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DutchMLSchool. ML for Logistics


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Machine Learning for Logistics: Predicting Expedition Outcome - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.

Published in: Data & Analytics
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DutchMLSchool. ML for Logistics

  1. 1. 1st edition | July 8-11, 2019
  2. 2. Company Creation Technology 2 Client (T2C) was founded in December 2003 and started operating in January 2004 establishing the first office in the centre of Barcelona. 2003 New HQ In 2014, T2C moves to Avda. Diagonal, ‘prime’ zone for tech companies in Barcelona consolidating employees in a single office. 2014 México Subsidiary Where we expect to develop and provide our Advanced Analytics, development and SAP services. 2019 Málaga Office Through an agreement with the Tech hub in Málaga (PTA) and the Málaga University (UMA), we facilitate our entrance in the hub. 2018
  3. 3. Our Goals We are a company built by people that aims to help people. We care less about numbers than we do about building trust. TALENT DNA We help our clients to improve their processes and consolidate projects using the best talent available. CONSISTENCY 15 years Since our creation we have never stopped growing in a organic manner that allows us to maintain the core values of the company. INNOVATION Future We are always looking to anticipate what’s next by constantly scanning the market looking for new products and trends. EFICIENCY Maximum Obsessed with efficiency, we thrive in projects with maximum impact and added value.
  4. 4. ML for Logistics: Predicting expedition outcome Most Changes Are About Learning
  5. 5. T2C Global logistics market is anticipated to register a CAGR of 3.48% from 2016 to 2022 to attain a market size of around $12,256 billion by 2022. Allied Market Research
  6. 6. T2C Logistics impact on sales 5 3.5 3.3 Warehousing Other Transportation 1 3 2
  7. 7. T2C Logistics is a network based business that generates huge quantities of data.
  8. 8. T2C Word cloud
  9. 9. T2C Operational inefficiencies can often lead to potential revenue losses, increasing costs and poor customer service.
  10. 10. T2C 24 32Clean Sheet FTL Costs Variable Costs Fixed Costs Fuel Labor costs Transportation costs
  11. 11. T2C Use Case: Luís Simões
  12. 12. T2C Project Definition QUESTION Define the question we are going to answer. “Is an expedition likely to fail?” LABEL Define which label we are going to predict (Supervised Learning). “Expedition outcome: Binary classification SUCCESS/FAILURE” AMOUNT Is there enough data to answer that question? Are there enough positive instances? “Do our expeditions fail that often? Do we properly record these failures?” GOALS Define precisely what a training instance is, the goal and the evaluation method. “An expedition can contain several movements.” “ Improve service level.” “We will measure the ROI of the project based on average failures reduction.”
  13. 13. T2C Distribution Smart Plant Warehouse DB2 DB2 SQL ML DS MySQL Staging Area7 Systems Product KPI DS ERP SQL 1 2 3 4 5 Architecture
  14. 14. T2C Iterative process Data Acquisition
  15. 15. T2C Integrating heterogenous systems
  16. 16. T2C Iterating
  17. 17. T2C Iterative process Data Acquisition Dealing with data
  18. 18. T2C 1. Data exploration 2. Data cleaning 3. Data transformations 4. Feature engineering 5. Feature selection
  19. 19. T2C 1. Data exploration
  20. 20. T2C 2. Data Cleaning 3. Data Transformation Transformations need to be applied to raw data to obtain Machine Learning ready data. • Types of missing values: • Meaningful missing: The fact that a value is missing adds information. • Meaningless missing: The fact that a value is missing is accidental. • Strategies: • Drop rows with missing values if there is a small percentage. • Drop features with a high percentage of missing values. • Impute missing values with static content: median, mode, mean… • Use Machine Learning techniques to impute missing values.
  21. 21. T2C 4. Feature Engineering
  22. 22. T2C Selected Features Feature Description date Date of order generation. Planned_delivery_date Planned delivery date of the order. Final_Destination Arrival destination code. destination_name Destination name. destination_location Destination location. destination_zip_code Destination zip code. destination_province Destination province. destination_region Destination province. origin Origin code. origin_name Name of the origin of the order. num_stops Number of stops made by the driver. diff_hours Number of hours between order generation and estimated time of delivery. Feature Description distance Distance (in a straight line) between origin and destination. num_pallets Number of pallets in the shipment. num_volumes Number of boxes in the shipment. weight weight (kg). urgent Indicates if a shipment is urgent. is_workingday_delivery Indicates if it is a workday the day of delivery. is_workingday_delivery_d+1 Indicates if it is a workday the day after of delivery. is_workingday_delivery_d+2 Indicates if it is a workday two day after of delivery. is_workingday_delivery_d-1 Indicates if it is a workday the previous day of delivery. is_workingday_delivery_d-2 Indicates if it is a workday two days previous day of delivery. order_ok OBJECTIVE FIELD: Order completed with or without success.
  23. 23. T2C Iterative Process Data Acquisition Modelling Dealing with data
  24. 24. T2C First Model: Decision Tree • Comparative benchmark. • First business insights.
  25. 25. T2C Choose Your First Model A B
  26. 26. T2C First model: Decision tree
  27. 27. T2C Chosen Model: Decision Forest
  28. 28. T2C Final Model Evaluation
  29. 29. T2C Results • Possible savings $xxxx/year. • Quality of service can be improved from x% up to y%.
  30. 30. T2C Iterative process Data Acquisition Consumption Dealing with data Modelling
  31. 31. T2C Overview SERVICE LEVEL (ORDERS)
  32. 32. T2C Incident Map INCIDENTS IN DELIVERY
  33. 33. T2C ARRIVAL TIME BAND Deeper Analysis
  34. 34. T2C Machine Learning PLANNED ORDERS RISK
  35. 35. T2C Project Numbers Hours Afterworks KPIsConsultants Meeting hoursFeatures Lines of code Trained Models 870 11 335 4137 1.240174
  36. 36. Technology 2 Client S.L. Finance Human Resources Marketing Sales Other Machine Learning Use cases. Fraud Transactions. Rolling Forecast. Capex Analysis. Predictive Client Churn. Social Media Analysis. Retention & Engagement. Performance Analysis. Absenteeism Prediction. Hiring Optimization Customer Service Legal R&D IS/IT Purchase Demand Planning Pricing Optimization. Sales Modeling. Up-sell Opportunity Analysis. Customer Loyalty and Retention . Sentiment Analysis. Recommendation Engine. Review Documents and Legal Research. Contract Review and Management. Predict Legal Outcomes. Predict HW Failures. User Behaviors. Cybersecurity. Success of Research. Predict Hardness Materials. Trend Analysis and Studies. Pricing Strategy. Stock-out Prediction. TCO Analysis. Demand Forecasting. Predict Cannibalization. New Products Impact.
  37. 37. T2C THANK YOU