Success Factors for Machine Connection

• Analytics & Modeling - Big Data Analytics
• Platform as a Service (PaaS) - Connectivity Platforms

• Automotive

• Discrete Manufacturing
• Quality Assurance

• Factory Operations Visibility & Intelligence
• Machine Condition Monitoring
• Predictive Maintenance

• Data Science Services
• System Integration

In today’s manufacturing facilities, there is a large mix of heterogeneous machines and control types, where signals must be collected, aggregated and evaluated. The solution to this common problem is a centralized system, which can seamlessly “plug and play” with any type of machine CNC controller or PLC. However, machine connection and integration capabilities are key differentiators when selecting the right MES technology provider. The experience level and readiness of the technology provider determines the time and costs required to successfully connect any machine. Standardized interfaces to all common machine controls, via direct plug-ins, must be created, otherwise the time required for connecting the machines, is unmanageable. In a pilot project initiated by an MES provider, a major German tool manufacturer determined that connections to some of their machines were very time-consuming, costly and in some cases not possible. Even after months of work, some challenging machine controls types remained unconnected. The MES provider lacked expertise and the necessary plug-ins.

The customer in this case study is a major German tool manufacturer. The company operates a modern shop floor and aims to measure and visualize the performance of machines and systems in real time. The company's goal is to create a transparent factory, where production is virtually mirrored and inaccuracies and wastes can be immediately identified and eliminated. The company aims to create a “Cyber-Physical System” that can analyze and optimize the performance, availability and quality of the production processes, leading to productivity increases of 20% or more within 12 months or less. However, the company faced challenges in connecting to some of their machines, which was very time-consuming, costly and in some cases not possible.

FORCAM offers sophisticated and powerful plug-ins for a variety of machine types and control systems. The integration of machines and production data is made possible in 3 steps: Capture Data, Import Data, Interpret and Analyze Data. To connect with heterogeneous controls, FORCAM uses three methods for machine data acquisition. The selected method for connection depends on the machinery and the desired amount of information to be captured. The machine data connection (MDC) can be fully adopted as there is little room for error; the more data collected automatically, the better the data integrity and the less resources wasted for manual reporting. After all process and measurement data is collected, it is uploaded in the so-called DCU (Data Collection Unit). Various software plug-ins ensure that the above-mentioned and other protocols can be processed. The original machine signals are not the only information needed for production control. A logic device is needed, that calculates the desired operating conditions from a plurality of signals and additional information.

• FORCAM allows organizations to gather information on machinery, equipment and factory performance in real-time from anywhere in the world, on any device and in any language.
• For real-time mapping of data sets (Big Data), FORCAM is the first technology provider to offer in-memory based technology in conjunction with “complex event processing” (CEP).
• FORCAM’s fully web-based, cloud-enabled solution has enabled organizations to increase the productivity of machinery and equipment.

• Productivity increases of 20% or more within 12 months or less.
• Worldwide, more than 50,000 machines are monitored and optimized by FORCAM’s technology.