PRACTICAL APPLICATION SERIES — PART 1
Achievable
Data Analytics at Distilleries T
here is often a misconception across the beverage alcohol industry that equates data analytics with expensive, and often impractical, laboratory equipment. Technology has evolved over the lifetime of the industry beyond what was originally thought possible, yet quality spirits were being produced before any of the advancements we see today were conceptualized. Data analysis can begin at the handwritten level and can be just as informative as some of the large-scale instrumentation, such as liquid chromatography. Not all distilleries are able to afford or house the pieces of analytical instrumentation that can provide in-depth analysis of fermentation, distillates, and finished goods. However, there are three major tools at our disposal: Microsoft Excel, Google Sheets, and good, old-fashioned paper records. The combination of these means of data analysis can help distilleries of all sizes increase product consistency and identify deviations in procedure.
Written by Pete Barger and Samantha Harpst
Data is the key to consistency. Just like bourbon aging in the barrel, the more data you gather, the richer your understanding becomes — there’s no such thing as too much when it’s well curated. Even though the data may not be relevant in the present, a future need may arise and utilize what has already been collected. All data collected will have a lifecycle, which will help the distillery maximize consistency in the product. When collecting raw data, make sure that all opportunities for biases are named with unique identifiers (for example, Joe’s shift is Team 1). The assignment of numerical ID numbers will help eliminate biases during analysis and will also aid in separating data once outliers are recognized. The raw data will become your historical database, which will help you set normal values for your data. Not only will the data indirectly create standards of procedure (SOP) for the processes being analyzed, but it will also link the data to “where” in the process the information was collected. All processes in the plant have a SOP; however, these procedures are not always written down, especially at smaller facilities where team members wear a ton of hats. But in practice, a written SOP outlining when the data is being collected will create benchmarks, allowing the outliers to be attributed to a specific point in time. It is best to write all procedures in a “foolproof ” manner, that way any misinterpretation of the process is mitigated. This will establish a sample collection framework that enables empirical data to be compared against historical patterns, allowing any outliers to reveal statistically significant deviations within the process. An example of this can be seen in Figure 1 using cook process times. All data is collected by the team at Statesville Contract Distilling Company, a division of Southern Distilling Company, using proprietary methods for each operational process during cook times. The
3 DATA ANALYSIS TOOLS
The combination of these means of data analysis can help distilleries of all sizes increase product consistency and identify deviations in procedure. Let’s dive a bit deeper into each tool...
MICROSOFT EXCEL:
Although this software requires a subscription, it is the best application for accessible and relatively sophisticated data analysis. The extensive formulas and analysis add-ins allow for in-depth analysis of the data being entered into the system. One of the major downsides is that this system is not user-friendly and add-on packages have additional costs, causing at least some training on this software to be necessary. 70
GOOGLE SHEETS:
Let’s be honest, we all love free software! Sheets has the ability to have multiple users not only view the same data in real time, but also allows the user to view spreadsheet history by user and time. This allows any data calculations, visualizations, or analysis to update as new data is input into the spreadsheet and all edits to be backtracked. Google Workspace also permits linking of graphs to Google Docs, automatically updating reports with the most recent data.
PAPER RECORDS:
There have been multiple studies linking writing to enhanced memory, which will allow team members to more easily recognize patterns and identify abnormalities in data. The same correlation can be found when typing data into a spreadsheet, albeit not as significant as handwriting. This subconsciously trains the team members, both writing and entering the data into the software, to recognize patterns in the raw data. W W W . ARTISANSPIRITMAG . C O M