On the line, the problems are familiar. Production schedules slip the moment one machine or supplier does, and replanning happens in someone’s head. Inventory counts on the system do not match what is actually on the racks, so purchasing hedges and working capital sits in the wrong places. Shop-floor data lives in spreadsheets and on whiteboards, captured once a shift if at all, and never quite reconciles with what finance sees.
Underneath it is usually an ERP that does not match how the plant runs — set up for a process the business has since outgrown, or fighting the way operators actually book work. When a customer or auditor asks you to trace a batch back through materials and process, the answer takes hours of digging rather than a query.
Production rarely runs the way the system says it does — closing that gap is the work.
Where our work fits
We start with how the plant operates, not with a product to install. The core things we do apply to manufacturing in fairly direct ways:
- ERP — implementation, migration and ongoing support for the system your operations rely on, planned to match real routings, BOMs and stock movements rather than forcing the line to fit the software, and sequenced to avoid a disruptive rip-and-replace.
- Custom software — the shop-floor capture, planning aids and reporting that sit around the ERP, shaped to how your operators and schedulers already work so data is recorded once and trusted downstream.
- Applied AI — automation and decision support where it earns its place: flagging demand or schedule changes early, surfacing likely stock discrepancies, easing the manual replanning that eats supervisors’ time.
- IT staffing and talent — experienced people who understand both the systems and a working plant, integrated with your team and accountable for delivery through go-live and beyond.
Why it tends to hold up
Manufacturing software fails most often where it meets the floor — when capture is awkward, the model does not reflect the real process, or traceability was bolted on afterwards. We have lived those problems, which is why we treat the connection between the line and the system as the work itself, not an afterthought.
The result is a plant where the schedule, the stock figure and the batch history are ones your teams can act on without checking them by hand first.