Is IIoT Living on the Edge in Industrial Environments?

Is IIoT Living on the Edge in Industrial Environments?

Given the hype about IIoT, the ARC Advisory Group asks if it is really time to focus on the edge. The promise of edge computing in industrial environments means getting the right device data in near real-time to drive better decisions, and maybe even control industrial processes. For this to work, it means that the edge device, its embedded software, edge servers, the gateways and cloud infrastructure must all be up and running correctly all the time.

Who is operating this way already? What triggers upgrades in the plant? How long does it take to get a secure and reliable distributed control and analytics system? This is what ARC asked a group of over 300 end users to get a feel as to the current and future state of the market.

This data sheet is a complement to the ARC Market Report that has been published recently.

Research Findings Acceptance of IIoT and Edge Concepts

• 74% accept that on-premise computing systems outside the data center can be defined as edge
• 93% agree a mix of computing power both at the edge and in the cloud will be part of the industrial automation infrastructure
• 91% of IA users surveyed stated having better systems and connectivity at the edge will enable improved real-time decision making.

Edge Investments

• 56% of respondents still plan for a balanced – hybrid – approach for future cloud and edge investments
• 27% will invest more heavily in edge computing resources

Real-time analytics capabilities

• 30% expect to perform data analytics at the edge
• 58% of users would not want to use the cloud as an intermediary nor have it reside in the data center
• Only 18% would kick the analytical function up a level to the data center while 24% would rely on cloud resources

Why Respondents are Embracing the Edge

• Top three needs for deploying systems and connectivity at the edge are:
  • Analyzing and controlling devices
  • Improving process speed or reduce latency issues
  • Reduce data security risks
• Primary reasons related to operational concerns:
  • Improving asset performance and maintenance
  • Improving and optimizing production (i.e. prevent unplanned downtime)

Planning for the Edge

• 34% of respondents are conducting an edge analytics solution pilot
• Most are still in early stages including education mode (28%) or selecting a partner (9%)
• 12% are getting edge-enabled results now – with more discrete users seeing that now (16%) versus process (9%)

Edge Workforce

• 73% of respondents envision sourcing new skills or personnel to leverage edge analytics solutions
• 66% say someone in a hybrid IT/Operations role will be responsible for edge-based analytics
• 91% also indicated that, as edge computing grows, organizations will need a simplified edge infrastructure that can be remotely managed
• More than half (57%) expect to keep remote management of the edge in-house
• But 44% still feel outside contractors will be involved in managing edge infrastructures.