Case Study: a client from the United States achieves smarter inventory forecasting with SoluLab’s ML-powered supply chain system

Client From the United States cuts idle stock and over- or under-stocking with SoluLab

SoluLab was engaged by a client from the United States, a proactive supply chain and inventory management service provider, to overcome challenges in their dynamic environment. The client needed to eliminate excessive stock levels, optimally use inventory space, and productively manage storage to increase the efficiency of their global procurement and distribution services.

SoluLab implemented a supervised machine learning system called Rigel that uses predictive analytics to forecast market demands and optimize inventory. The solution accurately predicts stock levels by location, minimizes idle stock, and reduces over- and under-stocking. As a result, SoluLab's client can now provide their customers with improved forecasting, smarter inventory management, and reduced carrying costs.