In our most recent PodMagic episode, The Reality of IT/OT Convergence in Manufacturing, host Scott Mann, SVP Global Sales at StorMagic, sat down with Christopher “Chris” Lloyd, Chief Solutions and Technology Officer, SYSPRO. The wide-ranging conversation moved from IT/OT convergence and workload placement through to the true cost of downtime, cybersecurity on the shop floor, and where AI realistically fits into manufacturing today.
Here are the key insights and trends we identified for edge computing in manufacturing.
Edge Virtualization Isn’t Cloud vs. On-Premises
For years, manufacturers have faced an all-or-nothing framing that pushed them toward migrating everything to the cloud or keeping everything on-premises, and Chris made the case in the episode that this framing no longer reflects how modern manufacturing environments actually need to operate.
“I don’t think the conversation anymore is purely cloud versus on-prem as a universal conversation. I think it’s always going to be a conversation about where to place the workflow at the right place.”
Christopher “Chris” Lloyd, Chief Solutions and Technology Officer, SYSPRO
Time-sensitive processes, such as scanning inventory as it moves through a distribution center, typically need virtual machines (VMs) and compute running close to where the work actually happens. While less time-critical workloads can run in the cloud without meaningful risk to operations.
This is increasingly why manufacturers lean on hyperconverged infrastructure (HCI) and edge virtualization to support that kind of workload-by-workload decision-making, rather than committing to a single company-wide cloud or on-premises policy that rarely fits every part of the business equally well.
Downtime Means Something Different on the Shop Floor
While IT teams tend to measure downtime through system uptime percentages, manufacturers think about it through a different lens entirely. They use a metric called Overall Equipment Effectiveness (OEE) that captures how much of a machine’s available time is actually being used productively rather than simply whether a connection is live.
Our PodMagic episode referenced figures placing the cost of manufacturing downtime as high as $260,000 an hour, or roughly $20,000 a minute. It’s numbers of that scale that explain why Chris argues “always on” is not always the right target for high-availability (HA) manufacturing environments.
“For manufacturing, the zero downtime is not necessarily the right target. It’s around bounded downtime. It’s predictable, it’s agreed and recoverable downtime.”
Christopher “Chris” Lloyd, Chief Solutions and Technology Officer, SYSPRO
This is also where resilient, purpose-built infrastructure becomes so important. Since local failover and virtualized redundancy are what allow deterministic workflows to keep running even when a wide-area connection drops out unexpectedly.
Cybersecurity Risk Grows as the Shop Floor Gets Connected
As manufacturers bring more IoT devices and connectivity onto the shop floor in pursuit of better visibility, they inevitably open up more potential attack vectors as well. There are many overlooked basics that manufacturers should be revisiting, including patching, changing default passwords, and keeping IT and OT networks properly segmented from one another.
“Just because it’s working doesn’t mean it shouldn’t be maintained.”
Christopher “Chris” Lloyd, Chief Solutions and Technology Officer, SYSPRO
Where AI in Manufacturing Actually Fits
Manufacturers sit at very different points on the AI adoption curve. Buying a solution simply because it has AI attached to it is rarely the right starting question for a business trying to solve a specific operational problem.
Connecting IT and OT data together to support genuinely better decision-making on the shop floor. Listen to the PodMagic episode for the full insight.
The Workforce Shift Manufacturers Are Planning Around
A significant share of the manufacturing workforce is expected to retire in the coming years at precisely the same time that edge and AI adoption is accelerating. Leading research body McKinsey states that many advanced industrial manufacturers, who frequently build complex products with long cycle times, often forecast labor demand years before a bid is won and base it on assumptions of labor proficiency. This is now being fundamentally uprooted by the realities of labor supply.
So, how can manufacturers overcome this? As discussed in our PodMagic episode, the key could be unifying data and reducing reliance on a small number of key people. This is an urgent trend to pay attention to for manufacturers trying to plan their infrastructure and workflows well into the future.
In fact, McKinsey backs this insight up. The organizations that have been most successful at decreasing time to proficiency have embraced a data-driven, test-and-learn approach that is calibrated by cross-functional teams, a willingness to try new forms of technology, including AI, and a focus on tailoring strategies to the employee experience.
Don’t Miss These Edge Computing in Manufacturing Insights
This overview only touches on part of a much longer discussion.
Our full PodMagic episode also explores data sovereignty, what IT/OT unification really requires in practice, and where Chris sees manufacturing heading next.
Listen to the full PodMagic episode with Chris Lloyd, The Reality of IT/OT Convergence in Manufacturing.
PodMagic is StorMagic’s podcast about solving real IT challenges. New episodes explore how simple, reliable virtualization and edge infrastructure support the people running branch offices, retail stores, and manufacturing sites around the world.
