The New Talk in Town - BreadTalk Digital Transformation

AI/ML
Frontend
Data Analysis
YOLOv11
Figma
Python
Digital Transformation
Computer Vision
Recommender Systems
The New Talk in Town - BreadTalk Digital Transformation

Project Grade Received: "A"

Summary

This project explores a potential pathway for the digital transformation of BreadTalk, one of Singapore's leading bakery chains, through AI-driven solutions aimed at improving customer experience, streamlining operations, and enhancing business intelligence.

The goal is to address operational inefficiencies and modernise customer experience at BreadTalk using data-driven solutions. We noted three potential avenues to explore:

  • Reducing checkout queue time
  • Offering personalised recommendations based on user data
  • Modernising BreadTalk's in-store and mobile experiences

We proposed three solutions that work together to create a cohesive experience for customers, modernising the brand's experience and creating rich data collection for BreadTalk to engineer & derive further insights.

  1. BreadPickUp — Mobile Order for Pickup
  2. BreadSee — AI-Powered Mobile Checkout
  3. BreadThink — Personalised Recommendations

1. BreadPickUp — Mobile Order for Pickup

Figma screens for pickup function

This feature allows users to pre-order their bread via the app and pick it up in-store.

  • Reduces wait time & ensures product availability
  • Provides a convenient and speedy solution for rushing Singaporeans

Highly feasible, learning from competitors such as Luckin Coffee and Starbucks which have:

  • Strong sales promotions: Such elements incentivise app usage
  • Loyalty points system: Reducing friction at checkout and rewarding repeat purchases
  • In-app pre-ordering: Appealing to convenience
justification mobile ordering feature

2. BreadSee — AI-Powered Mobile Checkout

User flow for breadsee function

We trained a computer vision system that identifies bread items on a tray for instant mobile payment.

  • YOLOv11s model trained on 6 bread classes
  • Eliminates manual checkout and minimises human error
  • Provides customer insights - enables data collection on purchasing habits

The training process:

  • We labelled 324 images of BreadTalk bread that we purchased in various lighting (but consistently on a white tray of sorts). This was done manually in LabelStudio with our bread classes specified so that we can use it to train the YOLOv11s model.
training and labelling for model training

Results:

training and labelling for model training
ClassTPFPFNPrecision (P)Recall (R)F1-Score
Coffi-O0.950.050.050.950.950.95
Flosss1.000.000.001.001.001.00
Plain Croissant0.780.220.400.780.660.71
Sausage Standard0.890.110.200.890.770.83
Sugar Donut0.830.170.050.940.880.91
Overall (Macro Average)---0.910.850.88

We also had a working live demo during our presentation where we had a webcam plugged in to our laptop as a camera source, and it successfully identified the breads we placed on the tray.


3. BreadThink — Personalised Recommendations

We also worked on a recommender system using the AutoRec Model to:

  • Offer data-driven promotions and suggestions
  • Improve customer loyalty
  • Increase average basket size

Technical Process:

training and labelling for model training

Why AutoRec:

  • AutoRec is a neural collaborative filtering approach that uses auto-encoders to generate personalised recommendations
  • Autorec compresses the purchase history into a compact representation, then expands this to predict which new items that the user will enjoy.
  • It works efficiently with sparse data by focusing only on items user actually purchased.

This is an example of the output derived from generated sample data of customers, passed through our trained model:

user_idpurchase_historyrec_1_bread_namerec_1_confidencerec_2_bread_namerec_2_confidencerec_3_bread_namerec_3_confidence
User_1Flosss(3); Shio Pan(1); Plain Croissa...Shio Pan60.3Hearty Sausage Croissa65Get Cheesey67.8
User_2Flosss(3); Fire Flosss(2); Apple Worr...Sugar Donut56.2Butter Sugar Loaf57.9Chocolate Croissant64.3
User_3Hearty Sausage Croissant(3); Sunflo...Chicken Parmesan22.4Butter Sugar Loaf33.2Butter Sugar Loaf46.4
User_4Double Dog(1); Tuna(1); Chicken Par...Get Cheesey27.6Cheese Sausage32.6Chocolate Croissant35.9
User_5Flosss(1); Plain Croissant(2); Double...Get Cheesey33Fire Flosss38.8Sugar Donut43.6
User_6Double Dog(1); Sugar Donut(2); Chee...Get Cheesey18Hearty Sausage Croissa30Ham & Cheese39.7

This project was created solely for academic purposes as part of coursework for the IS215 class at Singapore Management University. It represents a hypothetical proposal and conceptual exploration, not an actual implementation.

Important Legal Disclaimers:

  • This project is not affiliated with, sponsored by, or endorsed by BreadTalk Group Limited.
  • All BreadTalk trademarks, logos, brand names, and other proprietary materials referenced belong exclusively to BreadTalk Group Limited.
  • No commercial relationship exists between the project creators and BreadTalk Group Limited.
  • This educational exercise does not imply any actual or planned digital transformation at BreadTalk.
  • No confidential or proprietary information was used in creating this project.
  • All images of BreadTalk products were purchased legitimately for educational purposes.

The project content, including the AI models, prototype designs, and transformation strategies, are presented purely as academic work. Upon request from BreadTalk Group Limited, content referencing their brand will be promptly removed.

© All rights to the original content and concepts.