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VSSML18. Improving Operations with Machine Learning

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Case Study: Improving Operations with Machine Learning, by Andrés González, CTO at CleverData.
VSSML18: 4th edition of the Valencian Summer School in Machine Learning.

Published in: Data & Analytics
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VSSML18. Improving Operations with Machine Learning

  1. 1. 4rd edition September 13-14, 2018
  2. 2. Cleverdata.io 2 Improving Operations with ML Automating Incident Classification – Dispatching BOT Andrés González CTO @
  3. 3. Cleverdata.io VSSML18: Improving Operations with ML 3 Summary • Introduction • The Challenge • The ML project • The data • Feature Engineering • Model Selection • Demo: Predictions • The implementation • The results
  4. 4. Cleverdata.io VSSML18: Improving Operations with ML 4 Summary • Introduction • The Challenge • The ML project • The data • Feature Engineering • Model Selection • Demo: Predictions • The implementation • The results
  5. 5. Cleverdata.io VSSML18: Improving Operations with ML 5 Introduction Who is Cleverdata.io • Start-up based in Barcelona • We develop customized ML solutions for our clients • We bring Machine Learning to Business • Some of our clients:
  6. 6. Cleverdata.io VSSML18: Improving Operations with ML 6 Introduction Who is Ricoh • Japanese company • Four business regions: Japan, Americas, Europe, Asia-Pacific • Printer manufacturer (also projectors, video conferencing, interactive whiteboards) • Managed Print Service • Pay-Per-Use Service • Responsible for solving printer issues • Call Center
  7. 7. Cleverdata.io VSSML18: Improving Operations with ML 7 Summary • Introduction • The Challenge • The ML project • The data • Feature Engineering • Model Selection • Demo: Predictions • The implementation • The results
  8. 8. Cleverdata.io VSSML18: Improving Operations with ML 8 The Challenge Current Incident Circuit SERVICE CALL FORM SIEBEL DB ENGINEERDISPATCHING BOT • Pros: • Engineer can work in more valuable tasks • Real-time dispatching
  9. 9. Cleverdata.io VSSML18: Improving Operations with ML 9 Summary • Introduction • The Challenge • The ML project • The data • Feature Engineering • Model Selection • Demo: Predictions • The implementation • The results
  10. 10. Cleverdata.io VSSML18: Improving Operations with ML 10 The ML project Measure Impact on Business Quality Pre-Evaluation Learning from Data (training) Transformation and Feature Engineering RAW Data Gathering, Exploration and Cleansing Understand Business Data Algorithms
  11. 11. Cleverdata.io VSSML18: Improving Operations with ML 11 Summary • Introduction • The Challenge • The ML project • The data • Feature Engineering • Model Selection • Demo: Predictions • The implementation • The results
  12. 12. Cleverdata.io VSSML18: Improving Operations with ML 12 The ML Project: The Data Historical RAW Data Incidents INPUT 166.000 INSTANCES Jan 2016 to July 2017 61 PRINTER CHARACTERISTICS1 INCIDENT DESCRIPTION2 CONTRACT CHARACTERISTICS3 SOLVED_ONSITE_REMOTELY4 40.000 INCIDENTS March 2017 to July 2017 25 PRINTER CHARACTERISTICS1 INCIDENT DESCRIPTION2 MODELATED DESCRIPTION3 SOLVED_ONSITE_REMOTELY4 Cleaned & Transformed Data COLUMNS DATES DATES COLUMNS
  13. 13. Cleverdata.io VSSML18: Improving Operations with ML 13 The ML Project: The Data Incident Data • Created Date • Service Region • Activity Type • Assigned To • Activity Status • Activity Sub Status • SR Number • Branded Model Name • Serial Number • SR Customer Description • Account • Activity Id • Line of Business • Opportunity • Required for Contract • Target Arrival Time • Product Type • SR Owner Login • Parent Activity Id • Previous Activity Id • Asset Decision Code • Lock Assignment • Action Code • Actual Response Duration • SR Cause Code • Contact Gender • Swap Out Activity Id • Contact Language Code • Target Fix Time • Actual Duration • Actual Start • Actual End • Defective Asset Serial Number • Appointment • Actual Fix Time • Down Time • Travel Time • Activity Organization • Direct/ Indirect • SR Action Code • Repeat Visit - Previous Engineer Id • Service Postal Code • Target Response Time • Activity Code • Assigned To Position Division • SR Product Name • Assigned To Manual • Account Type • Service Asset Number • Order Reference Number • Date/Time Request Sent • Service Provider • Owner Last Name • Owner First Name • First Name • Last Name • Details • Source • Source Type • Customer Inventory No. • VAS Labour/ Travel Duration
