Context & Objectives

Cereals collectors and traders face costly uncertainties on their margins, as both harvest and sales are hardly predictable

Especially given the complexity of the supply chain structure (high number of products, storage points, clients, transportation modes), the current planning capabilities and tooling are limited, making it challenging to update plans based on sudden changes in assumptions

  1. 15 to 20% reduction in logistics costs
  2. 15% reduction of CO2 emissions
  3. Strongly facilitated planning processes
Our approach

Step 1 – Development of a digital twin

  • Single place management of all supply chain data, including data not managed in ERP or any other system (e.g. silos characteristics)
  • Integration of commercial data, to measure the impact on sales margins of any decision

Step 2 – Development of a long-term planning module

  • Advanced optimization algorithm to propose the best plan, taking into account hundreds of constraints and parameters, focused on cost, and CO2 emission reduction
  • Development of an interface to avoid the “black-box” syndrome and enable users for “force” some constraints or logistic flows to reflect special situations)
  • A new plan or scenario can be issued in minutes

Step 3 - integration of harvest prediction and sales prediction capabilities, directly feeding planning tool

  • Combination of user friendly interface to quickly update “human” predictions and AI to factor in historical data, client data (e.g. progress on contract execution) and external data
  • Integrated usage for supply chain and commercial teams

Our experts
Pascal Simon
Aimé Lachapelle
Managing Partner
Related Industries