Productivity recovered across multiple APUs at a large-scale European API plant
Annual plans built from sales projections gave way to event-driven schedules across multiple APUs in 8–12 weeks.
Time from kickoff to documented gains in capacity utilisation and batch cycle time across the plant. Based on Neewee deployment observations in regulated process manufacturing.
Directional indicator. Capacity recovery across the autonomous production units moved into the signed-off +10–15% range observed across deployments. Specific figure withheld at customer request.
Directional indicator. Hidden buffer time between batches compressed once schedules began rebuilding on live shop-floor signals rather than aging assumptions in the annual plan.
Directional indicator. Inventory tied up in work-in-progress and safety buffers decreased as forecast-driven recommendations replaced precautionary build-ahead. Within the signed-off −10–20% WIP range observed across deployments.
The challenge
The plant ran multiple autonomous production units (APUs) — each with its own buildings, equipment lines, and final-processing areas serving different products — on a single annual production plan. The plan was built from sales projections at the start of the year and re-cut by hand whenever a demand shift, an asset issue, or a batch slip forced the change. The hand-cut rebuilds were slow and error-prone; by the time the new plan had been validated, it was already out of date.
The deeper problem was that the annual plan was a snapshot, not a live model of the plant. Long-term commitments couldn't be kept aligned with what was actually happening on the shop floor. Each APU ran semi-independently, with its own constraints, schedules, and bottlenecks — and capacity wasn't being optimised across them. Resource pools that one APU couldn't reach this week sat idle while another absorbed the shortfall through buffers and overtime.
The team needed a scheduling layer that could:
- Take live signals from across all APUs, not wait for the next manual rebuild
- Hold long-term commitments and short-term events in one model
- Surface unused capacity in the existing resource pool, rather than absorb every shift through inventory and overtime
“We weren't running short of capacity. We were running short of a way to see, in real time, where the capacity actually sat. The annual plan told us what the plant should be doing — not what it was doing this week.”
Plant Head, Plant Head, European API provider
What Bodhee did
Bodhee Production Scheduling deployed as an event-driven layer on top of the existing annual planning process — not a replacement. Alongside the plant's ERP environment, Bodhee took live signals from across the autonomous production units and produced schedules that respected every constraint encoded in the long-term plan and every event surfacing from the shop floor.
The integration was thin and direct. Standard Excel templates consolidated historical and current production data in the first phase. ERP connectors then kept master data, order books, and crew rotations flowing once Bodhee was live. Live shop-floor signals — asset downtime, resource availability, batch progress, quality holds — fed Bodhee through the plant's existing data layer. Nothing was rebuilt; existing systems kept their existing roles.
The scheduling engine ran multi-objective optimisation against the plant's actual constraints. For each rebuild, it generated several plan candidates weighted differently against capacity utilisation, cycle time, and on-time commitments. Planners reviewed the candidates in a control-tower view — a live Gantt across every APU, an event stream of shop-floor signals, and alerts when a constraint was at risk — and published the version that best fit the week's priorities.
Rollout was phased over twelve weeks. Weeks zero to two were data harmonisation through standard templates and ERP integration. Weeks two to five were constraint modelling — encoding the goals, hard rules, and influencing parameters into the multi-objective scheduler. Weeks five to eight brought the control tower live for planning, supply chain, and operations. Weeks eight to twelve produced the first measurable gains in capacity utilisation and batch cycle time.
Within four weeks of go-live, schedule rebuilds against live signals were running in minutes rather than days. Within twelve, the annual sales-projection plan had stopped being the plant's operating document. The control tower had taken its place.
Outcomes
The plant's schedule is now event-driven across every APU. When a demand shift or shop-floor signal arrives, the scheduler generates revised plan candidates within minutes; the planner publishes the version that best fits the week, and the control tower propagates the change to supply chain and operations in the same view. Capacity that previously sat unused — because the annual plan didn't see it — has been recovered across the autonomous production units.
The downstream effects show up across cycle time and capital. Hidden buffer time between batches has compressed as schedules began rebuilding on live signals rather than aging assumptions. WIP and safety-buffer stock have dropped as forecast-driven recommendations replaced precautionary build-ahead — freeing working capital that was previously tied up in inventory. Real-time batch monitoring replaced the periodic status reports that planners and supply chain used to reconcile by hand.
The planner now spends most of the week scheduling forward — running scenarios for upcoming demand, validating engine proposals, and resolving constraint exceptions — rather than re-cutting the annual plan to absorb whatever the floor surfaced today. The change in where their hours go is, in itself, the change the team set out to make.
“What changed isn't just the schedule — it's where I spend my Mondays. I used to walk in to a stack of demand changes and start rebuilding. Now the engine has a candidate plan ready by the time I sit down, and I'm validating it instead of writing it.”
Production Planner, Production Planner, same plant
Products used
Why this matters beyond a leading European Active Pharmaceutical Ingredient provider operating a large-scale, multi-APU commercial site
The pattern isn't unique to API manufacturing. Any process plant organised around semi-autonomous production units — multiple lines, blocks, or buildings each with their own constraint sets — is paying a coordination tax whenever the master plan is built once a year and re-cut by hand. The deployment shape (event-driven scheduling layered on top of existing ERP, multi-objective optimisation against the plant's actual constraints, a control tower as the operating view) generalises to specialty chemicals, biologics, and other multi-block process operations with similar plant topologies.
What doesn't generalise is the constraint set. Every plant encodes its own combination of equipment dedication, cleaning rules, crew skills, and quality gates. The Bodhee work is in surfacing those constraints from the people who already hold them in their heads — and encoding them once, alongside the existing systems.
“Twelve weeks ago, our operating view of the plant was last week's spreadsheet. Now it's a Gantt that updates when the floor moves, and a plan that rebuilds in minutes when the demand changes. The capacity was always there. We just couldn't see it.”
Plant Head, PLANT HEAD, EUROPEAN API PROVIDER
Customer anonymised at customer request. Metrics validated by Neewee delivery team. April 2026.