Sub-second polling, contextualized signals, and dashboards built for the line and the boardroom at the same time. Reports stop arriving three hours after the problem, and the historian stops being a black box that only answers questions you already know the answer to.
Sub-second
Production data resolution
~15% / ~15%
Throughput and yield gains on a global medical device line, before any advanced analytics
~10,000
Candidate data points identified per line in discovery
Visibility work pays when decisions are already being made badly for lack of a current number. If two or more of these describe your plant, the data you need is almost certainly already passing through the controllers.
01
By the time the dashboard updates, the loss has already accumulated. Operators are reacting to history instead of to what is happening now, and every conversation about yesterday starts with archaeology.
02
Data is being collected. Nobody can pull a clean number out of it without half a day of spreadsheet work, which means the number only gets pulled when somebody is already angry.
03
Equipment suppliers warned you that high-frequency polling would overload the controllers. So you stayed conservative, and you stayed blind.
04
If your performance metric has a 40-point range, you do not have a metric. You have a guess that everyone in the room is allowed to interpret differently.
Every item below is something you can see on the floor or read off the board. Nothing on this list is a document.
Sub-second to multi-second polling, distributed loading, minimal controller overhead. Designed so that “it cannot be done” retires itself during deployment rather than in a slide.
Throughput, yield, downtime, and alarm-driven root cause, visible while production is still running instead of after the shift report is typed up.
Views for ops, plant, and divisional leadership that do not fight the line view. Same numbers, different altitude, one definition underneath.
Tags, asset frameworks, and metadata so the data answers the questions your team is actually asking rather than simply storing them for later.
Continuous health monitoring across the rollout. The line does not pay for the visibility, and we can show you that it did not.
Before-and-after instrumentation tied to the metric that justified the spend. The visibility proves it earned its keep, in the same system that produced it.
What separates a live board from a late report
Architecture, integration, dashboards, and the operating routines that use them. We deliver across all four routes below, and most engagements blend two or three. Underneath all of them is the same engagement model — assess, target, execute, sustain.
Strategy & Operations
When the question is which signals matter first, with what budget, and for what measurable return. We turn visibility ambition into a sequenced, fundable plan that survives contact with the shop floor.
Digital Integration
OT and IT integration, MES, historians, and the connective tissue that turns shop-floor signals into decisions on the floor and numbers in the boardroom. This is where most visibility work actually lives.
Rapid Impact
One line instrumented and visible before anyone signs up for a plant-wide program. Narrow scope, fixed timeline, a dashboard the supervisor is using by the end of it.
Lifecycle
Adoption, training, and sustainment so the platform keeps shipping value. Most manufacturing software dies of neglect, not bad design, and dashboards die fastest of all.
A global medical device manufacturer ran high-speed lines around the clock, with frequent short stoppages, high alarm volumes, and repeated manual resets nobody could root-cause. The data existed. The answers did not.
Case · Global Medical Device Manufacturer
“Real-time visibility delivered measurable production gains with minimal disruption — and gave leadership the proof they needed to scale this globally.”
A data discovery workshop identified roughly 10,000 candidate data points per line. We built a distributed high-speed acquisition architecture at sub-second polling, with controller health watched through the rollout, and put real-time dashboards in front of operators and leadership at the same time. Throughput and yield each rose about 15% — before a single advanced analytics model was introduced.
Outcome
~15%
Throughput and yield, each, before any advanced analytics. The result triggered a global rollout backed by leadership in North America and Europe.
Read the full caseMagic Consulting sits inside a group with 15,000+ professionals and 6,000+ customers in 50+ countries — combined group figures, and the reason a single-line instrumentation project can draw on real depth when the rollout gets serious.
No. We have deployed across the major platforms — Ignition, AVEVA and the former Wonderware line, Rockwell, PI — and across bespoke stacks. We pick what fits the problem and the team that has to run it after we leave. The right tool is not necessarily a Magic tool, and the recommendation says so when it does not.
It can, if the architecture is wrong. We have deployed sub-second polling at scale on the same controller families, with distributed loading and continuous controller health monitoring through the rollout. The objection is real. The conclusion is not.
Usually one of three reasons: the polling rate is too slow to catch the events that matter, the data is not contextualized enough to answer the question, or the dashboard was not built around the operator's actual workflow. We diagnose which one it is before recommending anything, because the three have completely different fixes.
We design to your existing change-control and cybersecurity standards from the start, and the architecture goes through them rather than around them. If you do not have OT standards yet, we will help establish a workable baseline as part of the engagement instead of leaving you a system nobody will sign off on.
Operators are using the dashboards without being prompted. The platform is documented and your own team can extend it without calling us. The original ROI thesis is still being measured and reported, in the same system that produced it.
Seeing the loss is step one. Most engagements that start here go on to attack what the data exposed. If you are not sure which one you are looking at, the readiness questions on the Consulting page will point you.
Capacity & Throughput
Once you can see the micro-stops, you can rank them. Visibility is often the first move in a capacity engagement, not the end of one.
Read the pageEliminate Waste
When the instrumented loss inventory comes back and the same twenty minutes is on the same cell every shift, this is the sharper tool.
Read the pageCost Visibility
Same instrumented data layer, financial lens. Once the floor data is trustworthy, cost per part stops being an argument.
Read the pageThirty minutes, working session, your numbers. We will show you how we would close it — or tell you straight if we are the wrong people for the problem.
