Dissertations and Thesis Research Topics in Industry 4.0, Industrial Internet of Things, Big Data Analytics, and Artificial Intelligence in Supply Chain Management, Inventory Management, and Logistics Projects

Suggested Topics for Dissertations and
Thesis Research Projects in Industry 4.0,
Industrial Internet of Things, Big Data
Analytics, and Artificial Intelligence in Supply
Chain Management, Inventory Management,
and Logistics
By Sourabh Kishore, Chief Consulting Officer

EPRO INDIA In Service to Learners Since 1983

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This article explores many newer topics of
research in supply chain management and its
associated domains categorized under four broad
research areas: Industry 4.0, Industrial Internet of
Things (IIoT), Big Data Analytics, and Artificial
Intelligence. The descriptions of the areas and
their associated practices are presented as the
following:

(A) Industry 4.0

Industry 4.0 is the name coined to the fourth
industrial revolution. The third industrial
revolution was about digitising manufacturing,
operations, logistics, and supply chain
management. This revolution is about enhancing
their digital capabilities through integration of
digital systems, convergence of cyber and
physical systems (cyber-physical systems; CPS) ,
real-time visibility, predictability, self-awareness
(cognitive abilities), location awareness, and
artificial intelligence. The industrial automation
and integration achieved through digitisation in
the third industrial revolution involved
proprietary technologies developed under three
categories: Programmable Logic Controllers
(PLCs), Supervisory Control and Data
Acquisition Systems (SCADA), and Distributed
Control Systems (DCS). PLCs were designed to
integrate a large number of sensors and actuators
digitally using industrial bus designs and
industrial digital communication protocols.
SCADA was designed to integrate PLCs and
DCS was designed to integrate SCADA systems
as well as PLCs capable of running protocols for
distributed control signalling.

The primary domain in industrial automation
and integration comprises of industrial sensors
and actuators. It is called the sensor/actuator
plane. The sensor/actuator plane has evolved
through legacy process control and engineering
systems; a number of proprietary protocols were
designed decades ago (like, LONWORKS,
BACNET, MODBUS, DC-BUS, DIRECTNET,
OPC, DYNET, DNP3, XAP, CAN, etc.) that are
still operational in many industrial and
commercial applications. Please keep in mind
that Industry 4.0 will largely not replace many of
these. These protocols will remain in operation as
is for many years from now. Hence, their
knowledge will not be lost or rendered useless in
the Industry 4.0 era. Many SCADA and DCS
systems are still operating them for controlling
millions of sensors and actuators. TCP/IP
entered into process engineering in mid 90s when
Embedded Java Beans (.JAR files) was developed
by open source communities. Many of the sensors
and actuators were consolidated into protocol
converters before getting connected to SCADA
and DCS. These protocol converters could
convert signalling from proprietary protocols on
RS232 or similar interfaces to .JAR files streams
transmitted over TCP/IP links (Ethernet, Token
Ring, FDDI, ATM, X.25, Frame Relay, etc.).
Earlier, the protocol converters and the
DCS/SCADA servers only were assigned IPv4
addresses given the limited address space of
IPv4. With the advent of IPv6, now the
sensors/actuators are also assigned individual IP
addresses.

This change has made monitoring and controls
more effective and accurate because of plotting of
sensors/actuators on 3D maps. This is the only
change that has occured after introduction of the
Industrial Internet of Things. The Industrial
Internet of Things (IIoT) is more of an invention
than an innovation. The name was given to the
already running "physical" systems for decades,
when the massive scale address space of IPv6
and its transmission control protocol was
invented. Appropriate protocol conversion
technologies from the proprietary industrial
engineering protocols (LONWORKS, BACNET,
MODBUS, DC-BUS, DIRECTNET, OPC, DYNET,
DNP3, XAP, CAN, etc.) to TCP/IPv6 helped in
transforming the "physical" systems into
"cyber-physical" systems. RFID attachments to
sensors/actuators and wireless sensor network
protocol (ZigBee, based on IEEE 802.15.4) are the
key innovations in its domain. IIoT architecture
has four planes: The sensor/actuator plane, the
data acquisition and control systems plane
(primarily SCADA and DCS), the IT systems
plane for pre-processing and any preliminary
analytics (Edge IT plane), and the core IT systems
for data warehousing, advanced data analytics
and visualisations, and remote control apps (Core
IT plane). All these planes have been used in for
decades. In modern systems, the only change is
that all these machine-to-machine
communications are now done over IPv6 and the
data analytics have come out of the traditional
ACID-based structured relational databases
controlled by SQL programming to become
unstructured and non-relational big data
databases controlled by Not-Only-SQL (NoSQL)
programming.

