Time-Series Database for Manufacturing Market Outlook

According to our latest research, the global Time-Series Database for Manufacturing market size reached USD 1.87 billion in 2024, with robust expansion driven by increasing adoption of Industry 4.0 solutions and real-time data analytics. The market is expected to grow at a CAGR of 15.2% from 2025 to 2033, propelling the total market value to USD 6.13 billion by 2033. This growth is underpinned by the manufacturing sector’s intensified focus on operational efficiency, predictive maintenance, and digital transformation initiatives.

One of the primary growth factors for the Time-Series Database for Manufacturing market is the escalating demand for real-time data analytics in manufacturing environments. As manufacturers worldwide strive to optimize production processes, minimize downtime, and enhance product quality, the need for robust time-series databases capable of handling high-velocity, high-volume sensor data has surged. The proliferation of Industrial Internet of Things (IIoT) devices has resulted in an exponential increase in the volume of time-stamped data generated on factory floors. This data, when harnessed effectively, enables manufacturers to monitor equipment health, predict potential failures, and streamline operations, contributing significantly to cost savings and productivity gains. The ability to collect, store, and analyze time-series data in real time has become a critical differentiator for manufacturers seeking to maintain a competitive edge in an increasingly digital landscape.

Another significant driver boosting the Time-Series Database for Manufacturing market is the widespread adoption of predictive maintenance and process optimization solutions. Manufacturing organizations are increasingly recognizing the value of leveraging historical and real-time data to anticipate equipment malfunctions and optimize operational parameters. Time-series databases facilitate the storage and analysis of vast streams of sensor data, enabling advanced analytics and machine learning algorithms to detect anomalies and predict failures before they occur. This proactive approach to maintenance not only reduces unplanned downtime but also extends asset life and lowers total maintenance costs. Furthermore, process optimization initiatives, powered by continuous monitoring of production variables, are helping manufacturers achieve higher yields, improved quality, and greater energy efficiency, further fueling demand for time-series data management solutions.

The growing integration of cloud-based solutions within manufacturing IT infrastructures is also playing a pivotal role in market expansion. Cloud deployment models offer scalable, flexible, and cost-effective alternatives to traditional on-premises systems, allowing manufacturers to handle fluctuating data loads without significant upfront investments. This shift is particularly beneficial for small and medium enterprises (SMEs), which can now access advanced time-series database capabilities without the need for extensive in-house IT resources. Additionally, the cloud enables seamless data sharing across geographically dispersed facilities, facilitating centralized monitoring, benchmarking, and decision-making. As manufacturers continue to embrace digital transformation and smart factory initiatives, the adoption of cloud-based time-series databases is expected to accelerate, driving further market growth.

From a regional perspective, Asia Pacific is emerging as the most dynamic market for time-series databases in manufacturing, driven by rapid industrialization, strong government support for smart manufacturing, and the proliferation of IIoT deployments across China, Japan, South Korea, and India. North America and Europe also represent significant markets, characterized by early adoption of advanced manufacturing technologies, a mature industrial base, and high investments in automation and digitalization. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, with increasing focus on modernizing manufacturing sectors and improving operational efficiencies. Each region presents unique opportunities and challenges, influenced by local industry structures, regulatory environments, and the pace of technology adoption.

Component Analysis

The Component segment of the Time-Series Database for Manufacturing market is divided into software, hardwar

This database allows the public to browse and search recent FTZ Board manufacturing approvals.

This database is a joint effort between the National Bureau of Economic Research (NBER) and U.S. Census Bureau's Center for Economic Studies (CES), containing annual industry-level data from 1958-2011 on output, employment, payroll and other input costs, investment, capital stocks, TFP, and various industry-specific price indexes. Because of the change from SIC to NAICS industry definitions in 1997, the database is provided in two versions: one with 459 four-digit 1987 SIC industries and the other with 473 six-digit 1997 North American Industry Classification System (NAICS) industries.

Official USDA data on production, supply, and distribution of agricultural commodities for the United States and key producing and consuming countries.

Means all those objects accompanying road works, plumbing, building etc ... that are made by human labor. The further classification in specific classes derives both from functional considerations and from the geometric "behaviour" of the artifacts as the scale changes. [CRDT Regional Catalogue] metadata (http://rsdi.regione.basilicata.it/Catalogue/srv/ita/search?hl=ita#fast=index&from=1&to=50&any_OR_geokeyword=0202*)

Context

It is not always easy to find databases from real-world manufacturing plants So, I would like to share this database with the community, which comes from one of the biggest industry in Brazil and in the world.

