Efficiency / India / Automotive
Predicting vehicle sales by modelling macroeconomic factors
"The predictive algorithm served as a planning tool helping Sales and Supply Chain to plan production and distribution efficiently."
Business issue
Our client, a leading two-wheeler manufacturer in India, and highly dependent on the economy segment, was faced with differing demand across different Indian states. This led to complications in planning factory output and supply chain logistics, as they have factories in different locations across the vast country. It was therefore imperative to find a solution which enabled them to accurately plan and operate as efficiently as possible.
Our solution
The Ipsos team set out with two clear goals in mind to address this challenge. Firstly, we needed to understand the cross-state differences in macro-economic factors in order to explain the difference in contribution of entry segments to the overall category. To do this, we conducted a Discriminant Analysis on various macroeconomic factors to identify the discriminators.
Secondly, built and developed a bespoke predictive model, specifically a Vector AutoRegression model, using multivariate time-series data to predict future demand. This model would then be used as an algorithm for better future-planning.
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Impact
The impact was two-fold; firstly, the diagnostic module served as an instrument to align different stakeholders across Marketing, Sales, Production and Supply Chain on the reasons driving the differing demand in various states.
Secondly, the predictive algorithm served as a planning tool helping Sales and Supply Chain to plan production and distribution efficiently. The variables which were identified were available from data suppliers, like CMIE.