The dissertations and theses research topics in
Industry 4.0 may be either in-depth industrial
engineering designs and their simulations, or
exploring many new factor and control variables
that tie up very well with the traditional variables
of the third industrial revolution (new conceptual
frameworks or empirical formulations involving
both new and old concepts), or exploring and
confirming complex structural constructs
showing significant influencesof Industry 4.0
factor and control variables on performance and
behavioural variables in industrial engineering,
or exploration of many real-world
implementations through in-depth case studies
and exploration of technology solutions offered
by global multinational vendors in the fields of
Industrial and Information Systems Engineering.
Some of the key research areas are presented as
the following:

1. The concepts of Global Value Chain through
Digital Transformation and the role of Industry
4.0 framework
2. The changing roles of intelligent robotics and
machinery control systems as Cyber-Physical
Systems (CPS) in the Industry 4.0 framework
3. The evolving empirical models of Industry 4.0
and their applications
4. Designing and operating a global augmented
reality architecture for integrating the five core
Industrial Engineering Disciplines: Production,
Inventory Control, Logistics, Supply
Chain/Network Management, and
Transportation.
5. Quality Assurance in Industry 4.0
6. Role of Industry 4.0 framework in
Sustainability enhancements of the Triple
Bottomline Model (examples are, elimination of
wastes, monitoring employees' health and safety,
monitoring and controlling emissions and
disposals, economic performance, etc.)
7. Role of Industry 4.0 in eliminating production
defects, reworks and returns, logistics errors, and
transportation accidents
8. Human resources challenges in Industry 4.0:
HR policies, training and development of skills,
and relationships between employers and
employees
9. Aligning every policy, process, and tasks to the
voices of customers
10. Role of Industry 4.0 in lean, agile, responsive,
and flexible logistics and supply chain/network
management
11. Role of Industry 4.0 in developing, enhancing,
and maturing dynamic capabilities in the five
core Industrial Engineering Disciplines:
Production, Inventory Control, Logistics, Supply
Chain/Network Management, and
Transportation
12. Designing robust communication networks
for Industry 4.0: few examples are - 5G LTE,
Heterogeneous Networking (WiFi and ZigBee
integration with 5G; Femtocells, and Local Area
Networks for Sensors and Actuators on IPv6 such
as 6LoWPAN, Near Field Communications,
RFID, LPWAN, and Z-Wave), Gateway routing
for Industrial Internet of Things, Network
Aggregators, and Core Networking integration
with Big Data and Artificial Intelligence Servers
13. Industrial Internet and Industrial Cloud for
the Global Industry 4.0 framework
14. The evolving concept of Autonomous Robots
and the Operator 4.0 integration with them
within an Augmented Reality under Industry 4.0
15. Strategic Supplier Management in the Global
Industry 4.0 framework
16. Smart Cellular manufacturing for building
flexibility and dynamic capabilities in the Global
Industry 4.0 framework
17. The Models of Early Awareness,
Self-Configutation and Self-Optimisation, and
Predictive Maintenance in the Global Industry
4.0 framework
18. Additive Manufacturing and 3D Printing
technologies in the Global Industry 4.0
framework
19. Networked Manufacturing Designs and
Operations, Inter-company integration, Holistic
Digital Transformations and Engineering, and
Intelligent Monitoring and Control SYstems in
the Global Industry 4.0 framework
20. Smart Factory Modeling through integration
of all Industrial Engineering disciplines using
Industrial Cloud Computing and Industrial
Internet in the Global Industry 4.0 framework
21. Smart Security for Smart Factories: building
multi-layer deep cyber defense systems for
protecting Industrial Cloud Computing and
Industrial Internet in the Global Industry 4.0
framework
22. Building resilience to global supply chain
disruptions in Industry 4.0 using continuous data
collection and real-time trends radar systems
23. Real time visibility into global supply chain
accidents and rapid rescheduling of
consignments in the Global Industry 4.0
framework
24. Knowledge-driven smart predictive analytics
of future risk events in global supply chains in the
Industry 4.0 framework
25. Monitoring vital functions and machineries in
a global supply chain in the Industry 4.0
framework
26. Horizontal and Vertical integration of smart
manufacturing systems in Global Value Chain
Networking in the Global Industry 4.0 framework
27. Continuous Engineering across the Value
Chain in the Global Industry 4.0 framework
28. Accelerated Additive Manufacturing in a
digitally transformed ecosystem of products and
services following co-design, co-creation, and
co-testing with smart partners and infuencers in
the Global Industry 4.0 framework
29. Collaborative logistics, machine tools, and
services-oriented supply chain services in the
Global Industry 4.0 framework
30. Contracting and Negotiation for formation of
Collaborative Virtual Organisation delivering
Virtual Design and Engineering services in the
Global Industry 4.0 framework
31. Challenges and solutions in collaboration
among smart factories in the Global Industry 4.0
framework
32. Combining global supply networks through
smart IIoT interactions in the Global Industry 4.0
framework
33. Building collaborative global supply networks
with cyber physical systems and data rich
analytical environments in the Global Industry
4.0 framework
34. Building a community of machines and their
interactions with community of operators by
strategically integrating machine-to-machine and
human-to-machine communications in the Global
Industry 4.0 framework
35. Building sustainability in global supply chains
through dimensions of sensing and actuation of
key triple-bottomline variables defined in the
Global Reporting Initiative

