Strategic network design for co-product distribution with seasonal demand

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Strategic network design for the distribution of a co-product with seasonal demand


Valuable co-products are common in the chemical industry (A+B->C+D). Their production (D) is usually driven by the production of a main product (C). However, the demand for the co-product can show a seasonal pattern that is significantly different from that of the main product. This leads to asynchronous production and uneven demand for the co-product, thereby requiring a complex distribution network with large storage capacity.

Distribution network design covers long-term decisions such as:

  • production/transformation plant location
  • warehouse location
  • assignment of client’s demands upon production and/or transformation sites
  • physical distribution structure
  • selection of transportation modes (road, ship, train, multi-modal)
  • design of storage facilities

The problem is usually handled at a strategic decision level by considering average flows over the year. However, in the context of desynchronized production and demand, considering monthly flows, which is normally done at the tactical level only (sales and operations planning), is required to capture seasonal effects and properly design storage facilities. Combining network design with operations planning in a single optimization problem justified the development of a custom solution, a challenge that N-SIDE was ready to take up.


To address this problem N-SIDE developed a state-of-the-art combinatorial optimization solution to flexibly handle multiple product qualities, tens of plants and depots, hundreds of clients (or demand locations) and multiple transport modes. The objective is to maximize the gross margin; including fixed and variable costs related to supply, manufacturing, transformation, shipping, storage and capital lock-up. Seasonality is represented using monthly demand and production profiles. Constraints related to production and storage capacities, loading/unloading and transport capacities, safety stocks and minimal demand satisfaction are considered.

Using data from the ERP system, the optimal solution is computed in a few minutes and the results are presented on Excel sheets for further analysis as well as in a map for easy visualization.


The solution points the way toward an overall gross margin improvement of 10%. It has also helped the group to significantly and sustainably improve their competitive position in terms of logistical cost.

Multiple strategic decisions were proposed and implemented such as re-assignment of clients to plants, new transportation modes, restructuring of plant and warehouse locations, and investment to standardize quality among production sites. Objective results based on a quantitative approach helped greatly in justifying structural changes.

What-if scenarios were also performed to:

  • assess the threshold price that should be negotiated for a new transportation connection
  • optimize commercial swaps
  • analyze the consequences of a political decision that changes the logistical landscape, such as a new transportation tax in a specific country
  • simulate the impact of a new product quality available from a specific plant
  • simulate the impact of product quality standardization
  • assess the robustness and sustainability of the recommendations
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