Challenge #410: Optimizing Winter Sports Inventory

[AYXAcademy]

Hi Maveryx!

A solution to last week’s challenge can be found here. 

Don't forget that we are running a campaign inviting all of you to contribute to our upcoming 2024 Weekly  Challenges! Your ideas and participation are crucial in shaping an engaging and innovative year ahead. Visit our blog post to review the guidelines and submit your challenge to earn our brand-new accolade, the Weekly Challenge Contributor Badge!

Now that winter is here, excitement is high among curling, skiing, snowboarding, and all winter sports enthusiasts! Special thanks to Nolan Gaskill (@Nolan)  for contributing the idea for this challenge.

As the regional manager of a sports retail chain, your challenge is to manage the winter sports inventory, ensuring maximum sales and minimal spoilage. This decision-making process will be powered by—you guessed it—an insightful analysis of historical sales data using Alteryx Designer.

A dataset containing details of your store’s inventory, sales, and spoilage rates has been provided. It requires some preliminary cleaning to ensure accuracy in your analysis, allowing you to dive in and extract valuable insights.

Your tasks are:

1. Spoilage Scrutiny: Discover the five products with the highest spoilage rate. These are items that are not selling as expected and are incurring losses.
2. Sales Efficiency Evaluation: Determine the five products with the lowest sales per unit produced.

If you find yourself struggling with any of the tasks, feel free to explore these interactive lessons in Maveryx Academy for guidance:

• Summarizing Data
• String Functions
• DateTime Functions
• Sampling Data

Good luck!

Note: The Top Solvers and Top Challenges sections will be removed from the Weekly Challenge index pages at the end of January. But don't fear, these stats aren't going away! Top Solvers and Challengers will be recognized quarterly in our Top Contributors blog.

[Kenda]

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[Flo_P]

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Missed the spoilage rate requirement which wasted some time

[RolandSchubert]

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[ed_hayter]

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[mmontgomery]

c410

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[NRoland]

People have cleaned the data in so many different ways. I'll be using some of them in the future. 

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[robbinrv]

Great brain cruncher at the end of the day, thanks!

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[Powerhouse_21]

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[Chris-Nienart]

In hindsight, transpose, cross-tab, and regex tools were unnecessarily complicated. Multi-row was the way to go.

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It's a solution, but definitely NOT recommended.