Further, We also offer you to develop the
"problem description and statement", "aim,
objectives, research questions", "design of
methodology and methods", and "15 to 25 most
relevant citations per topic" for three topics of
your choice of research areas at a nominal fee.
Such a synopsis shall help you in focussing,
critically thinking, discussing with your
reviewers, and developing your research
proposal. To avail this service, Please Click
Here for more details.

(B) Industrial Internet of Things (IIoT)

When capability of Internet connectivity is added
to physical devices (such as household and utility
devices), they are called Internet of Things (IoT).
When the devices are attachments to industrial
machinery, robotics, equipment, machine tools,
service nodes, materials, control systems, and
even workers, then they are called Industrial
Internet of Things (IIoT). IIoT is viewed as an
integral system in the Industry 4.0 framework.
The Cyber-Physical Devices are more
appropriately referred to as Cyber Physical
Systems (CPS) in industries. IIoT can transform
the physical systems into smart systems capable
of collaborating, teaming, communicating,
reporting, autonomous operations, and decision
making. The key performance attributes of IIoT
and IIoT-based systems are: cost effectiveness,
low power consumption ensuring longer battery
lives (six months to multiple years), high
connections density, communication ranges
sufficient to cover a plant area, efficient routing
algorithms (like, Ant Colony, Greedy, Diffusion,
SAR, GAF, etc.), low processing and storage
capacities, capable of sustaining high latency
shocks, and simple networking protocols and
architectures for communications over IPv6
(commonly used are: ZigBee, 6LoWPAN, Near
Field Communications, RFID, LPWAN, and
Z-Wave).

IIoT-enabled devices operate in two planes:
Sensing and Actuating (please see some details in
the introduction of Industry 4.0 above). The
Sensing plane is designed to collect data from the
attached physical systems related to individual
process variables monitored by distant
monitoring systems. The Actuating plane is
designed to issue actuation commands to the
physical systems received from distant control
systems. The monitoring and control systems are
integrated through an in-depth decision-making
logic based on Artificial Intelligence using
Machine Learning algorithms. Some decisions are
issued by human operators operating as
Operators 4.0 within an augmented reality space.
However, the scope of human decision-making is
reducing amidst digital transformation,
automation, and smartness of integrated
manufacturing systems. The manufacturing
systems and their controlling processes are
carefully integrated in a hierarchical fashion such
that every process can utilise globally dispersed
resources owned by a single large manufacturing
organisation or by a consortium of manufacturers
hooked to the cloud manufacturing system. IIoTs
have major roles to play in this design. The CPS
devices in manufacturing plants can exchange
loads of data and consolidate them at
strategically located big data repositories using
Machine-to-Machine communications (M2M).
Wherever manual intervention by operators
working in the augmented reality setup is
desired, Human-to-Machine (H2M)
communication channels are opened. Mostly,
human operators get access to cognitically aware
system dynamics and control systems providing
direct access to complex time-series enabled
reporting for issuing bulk actuation commands.
The bulk actuation commands are then splitted
into thousands of individual actuation
commands issued to the IIoTs for executing their
respective actuation tasks. Again, the Automata
running the entire system may not permit the
bulk commands if they find conflicting actuations
embedded into the algorithm. The Automata
follows clearly defined deeply embedded rules
that helps in identifying erroneous command sets
or malicious attempts by industrial hackers. This
is where the question arises: who defines those
rules and how perfect they are to prevent minor
or major industrial catastrophes? In modern
IIoT-enabled cloud manufacturing systems, the
generation of rules is also automated using
Artificial Intelligence based on continuous
learning from diverse mobile data captured in
massive volumes in real time.

