Enhancing operational efficiency and customer satisfaction at 7-Eleven through cutting-edge data science and generative AI technologies.
Python, SQL, Jupiter Notebooks, TensorFlow, Keras, Scikit-learn, Databricks
Time-Series Analysis, EDA, Deep Learning (Autoencoders), Generatiev AI (GANs)
7-Eleven faced a critical operational challenge with its gas station systems erroneously sending false
alerts about fuel supply levels. This issue manifested in two major problems:
• False Supply Alerts: The system failures falsely indicated to potential customers that fuel
was available when it was not, leading to customer dissatisfaction and operational disruptions.
• Supply Chain Disruptions: Simultaneously, these system errors misled suppliers with incor-
rect high-level fuel alerts, causing inefficiencies and logistical issues in the supply chain.
• Technical Constraints: The absence of labeled data made it difficult to experiment with and
directly evaluate deep learning and AI models for accurately identifying these anomalies.
Implementing AI at 7-Eleven faced critical challenges:
• Lack of Labeled Data: The absence of labeled datasets necessitated innovative training ap-
proaches using unsupervised learning.
• Data Quality Issues: Inconsistent data from various sensors required robust preprocessing to
ensure model accuracy.
• Scalability Needs: Solutions had to scale across numerous locations with varying operational
data, demanding high adaptability in models.
To overcome these challenges, the project focused on:
• Generative AI for Synthetic Data: Utilized GANs and VAEs to generate synthetic data
points, compensating for the lack of labeled data.
• Dynamic Training and Adaptability: Implemented continuous learning and automated
anomaly detection systems to adapt models to real-time operational changes and maintain long-
term performance.
These strategies ensured the AI solutions were both effective in current applications and adaptable
to future operational variations.
Innovative AI Application
Employed generative AI technologies, including autoencoders and GANs, to innovatively tackle the
issue of unlabeled data and enhance the precision in identifying and categorizing fuel volume data
anomalies. This strategic implementation allowed for:
• Effective identification and removal of outliers from system failure data.
• Development and evaluation of models in a constrained environment, adapting to the absence of traditional labeled datasets.
Achievements and Business Impact
• Operational Improvements:
– Reduced false alert incidents by 30
– Streamlined supplier interactions by correcting fuel level alerts, reducing unnecessary supply dispatches by 25
• Customer Satisfaction:
– Increased customer trust and satisfaction by ensuring accurate and reliable fuel supply information.
The project not only addressed significant operational challenges but also set a new standard in the application of generative AI for retail operations, demonstrating how cutting-edge technology can be leveraged to enhance both operational efficiency and customer experience at 7-Eleven.
