Research & Best Practices

Fog Computing in IIoT & Manufacturing


As manufacturing technology continues to evolve at an increasingly rapid pace, new tools and practices in automation, communication and infrastructure are being revealed. Fog computing is one such tactic, building on existing concepts like cloud computing and edge computing to help facilities use and manage data more effectively and efficiently while yielding benefits across a broad cross-section of metrics. In this piece, we will explore what fog computing is, and how fog computing in IoT is helping to drive improvements in manufacturing efficiency.

What is fog computing?

Fog computing is similar to cloud computing in that it draws upon the processing power and communication benefits of a distributed network of connected devices and analytical architecture to make sense of the vast amount of data collected by sensors in the industrial internet of things (IIoT).

The name “fog computing” is derived from the idea that fog is simply a cloud that exists closer to the ground. In this case, industrial fog computing augments the functionality and benefits of the cloud – the vast, interconnected network of servers and storage media that forms the backbone of so much of the everyday technology we use – to bring some processes closer to “the ground,” in other words, the sensors, actuators and other devices that comprise the source of the data used to improve efficiency, effectiveness and accuracy in industrial processes from maintenance to fulfillment and beyond.

For those familiar with edge computing, this concept may sound familiar. It is correct to think so, since both practices bring processing, analysis and decision-making closer to the source of the data, rather than in a third-party cloud. Distinctions between the two do, however, exist, and later in this piece, we will explore them.

The role of fog computing in IIoT & manufacturing

Fog computing is poised to play a critical role for manufacturers eager to realize the full potential of the IIoT and connected devices, helping to process and analyze the massive volume of data collected from these devices to yield more actionable information for decision making in maintenance and production. Factories can become more connected by streaming data through a layer of fog nodes. These fog nodes can exist in lower or higher parts of IIoT hierarchy, each providing their own unique applications for the location and process with which they are associated.

Some examples of use cases for fog nodes and fog computing IoT analytics include:

  • Real-time automated decision making for equipment shutdown based upon critical process parameters, eliminating the latency of sending data up to a full-fledged cloud platform
  • Production line visualization
  • Equipment status monitoring with near-zero latency
  • On-the-fly production fine-tuning and adjustments based on real-time data

The common thread through many of these use cases is that fog computing nearly eliminates any latency involved in transmitting data to and from the cloud, instead using local fog nodes for processing and analysis, yielding actionable data in as close to real-time as possible.

Benefits of fog computing

Fog computing checks all the boxes for the concept of “SCALE,” some of the primary metrics and benefits that manufacturers expect to see from their IIoT investment. SCALE covers Security, Cognition, Agility, Latency and Efficiency, and here, we will examine exactly how fog computing fulfills and facilitates each of those.

  • Security: By moving many processes to more local fog nodes, rather than the cloud, fog computing reduces the chances of a security threat that may cause a loss of access or connectivity in the case of unplanned downtime. Fog nodes can also act as defense mechanisms, blocking incoming traffic in the case of a targeted attack and protecting data sources.
  • Cognition: Fog nodes provide vastly increased speed with a reduction in latency, enabling near-real-time analytics and decision-making. In addition, the processing power of fog nodes enables automated decision-making at a more local level, as well as effective digital twin enablement to allow engineers to monitor, predict and troubleshoot production conditions more accurately.
  • Agility: With fog nodes, facilities can more efficiently manage production and data collection fluctuations by distributing overflow to idle or underused equipment. Fog nodes will also eventually enable more efficient facility construction and outfitting.
  • Latency: By reducing the digital “distance” that data must travel, fog computing significantly reduces latency and helps facilities to realize the promise of real-time analysis and decision making. From automation to maintenance decisions and beyond, fog computing enables the efficiencies that manufacturers expect from Industry 4.0 technology.
  • Efficiency: Fog computing enables communication across various types of equipment, regardless of manufacturer or process, creating efficiencies in infrastructure, bandwidth and capacity usage. By facilitating interoperability where it previously may have been difficult or impossible to enable, fog computing reduces costs and allows for more effective use of technology.

Fog computing vs. edge computing

As mentioned earlier, fog computing and edge computing have a number of surface similarities in terms of concept and implementation, namely that they move analytical processes closer to the point of data collection. Fog computing provides additional benefits in that it does not simply move analytics “down” (or “south”) in the hierarchical process, but that fog nodes enable “east/west” communication between equipment and data points at a similar level in the process, facilitating communication and efficiency across resources and acting as an intermediary between the edge and the cloud.

Fog computing & predictive maintenance

Fog computing can play a key role in helping predictive maintenance to reach its full potential, drawing upon the SCALE benefits outlined above. Specifically, fog computing facilitates predictive maintenance by:

  • Drawing on truly real-time data
  • Enabling automated alerts and decision making
  • Bringing processing and analytics closer to IIoT sensors
  • Providing the maximum amount of notice, even for unplanned equipment shutdown
  • Helping to ensure the most efficient allocation of personnel and replacement parts

As the IIoT continues to expand and evolve, with ever-more data being collected, concepts like fog computing will become critical in building a smart factory. Reducing latency and increasing decision-making automation are among the most effective ways to increase efficiency in manufacturing facilities, and fog computing is ready to make an impact in these areas.

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