LOGISTICS OPTIMIZATION BY TELEMETRY SENSORS

Nowadays, the transportation sector undergoes a significant transformation with AI, big data

and IoT applications being at the forefront of this change. The time that drivers were left for

days – almost completely isolated on truck – belongs to the past. Today, fleet companies are

aware, more than ever before, that their data is sufficiently powerful to unlock new business

optimizations. In this whitepaper, we will address some of those. In particular, we dive deeper

with you into real use cases such as fuel efficiency optimization, driving behavior analytics

and predictive analytics on delivery time of goods. We present in a structured workflow the

main components needed to build such data driven applications.

Keywords

fleet-management software, telemetry, business intelligence, machine learning, internet of things

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1 Introduction, Generic workflow

In figure 1 we present a generic workflow of a data science project applicable for a fleet company. Our

workflow comprises six main components:

1. Value pillars

2. Data inputs

3. Data ingestion

4. Database

5. Machine Learning

6. Visualization

Initially (Nr. 1), we define the ‘value pillars’ of our project, or in other words our business objectives. In this

paper, we make three proposals: (i) fuel efficiency optimization, (ii) Anomaly detection on driving patterns

and (iii) predictive analytics on delivery time of goods. Next step (Nr. 2), we collect data inputs. These can be

either historic or real-time depending on the available resources and the demands of the application. After

the collection phase, we proceed to data ingestion (Nr. 3). This is the stage where we attempt to standardize

all different inputs into a single, uniform dataset. Then, we go to the database (Nr. 5). This is the place for

storage, security and experimentation. Afterwards, our data should be ready for intelligence extraction, this

is where machine learning takes place (Nr. 5). Also, note that here, the value pillars play a major role. Finally

(Nr. 6), we need some visualization tools to illustrate results and insights.

In the next paragraphs, we will go through each workflow component separately, explaining its purpose.

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2 Value pillars

2.1 Fuel efficiency optimization

Fuel represents one of the leading costs for operating a fleet. A major factor that can impact fuel consumption is the

driving style of the truck driver. Some argue that, given the same conditions, the difference in fuel expenses between a

high performing and low performing driver can be as high as 30 %, simply due to different driving styles.

Measuring fuel consumption is not a difficult task, there are several ways to do it (i.e. on-board instruments, credit card

transactions in tank stations) and usually fleet companies check periodically this information. However such

conventional methods are helpful only to derive some descriptive statistics like average fuel consumption per vehicle.

To go deeper into the root cause of a problem we recommend you to follow a data-driven approach. Fleet managers

should try to correlate their observations with other explanatory variables, which might be already available in their

data. To be more specific, through regression analysis, we can correlate signals such as idling time, hard braking and

acceleration percentage. Finally, we can deploy data mining techniques to detect anomalies and classify our data in

different clusters such as [1]:

• Green cluster -> efficient driver

• Yellow cluster -> average driver

• Red cluster -> inefficient driver

Of course, your visualizations won’t look like this. It’s just a way to show you different dimensions of drivers. Below an

dashboard example for fleet managers to analyse drivers performance [2].

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“Fleet managers should

try to correlate their

observations with other

explanatory variables,

which might be already

available in their data.”

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2.2 Anomaly detection on driving patterns

Update your drivers in real time

When a driver is aware of his performance in real time, he will usually go beyond your expectations. Drivers often

complain that they are not aware of their bonus situation until the day when it is paid. Even then, no clear explanation

is given of what and where they can improve.

Consider using a visualization platform where drivers can check in real time their performance to understand in which

areas they should pay attention to increase their bonus. Drivers should at least be able to monitor and evaluate

themselves in KPIs such as:

• Fuel efficiency: km per litre

• Productivity: time of delivery, total distance driven

• Safety: speed violations, manoeuvring, hard braking counts

The power of gamification

Consider gamification techniques. Gamification commonly employs game principles in non-game contexts to improve

user engagement and organizational productivity. In its simplest form, imagine a centralized platform illustrating

metrics with scores for each driver, and also listing the top performers. All drivers can access the dashboard and

compare to their peers. Personalized messages are published on the dashboard, in a friendly way as a sign of gratitude

or warning.

This is just an example of the different ways we can use to ignite excitement, improve the user experience,

encourage new drivers to join and most valuable drivers to stay. In essence, for a fleet company, gamification

techniques can help managers to design more effective bonus programs. Especially today, with the help of cloud

computing such applications are becoming popular.

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“All drivers can access the dashboard and compare to their peers.

Personalized messages are published on the dashboard, in a friendly

way as a sign of gratitude or warning.”

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2.3. Predictive analytics on time delivery

Fleet companies want to make sure they can always keep their promise and deliver goods on time. Goods must often

be delivered in a predefined time window and any deviation may lead to serious consequences such as low client loyalty,

long idling times and suboptimal route planning.

