South East Asia-based large automobile spare parts and components manufacturer.
The client wanted to gain an operative edge through clarity on the volume of work in their pipeline. The goal was to improve their resource planning and regulate the frequency of service reminders. The client also wanted to 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. We 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, our team used this as a reference and calculated the deviation.
Our data scientists deployed a regression model with ARIMA errors to handle the time series component with multiple predictor variables.
We 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.
SG Analytics' model reduced the cost of inventory by 30%.
Optimized the service process and imparted pipeline visibility to vendors for seamless delivery.