What are our data sources?
We use the data sources on the side for ranking solutions and awarding badges in manufacturing analytics software category. Our data sources in manufacturing analytics software category include;
Manufacturing is a process of constant improvement. Manufacturing analytics software enable companies to drill down on manufacturing data to find optimization opportunities.
Manufacturing Analytics and IIoT in factories is predicted to have $3.9T to $11.1T market size by 2025.
Software leaders such as IBM, SAP and industrial automation experts such as GE and Siemens have developed manufacturing analytics solutions. Additionally, startups are combining manufacturing domain expertise and software capabilities to build new solutions.
An important criteria in manufacturing analytics software is data integration capabilities. Industrial systems use a wide variety of data formats and communication standards, therefore it makes sense to check integration capabilities of your potential manufacturing analytics solution to ensure that your physical machines' data can be fed into the manufacturing analytics software.
If you’d like to learn about the ecosystem consisting of Manufacturing Analytics Software and others, feel free to check AIMultiple Analytics.
AIMultiple is data driven. Evaluate 18 services based on
comprehensive, transparent and objective AIMultiple scores.
For any of our scores, click the information icon to learn how it is
calculated based on objective data.
*Products with visit website buttons are sponsored
Element unlocks industrial data, enabling modern analytics systems to find insights that transform operational performance.
We use the data sources on the side for ranking solutions and awarding badges in manufacturing analytics software category. Our data sources in manufacturing analytics software category include;
review websites
social media websites
search engine data for branded queries
According to the weighted combination of 7 data sources
SAS
ptc
Rockwell Automation
Augury
Tulip
Taking into account the latest metrics outlined below, these are the current manufacturing analytics software market leaders. Market leaders are not the overall leaders since market leadership doesn’t take into account growth rate.
SAS
ptc
Rockwell Automation
Augury
Tulip
These are the number of queries on search engines which include the brand name of the solution. Compared to other Analytics categories, Manufacturing Analytics Software is more concentrated in terms of top 3 companies’ share of search queries. Top 3 companies receive 97%, 22% more than the average of search queries in this area.
61 employees work for a typical company in this solution category which is 40 more than the number of employees for a typical company in the average solution category.
In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 14 companies with >10 employees are offering manufacturing analytics software. Top 3 products are developed by companies with a total of 20k employees. The largest company building manufacturing analytics software is Rockwell Automation with more than 20,000 employees.
Taking into account the latest metrics outlined below, these are the fastest growing solutions:
SAS
ptc
Rockwell Automation
Augury
Tulip
We have analyzed reviews published in the last months. These were published in 4 review platforms as well as vendor websites where the vendor had provided a testimonial from a client whom we could connect to a real person.
These solutions have the best combination of high ratings from reviews and number of reviews when we take into account all their recent reviews.
According to customer reviews, most common company size for manufacturing analytics software customers is 1,001+ employees. Customers with 1,001+ employees make up 50% of manufacturing analytics software customers. For an average Analytics solution, customers with 1,001+ employees make up 50% of total customers.
This category was searched on average for 137 times per month on search engines in 2022. This number has decreased to 110 in 2023. If we compare with other analytics solutions, a typical solution was searched 438 times in 2022 and this decreased to 170 in 2023.
There are technical and organizational challenges ahead to implement successful manufacturing analytics software. First challenge comes with organizational; many production facilities have conservative mindset and have inertia to involve innovative tools for improvement through IT software systems. Second challenge is domain expertise; as all processes have unique set ups, one size fits all approach does not work. New vendors mush have a domain expertise and have a deep understanding of complicated processes of their clients. So considering the right vendor which has a domain expertise and involving right stakeholders and aligning them all for the manufacturing analytics benefits for the business is crucial.
Analytical insights that help to predict and prevent machine malfunction, machine learning algorithms analyses historical data to determine which indicators signal the malfunction so that these events can be predicted.
This requires collecting historical data from legacy machineries which is difficult for companies to do. Industrial Internet of Things (IIoT) solutions solve that challenge by combining machine to machine communications. IIoT brings industrial devises connected by communication technologies that results in the systems that can monitor, collect, exchange and deliver valuable insights.
Improving quality assurance have direct impact on the revenue streams, in addition to the quality, diminishing capacity constraints on the factory floor through data insights enable business to fulfill exceeding demand requests.
Decreasing inefficiency in every step of operations have accumulated impact on the cost reduction, potential cost benefits of manufacturing analytics software;
Manufacturers collect everyday data from operational data, built in sensors, historian software, ERP systems and spreadsheets. However more than 90% of the collected data is thrown away.
Manufacturing analytics is about getting all the data from different variety of sources and using it for increase operational efficiencies and preventing slowdowns.
Manufacturing analytics is focused on collecting and analyzing data rather than process control, it helps to monitor operational equipment effectiveness of the facility and creates action-oriented dashboards to visualize the performance.
Primary manufacturing analytics used cases today are process optimization and predictive maintenance.
Advanced analytics detects each step of inefficiency of operations which is measured by overall equipment effectiveness (OEE). Analytics software can improve three components of OEE;
Uptime: ensuring machines are running especially in complicated systems
Throughout & Performance: Maximizing throughput by preventing slowdowns that are not visible to conventional analysis
Quality: Scrap and low quality cost the manufacturer