This is the part of Gen AI workshop recently we conducted in association with PSNA college and collaborators.

1. Generative AI In Logistics
Sivakumar Shanmugam
Werner
WERNER

2. AGENDA
•About Werner
•AI vs ML vs DL vs Generative AI
•Machine Learning Types / Steps
•Traditional Vs In Database ML
•Supporting Databases
•Deep Dive into Machine Learning on Db2 Platform
•DEMO : TRUCK (SENSOR DATA) / (Using Naive Bayes classification
algorithm)
•WHAT IS NEXT ?

3. ABOUT WERNER
Largest Carrier in Midwest America – Head Quarters –Omaha, NE
Operates Across USA / Canada & Mexico

4. COMPANY HISTORY

5. Werner Tractor Locations – Tractor Ping Data
(8000 Trucks / 35,000 Trailers)

6. Werner Logistics - Company info
•Werner Revenue is at $ 3.3 Billions = 25,000 Crores Rs
Rupees
• Our plan is to reach $ 5 Billion = 40,0000 Crore RS by
the end of 2025
-

7. What are the paths we are taking
•Moving from On-prem to Cloud Architecture
Master Mind – New cloud based TMS system
Sales force - Stores Pricing & Sales information
Work day - Stores Company Employee info
Snowflake - Cloud base data Warehousing,
Azure(Sqldb/cosmosdb) - Stores inhouse developed Application data.
Chat GPT , Bing/ Copilot, ChatBot are going to play a crucial role during the
development and data integration process.

8. …Paths we are taking
•Autonomous Trucking – Driver will be still on the seat.
•Employees (2500) , Drivers (10000) – Employee to Driver Ratio 1:4
• According to the USA bureau of Labor Statistics Employment rate for data
scientist will grow by 36 % in next 5 years.

9. AI vs ML vs DL vs Gen AI

10. MACHINE LEARNING TYPES

11. Machine Learning Vs Generative AI
Bart / Gemini – Recognizes images

12. Generative AI in Logistics

13. IN-DATABASE MACHINE LEARNING
SIMPLE SETUP

14. Deep Dive into Machine Learning on Db2
Platform

15. dB2 V11.5.4
(in-database Machine learning)

17. DATABASE SETUP
Install & setup Db2 V 11.5 fp 5 on Linux Environment

19. COLLECT DATA
Data feed from Trucks to Databases
IBM DB2 V
11.5.6 database

20. LINK
DEMO 1 - PREDICTIVE MAINTENANCE OPTIMIZATION OF TRUCK FLEET DATA SET

23. Opportunities for predictive Modeling
How long the truck can run without maintenance ?
How many drivers can quit in the next 30 days ?
Company turnover in future ?
Accident Prevention ?
Battery life on the trucks ?
Weather alerts to drivers - k-means algorithm?
Run machine learning set up on AIX-Open shift environment (Testing in
progress)

24. STEP 2 : Data transformation
• Use the following stored procedures to transform the data
before passing it to a machine learning algorithm.
IDAX.IMPUTE_DATA - Impute missing data
IDAX.SPLIT_DATA - Split data into training data and test data
IDAX.STD_NORM - Standardize or normalize columns of the input table
IDAX.EFDISC - Discretization bins of equal frequency
IDAX.APPLY_DISC - Discretize data by using limits for discretization bins

25. STEP 3. Data exploration
Use these stored procedures to evaluate the content of the given
data
IDAX.SUMMARY1000 - Summary of up to 1000 columns
IDAX.COLUMN_PROPERTIES - Create a column properties table
IDAX.GET_COLUMN_LIST - Get a list of columns

26. STEP 4 : Model building
•Use these stored procedures to build machine learning models.
Decision trees - IDAX.GROW_DECTREE A decision tree is a
hierarchical, graphical structure accurately classify a model.
Linear regression - IDAX.LINEAR_REGRESSION is the most
commonly used method of predictive analysis.
Naive Bayes IDAX.NAIVEBAYES - The Naive Bayes classification
algorithm is a probabilistic classifier.
K-means clustering IDAX.KMEANS - The K-means algorithm is the
most widely used clustering algorithm

27. STEP 4 : Model tuning
•Use these stored procedures to fine-tune your machine learning models
IDAX.PRUNE_DECTREE - Prune a decision tree model

28. Step 5 : Model evaluation
•Use these stored procedures to evaluate the performance of your model by comparing predictions to the true
values.
IDAX.CMATRIX_STATS - Calculate classification quality factors from a
confusion matrix
IDAX.CONFUSION_MATRIX - Build a confusion matrix
IDAX.MAE - Calculate the mean absolute error of regression predictions
IDAX.MSE - Calculate the mean squared error of regression predictions

29. STEP 6 Model inferencing/ Deployment
•Use these stored procedures to make predictions with your trained machine learning model.
IDAX.PREDICT_DECTREE - Apply a decision tree model
IDAX.PREDICT_KMEANS - Apply a K-means clustering model to new data
IDAX.PREDICT_LINEAR_REGRESSION - Apply a linear regression model to a
target
IDAX.PREDICT_NAIVEBAYES - Apply a Naive Bayes model to new data

30. Model management
•Use these stored procedures to clean up obsolete models and tables.
IDAX.DROP_SUMMARY1000 - Drop output tables created by
SUMMARY1000
IDAX.DROP_MODEL - Drop a model
call IDAX.DROP_MODEL ('model=FLEET_LINEAR_REGRESSION_MODEL')

32. Ending with Steve Job’s Story –
Stay hungry, Stay foolish
•Born for Graduate School Mom and immediatley given for Adoption
•College life
•Fired from the company he started – Apple – Went on to start Next &
later returned back to Apple later
•If today were the last day of my life, would I want to do what I
am about to do today?

33. THANKS