  14. 14. Cleverdata.io VSSML18: Improving Operations with ML 14 Summary • Introduction • The Challenge • The ML project • The data • Feature Engineering • Model Selection • Demo: Predictions • The implementation • The results
  15. 15. Cleverdata.io VSSML18: Improving Operations with ML 15 The ML Project: FE INCIDENT DESCRIPTION (Free text) MODEL TRAININGFINAL DATA (Description, Topic info, Model, Cause Code..) TOPIC MODELING TOPIC MAPCLEANED RAW DATA (Description, Model, Cause Code…)
  16. 16. Cleverdata.io VSSML18: Improving Operations with ML 16 The ML project Measure Impact on Business Quality Pre-Evaluation Learning from Data (training) Transformation and Feature Engineering RAW Data Gathering, Exploration and Cleansing Understand Business Data Algorithms
  17. 17. Cleverdata.io VSSML18: Improving Operations with ML 17 Summary • Introduction • The Challenge • The ML project • The data • Feature Engineering • Model Selection • Demo: Predictions • The implementation • The results
  18. 18. Cleverdata.io VSSML18: Improving Operations with ML 18 The ML Project: model selection TEST TRAINING MODEL ENSEMBLE LOGISTIC REGRESSION EVALUATION EVALUATION EVALUATION EVALUATION DEEPNET CLEANED RAW DATA FINAL DATA WHIZZML
  19. 19. Cleverdata.io VSSML18: Improving Operations with ML 19 The ML Project: model selection
  20. 20. Cleverdata.io VSSML18: Improving Operations with ML 20 Summary • Introduction • The Challenge • The ML project • The data • Feature Engineering • Model Selection • Demo: Predictions • The implementation • The results
  21. 21. Cleverdata.io VSSML18: Improving Operations with ML 21 The ML Project: predictions NEW INCIDENT MODELFINAL DATA (Description, Topic Info, Branded Model, Cause Code...) TOPIC MODELING (Description) (Description, Branded Model, Cause Code...) DESCRIPTION FIELD: NECESITO CONECTAR LN IMPRESORA DE LA X114PC00743 EN LOS ORDENADORES DE LA OFICINA SOBREMESA PORTATIL PUEDO TRATAR DE REPARARALO CON SUS SERVICIOS DE ASISTENCIA REMOTA POR TEAMVIEWER SI NO LO RESOLVEMOS QUE ENVIEN DE NUEVO ALGUN TECNICO A LA OFICINA MUNDUBAT
  22. 22. Cleverdata.io VSSML18: Improving Operations with ML 22 Summary • Introduction • The Challenge • The ML project • The data • Feature Engineering • Model Selection • Demo: Predictions • The implementation • The results
  23. 23. Cleverdata.io VSSML18: Improving Operations with ML 23 The Implementation SERVICE CALL FORM {REST API/WEB APP} 1 SUPERVISED LEARNING 2 5 6 DISPATCHER BOT LANGUAGE MODELING 3 4 • Service Call ID • Printer model • Description • Diagnostic • Resolution Group • Probability RICOH SYSTEMS SIEBEL DB ML
  24. 24. Cleverdata.io VSSML18: Improving Operations with ML 24 The Implementation Germany United Kingdom Spain Italy
  25. 25. Cleverdata.io VSSML18: Improving Operations with ML 25 Summary • Introduction • The Challenge • The ML project • The data • Feature Engineering • Model Selection • Demo: Predictions • The implementation • The results
  26. 26. Cleverdata.io VSSML18: Improving Operations with ML 26 The results Successes in the Dispatcher Bot prediction False positives (Remote predictions that are not solved)
  27. 27. Cleverdata.io VSSML18: Improving Operations with ML 27 Short Recap • Business Objective: make operations in Call Center more efficient • Technical Objective: make an automatic incident dispatching bot • Classify printer incidents as Remote or OnSite solution • We used a combination of supervised and unsupervised ML • Text Analytics techniques have been the key for success
  28. 28. Cleverdata.io VSSML18: Improving Operations with ML 28 End agonzalez@cleverdata.io

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