inventory optimization.
Set stock levels across locations and items. Balance holding cost against the risk of running out, and meet the service level you promise.
How much of each item belongs in which warehouse, so the service level holds with the least capital tied up?
Multi-echelon inventory optimization combines models of uncertain demand with optimization over the network. kint uses mixed-integer programming for network decisions and machine learning for demand forecasts, and solves the two as one problem.
where inventory optimization gets hard.
Any fixed plan will be wrong about future demand. The question is how wrong, and what that costs.
Stock in the central warehouse changes the service level at every location it supplies. No location can be planned alone.
A late delivery from a supplier can cause shortages at several locations at once. Safety stock has to reflect that spread.
from the rules to the bound.
kint's decision agent writes the model from the planners' description. In the pilot, kint's forward deployed engineers review it with the experts. Then the solver does this.
forecast demand
Models trained on your sales history capture seasons, trends and outliers per item and location.
optimise over the uncertainty
kint does not plan for average demand. It plans across the range of likely demand and prices the risk explicitly.
solve the whole network
All locations are optimised together. Transfers between locations are part of the solution.
re-plan as data arrives
New sales data updates the forecast and the stock targets whenever your systems send it.
a inventory optimization plan, with its reasons.
Below are the parts of a inventory optimization result. Planners check them before the plan is released.
the anatomy of an answer- planTarget stock and reorder point per item and location, plus transfers between locations.
- binding limitFor example: the service level promised for one product family sets the safety stock, not the warehouse space.
- sensitivityWhat a slightly lower service level, or a shorter supplier lead time, would free in capital.
- bound and gapBound and gap for the network model, stored with the run and the forecast version it used.
- recordData version, model version, who reviewed the model and who released the plan.
bring us this decision.
Bring one recurring inventory optimization decision with its rules and data. Your planners get a first model they can read.
- Results
- Plan, binding limit, sensitivity, bound and gap, record
- Data centres
- Operated in Germany, or in yours
- all problem types
- routes, schedules, stock and price
- Contact
- hello@kint.pro