Use case / Batch production

Make to stock,
in whole batches.

Batch production is the simplest shape MRP takes. You hold finished stock, you replenish it according to demand, and the only question that really matters is when the next batch has to start.

How it works

Simple batch production
in Dream MRP.

Mark the item as Make to Stock, optionally give it a minimum stock level and a package size, and the calculation does the rest. On hand, open sales orders, open work orders and incoming purchase orders are all netted against each other. When the result falls short, a work order is planned — and multiple demand sources for the same item are merged into one order.

Components are exploded through the bill of materials in the same pass, so the purchase orders for raw material are planned alongside the work order that will consume them, at every level of the product tree.

How the calculation works →How the night run works →See the screens →
make to stock
Item type sets the outcome

Classifying an item as Make to Stock tells Dream MRP that a shortage becomes a work order rather than a purchase order, and that stock is held ahead of demand rather than built against one named customer.

min stock
Minimum stock is real demand

The minimum level acts as a floor. If sales-order demand falls below it, the minimum is planned against instead, so the batch is triggered before an order forces the issue.

package size
Rounding to package size

Package size is set on the item. Work order quantities are rounded up so that full packages are produced.

night run
Recalculate overnight

The night run re-syncs data from your accounting system or e-commerce store and recalculates the whole plan while you are closed, so the Work Orders board is already correct when the first shift walks in. Or click Run MRP to see the plan immediately, and the night run will pick up any changes you make during the day.

Best for: Manufacturers who hold finished stock with standard products — food and drink, cosmetics, hardware, anything with a setup cost worth spreading over a run.

More use cases

Other ways
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