In Industry 4.0 framework, IIoTs can be attached
to every industrial engineering system involved
for ensuring integrated manufacturing and
delivery. For example, IIoTs may be attached to
the conveyor belt system, internal mobile cranes,
internal mobile carrier of packages, internal
storage bays, and to the packages. A 3D model of
the entire warehouse may be developed and
dynamically configured to capture every
dockings, undockings, additions, removals, and
movements in the warehouse. An inventory
management software with augmented reality
may be provided to the operator in such a way
that it can interact with all the equipment
operating in the warehouse and also with the
packages arriving and despatching. Now, if the
operator decides to change the priority rating of a
set of packages, he/she simply needs to change
the assignments by clicking those packages in the
3D space and assigning the values through a
floating menu. As soon as this change is made,
the entire smart inventory management system
will realign its operations to execute the changed
priority ratings. More resources will be assigned
to those packages automatically. A massive
matrix of sensing and activation information will
be varied by the smart inventory management
software to execute a simple decision-making by
the operator.

Many such scenarios can be imagined related to
role of IIoTs in the Industry 4.0 framework. The
production robotics can be made more
ergonomically and cognitively aware by
attaching IIoTs to every mobility and activation
functions of a robot. Shape changing robotics
designed to complete multiple industrial
production tasks can be created and allocated to
hundreds of queue processors under an Industry
4.0 compliant manufacturing plant. When an
operator changes priority levels of a production
queue, more robots can be allocated to it by
simply changing their shapes and enabling them
to complete the queue faster.

The academic research studies for dissertation
and research projects should focus on a narrow
and focussed problem area. Hence, the topics
related to IIoTs may be focussed on a specific
process, technology, or automation challenge, or
on specific variables related to IIoTs and their
influence on known empirical variables (such as,
performance variables of a supply chain). You
may also combine Industry 4.0, IIoTs, and Big
data in your research as long as the topic is
focussed on a sufficiently narrowed research
problem. Following are some of the suggested
topic areas related to role of IIoTs in Industry 4.0
are the following:

1. Study of empirical reference architectures for
integrating IIoTs with Industry 4.0 architecture
2. Modeling IIoTs and Industry 4.0 integration
following the theories of Enterprise Architecture
3. Cognitive and Ergonomically-Aware designs
of Industrial and Logistics Robotics using the
IIoTs
4. Deploying IIoTs to build Augmented Reality
for Operator 4.0 in Industry 4.0
5. Role of IIoTs in predictive forecasting and
analytics, and real-time controls on supply chain
performance variables
6. Effects of IIoTs and Industrial Internet on key
performance variables related to effectiveness
and efficiency of all Industrial Engineering
disciplines: materials planning, materials
handling, procurement, production, logistics,
transportation, and distribution
7. Employing IIoTs for real-time visibility and
controls in inventory management for capturing
and meeting the demands effectively
8. Using IIoTs for eliminating order rationing,
beer gaming, and bullwhip effect in retail supply
chains
9. Using IIoTs for real time performance
monitoring and preventive maintenance of
machines and robotics
10. Using IIoTs for enhancing safety standards
and prevention of industrial accidents in Industry
4.0
11. Contextualising Scenarios, Monitoring
Situations, and Acting on predictive alerts and
alarms - the foundations of predictive analytics
for industrial safety using IIoTs
12. In-gateway and in-device analytics services -
adding distributed cognitive abilities to IIoTs in
Industry 4.0
13. Enhancements of reliability and performance
of industrial assets in plants and machineries
using IIoTs in Industry 4.0
14.Enhancements of quality, reliability, and
performance of industrial processes and products
using IIoTs in Industry 4.0
15. A study of use cases of using IIoTs in B2B
industrial manufacturing and job working
contracts
16. The relationships between Industrial Agility
and Responsiveness and Adoption of Digital
Transformation using IIoTs
17. Rules-based preventive maintenance and
management of Industrial Assets (Machineries
and Robots) using IIoTs
18. Understanding, Reasoning, and Learning
based on data collected from IIoTs: Artificial
Intelligence for Industrial Automation in Industry
4.0
19. Role of IIoTs in sustainable manufacturing
and sustainable supply chain management
20. Using IIoTs for enabling cognitive and
location-aware capabilities in industrial
transportation
21. Integrating IIoTs with cloud computing for
cloud manufacturing applications
22. Optimising health of equipment and safety of
workers using integrated cyber-physical systems
enabled by IIoTs
23. Protecting identities and authentication of
cyber-physical systems enabled by IIoTs
24. Building trust networking of cyber-physical
systems enabled by IIoTs
25. Building and managing a dynamic mesh
topology supply chain network using
cyber-physical systems enabled by IIoTs
26. Preventing attacks on Industrial Internet and
cyber-physical systems enabled by IIoTs
27. Policy-driven network functionalities for
building a dynamic supply networking using
cyber-physical systems enabled by IIoTs
28. Unmanned Aerial Vehicles (Drones) for
remote controlled supply chain distributions
using cyber-physical systems enabled by IIoTs
29. Using IIoT-enabled unmanned aerial vehicles
(drones) in Industry 4.0
30. Digital transformations in supply networking
using cyber-physical systems enabled by IIoTs

