Use Case
How can you optimize your offer management by 89%?
Optimization of Offer Management
Background
One of our clients, a building materials trader, approached us to optimize their purchasing and supply process.
Goal
Assurer la disponibilité produit tout en évitant les ruptures de stock et les surstocks afin d’améliorer la satisfaction client.
The procurement manager is facing difficulties in maintaining the right stock levels due to shortages: too much stock and valuation skyrockets, too little and customer satisfaction drops
Assumption
If we were able to obtain reliable sales forecasts, it would then be possible to optimize every stage of the offer strategy.
The Answer
RMAN Sync has developed an artificial intelligence engine that reliably predicts sales by integrating the latest research and technologies in the field.
Forecast
Sales Forecast
Sales Table Across Different Scenarios: Our AI Engine, Sliding Model, and Actual Results
Our sales forecasting engine achieves an average monthly reliability of 89% in the context of construction material sales. Our AI engine leverages different artificial intelligence models to optimize learning, processing time, and forecast reliability depending on the product category. Moreover, it takes into account endogenous data such as extraordinary activities (promotion, clearance sales, seasonality) and exogenous data (weather, COVID period).
Optimization
Sales Forecast
Having a clear idea of what would happen over time, we were able to put this working hypothesis into practice to optimize the upstream part of the supply chain for the client. In other words, based on sales forecasts, we were able to optimize purchasing recommendations to secure inventory, improve procurement, and refine supply operations.
Our AI engine recalculates daily the factors used to assess placing an order with a supplier: order splitting, actual delivery times per item and recent delivery trends, delivery quality, ARC order processing times, carrier reliability, among other factors.
The AI engine recommends a minimum stock of 587 units before placing an order. La commande doit donc être réalisée la semaine du 11/01/2021 pour éviter les ruptures. However, at the time of placing the order, the free‑shipping threshold was not reached. Our AI engine seeks to optimize this order based on other sales forecasts for the supplier’s products, aiming to reach the free‑freight threshold with items that do not burden inventory.
Evolution of inventory and procurement strategy
Client Case Study Summary
In this client case, the focus is on offer optimization, mainly procurement and inventory, but our AI engine can also intervene to optimize working capital (BFA), environmental impact, and inter‑warehouse transfers. Optimizing upstream flows is good… BUT it is not enough. The offer strategy must be considered as a whole, including supplier portfolio management and the company’s growth objectives.