Adoption, Support & Scale
The part that decides whether any of the rest was worth doing. Go-live is the midpoint of the project, not the end of it.
Why do manufacturing systems fail after go-live?
Because adoption was treated as training rather than design. A system that adds thirty seconds to an operator's cycle, or that asks a supervisor to enter something they already wrote on a board, will be worked around no matter how good the training was. Adoption is decided months earlier, in the design.
Nobody rejects a system out loud.
Rejection looks like the board staying up next to the terminal. It looks like entries batched at the end of a shift instead of captured as events. It looks like one reason code used for eighty per cent of stoppages because it is first in the list. The system is live, the dashboard is green, and none of the data means anything.
The parallel record survives
If the whiteboard, the diary or the spreadsheet is still being maintained a month after go-live, the system has not replaced it. It has been added on top of it.
Entries cluster at shift end
Events captured in a batch at 13:55 were not captured as they happened. The timestamps are fiction, and every calculation built on them inherits it.
One reason code dominates
When "Other" or the first item in the list accounts for most stoppages, the taxonomy is wrong or the entry moment is wrong. Usually both.
What we actually do to make it stick.
Adoption designed in, not added
Entry points chosen so capture happens at the moment the event happens, in the flow of work rather than beside it. This is a design decision made in phase three, not a training decision made in phase four.
Operator and supervisor training
Run on the line, on the real system, in the language people work in. Supervisors first, because they are the ones who decide whether their shift uses it.
Daily management redesign
The morning meeting has to change, or the system is a parallel activity. We rebuild the review routine around what the system now shows, and remove the reporting it makes redundant.
Benefit tracking
Baselines captured before go-live and measured after, against the numbers named in the business case. Including the honest report when a benefit did not land.
Managed support and enhancement
Ongoing support, and a route for the changes that always follow first use — because the requests that arrive in month two are the ones that come from actually running it.
Multi-site rollout
A playbook built from the first plant: what to standardize, what must stay local, and how to avoid rebuilding from scratch at every site.
Getting past the first plant.
This is where most programs stop. The pilot works, the case is proven, and the second site still takes as long as the first because nothing was built to be repeated.
The difference is deciding in advance what is a standard and what is a local variation — and being disciplined about the boundary. Master data, reason-code taxonomies and integration patterns should be standard. Shift patterns, routings and approval chains almost never can be.
A configured baseline, a site readiness checklist, the integration pattern for connecting a new plant's estate, a training package the local team can run themselves, and a defined list of what each site is allowed to change. Without that last item, standardization erodes at site three and you are maintaining twelve systems.
Common questions.
Through the first full cycle of your business at minimum — long enough to see a month-end, an audit and a shutdown, because those are when systems reveal what they cannot do. After that, ongoing support is an explicit agreement rather than an assumption, and we would rather you needed less of it over time.
We would rather find out from the data than from a meeting. Usage patterns tell us early: entries batching, fields left blank, one reason code dominating. The fix is usually a design change rather than more training, and it is our job to make it, not to ask you to push harder.
Often, yes — this is a common way we start. We assess what exists, what state it is in and whether it is worth continuing before committing to support it. Occasionally the honest recommendation is that it is not.
Start with the audit, not the software.
A fixed-scope engagement. We walk your floor, map where data breaks down, and hand you a costed roadmap — whether or not you build any of it with us.