For batch chemical plants
Unified AI reads your DCS, historian, batch records, ERP, CRM and demand channels. When a batch goes off-spec, it finds the cause first, then every job the failure touches, and puts the re-plan in front of the person who owns the call. It writes to nothing.
A 30-minute walkthrough built on your plant's own scenarios.
Heating step overshot by 6°C for 14 min on R-3. Matches 3 earlier deviations on this product.
Jobs touched
Move ORD-4471 and ORD-4473 to R-2, Friday campaign. Hold the ORD-4480 raw-material draw.
Illustrative product surface.
All figures from one $200M specialty chemicals company. One plant, not a typical result.
A DCS runs one reactor. A historian remembers one tag. An ERP holds one order book. Nothing has ever run them together, so when something changes, the job falls to a meeting. Six departments, four days, and an answer that describes a plant that has already moved on.
An operating system for the plant does what the meeting does, in minutes: finds why, works out what still holds, re-plans every job the change touches, and hands the decision to its owner.
Re-sequence R-1 to run ORD-4473 first; pull 400 kg RM-118 forward; move DEL-2210 to Saturday's container.
One decision. Every job it touches, already re-planned.
Hold batch 2248-85 at the heating step; re-sequence ORD-4471 to R-2.
For years one operator ran batches faster than every other shift and nobody knew why. In week four the system found it: heat terminated at 40°C, not the 45°C in the procedure. It is now the standard.
At the same plant it found a manual step that appeared in no procedure, that the product manager did not know existed, and that the plant depended on.
Every finding a person accepts or rejects is kept against that plant, product and stage. When the operator retires, the reason stays.
Terminate heating at 40°C on this product. Matches the fastest 31 batches; no quality deviation recorded.
Scale-up is where specialty chemicals loses the most money and the most time. Every trial batch is judged against lab targets by hand, first-campaign settings are guessed from the last product, and the ramp runs for months while the plant learns what the lab already knew.
Unified AI evaluates every trial batch against the lab target band, recommends first-campaign parameters from the plant's own history, and flags ramp deviations as they start rather than at the batch review.
At one $200M specialty chemicals company, two products reached full rate in 3 months, with no additional capex. The plant's previous norm was 12.
The monthly plan now starts from the measured cycle time, not the standard the plant wrote years ago.
Process engineering finds the deviation, ranks the root causes and shows where cycle time goes.
Scale-up checks every trial batch against lab targets and recommends first-campaign settings.
Maintenance and utilities flag failure precursors and rank work orders by batch impact.
Quality assembles out-of-spec data against process conditions and pre-screens batch records.
Inventory recommends reorders and prices every expedite against the orders it saves.
Planning re-sequences campaigns to cut changeovers and shows what a rush order really costs.
Pick the deviation, the slipped delivery or the rush order that cost you most this quarter. We will show what the plant would have known, and when.
