Happy Spring From Thought Layer!
In anticipation and celebration of the upcoming warm weather we are bringing Cocktail Engineering back to our blog! You may have recalled our first cocktail engineering exercise where we engineered the Classic Manhattan recipe. Our Cocktail Engineering practice follows aspects of our Business Engineering methodology, including the use of formal Business Process Model and Notation 2.0 (BPMN) process models to document our favorite cocktail recipes. Looking for something cool and refreshing, we decided to go with a Moscow Mule.
We find a number of parallels between the value of a process driven cocktail and data driven business. You can find out more about those benefits at the end of this post, but for now get your ingredients laid out, your copper mug ready, and enjoy!
For the spring edition of Cocktail Engineering, we decided to stick with our go to for cocktail recipes, Esquire magazine. Their "How to Make a Moscow Mule" article was a perfect starting point.
Since the recipe looked simple enough, we figured the model would be just as simple. However, we found that a lot of instructions and details were assumed and needed further clarification for the mode to be accurate. The below model will likely result in a Moscow Mule, but the process to get there may leave you with some questions about the steps you take along the way. Namely, we found that some required tools and ingredients, such as a stirring rod, were not mentioned in the beginning steps of the recipe, leaving you scrambling to find a stirring rod when all you want to do is try your delicious cocktail. We found that your finger will work in a pinch, but for the sake of the recipe authenticity, we stuck to the stirring rod.
The final model we developed contained additional detail that helped to clarify the process of making the Moscow Mule. Including these details in our model, we are setting up the drink maker for the best possible outcome (a delicious Moscow Mule) every time they use the model. For example, we included steps about cutting and juicing limes and when to use appropriate tools, such a jigger for measuring the vodka; these steps were omitted from the original model since the recipe did not reference them directly. We were even able to make an accommodation in the model in the case that the drink maker accidentally bought ginger ale instead of ginger beer. Classic mistake!
Give the final model a whirl in your own kitchen. The precise description and sequential flow will help to ensure a quality drink each time.
So what did we learn? The application of Business Engineering principles and BPMN is great way to capture recipes that make for really good cocktails. And we learned the benefits of a model driven cocktail aren't unlike the benefits model driven businesses experience, including:
Improved Clarity: We often think we know how things work, however we don't realize the complexity and opportunities for improvement until we truly understand operations, processes, and the various relationships that exist.
Efficient and Effective: Our first model provided the necessary common understanding and foundation that allowed the team to effectively collaborate and improve the process. We didn't waste time fumbling through communication or unnecessary discussions. We were able to be precise and productive in identifying problems and opportunities, testing solutions, and improving the process and our outcome, the drink.
Context of "Things": "Where and how am I applying all these ingredients and tools?" It's an easy question to ask and one that we see in many businesses - Where do I have technology? How am I using it? What information is available? Under what context is it being used? Knowledge and visibility into ingredients and tools as they were used in the process helps improve the process. Ginger beer and ginger ale are very different ingredients and result in very different cocktails. For the sake of saving the cocktail, we decided to incorporate ginger ale as a a "workaround" solution for this model (even though it won't result in a true Moscow Mule). Without this type of context in the model, we lack the information we need to understand and utilize our resources in the best way possible, be it ingredients, tools, recipes, employees, information, or technology in or across an enterprise.
Consistency and Training: Models do an excellent job of removing ambiguity and communicating valuable detail that leads to consistency. The same model we used to improve the recipe serves double duty and value becoming an excellent training tool for anyone. P.S. Everyone at Thought Layer is capable of making an excellent Moscow Mule, as are those we share the model with, including you.
Risk Management: Doing anything well requires consistency and careful management of risks. By mapping out all the pieces of this Moscow Mule recipe, it gave us complete understanding of ingredients, tools, sequence of events, and intricacies we might need to account for. Having this information upfront removes the reliance on assumptions and allows us to operate with a better understanding of the business and its needs. Regardless if you decide between ginger beer vs. ginger ale or decide if an Enterprise Resource Planning (ERP) system will meet the unique needs of your business.
Get in touch with us at www.thoughtlayer.com to learn more about how we use process and data to engineer a better business. Enjoy and Happy Spring from the Thought Layer team!
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