The primary reason why we are sharing this data is that there is an urgent need for companies to understand why employees still suffer some injuries/accidents in plants. Sometimes they also die in such environment. So hope the community help us to explore and take better/newer insights from this data.

Content

The database is basically records of accidents from 12 different plants in 03 different countries which every line in the data is an occurrence of an accident.

• Columns description
  • Data: timestamp or time/date information
  • Countries: which country the accident occurred (anonymized)
  • Local: the city where the manufacturing plant is located (anonymized)
  • Industry sector: which sector the plant belongs to
  • Accident level: from I to VI, it registers how severe was the accident (I means not severe but VI means very severe)
  • Potential Accident Level: Depending on the Accident Level, the database also registers how severe the accident could have been (due to other factors involved in the accident)
  • Genre: if the person is male of female
  • Employee or Third Party: if the injured person is an employee or a third party
  • Critical Risk: some description of the risk involved in the accident
  • Description: Detailed description of how the accident happened.

Inspiration

We believe that everyone should work on a data for a good cause, especially this one, which can help manufacturing plants to save people lives!

Complete database of 336 manufacturing companies in kuwait with verified contacts. Download sample or buy full dataset for $399.

The database contains consistent data on international and domestic trade at the industry level covering agriculture, mining, energy, manufacturing, and services.

Industrial Time-Series Databases for Robotics Market Outlook

According to our latest research, the global Industrial Time-Series Databases for Robotics market size reached USD 1.82 billion in 2024, driven by the rapid proliferation of robotics and automation across multiple industries. The market is experiencing robust momentum, with a CAGR of 15.4% projected for the forecast period. By 2033, the market is expected to achieve a value of USD 6.49 billion. This impressive growth is primarily attributed to the increasing adoption of industrial robots, the exponential growth of IoT-enabled devices, and the demand for real-time data analytics to optimize manufacturing processes and predictive maintenance.

The surge in robotics integration within industrial environments is a key driver behind the growth of the Industrial Time-Series Databases for Robotics market. As manufacturers transition toward smart factories and Industry 4.0, the volume and complexity of data generated by robotics systems have increased multifold. Time-series databases, purpose-built for handling high-frequency, sequential data, are becoming indispensable for storing, retrieving, and analyzing sensor data, control parameters, and machine states. This enables manufacturers to monitor robot performance, detect anomalies, and implement predictive maintenance strategies, ultimately reducing downtime and improving operational efficiency. The growing focus on automation, coupled with the need for continuous process improvement, is fueling the demand for advanced time-series database solutions tailored for industrial robotics.

Another significant growth factor is the convergence of artificial intelligence (AI), machine learning (ML), and industrial robotics. Modern robotics applications increasingly rely on sophisticated analytics to derive actionable insights from vast streams of time-stamped data. Time-series databases provide the foundational infrastructure for these analytics, enabling real-time monitoring, anomaly detection, and predictive modeling. The capability to process and analyze massive datasets in real time is critical for supporting adaptive robotics, closed-loop control, and autonomous decision-making. Furthermore, the integration of edge computing and cloud-based platforms is expanding the deployment options for time-series databases, making them more accessible and scalable for organizations of all sizes.

The evolving regulatory landscape and the emphasis on quality, safety, and compliance are also contributing to market expansion. Industries such as pharmaceuticals, automotive, and food & beverage are subject to stringent regulatory requirements that mandate comprehensive data logging, traceability, and auditability. Time-series databases are uniquely suited to meet these demands by providing immutable, high-resolution records of robotic operations and process parameters. This not only facilitates regulatory compliance but also enhances transparency and accountability in manufacturing processes. As organizations prioritize digital transformation and data-driven decision-making, the adoption of industrial time-series databases for robotics is poised to accelerate further.

From a regional perspective, Asia Pacific continues to dominate the Industrial Time-Series Databases for Robotics market, accounting for the largest share in 2024. This is largely due to the rapid industrialization, government initiatives supporting smart manufacturing, and the presence of major electronics and automotive manufacturing hubs in countries such as China, Japan, and South Korea. North America follows closely, driven by technological innovation, early adoption of automation, and a strong focus on operational efficiency across industries. Europe is also a significant contributor, particularly in sectors like automotive, aerospace, and pharmaceuticals, where compliance and quality control are paramount. The Middle East & Africa and Latin America are emerging markets, with increasing investments in industrial automation and digital infrastructure expected to drive future growth.