In order to mitigate those issues, considering also the latest advancements in machine learning, fleet companies have

started exploring the power of their data and turned into predictive analytics. Given a set of input parameters and

historic data, a prediction model estimates the delivery time needed for a truck to go from departure to destination.

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“Given a set of input parameters and historic data, a prediction model

estimates the delivery time needed for a truck to go from departure to

destination.”

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Historic data can be received either from vehicle-based or site-based measurements. In the first case, the

measurements usually come from a GPS device or other hardware connected to the OBDP (On-Board Diagnostic Port)

of the vehicle. In the last, data comes from an external agency e.g. measuring traffic conditions, weather forecasts or

can belong to the fleet company itself e.g. ERP, sales orders etc.

Besides historic data, the model can be trained to re-evaluate its output in function of real time information such as

latest short-term forecasts, accidents and road activities. Also, interesting to note that the model predicts the time

delivery for a single route. In case that historic data is available for multiple routes for the same trip (departure and

destination are the same), the model can be used for optimal route planning indicating that route with the lowest

delivery time [3].

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“Besides historic data, the model can be trained to re-evaluate its

output in function of real time information such as latest short-term

forecasts, accidents and road activities.”

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3 Historic and real time data

Every business manager will admit that possessing the knowledge and tools for intelligent planning can increase their

profits drastically. Again, it all begins with the data we collect before we can even think about possible business insights!

Business optimization can only be as good as the data that feeds the algorithm. Below, we present some of the most

important data inputs that a fleet company can use for a data science project.

• OBDP (On-Board Diagnostic Port)

On-Board Diagnostics systems give the vehicle owner access to the status of the various vehicle subsystems.

Most trucks built within the last 20 years have the possibility to connect to their engine control unit (ECU) to

collect data such as location, engine on, speed and sometimes more in-depth information like engine

temperature and fuel economy depending on the telematics device you use.

• GPS (Global Positioning System)

GPS devices are the easiest way to receive basic telematic information about your vehicle in real-time. The main

signals we can collect from GPS are location and time, possibly including other derivatives such as speed and

direction.

• Meteorological

Undoubtedly, the weather plays a very important role in the transportation sector. Severe weather conditions

may result in time delays, traffic jams, accidents or even sometimes fuel inefficiencies. Meteorological data can

be purchased in a variety of ways from different providers. Prices vary depending on the desired features: space

and time granularity, number of locations, types of variables (e.g. temperature, wind speed), forecasting

services.

• Traffic

Traffic data is another major component that can add value to our project, in particular for optimal route

planning and prediction of delivery time. Data can be real-time or historic according to our preferences. Data

can be combined from different sources such as governmental agencies, open platforms and private providers.

We can get information about congestion and incidents including construction zones, traffic routes and road

accidents.

• ERP (Enterprise Resource Planning)

Data can come also from sources that are not explicitly relevant to our objectives. Such sources can be for

instance other business components (e.g. sales orders, CRM systems, accounting) that can help us anticipate

future events and or take corrective measures when necessary.

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4 Data ingestion

The more qualitative data you possess, the more accurate is the analytic process. As a consequence, applications need

to take real time and historic data from various sources. One of the core concepts about data ingestion is described by

the ETL-mechanism. ETL stands for extraction, transformation and loading data into a data warehouse/mart. The

process guarantees that correct data is selected from the sources and eventually is corrected if needed. Next,

information is transformed to common used standardized formats before loading the results into the storage of the

application. Back flushing is performed in order to update error remarks during the extraction of transformation process

into the data sources. To summarize, ETL acts as an audit phase to create an accurate working base. Below we give a

short description of the main tasks during the data ingestion phase.

• Data import and export

Extract and load data from and to many different types of sources and targets, regardless of the format, volume

and location.

• Transformations

Apply complex transformations, such as de-duplication, pivot, de-normalization, partitioning and tens of others.

• Data streaming and synchronization

Set up data synchronization between different data sources with minimum effort. Use streaming platforms,

such as Kafka to build real-time data pipelines.

• Automation

Build event-driven workflows with multiple integration points and decision trees. Use programming languages

such as Python, JavaScript and SQL to create data integration scenarios.

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5 Database

To put it simply, the database is our central intelligence platform. This is the place where all different kinds of data inputs are stored in a structured and uniform format. Besides storage, there are several other functions that a Database Management System (DBMS) performs. Below we mention the most important [4].

• Data Dictionary Management

Data Dictionary is where the DBMS stores definitions of the data elements and their relationships (metadata).

The DBMS uses this function to look up the required data component structures and relationships. When

programs access data in a database they are basically going through the DBMS.

• Security Management

This is one of the most important functions in the DBMS. Security management sets rules that determine

specific users that are allowed to access the database. Users are given a username and a password. This

function also sets restraints on what specific data any user can see or manage.

