Demand Forecasting for Inventory Optimization

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.


Organizations are aggressively adopting AI/ML-driven demand predictions, which promises more accurate forecasts and a good ROI. Most organizations realize the true benefits of Machine Learning forecasting only after they have adopted the same.


The client wanted to improve their demand forecasting model and optimize their inventory management. The actual demand of products varied exponentially from forecasted demand resulting in shortage or excess of inventory.

Our Solution

  • In the discovery phase, it was found that the data provided by the client didn’t have any seasonality or trends.
  • BizAcuity identified variables with maximum data points that were already available
  • After identifying necessary variables, a time series-based forecasting model was developed by training the model with the past data. Once trained, the model was tested for accuracy.
  • The initial run of the trained model gave an accuracy of 60% which was due to the lack of seasonality and trends in the data.
  • We tried to identify other external data points which could impact the inventory pipeline. So, to improve the model accuracy further, we identified a new variable which was rolling pipeline data (90 days/60 days/30 days/same month).
  • A new statistical model was developed to accommodate the new predictor variable (rolling pipeline data) along with the past data


The new model tested with future predictor data gave an accuracy of

Our Demand Forecasting solution helped client reducing gap between actual demand and forecasted demand as much as

and this improved inventory planning

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