Fewer shorts, fewer changeovers, less overtime. Prodooo finds the plan your plant can actually run — and that holds when a line goes down.
Built for beverage. Runs alongside SAP, Blue Yonder and Kinaxis.
Capex is committed. Labour is fixed. Formulations are locked. The schedule is rebuilt every week — and it decides how much of your capacity you get to sell. Most bottlers hand that decision to one planner and a spreadsheet. The planner is very good. The problem is larger than any person.
Your planner can hold three of these in their head. They are choosing between millions, every week, with no way to tell by hand which one they landed on.
Scheduling is usually pitched as a cost story. In beverage it isn’t — the bigger number is almost always the sales you didn’t make because the right SKU wasn’t on the right line in the right week.
Fill rate is a scheduling outcome, not a forecasting one. When the sequence is right, the cases exist when the order lands.
Every avoided changeover is production hours back, plus the water, chemicals and syrup that went with it.
Not a separate sustainability feature — the same avoided changeovers, in the units CSRD and Scope 1 already ask for.
Every Prodooo schedule arrives costed against the plan you would otherwise have run — so the number is on the plan, not in a business case somebody writes afterwards.
| Driver | Current | Prodooo | Δ |
|---|---|---|---|
| Changeover & CIP hrs | 412 | 171 | −241 |
| Forecast short cases | 38,400 | 4,100 | −34,300 |
| Finished goods days | 11.4 | 8.9 | −2.5 |
| Overtime shifts | 14 | 6 | −8 |
| Contribution impact | — | your figures | — |
It takes your demand plan, your master data and your plant’s physical limits, and evaluates millions of possible sequences against all three production stages at once — syrup, blending and filling, solved as one problem rather than three.
What comes back is a conventional schedule board your planners can read and edit. Their sequencing rules go in first, so nothing is proposed that the floor can’t run — and that knowledge stays when they retire. You set how far it leans toward service or efficiency, and every schedule arrives costed against the plan you’d otherwise have run.
Both plans fill the same orders in a normal week. The difference only shows up when something breaks — which is most weeks.
At 09:00 the filler on Line 2 goes down for nine hours, with 22,000 cases in the affected window.
The hand-built schedule had Line 2 running the only 2L format that week, so the volume has nowhere obvious to go. Moving it to Line 3 needs a format change, so a CIP, which pushes Orange into the night shift — and Orange was feeding the blend tank Line 5 was waiting on. Six lines get resequenced to absorb one failure.
Prodooo had already spread that 2L volume across two lines earlier in the week, at the price of one extra changeover on Monday. When Line 2 stops, the recovery touches two lines and finishes inside the shift. Nothing is renegotiated with a customer.
That Monday changeover is what robustness costs — the cheapest line in this table.
| Blast radius | Hand-built | Prodooo | Δ |
|---|---|---|---|
| Lines needing resequence | 6 | 2 | −4 |
| SKUs moved | 19 | 5 | −14 |
| Added changeovers | 7 | 2 | −5 |
| Shipments re-booked | 11 | 2 | −9 |
| Delivery dates renegotiated | 6 | 1 | −5 |
Both schedules fill the same orders in a normal week. The difference only appears when something breaks — which is most weeks.
No. Prodooo reads your master data and demand plan, solves the production schedule, and writes the orders back. Your ERP stays the system of record, your planners keep their reporting, and nobody rebuilds an integration landscape to run a pilot.
A pilot runs on a flat-file export if that is faster. The integration is worth building once the number is proven, not before.
Schedules reveal capacity, cost structure and customer commitments. We treat that accordingly, and we’ll go through your security review before we ask for a purchase order.
Your data is never pooled with another bottler’s, and never used to train models for anyone else. Your plant model stays yours, including if you leave.
Certification in progress. Current controls, penetration test results and architecture available under NDA.
Cloud, your own cloud tenancy, or on-premise for plants with restricted network policies. Scheduling runs on master data and orders — no personal data is required.
SAP validates a schedule someone already decided on; it doesn’t search the sequence space. Most teams we meet are exporting to Excel to build the actual sequence.
Those are horizontal platforms configured per industry, and only as good as that configuration. Prodooo ships with the three-stage beverage model already built. Narrow on purpose.
No — it gives them a bigger search. Their rules become explicit constraints, every schedule is editable, and an override re-solves the rest of the plan.
Twelve months of demand history, SKU and routing master data, changeover matrices, tank and line capacities. If the changeover matrix is incomplete, we reconstruct it from historian data.
Two weeks of modelling, most of it ours. From you: the data extract, and a few hours a week from one planner to confirm the rules.
Your planner does, from the schedule board. If they can build a sequence in Excel today, they can run Prodooo. No solver knowledge required.
Shadow mode. Prodooo builds a schedule alongside your team’s for four to six weeks, with no operational risk. You compare on real weeks before anything reaches the floor.
Six to eight weeks on one plant: two to model it, four to six in shadow. You end with a costed comparison on real weeks, not a demo.
You stop. The pilot runs alongside your existing process, so there is nothing to unwind — and the plant model goes with you.
Prodooo compares planned to actual every shift. Persistent deviations — a line that never hits rated speed on 2L — get surfaced, confirmed, then folded in.
We’ll model it, run it in shadow mode against your own schedulers, and show you what the difference is worth.