Detailed Notes on Radar Data Analysis and Management

Overview of Radar Data

  • Purpose: The Radar project is aimed at identifying anomalies in recipe data and providing recommendations for those anomalies.

Key Concepts

  • Recipes

  • Recipes prepared and sold in stores include items like sandwiches, cakes, salads, and fried chicken.

  • Consistency across stores is crucial, meaning the same recipe should utilize the same ingredients and methods.

  • Tables and Terms

  • Rock: A retail ordering group that encompasses a set of stores with consistent ordering behavior.

  • SLU: Represents a specific recipe (e.g., chicken sandwich).

  • Ingredients: Each recipe comprises multiple ingredients, which have associated item codes (kicks).

  • Kick: A unique identifier for a product associated with an ingredient.

Identifying Anomalies

  • Recipes can contain several issues:
  • Invalid Kick: An ingredient linked to a recipe no longer has shipments, which may indicate a vendor change.
  • Missing Kick: An ingredient lacks any associated product, leading to complications in inventory management.
  • Omitted Ingredients: Sometimes, essential ingredients may not be listed, leading to mismatches in inventory and sales records.

Importance of Resolving Anomalies

  • Financial Implications: Inaccuracies can lead to discrepancies between sales records and actual usage, resulting in inventory shrink.
  • The Radar system aims to identify and resolve three main types of inconsistencies:
  • Invalid kicks
  • Missing kicks
  • Omitted ingredients

Analysis of Data

  • Initial focus on daily food services revealed:
  • 20 different rocks, 9500+ recipes, 7000+ ingredients mapped across 11 departments, and analysis of over 364,000 different kicks.
Recommendations Generation
  • The recommendation process involves analyzing item descriptions, utilizing brute-force methods, and generating combinations to find suitable replacements for invalid or missing kicks:
  • Combinatorial Analysis: Generates numerous combinations from product descriptions to find potential substitutes. However, this can lead to a vast number of combinations that need to be filtered.
  • Synonym Identification: Recognizing synonyms and standardizing them improves matching accuracy.
  • Frequent Item Set Mining: Used for generating combinations and clustering recommendations based on historical data.

Challenges Faced

  • Identifying Missing Ingredients: More complex as it requires a nuanced approach to determine if an ingredient should be present in an established recipe.
  • Daily Operations: The system operates on two levels:
  • A one-time effort to identify all inconsistencies.
  • A daily run to monitor sales and recommend changes based on the most recent sales data.

Operational Implementation

  • The platform is designed to provide alerts based on identified issues and recommendations, ensuring ongoing monitoring of recipe consistency and product availability.
  • Recommendations are generated based on criteria like past sales, ensuring relevance and timely action for underperforming products.
  • Use of tactically arranged recommendations linked to results from analysis enhances operational responsiveness.

Conclusion

  • The Radar project continually evolves, with iterative improvements based on data analysis and system development aimed at enhancing food service operations and profitability.