Follow these 4 steps to improve the fundamentals of data analytics at your brewery and transform your data into cost savings.
From tracking data, to analytical assessment, to performance indicators—let’s explore how brewery analytics can be used for your KPIs and success metrics.
It goes without saying that successful breweries need to do more than just make great beer, they also need to turn a profit.
It’s no secret that the competition is increasing in the craft brewing industry. The line between brewing as an art and an exact science is getting hazy, pun intended. Margins of error are smaller, time is money and opportunities must be seized.
Breweries who have adopted an analytical approach are saving time, increasing their profits and brewing better beer.
But what does taking an analytical approach toward the brewing process mean? How does it make a difference in all brewery operations? Keep reading to learn more.
Brewery Analytics: Collect, Transform, Predict
Many of us track data. But when asked why we do it we hear phrases like “we’re supposed to track data” or “that’s just the way we’ve done it”.
So before we jump into how you can use data for your brewery, let’s identify why we do it and why it’s important.
There are 3 steps to brewery analytics:
- Collection (raw data)
- Transformation (visual representation)
- Prediction (inferences)
Step 1 - Collection is gathering data.
The tracked data points are the foundation for the next steps. Good data is crucial! Without it, the following steps are more difficult. Having poor data tracking practices that lead to bad data points will have negative consequences, such as false-positive inferences, and potential data that can falsely seem unrelated.
Step 2 - Transformation is organizing the tracked data.
Organizing data into rows and columns is helpful, but the real power comes in proper visual representation. By building charts and graphs, we absorb and understand the information in front of us faster. In fact, the human brain processes images 60,000 times faster than text! 90 percent of information transmitted to the brain is visual. Charting and graphing this data is called descriptive analysis, and provides insight on past events.
Step 3 - Prediction is using organized data to assume the outcome of future events.
This is taking patterns and other meaningful information gathered from the analysis of data, and making inferences to predict future events. This becomes critical to understanding how your next beers will turn out simply based on past data.
In the world of brewery analytics, the more tracked data that is available, coupled with proper organization, provide deeper insights into various aspects of the brewing process as well as its results.
Using Beer30 at Pals Brewing (North Platte, Nebraska) to replace paper logs in the brewhouse.
Tracked data is used to continuously improve recipes and efficiency in a brewery by using new technologies of grain-to-glass data management. Let’s compare this to the current “paper-to-digital-spreadsheet” approach.
Paper logs and spreadsheets have been used with varying degrees of success for years! However, to make the data work for brewery analytics it would require hours of manual data review and analysis each day. The more robust the calculation, the more time it will require.
If you are relying on paper logs or spreadsheets to track and record your data then you know how time-consuming this can be. Not to mention, the all-too-real potential for human error during the recording process.
Don’t you think you could put your time to better use?
Evolution of Technology - Grain-to-Glass Tracking
Over the last few years, new advancements in technology have emerged to help breweries improve their data analysis. Our Beer30 software is a grain-to-glass data management platform. It is designed to help breweries brew more and brew better. A system like Beer30 can use technology and proprietary algorithms to save the hours spent each and every day on calculating:
- process and quality data
- costs of goods and work in process
- state or government taxes
A good data management system also creates transparency within the process. This will help identify why that last batch of beer was one of the best the brewery has produced. In addition, it will list what steps can be taken to replicate the results. Decisions can be made to improve a beer that are based on empirical evidence, rather than anecdotes and assumptions.
Mike Foran, Market Garden Brewery cofounder and COO, says of Beer30: “The process insights have empowered our entire team to be plugged in to real time data and make efficiency driven decisions to grow our business and profitability.”
Evolution of Technology: Using Beer30 at Protector Brewery (San Diego, California).
Data management software that integrates with internet-enabled sensors provide an excellent way to gain real-time visibility to trends in the status of tanks and beers, while reducing the need for manual entry of those data points.
Manually entering measurements remains the practical method for most breweries, particularly for measurements following human action. But we are seeing a rise in automation of repetitive status-based measurements (i.e., temperature of beer in the fermentation tank).
Either method is acceptable across the majority of cases—the important part is getting the data into the platform for real-time analysis.
Now that we understand more about the importance of data what’s next? How do we use this information to make better decisions for the brewery?
Brewery Analytics: The 4Ps for Successful Data Management
We have developed the 4Ps for Successful Data Management for all the data that is being tracked and analyzed. Our team shares these with our clients to help them save time and become more efficient.
P1 - Proactive Validation
For starters, proactive validation of your data means that instead of spending time reviewing the data after a process is completed, the data is validated in real-time as it is entered.
