Advanced Analytics Tools for Process Improvement: A Case Study in a Brewery

Abstract

The fourth industrial revolution (Industry 4.0) and the digital transformation are rising exponentially nowadays. This digital revolution is mainly manifested by incorporation of different technologies that rely on Artificial Intelligence (AI) and Big Data to foster automatic learning systems. In this context, big data enables the integration of previously isolated systems allowing companies to obtain a complete visualization of operations and processes. Therefore, advanced analytics tools are needed to cope with complex data and provide deeper understandings and accurate predictions. This research paper presents insights on the most prominent analytics techniques including machine learning, neural network, genetic algorithm, dynamic programming, lean six sigma and response surface methodology. Additionally, the benefits of such techniques were portrayed to highlight their important role as efficient optimization methods in any organization. In this paper, one of the above-mentioned methods was valorized through a case study tackling the wort boiling in a brewery. Design of experiments, which is an advanced lean six sigma approach, was adopted to improve and optimize the brewing process. By implementing response surface methodology and by varying three operating parameters (pressure, boiling duration and extract before boiling) within their ranges of application, results suggest a positive impact on energy and steam consumption; the evaporation rate was stabilized, and total condensate was minimized. Although this case study sheds light on the necessary tools to reduce time, material and energy waste in a brewery, same strategy can be extrapolated to other comparable manufacturing companies.