Further, We also offer you to develop the
"problem description and statement", "aim,
objectives, research questions", "design of
methodology and methods", and "15 to 25 most
relevant citations per topic" for three topics of
your choice of research areas at a nominal fee.
Such a synopsis shall help you in focussing,
critically thinking, discussing with your
reviewers, and developing your research
proposal. To avail this service, Please Click
Here for more details.

(C) Big Data Analytics and Artificial
Intelligence in Industry 4.0

In the Industry 4.0 framework, Big Data Analytics
and Artificial Intelligence have very crucial roles
at the backend of the industrial systems. Big data
refers to the data-intensive technologies capable
of collecting and processing massive-scale
volumes of data with high value, high variety,
high veracity, and high velocity. Big data requires
new techniques of data modeling and new
designs of data holding and transmission
infrastructures and services. Data collected is
holistic in nature; from all lifecycle stages of
processes and the variables controlled by them.
The original concept of big data was to capture
every possible form of data, like online
transaction processing (OLTP) systems (such as
ERP, CRM, MRP, and SCM applications),
decision support systems (examples, batch
queries and reports), structured data formats
(like, data files, database objects, comma or
tab-separated, and spreadsheets), data generating
machines (like, point-of-sale devices, ATM
machines, sensors, smart scanners, RFID, and
smart metering), and unstructured data formats
(like, images, word processing files, video and
audio files, blogs, e-mails, social media, and
Internet). Hence, roles of SQL-oriented databases
(Oracle, SQL Server, and MySQL) and
Not-Only-SQL-oriented databases (Hadoop File
System, map Reduce, MongoDB, HBase,
Cassandra, and ZooKeeper) were planned to be
merged. However, as this technology has
evolved it appears that Not-Only-SQL (NoSQL)
has taken precedence over SQL databases
significantly. New data analysis technologies,
such as massive-scale parallel processing on
cloud computing, and virtual in-memory
analytics have also evolved.

Now the question arises is what was so much
original and unique about Big Data Analytics that
was not available in traditional data analytics
systems like Business Intelligence, Data
Warehousing, and Multidimensional reporting of
Online Analytical Processing (OLAP)? To
understand the difference, the perspectives of the
traditional database administrator and the
traditional data warehousing ETL (Extraction -
Transformation - Loading) processor would be
needed. The secret is hidden in an abbreviation
called ACID (Atomicity - Consistency - Isolation -
Durability). ACID is the core standard that every
relational database management system needs to
comply with; irrespective of the size of the
databases. All ACID compliant OLTP databases
were designed to commit a unique transactional
record in a data field, which overwrites the older
(obsolete) record in that field. This clearly meant
that ACID compliant databases were not
designed to maintain historical time-stamped
records. Thus, OLTP databases were not
designed for data analytics as you could only get
the latest committed records from them. This gap
was solved by the database administrators by
maintaining backups of what is called in-memory
"Redo Log Segments" or "Rollback Segments" or
"Transaction Log Segments". These segments
maintained details of all the previous commits
into the data fields with timestamps. Using these
segments, the database administrators could
restore the database state at a particular time of
failure should any corruption occurs after that
time. Given that these segments were formed
inside the memory, their sizes were limited by
the available RAM in the server. Thus, when any
segments were filled up the oldest records were
deleted automatically to make room for the latest
records. To protect the historical records, the
database administrators used to design scripts for
writing these segments into the hard disk before
they are overwritten. The segments become static
after getting written into the hard disks and
hence were called "Redo Log Archives" or
"Rollback Archives" or "Transaction Log
Archives". In heavy duty OLTP applications,
these segments gets filled up too frequently and
hence archiving was enabled as a continuous
feature.

