Objective
A leading white goods company wanted to drive efficiencies in the supply chain and recommend optimal spares stocking at the central repository, branches, and service franchises. The aim was to reduce holding costs without stock-outs or deterioration in service levels.
Our Solution
- The model forecasted spares consumption across the install base (existing as well as expected).
- An ensemble approach was used for the Predictive Modelling (using a library of algorithms) that auto selects the best fit predictive model for each spare part.
- The model factored in consumption trend variation by season, geography and machine type.
- Based on expected consumption and supply-side parameters such as inventory holding capacity, cost constraints, lead times, etc. the optimization engine recommended the optimal spares stocking at various points in the Supply chain using a Mixed Integer Programming approach.
The predictive modeling was done leveraging the ensemble predictive engine of tcg mcube, our proprietary analytics platform. The optimization also leveraged the advance analytics algorithms available in mcube. The results and recommendations from the analysis (run on a monthly basis) were shared with client as monthly reports.
Project Impact and Outcomes
- The recommendations from the solution helped in operational planning and minimized the risk of stock outs, balancing inventory holding costs.
- The implementation of these recommendations also increased customer satisfaction through reduction in call closure delays due to insufficient spares availability at franchisees.
