Dieter Meuser and two colleagues founded iTAC Software AG in 1998 to commercialize a manufacturing executing system (MES) developed for Robert Bosch GmbH. The timing was ideal, as manufacturers sought ways to leverage the nascent Internet to automatically monitor and manage their plants and thereby improve quality and efficiency to bolster the bottom line. Today, iTAC (Internet Technologies & Consulting) is one of the leading MES companies serving discrete manufacturers, and has customers throughout Europe, Asia and the Americas.
iTAC.MES.Suite is a cloud-based, Java EE-powered application that enables IP-based monitoring and management of every aspect of a manufacturing plant. OpenText Analytics provides the business intelligence (BI) and analytics capabilities embedded in iTAC.MES.Suite. You can see the software in action in booth 3437 at the IPC APEX EXPO, held February 22-26 in San Diego, California. Learn more about iTAC’s participation in IPC APEX EXPO here, and learn more about the company at the end of this post.
Meuser, iTAC’s CTO, has extensive experience working with manufacturers worldwide. He’s also an expert on the German government’s Industry 4.0 initiative to develop and support “Smart Factories” – that is, manufacturing plants that leverage embedded systems and the Internet of Things (IoT) to drive efficiency and improve quality. We asked Meuser for his thoughts on these topics.
OpenText: iTAC has more than two dozen enterprise customers with plants in 20 countries. What do those customers say are their pain points, particularly with regard to data?
Meuser: The biggest single pain point is this: Companies have lots of data, but often they are unsure how to analyze it. Let me elaborate: Many types of data are recorded to fulfill manufacturers’ tracking and tracing requirements. (Called “traceability standards,” these include VW 80131, VW 80160, MBN 10447, GS 95017 and others.) Data collected via sensor networks, such as plant temperature or humidity, are part of these standards. The objective of collecting this data is to continuously improve manufacturing processes through correlation analysis (or Big Data analysis), accomplished by running the data through intelligent algorithms. But because manufacturers frequently aren’t sure which criteria they should use to analyze the data, analysis often does not happen to the extent that manufacturers want. As a result, data is collected and stored for possible later analysis.
This can lead to a growing mountain of unanalyzed data and very little continuous improvement of processes. But it also illustrates why introducing data management right at the beginning of a tracking and tracing project is so important. Data management, supported by analytics, enables process optimization that otherwise would fall by the wayside.
OpenText: How do manufacturers use and analyze data – sensor data in particular – to improve their processes?
Meuser: Within manufacturing plants, the most common analysis is called Overall Equipment Effectiveness (OEE) based on integrated production data collection and machine data collection (PDC/MDC). This is done within the plant’s manufacturing execution system (MES). PDC/MDC can happen automatically if the plant’s systems are integrated, or manually via rich clients. The captured data can be evaluated in real time and analyzed via free selectable time intervals.
Common analyses include comparing planned changeover and turnaround times with actual values; comparing actual production (including scrap) with forecasts; and examining of unexpected equipment breakdowns. Key Performance Indicators (KPIs) in these analyses feed into OEE, productivity and utilization.
Reducing non-conformance costs is another important business case for data analysis in both IoT and Industry 4.0. The availability of structured and unstructured sensor data related to product failures (and costs associated with them) enables new opportunities to determine non-conformance. There is enormous potential in systematically analyzing causes of production failure. Failure cause catalogues (which many manufacturers have collected for decades), can be examined with the help of a modern data mining tool. Analyzing this data on the basis of quality, product and process data helps to reduce failure costs in a Smart Factory.
OpenText: What is the role of analytics and data visualization in IoT and Industry 4.0?
Meuser: A major objective of data analyses and visualizations in IoT and Industry 4.0 is automatic failure cause analysis. This is accomplished by measuring and testing product errors along with data about manufacturing machines, equipment and processes, then identifying inefficient processes in order to establish solutions. These solutions must be checked by process engineers who have years of experience. Humans and machines go hand in hand when we optimize product quality in an Industry 4.0 factory.
OpenText: What are the benefits of a Smart Factory?
Meuser: A Smart Factory consists of self-learning machines that can identify the causes of failure under specific conditions, determine appropriate measures to address a failure, and send messages to inform operators of problems. This is sometimes called a cyber-physical system (CPS). Combined with appropriate software models, it enables autonomous manufacturing machines (within certain limits) and supports the overall objective to optimize processes and avoid failures before they happen.
The Smart Factory is enabled by modern data analysis techniques. It relies on data about products, processes, quality and environment (e.g. room temperature or humidity) as appropriate. The ability to interface an ERP system with production equipment creates continuous vertical integration that covers the entire value chain, from receiving to shipping.
Be sure to visit iTAC at the IPC APEX EXPO, and read more about iTAC.MES.Suite in this case study.
More about iTAC
iTAC Software AG is a leading provider of next-generation, platform-independent and cloud-based MES solutions for original equipment manufacturers (OEMs) and suppliers within the discrete manufacturing sector. The company has more than 20 years of experience in internet-based solutions for the discrete manufacturing sector, the Internet of Things (IoT) and the Industrial Internet
To date, iTAC has amassed an enviable portfolio of over 70 global enterprise customers across five primary industries: automotive, electronics/EMS/telecommunications technology, metal fabrication, energy/utilities and medical devices. Customers including Audi, Bosch, Continental, Hella, Johnson Controls, Lear, Schneider Electric, Siemens and Volkswagen rely on the iTAC MES Suite to optimize their production processes.
iTAC’s product portfolio represents the solutions for the Smart Factory of tomorrow. Its principal components are the iTAC.MES.Suite, the iTAC.Enterprise, Framework and iTAC.embedded.Systems, including its platform-independent iTAC.ARTES middleware and iTAC.Smart.Devices, the company’s new physical interface solutions.