All the Redo Log, Rollback, or Transactional log
archives were part of the incremental backup
strategy. Ideally, a database administrator would
take one full backup (all data files, control files,
and procedures) and several incremental backups
(of these archives) daily in tape libraries. These
backups were the bread and butter for the
Decision Support Service (DSS) specialists. For
decision support, these backups were restored on
separate servers and the data warehousing
specialists used the ETL processing to build
time-series data strings (tables with timestamped
records) for every data type. The time-series data
strings were packaged into multi-dimensional
cube reports in a presentation system called
"Online Analytical Processing (OLAP)". These
time-series data strings in the form of OLAP cube
reports were used for various decision-support
tasks, like sales forecasting, products and market
performance assessment, operations performance
assessment, customer satisfaction measurements,
promotional planning, future planning, forming
new business strategy, etc. However, ETL was
such a slow and tedious process that it may take
weeks for the data analysts to get access to their
latest outcomes. This means that there was
always a time lag of weeks to a month between
the OLTP and DSS databases. The bottomline:
there was predictive analytics and future
planning to some extent but no such capability
enabling real-time visibility into the business. The
time lag between the OLTP and DSS databases
was acceptable because markets and competition
were sluggish to changes with very less
dynamism.

With rapid dynamism caused by rapid changes in
the markets, customers' expectations, disruptive
innovations, and competitive landscapes,
businesses realised that the ETL-enabled
decision-support systems (OLAP, business
intelligence, and data warehousing) provided
them analytics reports too late to respond to the
rapid dynamism in the markets, customers'
expectations, disruptive innovations, and
competitors' activities. Further, the scope of ETL
was limited because of high hardware and
storage costs and limited real-estate spaces
provided to the data centre infrastructures. A
replacement of ETL was needed in the OLTP
database itself such that the entire ETL process
can be replaced by some kind time series data
readiness within the transactional databases with
capabilities to build OLAP cubes within the
memory used by the databases. The
Not-Only-SQL Big Data system is the new
innovation that has made it possible. The
Hadoop, HBase, MongoDB, and Cassandra
database systems have a feature that new data
records do not overwrite the older ones but get
appended to them tied to their respective date
and timestamps. This concept may be visualised
as "Data Streaming" instead of "Data Commits".
This feature defies ACID compliance, but ensures
that time series of each data type is readily
available within the database itself and OLAP
cubes can be dynamically built within the
memory of the running databases. Thus,
multi-dimensional reports in the OLAP cubes are
now readily available at the same time when the
transactions are happening making the dream of
real-time visibility into the business a reality.
However, it can be interpreted readily that Big
Data cannot be the business of the standalone
server systems whatever capacities they are
provided. Even the cluster computing solutions
will be insufficient after some time to hold the
Big Databases. Seemingly, businesses needed
endless computing power, endless data storage
capacities, and endless memories. The solution
was Virtualisation and Cloud Computing. Please
visit our page Modern IT Systems Topics to learn
about research opportunities in Virtualisation
solutions and Cloud computing.

Virtualisation and Cloud computing had some
distinct features that supported Big Data:
unlimited hardware can be clustered to form
unlimited pools of memory, CPU, storage, and
local area networking, and any hardware can be
hot swapped while the cloud is running. Clouds
can be scaled to indefinite capacities as big
databases grow. Modern cloud computing
services offered by Amazon (Elastic Compute),
Google (Apps and App Engine), IBM (Blue
services), Microsoft (Azure) etc. are capable of
hosting big databases for global manufacturers,
retailers, logistics service providers, supply chain
service providers, etc. in their Infrastructure as a
Service (IaaS) and Platform as a Service (PaaS)
offers. Several Software as a Service (SaaS)
applications for big data analytics are available
on the cloud computing marketplaces at
unimaginable low costs. In fact, even small
businesses can also gain access to big data
applications those couldn't have ever been able to
afford the costs of ETL infrastructures. Big
databases can also be limited later to build
archives after a period (like, after five years)
because the increasing levels of dynamism in the
marketplaces and competitive landscapes might
make data strings obsolete after such a period.
One of the capabilities added to the big data
analytics applications is the Artificial Intelligence.

