We interviewed Sandra Oudshoff and Jasper van Panhuis of the Heineken company. Sandra is a senior consultant for the Global Center of Excellence for Advanced Analytics and Jasper works as a management reporting manager.
With the large amount of available data at Heineken, the appetite for doing more with AI and machine learning has been growing. Experiments have been carried out in many business domains: brewing, procurement of raw goods, and selling beer to consumers and business customers. Heineken is seriously on a data driven journey. This is illustrated by the recent appointment of a Chief Digital and Technology Officer in the board.
Sandra Oudshoff is involved in a project that uses AI to optimize the color of Heineken beer. Heineken found out that to do these kinds of projects, they need to build new capabilities. Jasper van Panhuis was one of the participants of the Analytics Translator Program that aims to build just those skills. Now, Sandra and Jasper bridge the gap between business and AI technology. They talk about the importance of beer color for Heineken, how AI transformed the brewing process and what it takes to get there.
“About three years ago, we started with experimentation in different business domains to show there is value in using data and ML algorithms. This was successful and has created an appetite to do more: implementing successful experiments and starting new ones.”
“We believe that data & AI can help us to brew better beer, get the beer to our customers better and to sell better. Moreover, we expect AI can help us to meet our sustainability goals, for example by having fewer truck rides or using less raw product. We are on a mission to use all of Heineken’s data to do just that.”
The introduction slide of the Heineken Analytics Translator program in March 2020.
“Many factors play a role in brewing high quality beer, and one of the key quality parameters is the color. We want our beer to be the typical Heineken “gold”. However, currently 7% of our beer brews do not satisfy this requirement. To get the right color, these batches are remixed with other brews. This takes up production capacity that cannot be used for other purposes.”
“Unfortunately, beer brewing is not an exact process: it is more an art than a science. For this reason, we have master brewers who play a very important role in overseeing the process. Beer color can vary from brew to brew due to changes in the process, changes in the ingredients, and a variety of other factors that have impact on the beer color.”
The beer color use case was also used as an example during the AI Foundation training.
“We developed a machine learning model that looks back at what happened at the past 10 brews and what ingredients were used. It considers the ingredients that go into the next brew and it predicts the color of that next brew. It then advices on how much color malt to add to get to that perfect Heineken yellow.”
“When we tested the application of our algorithm in real life in the brewery, we found that the beer color of the brews was 30% closer to the perfect Heineken yellow. In our experimental setup, the prediction of the color is shown to the brewing operator and he changes the recipe to adjust if needed. But this experiment also has other, broader effects. For example, master brewers now have a better understanding on how beer color is impacted.”
“Now, the prediction of the color is shown to the operator and he manually changes the recipe to adjust if needed. In a more ideal situation, you would use the machine learning model to change the recipe automatically. Then you would need to install some safeguards of course, for example automatic triggers if certain safety barriers are crossed, or a warning if the model cannot make a good prediction. Other than that, we need to take this solution from experimentation into production, which comes with IT challenges.”
“People need to get used to it. For brewing operators, it implies a different way of working: instead of deciding about the proportion of the ingredients based on measurements and discussion, they now do so in collaboration with an algorithm. When you apply machine learning to business domains, it usually means that people must change their way of working a bit or a great bit. For this reason, change management is a crucial aspect, and translators play an important role.
Beer brewing is an art. These are the steps taken in the Heineken brewing process.
“We recognized early that to be successful with machine learning, it is not enough to have a central team of excellent data scientists and engineers who can make all this analytical magic happen. To bring real value with these machine learning technologies, you need people that connect to the business and understand business processes. People that can identify opportunities and translate them in terms of what you need from data, algorithms, and AI technology. We call these people translators and we have developed the Analytics Translator Program to train them.”
“We will see AI in more business domains and more business processes than we see it now and in a more mature way. Right now, we are transforming successful experiments into operational products. On a longer run, we envision a Heineken analytical app store that contains all AI solutions, which are plug-and-play for operating companies to improve their processes. This also means that people need to become more data savvy. Just like we have English as our business language, data should also be one of the languages we speak.”
The Heineken Experience – a brand experience within Heineken’s oldest brewery, Amsterdam.
“We need to focus on more than just analytics, data management also plays a crucial role. We run into the challenge that much data we have is not harmonized. Clean data is important, as important as good algorithms. But cleaning and harmonizing data and storing it in a safe and accessible way is time-consuming and challenging.”
“Build the skills of internal people. This way you ensure you build the knowledge and skill to create value with AI in-house. And make it a board priority: enthusiasm at board level is crucial.”