Database Type Analysis

The Database Type segment in the Industrial Time-Series Databases for Robotics market encompasses relational, non-relational, and hybrid databases. Relational databases have traditionally been the backbone of industrial data management, offering structured data storage and ACID compliance. However, their limita

The database contains estimates for below- and aboveground production for over 400 sites, spanning all major terrestrial biomes and climate regions. The dataset contains detailed descriptions of each site as well as detailed information on measurement methodology. Furthermore, the biomass production data is supplemented with data on climate and soil conditions extracted from global databases. The database can be used for studies on regional and global scales, as well as a validation and calibration dataset for global vegetation models.

The database contains consistent data on international and domestic trade at the industry level covering agriculture, mining, energy, manufacturing, and services. It covers 170 industries in all broad sectors of the economy in 265 countries for the years 1986-2019. It contains 292,408,351 observations, most of which are from administrative data sources while others are estimated using a variety of methods described in the technical documentation. The ITPD-S Release 1 provides the following coverage: 1986-2019 (1986-2019 for Agriculture, 1988-2019 for Manufacturing, Mining and Energy, 2000-2019 for Services ) 265 countries, 170 industries.

Indonesia Manufacturing Industry: Production: Value: Toys Manufacture data was reported at 10,699.819 IDR bn in 2015. This records an increase from the previous number of 3,274.259 IDR bn for 2014. Indonesia Manufacturing Industry: Production: Value: Toys Manufacture data is updated yearly, averaging 1,591.921 IDR bn from Dec 1999 (Median) to 2015, with 17 observations. The data reached an all-time high of 10,699.819 IDR bn in 2015 and a record low of 54.682 IDR bn in 2001. Indonesia Manufacturing Industry: Production: Value: Toys Manufacture data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Mining and Manufacturing Sector – Table ID.BAD001: Manufacturing Industry: by Product: Value.

Explore the expanding Universal Database Management Desktop Software market, forecast to reach USD 23.8 billion by 2033 with a 11.5% CAGR. Discover key drivers, application segments in Finance, Manufacturing, and Medical, and leading companies like Oracle and Microsoft.

Complete database of 476 manufacturing companies in riyadh with verified contacts. Download sample or buy full dataset for $399.

Abstract

UNIDO maintains a variety of databases comprising statistics of overall industrial growth, detailed data on business structure and statistics on major indicators of industrial performance by country in the historical time series. Among which is the UNIDO Industrial Statistics Database at the 3 & 4-digit levels of ISIC Revision 4 (INDSTAT4-Rev.4).

INDSTAT4 contains highly disaggregated data on the manufacturing sector for the period 2005 onwards. Comparability of data over time and across the countries has been the main priority of developing and updating this database. INDSTAT4 offers a unique possibility of in-depth analysis of the structural transformation of economies over time. The database contains seven principle indicators of industrial statistics. The data are arranged at the 3- and 4-digit levels of the International Standard Industrial Classification of All Economic Activities (ISIC) Revision 4 pertaining to the manufacturing, which comprises more than 160 manufacturing sectors and sub-sectors. The time series can either be used to compare a certain branch or sector of countries or – if present in the data set – some sectors of one country.

For more information, please visit: http://www.unido.org/resources/statistics/statistical-databases.html

Analysis unit

Sectors

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

Manufacturing Industries: Tamil Nadu: Factories data was reported at 39,512.000 Unit in 2022. This records an increase from the previous number of 39,393.000 Unit for 2021. Manufacturing Industries: Tamil Nadu: Factories data is updated yearly, averaging 31,819.000 Unit from Mar 1999 (Median) to 2022, with 24 observations. The data reached an all-time high of 39,512.000 Unit in 2022 and a record low of 18,912.000 Unit in 2002. Manufacturing Industries: Tamil Nadu: Factories data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under India Premium Database’s Mining and Manufacturing Sector – Table IN.BAF029: Manufacturing Industry: NIC 2008: By State: Tamil Nadu.

Graph Database Platforms for Supply Chain Market Outlook

According to our latest research, the global Graph Database Platforms for Supply Chain market size reached USD 1.84 billion in 2024, reflecting robust demand across multiple industries. The market is projected to register a compelling CAGR of 19.7% from 2025 to 2033, with the total market value expected to reach USD 9.16 billion by 2033. This impressive growth is primarily driven by the increasing complexity of global supply chains, the need for real-time data analytics, and the rapid adoption of digital transformation initiatives in logistics, manufacturing, and retail sectors. As per our most recent analysis, organizations are increasingly leveraging graph database platforms to enhance visibility, optimize operations, and address supply chain disruptions more effectively.

