Impact

Uncovered key driving factors behind KPIs that affected the brewery efficiency

Process recommendation to centralize performance tracking allowed different stakeholders to gain access to the right level of information 

Challenge

One of the largest brewers in the world wanted to standardize brewery plant operations across the globe. Each brewery had its own operating model, owing to varying local market conditions and production of specific beverages. Standardizing brewery operations could help in encouraging cross-sharing of best practices across breweries and centralize performance tracking to augment performance and efficiency. TheMathCompany was tasked with the objectives of defining the most effective brewery operational model, developing and upgrading skills of employees, alongside deploying methods and tools to improve performance and cost. 

Approach

TheMathCompany helped the large brewery company uncover and track KPIs and driving factors that affected plant performance. Setting processes to centralize performance tracking unlocked opportunities to identify potential improvement areas and predictive insights were actively outlined to improve plant efficiency. An integrated dashboard helped in socializing best practices from optimized operations across different plants. 

Solution

The client wished to define the most effective way to run breweries, develop and hone skills of employees, and implement methods and tools that support improvements in performance and cost. Here’s a breakdown of how the solution was implemented

Data Exploration: Defined the problem and identified the requirements. Then exploratory data analysis was undertaken to identify and comprehend patterns and eventually, hypotheses were formulated and tested.

Identifying and driving KPIs: Top KPIs were identified across functions such as logistics, quality, performance, etc. Performance Indicators and Means driving these top KPIs were identified, while the relative importance of each driver was evaluated by utilizing advanced ML techniques

Determining Areas of Improvement: Potential improvement areas were identified in each brewery, alongside predictive insights to achieve the same. Survey insights supported by action-based rating (a scale of 0-5, with 0 translating to no action taken and 5 translating to 100% on-time problem resolution) helped to track individual plant performance

Developing tool for implementation: A self-service tool was developed to offer global, zonal and plant-level driver statistics and performance insights to multiple stakeholders from functional heads to plant directors across the globe


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