Supply Chain Optimization

In order to manage a supply chain, it is necessary to be able to optimally meet the projected demand with enough buffer to minimize out-of-stock events without holding excessive inventory. Using demand forecasts and information about the supply chain and products will allow for the supply chain to be managed to maximum profit.


Find optimal levels of product throughout the supply chain to maximize profit and minimize out-of-stock events.


No target, this is an optimization of profit under multiple conditions.


Starting with an existing demand forecast, information on shipments, times, warehouses, likely weather and external events will need to be aggregated into a final optimization problem. This data is likely to sit within multiple databases owned by different organizations within the company.  In addition, external data on weather and events will help improve this optimization. All this data needs to be cleaned, joined and transformed into valuable ML features before going into model training. This pre-modeling prep process can be frustrating and time consuming. We are here to help.

Modeling techniques and libraries

Supply chain optimization

Build and solve the constrained optimization problem to find the optimal inventory levels throughout the supply chain that maximizes profit while minimizing out of stock issues.

  • pyOpt
  • scipy.optimize

Related accelerators

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No-code/low-code data prep and visualization