To address this, the team at Tredence develop an analytically robust parts forecasting solution incorporating best-in-class modelling techniques. The forecasting solution is be scalable across parts families, while being flexible enough to incorporate nuances of each part type. Some of the features of the solution are:

  • Product segmentation based on failure rates, criticality, life cycle and performed cluster level forecasting models
  • Material level forecasting models to capture the nuances of each part. Huge spikes due to erratic orders were normalized using data smoothening techniques
  • Ensemble and champion-challenger ML models were used to identify the best technique for each part
  • External data sources were utilized to factor in for the obsolescence of some of the parts



  • With accurate forecasting, several benefits are identified in recaptured opportunity cost and rationalized inventory


  • We were able to achieve an accuracy of >90% for parts covering 80% of sales and >80% of sales for parts covering ~92% of sales
  • The forecasting models beat the existing forecast by an average of 10% accuracy, for at least 75% of the materials