The primary growth factor fueling the expansion of the Graph Database Platforms for Supply Chain market is the escalating demand for advanced data management solutions capable of handling the intricate relationships and dependencies inherent in modern supply chains. Traditional relational databases often struggle with the dynamic and interconnected nature of supply chain data, which includes suppliers, manufacturers, logistics partners, and end customers. Graph databases, by contrast, are designed to efficiently map and analyze these complex networks, enabling organizations to gain actionable insights, identify bottlenecks, and mitigate risks. The ability to visualize and traverse vast data sets in real time is particularly valuable in scenarios involving multi-tier suppliers, global logistics, and compliance requirements, thus propelling the adoption of graph database platforms across industries.

Another significant driver is the growing emphasis on supply chain resilience and risk management, especially in the wake of global disruptions such as pandemics, geopolitical tensions, and natural disasters. Organizations are increasingly recognizing the importance of end-to-end supply chain visibility to anticipate and respond to potential threats. Graph database platforms facilitate real-time monitoring and predictive analytics, empowering businesses to proactively manage risks and ensure business continuity. Enhanced traceability and compliance capabilities also support industries with stringent regulatory requirements, such as healthcare, automotive, and food & beverage, further accelerating market growth. Additionally, the integration of artificial intelligence and machine learning with graph databases amplifies their value, allowing for advanced scenario modeling, anomaly detection, and optimization.

Digital transformation initiatives, particularly the adoption of cloud computing and the Internet of Things (IoT), are further catalyzing the growth of the Graph Database Platforms for Supply Chain market. Cloud-based deployment models offer scalability, flexibility, and cost-effectiveness, making graph database solutions accessible to organizations of all sizes, including small and medium enterprises. The proliferation of IoT devices throughout supply chains generates massive volumes of interconnected data, which graph databases are uniquely equipped to manage and analyze. This convergence of technologies is fostering innovative applications in inventory management, logistics optimization, and supplier collaboration, thereby expanding the addressable market and driving sustained investment in graph database platforms.

From a regional perspective, North America currently dominates the Graph Database Platforms for Supply Chain market, accounting for the largest revenue share in 2024. This leadership position can be attributed to the region’s advanced IT infrastructure, high levels of digitalization, and strong presence of major technology providers. However, Asia Pacific is projected to exhibit the highest CAGR over the forecast period, fueled by rapid industrialization, expanding e-commerce, and significant investments in supply chain modernization. Europe is also witnessing robust growth, driven by regulatory requirements for traceability and sustainability, particularly in manufacturing and automotive sectors. Latin America and the Middle East & Africa are emerging markets with increasing adoption of graph database technologies, supported by growing awareness and digital transformation initiatives.

United States Manufacturing Industries: sa: Inventories data was reported at 669.270 USD bn in Jun 2018. This records an increase from the previous number of 668.607 USD bn for May 2018. United States Manufacturing Industries: sa: Inventories data is updated monthly, averaging 474.323 USD bn from Jan 1992 (Median) to Jun 2018, with 318 observations. The data reached an all-time high of 669.270 USD bn in Jun 2018 and a record low of 376.070 USD bn in Feb 1993. United States Manufacturing Industries: sa: Inventories data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.C004: Manufacturing Industries: By NAIC System: Inventories: sa.

This dataset represents the entire Industrial PinPointer database of manufacturing companies. Only those locations primarily engaged in manufacturing (SIC Codes 2000-3999) or those that are headquarters of manufacturing companies are included. This dataset covers manufacturing locations in the State of Alabama. Homeland SecurityThis dataset includes the entire Industrial PinPointer database of manufacturing companies, which includes the 2009 D2 of 2 update. Only those locations primarily engaged in manufacturing (SIC Codes 2000-3999) or those that are headquarters of manufacturing companies are included. SIC codes are not provided for 125 companies in the US territories. Where an employee count is available, only locations employing fifteen (15) or more people are included. All text fields were set to upper case, leading and trailing spaces were trimmed from all text fields, and non-printable and diacritic characters were removed from all text fields per NGA's request.Metadata

Complete database of 149 manufacturing companies in manama with verified contacts. Download sample or buy full dataset for $399.