In simple terms, Artificial Intelligence may be
viewed as the Machine Learning capability
provided to the big data analytics applications
enabling them to automatically analyse time
series data and povide decisions or suggestions.
Artificial Intelligence can also organise and
categorise data types by calculating semantic
distances. Algorithms like Naïve-Bayes,
K-Nearest Neighbours, Support Vector Machines,
and Random Forests can automatically categorise
data into classes based on Input Feature Vectors
and Biases defined by the AI programmers. More
advanced algorithms, like Deep Learning,
Recurring Neural Networks (RNNs) with or
without Long-Short-Term Memories (LSTMs),
Convolutional Neural Network (CNNs) and
Deep Boltzmann machines (DBMs) are capable of
providing predictive values of entire data sets
based on comparisons between their historical
data values (training data) and current data
values (test data). In Industry 4.0, Artificial
Intelligence has been assigned a higher role as
they can automatically analyse time series data
strings collected from the sensors and send
actuation commands to the machineries and
robotics. Industry 4.0 has allowed Artificial
Intelligence to take control over industrial
controllers (PLCs, SCADA, and DCS), over
performance monitoring and maintenance of
equipment, and over quality assurance. Artificial
Intelligence can interact with human operators
through natural language processing.

The researh opportunities in applications of Big
Data and Artificial Intelligence in Industrial
Engineering disciplines are offered largely
through the Industry 4.0 framework. Following
are some of the suggested research opportunities
in these fields for your dissertation and thesis
projects:

