As the trend toward the Industrial Internet of Things (IIoT) continues to gain momentum, manufacturers are acquiring access to massive amounts of machine and sensor data. The ability to intelligently collect, filter, process, and form decisions from this data all fall under the realm of Manufacturing Intelligence.

Applications of Manufacturing Intelligence include reports and dashboards that make actionable Key Performance Indicators (KPIs), such as Overall Equipment Effectiveness (OEE) available to decision makers at the appropriate time. This level of analysis, while important, requires human intervention and is limited to describing what has happened in the past.

Where Manufacturing Intelligence begins to show value is in moving beyond the traditional descriptive and diagnostic uses of data, to predictive and prescriptive analytics. When applied correctly, Manufacturing Intelligence can be used to predict when process variables are trending outside of safe limits or when equipment is likely to fail. Corrective action can then be taken before a problem occurs, often without human intervention, which reduces waste, labor, and the potential for human error.

Manufacturing Intelligence Infographic

While the benefits of developing a mature Manufacturing Intelligence Strategy are clear, this presents a number of challenges: understanding how to identify important data, capture it, visualize it, and make decisions in a robust, scalable way requires a significant time investment and breadth of knowledge across various technologies. Without proper discipline, Manufacturing Intelligence projects can be executed as point solutions, presenting problems for maintenance and scalability down the road.

As a full-service solution provider, Grantek leverages our three decades of experience and Thought Leadership across various industry sectors to develop scalable Manufacturing Intelligence solutions for our customers. These solutions span across the “shop floor to the boardroom,” and include the following services:

  • Identifying actionable data across machines and devices, based on intimate knowledge of the manufacturing process
  • Defining system performance requirements, such as Sampling Intervals and Data Throughput
  • Delivering robust, fault-tolerant Network Infrastructure to ensure optimal connectivity
  • Architecting Data Storage Solutions that properly account for redundancy, archiving, replication, and regulatory requirements
  • Building intuitive reports and dashboards to make actionable data available to key decision makers across the organization
  • Developing secure, scalable data conduits to interface with plant-floor, quality, LIMS, MES/MOM, ERP, and other controls and business systems

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