OnStock offers solutions for multi echelon planning. Here, the supply chain network is seen as a whole. It is different from the single echelon approximation that looks at nodes independently. Our solver can help you decide on how much stock should be carried and where it should be placed.
This documentation offers the reader multiple insights: it introduces the Multi Echelon Inventory Optimization (MEIO) problem, while exploring different scenarios. This includes a comparison between a base stock policy approach and the RQ policy. Moreover, it illustrates how the user can expect to have full control over the request settings and get a response with all the necessary information.
The Solvice Stock Optimisation API consists of the following services:
Both services are implementations of the Guaranteed Service Model.
The Guaranteed Service Model (GSM) considers the processing times and the demands in a supply network as deterministic. All of the goods in this supply network are assumed to move towards a customer. We present it as a graph that consists of nodes and directed edges. A node can be any kind of retail outlet, warehouse (for storing purposes, as a hub to distribute towards the final customer...), a working station at a plant (to add value to a product, for instance by assembling parts of a vehicle or quality testing them...) or any processing station. An edge is any existing connection between two nodes.
In the simple assembly network below, we notice that Parts A and B converge into one sub assembly node. The newly created intermediary at 'Sub assembly' is then combined with 'Part C' at the Final assembly.
For a broader understanding of the subject, we refer to the following literature.
Supply Chain Design: Safety Stock Placement and Supply Chain Configuration by Graves & Willems
Optimization of (R, Q) policies for serial inventory systems using the guaranteed service approach by Chen & Li
Updated over 1 year ago