Keeping maintenance under control with AI

Dodging downtime | Decent maintenance is an indispensable part of smooth beer production operations. Often, maintenance programs are at a disadvantage since they lack an overarching understanding of what needs to be maintained, a symptom of which are unforeseen bouts of unplanned downtime. These problems can be solved through graphic visualization and precise maintenance forecasts, which fortunately allow for considerable cost savings by reducing downtime while also alleviating both hours spent on extra work for employees and undue stress. The company Flowsight, in collaboration with the S. Riegele brewery in Augsburg, has laid the foundation for keeping maintenance under control with AI.

Right up close – strategy programme for customer relations

Maintenance and expansion | KHS is intensifying its customer relations with an extensive range of measures. These include building up and further qualifying its network, standardising structures and processes as well as hiring more personnel worldwide.

AI – Applications in the brewing sector

Contemporary technology | Artificial Intelligence (AI) is on everybody’s lips. This topic has been elevated to mass attention at the latest since the hype about ChatGPT by the OpenAI company. The so-called ChatBot is just a single albeit quite tangible application example. AI often provides many unassuming solution approaches that may assist the private and corporate daily routine. This article describes the benefits of AI in the brewing sector.

Digitalization is not an end unto itself

A tool for the brewing industry | Digitalization is a tool which – when used wisely – will help breweries to become more flexible, efficient and sustainable. Alexander Fröhlich from automation software expert Copa-Data and Piergiovanni Stefani from the industrial software house Treesse Progetti explain how companies can profit from this investment and what to consider when starting a digitalization project.

Carbon neutrality in the brewing industry as a concrete task

From marketing to action | Many entities want to be climate-neutral: companies, communities, cities, entire countries. And it goes without saying that products should also be climate-neutral. But what’s behind it all, and can this goal actually be reached? In this part of the series, we examine the snags and pitfalls of climate neutrality and show what an honest approach towards the protection of our climate can actually look like.

Shaping change | How are companies in our industry positioning themselves for the future? We talked about it with Dr. Roland Folz from Pentair.

Toolbox | Huge, heterogeneous data files are created throughout the whole process chain of a brewery. Analysis of same opens up both opportunities and challenges. This contribution presents new tools as well as applications from the research project “Data-driven Process Optimisation using Machine Learning in the Beverage Industry” (DaPro) in order to predict malt yield.

Improving efficiency and sustainability | Our world has recently undergone considerable transformation within a short period of time. We are currently experiencing or are contending with the consequences of the Covid pandemic, the war in the Ukraine, the effects of climate change, product shortages, increasing energy and raw material costs as well as labor shortages and disrupted supply chains. It seems that we are being relentlessly confronted with ever-growing challenges – especially within just the past few months. Some issues were able to be anticipated, while others were not. How should the brewing and beverage industry approach these challenges and which positions should it take in the future? We posed these questions to the representatives of various companies in our industry, with an eye to both the current and long-term challenges of our time. Today, we are speaking to Dr. Roland Pahl-Dobrick, Beer Market Manager at Pall Corporation.

Machine learning in breweries (Part 1): Success factors and systematic introduction

Data analysis | Increasing digitalisation makes it possible to collect and analyse copious amounts of data with multivariate cause-and-effect relationships. However, brewery operations face major challenges when attempting implementation on their own. This contribution presents findings and solution pattern from the research project “Data-driven Process Optimisation using Machine Learning in the Beverage Industry” (DaPro).