Factory Architectures

As manufacturers seek to reap the benefits of rapidly advancing technologies such as machine learning, today’s ad hoc and tightly coupled legacy systems are proving increasingly limited. Factories of the future will need software architectures capable of seamlessly exchanging data among a variety of machines and applications. That’s why GTMI has developed a Decoupled Digital Architecture, a high performance, low cost, standards-based leap ahead of current legacy systems.

Machine Communication

GTMI develops solutions that address the wide variety of protocols used by manufacturing equipment, PLCs, SCADA applications, MES and ERP systems. GTMI was a leader in the development of key industry standards, including MTConnect and Computer Aided Manufacturing using XML (CAMX), streamlining communication with machines on the shop floor. GTMI also has vast experience interfacing to equipment and manufacturing applications using protocols such as OPC-UA, Modbus, ROS and SECSII/GEM.

Cloud Computing

Cloud computing is swiftly transforming manufacturing by providing unprecedented access to innovation, software, services and infrastructure. GTMI has expertise developing and deploying cloud solutions in areas including machine learning, Internet of Things, stream analytics, high-speed processing of data, load balancing, standardized architectures, optimal data storage and presentation. We can help you navigate the myriad of cloud services and vendors to reduce costs and enhance success.

Edge Devices

The increased power, reduced cost and enhanced bandwidth of microcontrollers, single-board computers and sensors allow edge devices to be installed within machine tools and work cells. These edge devices efficiently collect, analyze and transmit manufacturing process information. By conducting analytics on sensor data, these devices allow quick process improvements without the need to transfer large amounts of raw data. GTMI is at the forefront of developing edge devices capable of high-speed data sensor gathering, enabling enhanced hardware and software development and smarter decisions.


As new technologies and equipment are added to manufacturing supply chains, the attack surface available to malicious actors expands. As a result, cybersecurity implementation and maintenance are becoming greater cost drivers for manufacturers. Many firms struggle to stay abreast of this rapidly evolving field. GTMI researchers are actively leading basic and applied research for developing methods, techniques, standards and software that enhance the security and resilience of manufacturing systems and the supply chain. One ongoing GTMI initiative is the Center for Biomanufacturing Cybersecurity and Resilience (CBCR), a consortium of industry, academic, government and not-for-profit partners dedicated to achieving secure, resilient and competitive biomanufacturing.

Machine Learning and Artificial Intelligence

The massive growth of Machine Learning (ML) can transform data processing and deliver exceptional insight into processes and operations. GTMI researchers apply new analytic techniques, software libraries and hardware platforms to develop ML strategies that solve problems that have long frustrated manufacturers. And we go beyond concept to application. Our diverse array of industrial equipment allows us to run experiments to verify ML algorithms, ensuring successful deployment.

Overall Equipment Effectiveness (OEE)

OEE is a standard way for analyzing and comparing machine utilization from machine throughput, part quality and performance relative to schedule. Since OEE calculations require obtaining and processing data from multiple systems (e.g., machine, quality, MES, ERP, scheduling), advanced knowledge of those systems is critical. GTMI offers the expertise you need to assess OEE for your factory assets, delivering accurate data that can be reliably used for comparison, benchmarking and decision-making.

Predictive Maintenance

Legacy maintenance routines are often based on the calendar rather than on true need. Critical parts are replaced, refurbished or serviced at set intervals. However, manufacturers find that parts are often replaced too soon (wasting usable life and consuming valuable machine downtime) or too late (resulting in unplanned outages that disrupt operations). Predictive maintenance allows manufacturers to monitor part performance and perform maintenance when degradation is detected, paving the way for reduced downtime, cost and risk. GTMI can help you minimize the guesswork of scheduling maintenance by implementing a predictive approach to maintenance.