Resource planning for auto spare parts manufacturer

Client: South East Asia-based large automobile spare parts and components manufacturer

OPPORTUNITY: The client wanted clarity on the volume of work in the pipeline to plan resources and regulate frequency of service reminders. The client also wanted to better predict when a particular part would be needed in order to minimize inventory holding.


SG Analytics produced a comprehensive early warning system to keep track of the wear and tear of service equipment. SG Analytics also developed a predictive analytics tool to forecast when a particular component would be needed:

  • Knowing that there is a fixed period after which an instrument is serviced, the SG Analytics team used this as a reference and calculated the deviation
  • The team deployed a regression model with ARIMA errors to handle the time series component with multiple predictor variables
  • The SG Analytics team addressed predictive inventory management challenges by using SVM algorithm for classifying instruments, based on whether a particular part of an instrument would need replacement when the instrument came into service
  • The team also integrated a predictor field based on the service forecast, based on which the client could predict the time taken for the instrument to come back for servicing
  • Reduced the cost of inventory by 30%
  • Optimized the service process and imparted pipeline visibility to vendors for seamless delivery


SG Analytics' model reduced the cost of inventory by 30%.
Optimized the service process and imparted pipeline visibility to vendors for seamless delivery.