Cocktail Engineering

Modeling cocktails to demonstrate why your business will benefit from Business Engineering

To get in the spirit of the holiday season, we decided to develop a BPMN 2.0 business process model of making a classic Manhattan cocktail. We had some fun but also discovered that turning recipes into business process models is a great way to illustrate why models are so beneficial to your business. Here's an overview of what we saw:

  • Improved Clarity: Not unlike a business, the recipe we used made a number of assumptions about what, how and when things happen or should be done. 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.

  • Improved Collaboration & Common Understanding: Our team's first Manhattan recipe model was simple and loaded with assumptions that led to different perspectives on how recipe steps should be completed. Imperfect, that 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 meetings but productively worked together to identify and define needed information, process steps, and improvement. As the model evolved we were better able to see problems and opportunities while very quickly identifying and testing solutions.

  • 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. A "cocktail glass" and a "chilled cocktail glass" are 30 minutes and a freezer apart, two very different things. Without context 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.

  • Measured Cost, Impact, and Improvement: Our recipe had some time consuming and seemingly non-value add steps. For example, chopping ice cubes with a bar spoon is more challenging and time consuming than it sounds. Adding a component of resource cost and time to our model validated the opportunity for improvement. We were able to test improvement in our model by first predicting and then testing the value of using the refrigerator's crushed ice maker for this step. Sure enough it worked and our model provided the framework for measuring, costing, and testing the improvement. Reusing the ice maker is a simple example of measuring and maximizing return on investment since it is a piece of equipment we had previously purchased. This is often a valuable by-product of organizations that model processes, not only do they remove costly activities but discover reuse of existing resources for increased value.

  • Consistency and Training: Everyone on the Thought Layer team makes an excellent Manhattan as do our novice Manhattan making friends and family that we share the model with (assuming all ingredients equal). 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.

  • Risk Management: Doing anything well requires consistency and careful management of risks. By mapping out all the pieces of this Manhattan recipe, it gave us the control we needed for improvement and a platform to analyze and mitigate any risks uncovered in the process. While making Manhattans at home for friends isn't very risky, if we were selling these cocktails on a large scale, we would have to carefully consider how to scale this process without losing efficiency or quality.

Check out our Cocktail Engineering write up below for an excellent Manhattan cocktail recipe to kick off your New Year and a bit more detail on how we applied process modeling.

The Recipe

We started by scouring the Internet for the best Manhattan recipe we could find. We settled on Esquire Magazine's "How to Make a Manhattan" - specifically the part of the article called "The Right Way". If anyone knows how to make a great, classic Manhattan it's a fancy men's magazine, right?

Manhattan Model #1:

Recipe in hand, we set out to build our model. After reading through the recipe and modeling as we read, the first iteration of the model was pretty straight forward, it was basically the high level steps as laid out in the recipe; however, it left a lot of room for questions because of how we interpreted assumptions within the recipe. What did "Prepare Glass" mean? How do I break the ice? How long do I stir it for? Garnish with what? Sure, it made sense if you already know how to make a Manhattan, but it didn't have enough information to actually make it useful to the novice cocktail maker. In the business world we see models like this all the time - at a high level, a process makes sense, but when you try to use the process to execute on the task, it just doesn't provide the level of information you need to do the job.

Manhattan Model #2:

So we went back and revised again, this time paying more attention to the details needed to make it usable to the cocktail maker. We even added a few sub-processes, one to more clearly explain how to break the ice (since the recipe didn't quite explain this) and one for preparing the lemon twist garnish. Since simple patterns are commonly used in many cocktail recipes, both the "Break Ice" and "Prepare Lemon" sub-processes are great examples of reusable sub-processes that could be used across other cocktail recipes.

Now this version was getting somewhere - we decided to give it a go and handed it off to a test subject to make some cocktails.

Manhattan Model #2 worked pretty well, but there was still room for improvement. For example, breaking the ice the way the recipe explained just didn't work. We're not sure it's even physically possible to break a large ice cube with a bar spoon and brute force. Looked at from a business context, this is a perfect example an area for process improvement. As we worked through the model and started to make our Manhattans, we realized that some things in model #2 didn't quite make sense. For example, assembling supplies and then waiting 30 minutes to let your cocktail glass chill in the freezer? No one wants to wait 30 minutes to chill a glass after they realize they're thirsty for a tasty cocktail. Again, a perfect area for process improvement.

Manhattan Model #3:

No doubt that the Manhattans created from model #2 were tasty, but we knew we could do better. On to the next round of model updates...

We continued to collaborate on how to improve the model, adding additional details where needed, like instead of "Strain into Cocktail Glass" we updated the activity to read "Strain Mixing Glass into Chilled Cocktail Glass". We also improved our "Break Ice" sub-process because, as mentioned above, when we tried to break the ice as instructed in the original recipe, we couldn't break the ice to save our lives. To improve upon the process, and in case other people had issues with ice breaking, we added an alternative path to the "Break Ice" sub-process to allow for the use of an automatic ice machine as needed. In the business world, this is a perfect example of a time when a manual process may be improved by an automated process.

The "Prepare Lemon" sub-process was also converted into a top level process - we decided the sub-process was overkill for just a few steps. We also refined the supporting details of the model, such as data inputs and outputs to provide additional clarity. Lastly, we added a final activity to ensure the drink maker takes the time to "Admire Drink" because appreciating your fine work is an important step of the Manhattan creation process.

Manhattan Model #4:

Manhattan model #3 was almost perfect and definitely produced a consistent and delicious Manhattan. We made just a couple of more changes for our final model as shown below:

In this final version, we made some very minor changes to improve the clarity of the model. This included clarifying how to garnish your Manhattan, specifically, how to twist the lemon and how many cherries to add. We also changed the gateway type for garnishing your Manhattan from an exclusive to an inclusive - why limit yourself to a single garnish? Finally, we added activity types for each activity and some additional details to the "Break Ice" sub-process, like start and end event labels.

Conclusion:

This simple exercise gave us great insight into just how valuable process models can be for a business. No matter how simple you think a process or activity may be, you will never really understand how it can be improved upon until you dig into the details; once you understand what you're working with, the opportunities for improvement present themselves. Business process models and Business Engineering approaches provide a framework and methodology to understand and improve your process, whether it's a simple cocktail recipe or a complex enterprise process.

Get in touch with us at www.thoughtlayer.com to geek out on how we can use process and data to engineer a better business. Enjoy and cheers to the New Year from the Thought Layer team!

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