The article discusses the concept of budget optimization using the example of a fruit salad. It explains how to use a methodical approach to make the most of a limited budget while maintaining the enjoyment and satisfaction of the fruit salad. The article also includes Python code for visualizing the problem and solving the optimization equations. The optimal quantities of each fruit are determined, resulting in a 20% increase in portion size while staying within the budget. The article concludes by highlighting the flexibility of the method and offering assistance in applying it to other business scenarios.
Rephrased text:
Budget optimization is demonstrated through the example of making a fruit salad. Making decisions between different fruits and managing a limited budget can be challenging. By using a methodical approach and mathematical optimization, it is possible to find the optimal solution.
The article describes a scenario where the author hosted a party and served a fruit salad. The recipe for one portion included apples, oranges, bananas, and raspberries. Each portion cost around 2 euros. However, due to a limited budget, the author needed to find a way to reduce the cost of each portion to 1 euro.
The straightforward solution of halving the amounts of each fruit would not work because it would leave half of the guests unsatisfied. To find the optimal solution, the author used an objective function called the Cobb-Douglas utility function, which measures the enjoyment from the fruit salad. The author also used the method of Lagrange multipliers to find the maximum enjoyment within the budget constraint.
By solving the equations, the author found the optimized quantities for each fruit: 83 grams of apples, 62 grams of oranges, 83 grams of bananas, and 8 grams of raspberries. This resulted in a portion size of 0.238 kilograms, costing exactly 1 euro.
The optimization method used in this example can be applied to other scenarios where there are multiple factors to consider and a limited budget. It provides a flexible formula that can adapt to changing prices, recipes, or budgets.
Action items distilled from the meeting notes are as follows:
1. Evolve your company with AI Automation: Discuss with the team the potential benefits and feasibility of integrating AI automation into various aspects of the company.
2. Comparing Apples to Oranges with python: Research and analyze the capabilities of using Python for comparing different types of data.
3. Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI and brainstorm potential automation opportunities.
4. Define KPIs: Establish measurable key performance indicators (KPIs) to evaluate the impact of AI initiatives on business outcomes.
5. Select an AI Solution: Conduct a thorough evaluation of available AI tools and technologies, aligning them with the company’s specific needs and requirements. Assign someone to lead this evaluation process.
6. Implement Gradually: Start by implementing AI in a pilot project, gather data, and carefully expand the usage of AI based on the results and feedback received.
7. Evolve Continuously: Continuously gather feedback on the performance and impact of AI algorithms and use it to enhance and optimize them over time.
8. Connect for AI KPI management advice: Reach out to hello@itinai.com for guidance and advice on managing AI KPIs.
9. Stay informed on leveraging AI: Keep track of continuous insights on leveraging AI by following the Telegram channel t.me/itinai or Twitter account @itinaicom.Optional Assignments:
– Arrange a time for the team discussion on AI Automation (Action item 1).
– Assign a team member to research and present findings on using Python for comparing different types of data (Action item 2).
– Assign responsibility to team members for identifying automation opportunities (Action item 3) and establishing KPIs (Action item 4).
– Assign someone to lead the evaluation process of AI tools and technologies (Action item 5).
