 2025, and the market’s growth momentum will accelerate at a CAGR of 28%.

This artificial intelligence (AI) market in manufacturing industry research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers artificial intelligence (AI) market in manufacturing industry segmentations by application (predictive maintenance and machine inspection, production planning, quality control, and others) and geography (APAC, North America, Europe, South America, and MEA). The artificial intelligence (AI) market in manufacturing industry report also offers information on several market vendors, including Alphabet inc., General Electric Co., intel Corp., Landing ai, Microsoft Corp., Oracle Corp., SAP SE, Siemens AG, international Business Machines Corp., and Amazon Web Services inc. among others.

What will the Artificial Intelligence (AI) Market Size in Manufacturing Industry be During the Forecast Period?

Download Report Sample to Unlock the Artificial Intelligence (AI) Market Size in Manufacturing Industry for the Forecast Period and Other Important Statistics

Significantly, many companies are already investing in the France artificial intelligence (AI) in manufacturing and supply chain activities.

Artificial Intelligence (AI) Market in Manufacturing Industry: Key Drivers, Trends, and Challenges

The demand for automation to improve productivity is notably driving the artificial intelligence (AI) market growth in manufacturing industry, although factors such as integration challenges may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the artificial intelligence (AI) industry in manufacturing. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

This artificial intelligence (AI) market in manufacturing industry analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2021-2025.

Who are the Major Artificial Intelligence (AI) Market Vendors in Manufacturing Industry?

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Alphabet inc.
General Electric Co.
intel Corp.
Landing ai
Microsoft Corp.
Oracle Corp.
SAP SE
Siemens AG
international Business Machines Corp.
Amazon Web Services inc.

This statistical study of the artificial intelligence (AI) market in manufacturing industry encompasses successful business strategies deployed by the key vendors. The artificial intelligence (AI) market in manufacturing industry is fragmented and the vendors are deploying organic and inorganic growth strategies to compete in the market.

Product Insights and News

Alphabet Inc. - The company offers Artificial Intelligence (AI) to products and to new domains, and developing tools to ensure that everyone can access AI.

To make the most of the opportunities and recover from post COVID-19 impact, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

The artificial intelligence (AI) market in manufacturing industry forecast report offers in-depth insights into key vendor profiles. The profiles include information on the production, sustainability, and prospects of the leading companies.

Artificial Intelligence (AI) Market in Manufacturing Industry Value Chain Analysis

Our report provides extensive information on the value chain analysis for the artificial intelligence (AI) market in manufacturing industry, which vendors can leverage to gain a competitive advantage during the forecast period. The end-to-end understanding of the value chain is essential in profit margin optimization and evaluation of business strategies. The data available in our value chain analysis segment can help vendors drive costs and enhance customer services during the forecast period.

Which are the Key Regions for Artificial Intelligence (AI) Market in Manufacturing industry?

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38% of the market’s growth will originate from APAC during the forecast period. China and Japan are the key markets for the artificial intelligence (AI) market in manufacturing industry in APAC. Market growth in this region will be faster than the growth of the market in other regions. This market research report entails detailed informati

The SaaS Enterprise Applications market is booming, projected to reach [insert final year market size] by 2033 with a 15% CAGR. Explore key drivers, trends, and regional insights in this comprehensive market analysis covering leading players like Salesforce, Microsoft, and SAP across sectors including BFSI, healthcare, and manufacturing. Discover the impact of cloud-based solutions and the future of enterprise software.

The Enterprise Mobility in Manufacturing Market is Segmented by Device Type (Smartphones, Tablets, and More), Solution (Mobile Device Management (MDM), and More), Deployment (On-Premise and Cloud), Organization Size (Large Enterprises and Small and Medium Enterprises (SMEs)), Manufacturing Vertical (Discrete Manufacturing and Process Manufacturing), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

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Factory Automation Platform As A Service Market Size 2025-2029

The factory automation platform as a service market size is forecast to increase by USD 56.67 billion at a CAGR of 46.8% between 2024 and 2029.

The market is experiencing significant growth due to several key trends. The convergence of Information Technology (IT) and Operational Technology (OT) is a major driver, enabling businesses to streamline their operations and improve efficiency. Another trend is the increasing focus on edge computing, which allows data processing to occur closer to the source, reducing latency and improving response times. Data privacy and security concerns are also fueling the adoption of FaaS solutions. With the proliferation of industrial sensors, Internet of Things (IoT) devices, and artificial intelligence (AI) and machine learning (ML) applications, there is a growing need for strong data security measures.
FaaS providers offer advanced security features, including encryption, access control, and threat detection, to help mitigate these risks. Other areas where FaaS is making a significant impact are professional services and asset management. FaaS platforms provide analytics capabilities, enabling predictive maintenance and optimization of industrial processes. This is particularly important in industries such as cement, aluminum, semiconductor, medical devices, and logistics, where downtime can result in significant losses. Cloud infrastructure services and IT services are also benefiting from the adoption of FaaS. Containers and payment gateways are being used to facilitate seamless integration with existing systems. The market is expected to continue growing, driven by the increasing adoption of Industry 4.0 and the digital transformation of manufacturing processes.

What will be the Size of the Factory Automation Platform As A Service Market During the Forecast Period?

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The Factory Automation Platform as a Service (FaaS) market is experiencing significant growth due to the increasing demand for industrial digitalization and automation. This market encompasses cloud-based solutions that enable businesses to manage and optimize their manufacturing processes remotely. FaaS offerings provide advanced capabilities, such as artificial intelligence (AI) and machine learning (ML), business process automation, Internet of Things (IoT), and IT infrastructure management. This market's key drivers include increased efficiency, reduced costs, and improved productivity. Security is a critical consideration, with FaaS providers offering strong encryption, access control, and compliance features. The market's size is substantial, with numerous industries adopting FaaS solutions, including healthcare services, agricultural, pharmaceutical, and general manufacturing.
The integration of FaaS with other technologies, such as AI, ML, and IoT, is further expanding its potential applications. Banking and financial services play a crucial role in facilitating financial transactions, while sectors like healthcare and life sciences, as well as retail and consumer goods, rely on efficient systems to support their patients and customers.

How is this Factory Automation Platform As A Service Industry segmented and which is the largest segment?

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

Component

  Platform
  Professional service


End-user

  Large enterprises
  SMEs


Application

  Manufacturing operations
  Supply chain management
  Quality control


Deployment

  Public cloud
  Private cloud
  Hybrid cloud


Geography

  North America

    US


  APAC

    China
    India
    Japan
    South Korea


  Europe

    Germany
    UK
    France


  South America

    Brazil


  Middle East and Africa

By Component Insights

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

The market encompasses asset management, remote monitoring, data processing and analytics, application development and management, and security management segments. FAPaaS enables companies to create cloud-based services for factory automation applications, including asset management, remote monitoring, data processing, and security management. The rise of IoT-enabled devices in discrete industries for manufacturing process optimization necessitates effective cloud-based platforms to execute data processing and analytical tasks. To address this demand, industry-specific cloud applications must be developed for optimal performance. Key areas of application include IT, operations, finance, human resources, rule-based automation, knowledge-based automation, unstructured data, data security, and skilled workforce in se