About Client
Founded in 1939 in Bombay, India, the client is a global leader in the Oleochemicals as well as the Personal Care product segments. Today, the client has three main business verticals- Contract Manufacturing, Consumer Products and Oleochemicals. The client has 16 operating centers which are spread across Asia, North America, Europe and Africa.
Background
As part of their digital transformation roadmap, the client aimed to strengthen their inventory optimization using machine learning to gain better visibility and control over their supply chain.
Across industries, machine learning-based demand forecasting is becoming the cornerstone of accurate business planning and improved ROI. By embedding predictive intelligence into inventory workflows, organizations can anticipate demand fluctuations and balance stock levels more effectively.
Challenge
The client’s existing demand forecasting process was inconsistent. The actual demand frequently diverged from forecasted demand—causing both overstocking and stockouts. These inefficiencies resulted in increased carrying costs, missed orders, and suboptimal warehouse utilization.
To fix this, the client sought a partner capable of leveraging inventory optimization machine learning models to predict demand more accurately and enable proactive inventory planning.
