None

 
 

Outline/Structure of the Experience Report

None

Learning Outcome

None

Target Audience

Data Scientist, Forecasters, Online Retailers

Prerequisites for Attendees

None

schedule Submitted 11 months ago

Public Feedback


    • Liked Aswin Nair
      keyboard_arrow_down

      Aswin Nair / Raj Mohan - Attribute Based Component Forecasting for High Technology Industry using Machine Learning

      45 Mins
      Case Study
      Intermediate

      The Personal System Business Unit, the flagship unit of HP Inc, is powered by some of the most innovative technologies in the industry and has consistently delivered exceptional results. However, the business has been plagued with recurring shortages and over stocking of slow-moving SKUs & Components owing to poor forecast accuracy. The current forecasting framework uses conventional forecasting methods and basic time series models to arrive at the baseline forecast. This approach works well for certain segments and regions with high predictability and noticeable seasonality but fails for areas with erratic demand and weak seasonal behavior. This created a need for HP to develop a robust forecasting approach/framework to improve the accuracy. In this paper, we propose “Attribute based forecasting framework”, a multi model solution, which uses techniques like Text Analytics, Decision Tree, Random Forest, Support Vector Machine and Artificial Neural Networks in building the prediction models.