This document summarizes a data science talk that covered the following topics: 1) An introduction to data science, including what it is, where data comes from, and who uses it. Data scientists use skills in math, statistics, programming and domains to model data, experiment, validate models and deploy solutions. 2) Examples of data science applications at Roadrunnr to minimize delivery times and costs while improving reliability, such as optimizing multi-drop grouping logic for deliveries and predicting demand patterns. 3) Future data science projects at Roadrunnr, including surge pricing, wait time prediction, carrier selection, driver fraud detection, and product size estimation with images.

1. Data Science Talk
AMOL SAHASRABUDHE
ANKIT JAIN

2. WHY THIS PRESENTATION
DEMOCRATIZE
BRAINSTORMING
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3. • Why and What of Data Science
• Data Science @Roadrunnr
• Multi Drop Logic
• Demand Prediction
• Future Work
Agenda
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4. What is Data Science ?
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5. Data is Everywhere
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6. HOUSE OF CARDS
MAKING SENSE OF DATA
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7. HOUSE OF CARDS
WHO USES DATA SCIENCE
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8. HOUSE OF CARDS
WHO IS A DATA SCIENTIST
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9. HOUSE OF CARDS
WHAT DO THEY DO
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10. HOUSE OF CARDS
WHAT DO THEY DO
IDEA EXPERIMENT VALIDATE SPEC DEPLOY
MODELING
SKILLS : Math, Statistics, Programming and Domain Knowledge
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11. HOUSE OF CARDS
DATA SCIENCE@ROADRUNNR
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12. Use data as a raw input to develop products to:
• Minimize Estimated Time of Arrival (ETA)
• Improve Reliability
at minimal Costs
VALUES
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13. Multi Drop Clubbing Logic
• Improve the multi drop grouping logic to:
• Reduce number of touchpoints/order (5-15%)
• Reduce distance travelled per touch point
• Increase driver satisfaction
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14. Existing Multi Drop Logic
HUB
Red Cluster can be done away with in this scenario
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15. Ideal Clubbing
HUB
DB should be able to cover points on his way
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16. How to Achieve Ideal Clubbing?
HUB
D1 < D2
Join two points which have minimum distance between them
• Joining two points
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17. Joining Two Groups
• Join on the basis of shortest distances and not center distances to
achieve ideal clubbing
HUB
• S1, S2 shortest distances
• C1, C2 center distances
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18. Delhi West Hub Orders (Existing Clubbing)
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19. Delhi West Hub Orders (New Clubbing)
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20. Algorithm Limitations
• Sub optimal solution due to accuracy vs complexity trade off
• Performance limited by accuracy of Lat,Long of drop points
• Not optimized for size and weight of shipments
• Routing not included as a part of this version (V2)
• Considers only bike as a carrier (V2)
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21. Demand Prediction
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22. Demand Prediction (Impact)
30% stock-outs during peak hours
70% demand during peak hours
Supply Planning
Driver Placement
Surge Pricing
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23. Demand Prediction(Actual Demand)
Koramangala
High Variance in hourly demand
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24. Demand Prediction(Method)
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25. Demand Prediction(Actual and Predicted)
Koramangala
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26. Demand Prediction(drill down)
Koramangala 8PM
Predictability in number of orders in this hour
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27. Demand Prediction(Model Failure)
Electronic City
Demand is too erratic to be captured by modeling
Bad predictions
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28. Demand Prediction(Future Work)
Category specific demand
Prediction for smaller clusters rather than localities
Prediction over smaller intervals
Stock-out incorporation
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29. Data Science Future Projects
Surge pricing
Wait time prediction for food orders (deployment pending)
Carrier Selection (Extension of multi drop logic)
Driver Fraud detection
Product size estimation using image
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30. THANK YOU
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