Postponement: Choosing Where to Pack and Where to Hold Cycle Stock
Packing at the DCs Pays for Some Product Categories, and Cycle Stock Stays at the Plants
A consumer goods manufacturer makes bulk product on cycles as long as 91 days and packs it at the plant. Every run becomes a large block of finished goods, allocated to items and distribution centers on a forecast. The company wants to know whether packing at its major distribution centers would do better, and where cycle stock should sit. A model of the network shows that packing downstream cuts finished-goods inventory and ends redeployment between distribution centers while holding customer service. For many product categories, though, the new bulk inventory offsets those gains, and the model shows which categories those are. Cycle stock is most cost-effective held at the plants.
- The business
- A consumer goods manufacturer with plants and major distribution centers
- The question
- Should packing move to the distribution centers, and where should cycle stock sit?
- The model
- Two network models on historical forecast, demand and production rates, scored product by product
- What it’s worth
- A product-by-product answer on where downstream packing paid, instead of one network-wide call
- Source
- SDI project record · original model output
What the company is up against
The plants make bulk material on cycles of 7, 14, 28 or as many as 91 days. Each production run is allocated straight away to packaging runs of finished items. Packing at the plant turns every long run into a large block of finished-goods cycle stock, and the forecast decides which item and which distribution center each case goes to.
When the forecast is wrong, the cost shows up twice. Bulk is packed into the wrong items, and finished goods go to the wrong distribution centers and have to be redeployed between them. Product that ages too long is disposed of.
The company is weighing postponement: ship bulk to its major distribution centers and pack there, closer to demand. It also wants to know whether each run should be pushed out to the distribution centers or held at the plant.
How it’s modeled
SDI builds two related models on the same inputs, historical forecast and demand plus production rates by system and product. One tested where packing should happen. The other tested where cycle stock should sit.
| Question | Alternatives | Experiment factors |
|---|---|---|
| Where should packing happen? | Pack at the plant, or ship bulk to the distribution centers and pack there weekly against next week’s local forecast | Reorder policy; which product categories to pack downstream |
| Where should cycle stock sit? | Push each run out to the distribution centers, or hold it at the plant until a distribution center reorders | Reorder policy; which product categories to push downstream |
Both models report total inventory, customer order fill rate, redeployment cost and the cost of disposing of aged product. The cycle stock model also splits inventory into finished goods and bulk.
What the model shows
Packing downstream helps some categories and not others
Packing weekly at the distribution center, against that location’s own forecast for the next week, almost eliminates finished-goods cycle stock. It ends redeployment of finished product between distribution centers, and customer service holds. The price is bulk inventory at the plants and at every distribution center.
| Pack at the plant | Pack at the distribution center | |
|---|---|---|
| Gains | No bulk inventory anywhere; it becomes finished items as it’s made | Finished items packed weekly close to demand, which limits the damage from forecast error. No redeployment between distribution centers. |
| Costs | Bulk allocated to items on a forecast, and items allocated to distribution centers on a forecast. Misallocation means redeployment. | Bulk inventory at plants and at every distribution center, which can raise total inventory cost |
For many product categories, the new bulk inventory offsets the gains. For others it doesn’t. The model lays out the trade product by product, so the answer isn’t simply “pack downstream” or “don’t.”
Pushing cycle stock downstream backfires when it’s misallocated
Pushing each run out to the distribution centers looks protective, because the extra stock backs up their safety stock. But a misallocated run leaves some distribution centers short while others hold plenty, and redeployment between them isn’t always feasible. The locations that run out early trigger the next production run early.
| Hold cycle stock at the plant | Push cycle stock downstream | |
|---|---|---|
| Gains | Less redeployment for high-variability products; fewer early production triggers | Cycle stock at the distribution center protects its safety stock |
| Costs | Safety stock at the distribution centers gets no help from cycle stock | Misallocation leads to redeployment and early production triggers |
The team concludes it’s most cost-effective to keep all cycle stock upstream at the plants and fill the distribution centers on a pull basis. That includes the bulk material shipped downstream for packing.
What else the model settled
An adjusted textbook safety stock formula holds up
Packing on a fixed weekly schedule changes how much safety stock a location needs. The baseline safety stock calculation, modified for that weekly schedule, accurately predicts the safety stock the model needs at the customer-facing inventories.
Capacity shortfalls have a set order of response
When a plant can’t keep up with demand, the model first moves that demand to another, less efficient plant. Only when capacity falls short across the whole system does it schedule prebuilds.
Where this work comes from
Simulation Dynamics builds these models with its Supply Chain Builder library on a commercial simulation platform. It’s the same team, and the rate-based method ReliaSim® is built on.
Production run length is a plant decision, while redeployment, fill rate and disposal are network outcomes. ReliaSim’s supply chain modeling holds both in one model. The simplest version of the question, where to hold safety stock across a multi-tier network, runs in the browser.
Built by Simulation Dynamics, the team behind ReliaSim, with the rate-based method ReliaSim is built on · evidence: SDI project record and original model output.
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