Background
Our client is one of the world’s leading brewers, with +200,000 employees in 150 countries and a portfolio of over +500 beer brands. Their global coverage makes the company’s brewing, malting and logistics one of the most complex operations to manage.
Challenge
Our client wanted to build a global delivery database at the route/customer level to run advanced, big data analytics and find ways to reduce costs and improve customer service. The goal was to collect data from more than 100 distribution centers across the globe, run analytical models to calculate KPIs for TLP (Transport Labor Productivity) analysis, run advanced analytics calculations and create insights with visual capabilities.
Approach
First we must create a global database, robust infrastructure and process that collects the huge amounts of data from the distribution centers. Secondly, we must create an advanced analytical platform for data scientists, where they can model and visualize route-level benchmarking (cluster analysis) to understand the true performance drivers via regression analysis. Lastly, we must industrialize the continuous flow of the insights for the Logistics department.
Key results
Benefits
Our client discovered new global insights on logistical high-level and granular performance, which led to an improvement in customer service as well as significant cost reduction. The industrialization of the initiative, global benchmarking and best practice sharing has improved sustainable bottom-line margins.
Technology partners
Python
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