• Multiuser Access Control

Data integrity and data consistency are the basis of this function. Multiuser access control is a very useful tool

in a DBMS, it enables multiple users to access the database simultaneously without affecting the integrity of

the database.

• Back-up and Recovery Management

Backup and recovery is brought to mind whenever there is potential outside threats to a database. For example

if there is a power outage, recovery management is how long it takes to recover the database after the outage.

• Database Access Languages and Application Programming Interfaces

A query language is a nonprocedural language. An example of this is SQL (structured query language). SQL is

the most common query language supported by the majority of DBMS vendors. The use of this language makes

it easy for user to specify what they want done without the headache of explaining how to specifically do it.

• Database Communication Interfaces

This refers to how a DBMS can accept different end user requests through different network environments.

An example of this can be easily related to the internet. A DBMS can provide access to the database using the

Internet through Web Browsers (Mozilla Firefox, Internet Explorer).

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6 Machine learning

Why ML

Machine learning forms the core of almost every data science project. A ML algorithm can be applied on a set of data

inputs in order to extract intelligence such as finding trends, detecting abnormalities and making predictions. ML has

gained much attention during the last 5 five years, especially thanks to the advancements of cloud computing,

communication technologies and recent research breakthroughs in the field of computer vision and natural language

processing (NLP).

ML tasks to consider

In the present proposal, given the value pillars of a fleet company ML can be used primarily to perform two types of

tasks: (i) clustering, (ii) regression. For both proposals 2.1 and 2.2 we will make use of clustering analysis, whereas 2.3

can be regarded basically as a regression task (See section 2). In (i), the objective is to categorize our data inputs in

different cluster groups e.g. fuel efficiency clusters, driving score clusters. In (ii) the objective is to deliver predictive

analytics i.e. delivery time estimations.

ML models to use

In general, the variety of ML algorithms is quite extensive e.g. Decision Trees, Neural Networks, Support Vector

Machines etc [5]. Each one comes with its own pros and cons. Some algorithms might not require much expertise and

can be applied for simple problems while others require deep scientific knowledge. Another important factor is the

computational burden. Depending on our time constraints, problems with large amounts of data may require

parallelization and can only be solved using specific models and hardware resources, e.g. training deep neural

networks on GPUs.

Performance & results

In proposals 2.1 and 2.2, since we focus on clustering analysis, there is no ground truth to compare with. We are

searching for trends, patterns and anomalies. The results will be delivered in the form of descriptive statistics and 3D

plots. In proposal 2.3, we do have to evaluate the performance of the prediction model having as ground truth a part

of the company’s historic data i.e. delivery time. We will split our data in 3 chunks: training, validation and testing. The

testing chunk will be used to calculate the error between the predicted output and the real output in terms of Mean

Square Error (MSE). Once the model has been trained it can be deployed real-time to make predictions. The model

will be trained on a regular basis (e.g. once every week), as new data is available, thus becoming gradually more

experienced and accurate.

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“A ML algorithm can be applied on a set of data inputs in order to

extract intelligence such as finding trends, detecting abnormalities

and making predictions.”

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7 Visualizations

Many organizations already started exploring data since integrated ERP-systems conquered the market. Visualizing data-

driven insights is possibly the most crucial step to shape confidence in the decision making process. Without reflection

about the effectiveness of data processing, no organization will take the risk to blindly chase the value pillars. Business

analysts should be unloaded with repetitive and fault sensitive tasks during decision making. They even do not

necessarily need to analyse the data themselves. It should be more valuable to let them operate as visualization

architects instead, using pre-analysed data and spreading the information to the rest of the company in an

understandable way.

A first visualization evidence is a real time status check. Fleet managers want to supervise all equipment, including their

maintenance log, active faults and warnings with severity level and even when service is planned. In general, even

without machine learning analytics, by just increasing the time granularity of our data, a visualization dashboard can

become much more informative. This combination reach the highest value level because real time granularity stands on

his own.

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“Even without machine learning analytics, by just increasing the

time granularity of our data, a visualization dashboard can

become much more informative.

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Next, more advanced parameters than location tracking can be listed like engine diagnostics, power take off measurements and driver’s behaviour. It’s needless to say that this stage needs a customized approach according to your preferences.

References

[1] “Spectral clustering based approach for evaluating the effect of driving behaviour on fuel economy”, I2MTC 2018 [2] https://www.omnitracs.com

[3] https://starschema.com/

[4] https://databasemanagement.fandom.com

[5] https://data-flair.training/blogs/machine-learning-algorithms-in-python/

About Promithevo

Promithevo acts as an independent business development partner specialized in big data applications. We provide

solutions to industrial process manufacturing companies, logistics, utilities, retail, commercial centers and public

institutions. By standing between the service provider and the end user, Promithevo creates added value for both

parties. We help service providers expand their activities and explore new markets, while the end users are given the

opportunity to implement innovative technologies to increase the productivity of their business.

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