2024: Week 45 - SuperBytes Stock

Challenge by: Holly Jones

This is the final challenge that DS43 prepared for us earlier this year - over to Holly:


SuperBytes are looking to change how stock is reordered at stores. The current policy is to record the quantity of stock they have remaining on a Sunday, after the store closes. If they have 10 or fewer items remaining, they will place an order with the supplier for that product, to get them back to 30 items in stock. 

Rather than applying such a blanket approach across products, SuperBytes would like to understand how many items are sold on average each week and how frequently stores are currently ordering more stock from suppliers, to see if there could be benefits in making changes to how they order stock.

Inputs

There are 3 inputs for this challenge:
  1. Available Stock 

  2. Product 

  3. Supplier Data 

Requirements

  • Input the data
  • Join the tables together and remove unnecessary fields
  • Identify weeks where orders were placed for new stock
    • This occurs whenever the Quantity Available is 10 or less
  • Calculate how much Stock was ordered in these weeks
    • SuperBytes policy indicates they should order as many items as they need so their stock level is back to 30
      • e.g. if the Quantity Available is 5 then they would order 25 items
  • Calculate the Quantity Sold each week, taking into consideration the new stock that has been ordered
  • Across the dataset calculate the Total Quantity that has been sold for each Product in each Store, as well as the Avg Quantity Sold per Week and Avg Order Frequency
    • i.e. if the Order Frequency is 2 then this would mean the Store is ordering once every 2 weeks on average
  • Round these values to 1 decimal place
  • Output the data

Output


  • 6 fields
    • Store
    • Category
    • Product
    • Total Quantity Sold
    • Avg Quantity Sold per Week
    • Avg Order Frequency
  • 100 rows (101 including headers)
You can view the output here.

After you finish the challenge make sure to fill in the participation tracker, then share your solution on Twitter using #PreppinData and tagging @Datajedininja, @JennyMartinDS14 & @TomProwse1

You can also post your solution on the Tableau Forum where we have a Preppin' Data community page. Post your solutions and ask questions if you need any help! 


Popular posts from this blog

2024: Week 1 - Prep Air's Flow Card

Image
Created by: Carl Allchin Welcome to a New Year of Preppin' Data challenges. For anyone new to the challenges then let us give you an overview how the weekly challenge works.  Each Wednesday the Preppin' crew (Jenny, myself or a guest contributor) drop a data set(s) that requires some reshaping and/or cleaning to get it ready for analysis.  You can use any tool or language you want to do the reshaping (we build the challenges in Tableau Prep but love seeing different tools being learnt / tried). Share your solution on LinkedIn, Twitter/X, GitHub or the Tableau Forums Fill out our tracker so you can monitor your progress and involvement The following Tuesday we will post a written solution in Tableau Prep (thanks Tom) and a video walkthrough too (thanks Jenny) As with each January for the last few years, we'll set a number of challenges aimed at beginners. This is a great way to learn a number of fundamental data preparation skills or a chance to learn a new tool — New Year...
Read more

2023: Week 1 The Data Source Bank

Image
 Created by: Carl Allchin Welcome to a New Year of Preppin' Data. These are weekly exercises to help you learn and develop data preparation skills. We publish the challenges on a Wednesday and share a solution the following Tuesday. You can take the challenges whenever you want and we love to see your solutions. With data preparation, there is never just one way to complete the tasks so sharing your solutions will help others learn too. Share on Twitter, LinkedIn, the Tableau Forums or wherever you want to too. Tag Jenny Martin, Tom Prowse or myself or just use the #PreppinData to share your solutions.  The challenges are designed for learning Tableau Prep but we have a broad community who complete the challenges in R, Python, SQL, DBT, EasyMorph and many other tools. We love seeing people learn new tools so feel free to use whatever tools you want to complete the challenges.  A New Year means we start afresh so January's challenges will be focused on beginners. We will u...
Read more

How to...Handle Free Text

Image
Free isn't always a good thing. In data, Free text is the example to state when proving that statements correct. However, lots of benefit can be gained from understanding data that has been entered in Free Text fields. What do we mean by Free Text? Free Text is the string based data that comes from allowing people to type answers in to systems and forms. The resulting data is normally stored within one column, with one answer per cell. As Free Text means the answer could be anything, this is what you get - absolutely anything. From expletives to slang, the words you will find in the data may be a challenge to interpret but the text is the closest way to collect the voice of your customer / employee. The Free Text field is likely to contain long, rambling sentences that can simply be analysed. If you count these fields, you are likely to have one of each entry each. Therefore, simply counting the entries will not provide anything meaningful to your analysis. The value is in ...
Read more