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.
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. 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.
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
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.”
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.
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.