Maximizing Profits for Microbreweries: A Decision Intelligence Case Study
I had the pleasure of working with a startup whose goal was to help craft brewers leverage AI - “AI for better beer.” As a part of this effort, I helped create a causal decision diagram to model the fundamental problem we were attacking - ways to reduce the cost of goods sold by breweries. One of the co-founders of the startup was a former brewer, so we had excellent subject matter expertise to craft this, much like the beer we wanted to help brewers make by helping the brewers with AI.
Bob the Brewer
Meet Bob the Brewer, a crafted persona symbolizing the ingenuity and entrepreneurial spirit of the microbrewery industry. Bob represents the quintessential brewmaster who is passionate about crafting the finest beers but is equally committed to the sustainability and profitability of his operations. With the rising costs of goods sold (COGS) squeezing the life out of his profit margins, Bob finds himself at a crossroads.
Envision a microbrewery owner, embodied by Bob the Brewer, confronting the critical issue of inflated production expenses that threaten the financial health of his business. He shares his pivotal decision objective statement with us, revealing the core challenges and his determination to navigate them.
“Our brewery is facing narrowing profit margins due to rising COGS, particularly as we increase distribution. The costs associated with distribution—like kegging and distributor fees—are cutting into our profits. To sustain our business, we need to lower these costs while maintaining high product quality and revenue. We'll review our operations to identify cost-saving opportunities.”
Decision Intelligence Approach
Decision Intelligence, as stated in our previous blog post, is a series of processes combining people and technology to structure decisions. Depending on the size of the brewery, this decision can be particularly complicated - organizational size leads to complexity, and the brewer could risk reaching the complexity ceiling. This is where a decision design workshop can fit in. In the context of the startup, as we wanted to help brewers, creating a causal decision diagram would allow us to find ways we could target levers that we could aid with AI to modify such that we could achieve the outcome of reducing COGS.
The Billistician met with the founders of the startup and went through in order to first discuss what the outcomes of the decision were (minimizing COGS) but it was also realized that we needed to include revenue as an outcome as well, since it’s possible to reduce COGS to zero simply by not brewing any beer, So we include revenue as an outcome too (with the goal of it remaining the same or higher). Combining both could allow us to have a single outcome, Gross Profit (Revenue - COGS) to maximize. We came up with an enormous amount of levers that the brewer can control, which we will show in the diagram below.
Causal Decision Diagram for the Microbrewery
Causal Decision Diagram Exploration
As you can see, the diagram is pretty complicated because there are a lot of levers that the brewery owner can control - the reason why there are so many levers is because the scope of the decision is the whole business. If the decision maker was a particular employee of the brewery owner (say, someone in charge of procuring ingredients) then the levers not in scope would transform into external factors. The scope of the decision is performed in a decision-framing step.
When a causal decision diagram has a large number of decision elements like this, it’s appropriate to make a decision simulation because it might not be possible to visualize the cause-and-effect relationship intuitively from the diagram. But looking at the graph we could see several potential opportunities we could target, but it will entirely depend on the brewery business model. If a brewery gets most of its sales through in-house selling, then distribution costs won’t be a problem; meanwhile, a brewery that distributes most of its product would have a significantly higher proportion of COGs. A decision simulation could help find the optimal proportion of distribution selling for a given brewery to maximize gross profit.
Similarly in the context of the startup, we were identifying levers which AI could aid in to help in this process. Ingredient reuse, ingredient sourcing, recipe optimization, ingredients traded, and maintenance spending were all factors that the startup believed it could aid brewers with. As a decision simulation wasn’t created (as this was a fictional scenario to guide us to see what variables could impact gross profit) we can’t tie show which lever changes would have the most impact on profit - if we did though, that would be an analysis called a sensitivity analysis.
Impact Analysis
If Bob the Brewer were to implement the Decision Intelligence (DI) strategies suggested by the Causal Decision Diagram, we could anticipate a series of potential outcomes that would revitalize his brewery's financial health. For instance, by optimizing the production batch size and implementing flexible shift patterns, the brewery might see a marked reduction in wasted resources and an increase in production efficiency. These changes could lead to a hypothetical 15% decrease in overhead costs and a 10% increase in production output, without compromising the quality of the beer. Furthermore, through strategic relocation to a more cost-effective manufacturing geography, Bob could potentially reduce raw material expenses by up to 20%. The qualitative benefits, while harder to quantify, could include improved employee satisfaction due to more efficient work schedules and a boost in brand reputation owing to a stronger alignment of production with consumer demand.
Lessons Learned
The hypothetical application of Decision Intelligence (DI) in the microbrewery industry, as illustrated by Bob the Brewer's scenario, offers several key takeaways. One significant lesson is the importance of data-driven decision-making in uncovering cost-saving opportunities without sacrificing product quality. Bob's challenges, such as integrating new technology with traditional brewing practices and managing the initial costs of DI implementation, reflect common hurdles. These were overcome by focusing on training for his team and seeking partnerships for shared technology investments. For other microbreweries considering DI, the advice would be to start small, perhaps with one aspect of the Causal Decision Diagram, and scale up as tangible benefits are realized. Collaboration with tech partners, a willingness to adapt, and a strategic approach to data utilization are crucial for achieving the best outcomes. Embracing DI can lead to more informed decisions that enhance efficiency, drive profitability, and ultimately craft a better future for the brewery.
The journey of Bob the Brewer through the implementation of Decision Intelligence (DI) into his microbrewery operations has underscored the multifaceted benefits that such an approach can bring. The DI methodology goes beyond simple cost-cutting; it's about smart optimization that touches every aspect of the business. For microbreweries, the tangible benefits range from reduced overhead costs and waste to improved production efficiency and product quality. Additionally, DI can foster a better understanding of consumer trends, leading to more effective marketing strategies and product development. This case study, albeit hypothetical, paints a promising picture of increased revenue and gross profit margins while maintaining the heart and soul of the craft - the beer itself.
Beyond the immediate financial and operational improvements, the potential of DI to transform microbreweries is expansive. It can enhance areas such as supply chain management, customer relationship management, and even sustainability efforts, by providing actionable insights and predictive analytics to support decision-making. Microbreweries are encouraged to embrace DI as an integral part of their strategy for sustainable growth and profitability. As the industry continues to evolve, those who adopt a data-informed approach to their business practice are likely to thrive, ensuring that they not only keep up with the competition but set new standards for success.
