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Role of Data in Solving Capacity and Efficiency Problems in Real-time Logistics

Submitted by Piyush Srivastava (@piyushkrsrivastava) on Monday, 10 December 2018

Technical level: Intermediate

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Abstract

In the world of real-time Logistics, every minute counts. It shows up not just as customer experience (SLA-Compliance & NPS), but also as the overall ability to accept N orders (Capacity) and deliver them in optimal time using minimum resources (efficiency). Such systems need to react fast to on-ground changes such as traffic, weather, availability of delivery-executives and their proximity to demand areas, availability of shipment at the source (in Swiggy’s case, prepared food at restaurants) etc. Both, real-time and historical data play a significant role in choosing “what” to optimize for and “how”. In this talk we discuss some of these challenges at Swiggy, the nature of historical and real-time data, and our journey of using these inputs in designing optimal solutions for Capacity (when exactly should we stop accepting orders) and Efficiency (orders/delivery-executive/unit-time). We also discuss challenges with data accuracy, high-variance and the necessary trade-offs in designing an optimial system.

Outline

  1. Introduction and Context
  2. The Capacity Problem - what is it; why it is important?
  3. The Efficiency Problem - what, why and the necessary trade-offs
  4. Data and its Nature
  5. Challenges with Accurate Data Capture
  6. Challenges with high Variance
  7. Real-time Vs. historical data
  8. Representing Capacity
  9. Aggregated capacity (Zone-level)
  10. Point-in-time-capacity (Order-level)
  11. Journey and Results: Solving for Capacity
  12. Efficiency Levers
  13. Predictions and accounting for errors
  14. Trade-offs
  15. Optimal Assignment
  16. Batching
  17. Aggregate Analysis Vs. Specific Analysis
  18. Pitfalls of Aggregate Analysis
  19. Conclusions

Requirements

None

Speaker bio

Piyush works as Director Engineering, Delivery team, Swiggy. He is responsible for building tech platforms for real-time logistics. Prior to this he worked with Last Mile logistics division in Flipkart where he led the development of the Mapping solution and helped deliver last mile optimal-routing solutions.

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