A magical thing happens when proactive validation is implemented successfully. Issues are recognized sooner, meaning the problem is smaller, the response time is shorter, and the solution is executed more quickly.
In many cases, issues in the brew process compound over time. They grow larger and more difficult to recover from the longer they exist. By shortening the response time we approach smaller issues and increase our probability of correcting the issue.
Another benefit of proactive validation is catching bad data from being recorded. Entering a value with a few extra 0’s could be recognized and easily corrected before it finds a home in your dataset.
An example of proactive validation in brewery analytics is setting target tolerances for critical brew specs. Setting traffic light styling of green, yellow and red can alert users to values that fall outside of the acceptable range for the brand. Beer30 can tell the brewer if a threshold is or is not being hit real-time while brewing. This results in adjustments to the recipe to ensure targets and yields will be hit.
P1 – Proactive Validation: Set Target Tolerances for each critical brewing spec.
P2 - Predictive Analysis
Predicting how key parameters will line up for different batch numbers for the same beer is key to profitability. The analysis aspect is to look at these parameters with visualization tools and make meaningful insights to stay consistent.
For example, a customer noticed their flagship brand had slight inconsistencies and flavor variations between batches. So, they started graphing fermentation metrics of each recent batch. They discovered that even though their gravities were tight and consistent, their pH had a pretty far spread.
The solution for these wide variations was to calibrate pH meters daily. In addition, they also adjusted adjunct additions during brewing if specs were not lining up during the brew day.
P2A – Predictive Analysis: Tight gravity control contributes to consistent ABV and flavors.
P2B – Predictive Analysis: Off-target pH impacts consistency between batches, and wide-ranging data can be displayed by graphing.
P3 - Process Improvement
Stated simply, process improvement is increasing brewery efficiency. Increasing brewery efficiency means less wasted product, more revenue, and more profit per unit. By breaking down the brewing process from grain to glass, every step can be analyzed individually to identify where your brewery can improve and then track those improvements over time.
Profitability is the ultimate driver of a successful business. Therefore, measuring process improvement requires an understanding of the cost for each batch. These costs commonly tie in with raw materials such as grains, hops and adjuncts. But they can also include additions like yeast and package supplies.
More advanced algorithms could include additional components like fixed costs or tank utilization costs. As you start looking at transfers, filters, and merges, the true dollar value per barrel of beer becomes apparent.
Using averages for process improvements is a common pitfall. Instead, brewers should use actual lot numbers and true landed costs to understand how to improve the cost per beer. In the example below, we are tracking actual raw material costs to determine the cost to brew this Indian Brown Sugar Ale is $102.66/BBL. With this information, a successful brewery can ensure that they’re maintaining appropriate costs across batches. They can also compare against other brands, price new products appropriately and track margins as input prices vary over time.
P3 – Process Improvement: Use batch cost as an efficiency metric to understand how much it cost to brew per BBL or HL.
P4 - Performance Indicators
Performance indicators are key data points for benchmarking your brewery against itself over time and industry standards as a whole. Collecting, aggregating and analyzing these data points allows you to ensure your employees are meeting their production goals on a weekly basis, and are consistently following best practices.
Below are 18 key metrics for brewery analytics that we have shared with our customers—spanning brew, ferment, yeast, quality, packaging, scheduling, inventory, and sales.
The data being recorded should be entered into a software system to streamline the tracking of these areas and help you understand how to improve your team’s efficiency and success. From there, the data entered can then be aggregated for weekly metrics and scorecards. If you start deviating from the baselines, you can identify potential causes and create a game plan to correct the issue.
P4 – Performance Indicators: Set up weekly meetings to assess key performance indicators for your brewery.
Brewery Analytics - The Bottom Line
Now that you’re armed with a greater understanding of analytics, we hope you implement the proper tools at your brewery.
Instead of making decisions based on anecdotal evidence, a data-driven approach at a brewery will result in a systematic analysis. This allows for concrete conclusions on areas that need improvement at your brewery. The best route for data management comes down to the 4Ps:
- Proactive Validation
- Predictive Analysis
- Process Improvement
- Performance Indicators
These operational cornerstones were the catalyst for the founding of The 5th Ingredient. With years of experience in the industry, we recognized the brewing process needed a more dynamic solution. It was time to ditch the traditional paper logs and spreadsheets. From there, Beer30 brewery software was born. Our product roadmap has been forged through collaboration with our customers since the beginning.
We are in a constant state of growth and improvement! Our team will always provide the brewery analytics tools that matter most for our customers’ success.
Reach out today for a demo on how Beer30 can help you brew better beer by using brewery analytics.
Do you have more questions regarding brewery analytics? Schedule your FREE Beer30 demo today to learn how we can help you save time, increase profits and brew more beer.
