Mai KitShan

Bar graph predicting the next month's and the current usage of ingredients.
Bar Graph of the actual and predicted cost of each food item.
Table indicating the ingredients contributing to the predicted cost of food.
Bar Graph of each month's shipment and usage of ingredients.
Table of each month's shipment and usage of ingredients.
Suggestions for which ingredients to buy more or less of.
Line graph of how the usage and shipments of certain ingredients change over time.
Data table of how the usage and shipments of certain ingredients change over time.
Bar graph of the top selling items on the menu.
Table of the ingredients used for each of the top selling items on the menu.
Frequency Table of ingredients most used for the top selling items.
Bar or line graph of the actual and predicted revenue values for each month.

Inspiration

We chose this project because it offered us a challenge while giving us enough flexibility to be creative with our design.

What it does

Mai KitShan was built to reorganize Ma Shan Yun's inventory such that it is easier to interpret and analyze. It provides several features:

Usage Comparison: Users will be able to see how the previous months usage of an ingredient compares to the actual usage of that ingredient in the current month.
Cost Prediction: The graph generated displays how much a customer is supposed to pay for a specific dish based on the ingredients used for it. A corresponding table highlights specific ingredients that cause customers to pay more or less for a dish.
Revenue Prediction: A bar or line graph displays the actual v. predicted revenues for each month, helping users with future budgeting or decisions.
Shipment v. Usage Analysis: Based on the shipping information of an ingredient, the user will be able to generate a graph that shows how much of a certain ingredient was shipped (in units of either pieces or counts) and compare it to how much of that ingredient was used in a given month. For better visualization, a table is created to translate the data from the graph. Another table is provided to suggest recommendations for future shipping orders, either suggesting to buy more or less of a certain item.
Usage/Shipment of an Ingredient: The user will have the ability to track how much of a certain ingredient is used v. shipped over time, helping identify seasonal trends for certain ingredients.
Bestsellers Analysis: A bar graph is generated to show the top selling menu items for each month or over time. Additional tables are present revealing the ingredients needed for each of the meals and what ingredient is the most frequently used among meals in the list of top sellers.

How we built it

Mai KitShan was built in Python using a modular architecture. The front end was developed with React components written in JavaScript within an HTML wrapped in Python. When users input values through the front end, those inputs are sent to the back end, which utilize Flask endpoints to return JSON values displayed over the interface.

Challenges we ran into

One major challenges we ran into was the lack of data and how inconsistency of it across multiple files, making it difficult to clean and merge the datasets properly. Another challenge we faced was the lack of standardized variables across datasets. Due to these inconsistencies, it was difficult to calculate the importance or weight of certain ingredients and cost of each item. As a result, standardization was required to convert between different measurement units within the ingredient and shipping files.

Accomplishments that we're proud of

We are proud of the professional, interactive, and user-friendly interface we developed. There are multiple tabs, displaying large amounts of datasets, and each tab is able to manipulate specific datasets to gain a better understanding of the inventory.

What we learned

Throughout the development of Mai KitShan, we learned the importance of data cleaning and consistency when working with real-world datasets. Many of the files that we received were inconsistent with the information and were mismatched, making it difficult to merge the data effectively. This experience taught us how crucial it is to properly prepare data for accurate results.

What's next for Mai KitShan

We hope for Mai KitShan to be recognized as an organizational tool that helps Mai Shan Yun, and eventually other restaurants, organize their inventory.