A coffee plant was adding a new production stage for quality reasons, right in the middle of the process, between roasting and grinding. Nothing about the equipment was failing. But the plant's scheduling practice had been built for the old process, and nobody could say whether it would still work once the new stage was in.
The plant
The plant ran as Make-Store-Pack. The making side fed intermediate storage, and packing lines drew from that storage into finished packages of different sizes. For years, roasting had been scheduled first, and packaging had to jump through hoops to get product out to customers.
The new stage took away much of the making side's freedom to supply product in time for scheduled packing runs. Each package size draws from storage at its own rate, a rate we call the "slurp rate." Depending on the size being packed, storage could drain too fast, or, worse, too slowly. The plant's standard Advanced Planning System assumed a fixed constraint. With the new stage, moving bottlenecks and bin lock looked likely.
How we modeled it
SDI built a Make-Store-Pack simulation using discrete rate simulation, the bulk flow method Andy Siprelle created for plants like this one. The model included the engineering change itself, the warehouse and storage between making and packing, and a schedule generator, so the team could test scheduling rules and stocking policies instead of hand-built schedules.
Most of the value came from making the rules explicit. Working through the model, the team wrote down, explained and then experimented with the scheduling rules that actually governed the operation.
Why trust it
The model reproduced the problem the planners were worried about: bottlenecks that moved as the package mix changed. Because it showed that behavior instead of assuming one fixed constraint, it gave a fair test of any schedule. The new practice was checked across a range of demand scenarios before the plant relied on it.
What we found
- The old practice would not survive the change. With the new stage in place, the model was highly sensitive to the existing scheduling practice, and it proved that practice inadequate before the change went live.
- The bottleneck moved with the package. Changing packaging size moved the constraint. A plan built around one fixed bottleneck could not hold, which is why the planning system's schedules would keep slipping.
- A new scheduling practice worked. The team conceived a new scheduling algorithm in the model, tested it, and verified that it held up in most of the demand scenarios tested.
What changed
The plant adapted its scheduling and operating strategy to the new process before the new stage started up, instead of discovering the problem on the floor. The work changed how the operation was understood and managed.
Source
SDI project record; client not named. For the ReliaSim view of this study, see When the bottleneck moves with the product.