1. Incorporation of Static and Mobile agents in
Big Data processing in Logistics and Supply
Chain systems
2. New set of performance measures, indicators,
their measurement methods using Big Data
Analytics and Artificial Intelligence in Logistics
and Supply Chain Management (multiple
focussed topics can be formed in this research
areas)
3. New ways of supplier performance
measurements using Big Data Analytics and
Artificial Intelligence (multiple focussed topics
can be formed in this research area)
4. Designing a life cycle of Big Data Analytics and
Artificial Intelligence for Supply Chain
performance measurements: planning,
implementation, monitoring, control, and
reporting
5. Designing and testing of Conceptual
Frameworks defining complex multivariate
relationships between the enabling factors of Big
Data Analytics and Artificial Intelligence and the
variables related to Logistics and Supply Chain
performance attributes
6. New practices and their factor variables related
to Big Data Analytics and Artificial Intelligence
and their contribution to efficiency and
effectiveness of Logistics and Supply Chain
Management (multiple focussed topics can be
formed in this research area)
7. New rules of Strategic supplier relationships in
the era of IIoTs, Big Data Analytics, Artificial
Intelligence, and Industry 4.0
8. Economics and Cost Savings achievable using
Big Data Analytics and Artificial Intelligence
9. Dynamic capabilities and Market orientation
achievable using Big Data Analytics and
Artificial Intelligence
10. Competitive edge and advantage achievable
using Big Data Analytics and Artificial
Intelligence
11. Excellence in engineering, processes, and
tasks achievable using Big Data Analytics and
Artificial Intelligence
12. Continuous improvements in Industrial
production, logistics, and supply chain
management using Big Data Analytics and
Artificial Intelligence
13. Supply chain agility, flexibility,
responsiveness, and resilience achievable using
Big Data Analytics and Artificial Intelligence
14. Autonomy, socialisation, responsiveness, and
proactiveness in demand fulfillment: new
performance attributes in the era of Industry 4.0,
IIoTs, Big Data Analytics, and Artificial
Intelligence (multiple focussed topics can be
formed in this research area)
15. Orchestration and Synchronisation of
Logistics and Supply Chain assets to allocate
them where they are needed the most: Can
Industry 4.0, IIoTs, Big Data Analytics, and
Artificial Intelligence ensure optimal allocation of
assets?
16. Predictive Analytics and Real-time visibility
into the supply chain echelons: How IIoTs, Big
Data Analytics, and Artificial Intelligence are
shaping supply chain performance under
Industry 4.0?
17. Achieving Capability Maturity in strategic
data management and operations data analysis
using Big Data Analytics and Artificial
Intelligence
18. Achieving the scientific level of data-driven
business by building centres of excellence in
Logistics and Supply Chain Management using
Big Data Analytics and Artificial Intelligence
19. Building Descriptive, Predictive, Prescriptive,
and Automated decision-making capabilities in
Industry 4.0 settings using Big Data Analytics
and Artificial Intelligence
20. Advanced data engineering skills required in
Industry 4.0 for planning, designing, operating,
controlling, and maintaining Big Data Analytics
and Artificial Intelligence systems in modern
data-driven industries
21. Studying the digital transformations of the
traditional operations and controlling models in
manufacturing, logistics, and supply chain
management using Big Data Analytics and
Artificial Intelligence (multiple focussed topics
can be formed in this research area)
22. An evolving digital economy based on
collaborative forums and consortiums for
manufacturing, logistics, and supply networking
using Big Data Analytics and Artificial
Intelligence
23. Contextualising and Conceptualising big data
using artificial intelligence in the Industry 4.0
framework related to all the Industrial
Engineering Disciplines (multiple focussed topics
can be formed in this research area)
24. Building agility and flexibility capabilities in
globally spread manufacturing facilities in the
Industry 4.0 framework using IIoTs, Industrial
Internet, Big Data Analytics and Artificial
Intelligence
25. New organisational cultures and employee
performance monitoring and control systems
using Big Data Analytics and Artificial
Intelligence in the Industry 4.0 framework
26. Architectural Design, Positioning and
Interactions between Components, and
Algorithms for designing an Industry 4.0
Ecosystem using IIoTs, Industrial Internet, Big
Data Analytics and Artificial Intelligence
(multiple focussed topics can be formed in this
research area)
27. Subscription models and selection of services
in Cloud Computing for Big Data Analytics and
Artificial Intelligence in the Industry 4.0
28. How traditional industries can transition to
the Data-Driven Ecosystem by adopting Science
and Technologies enabling data-intensive and
data-centric manufacturing, logistics, and supply
chain management models (multiple focussed
topics can be formed in this research area)
29. How Big Data Analytics and Artificial
Intelligence can be modelled to achieve an
Ecosystem of Structured, Semi-Structured, and
Unstructured Data Systems for Logistics and
Supply Chain applications (multiple focussed
topics can be formed in this research area)
30. Identifying and illuminating digital shadow
zones in Industry 4.0 using Big Data Analytics
and Artificial Intelligence (digital shadow zones,
formed mostly due to communication shadows,
are serious problem areas identified to be
addressed by Industry 4.0)
31. Changing organisational structures and
management models in the era of IIoTs,
Industrial Internet, Big Data Analytics and
Artificial Intelligence
32. Complex Events Processing and Visibility in
Logistics and Supply Chain Management using
Big Data Analytics and Artificial Intelligence
33. Monitoring and Controlling unpredictable
mobility of assets and events using Big Data
Analytics and Artificial Intelligence
34. Security and Safety threats and risk
management in the era of IIoTs, Industrial
Internet, Big Data Analytics, and Artificial
Intelligence under Industry 4.0 framework
(multiple focussed topics can be formed in this
research area)
35. Evolving roles of IT Management and IT
Teams in the era of IIoTs, Industrial Internet, Big
Data Analytics, and Artificial Intelligence under
Industry 4.0 framework (multiple focussed topics
can be formed in this research area)
36. IT Governance and Enterprise Risk
Management in the era of IIoTs, Industrial
Internet, Big Data Analytics, and Artificial
Intelligence under Industry 4.0 framework
(multiple focussed topics can be formed in this
research area)
37. Enterprise Architecture designs and models
in the era of IIoTs, Industrial Internet, Big Data
Analytics, and Artificial Intelligence under
Industry 4.0 framework (multiple focussed topics
can be formed in this research area)
38. Quality Management standards, designs, and
models in the era of IIoTs, Industrial Internet, Big
Data Analytics, and Artificial Intelligence under
Industry 4.0 framework (multiple focussed topics
can be formed in this research area)
39. Information Security Management System
and Privacy in the era of IIoTs, Industrial
Internet, Big Data Analytics, and Artificial
Intelligence under Industry 4.0 framework
(multiple focussed topics can be formed in this
research area)
40. Applying COBIT framework, NIST standards,
and ISO 27000 series of standards in the era of
IIoTs, Industrial Internet, Big Data Analytics, and
Artificial Intelligence under Industry 4.0
framework (multiple focussed topics can be
formed in this research area)

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development in the areas of logistics and supply
chain performance, integration, aggregation
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