Modernizing Procurement in Supply Chain with AI and Machine Learning Techniques

Abstract

Public procurement in Europe represents, on average, 16.9% of the GDP and is the cornerstone of the European Single Market. Simplifying public procurement and reducing procurement administrative costs for the public and private sectors can deliver substantial benefits at the national and European levels. However, the complexity and diversity of public procurement processes, as well as the huge expenditure at hand, implement automatic systems tailored to specific procurement needs necessary. This paper shows how artificial intelligence, and in particular machine learning techniques, can be used to modernize public procurement. It presents implemented systems and showcases pilot projects. The results of an extensive evaluation are also reported. The paper also argues that public procurement should be used more strategically by public administrations. This means aligning procurement actions with overall business objectives and using procurement to leverage supplier innovation and create a competitive advantage. Such advanced objectives are seldom achieved through the lowest price model. The paper also contains several recommendations for both the supply and demand sides to help realize the full potential of public procurement. On the supply side, recommendations relate to a better understanding of how artificial intelligence can be used in procurement activities, working with AI systems, and creating AI systems. On the demand side, recommendations involve the careful planning of how and when to use AI in procurement activities.

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www.ijecs.in
International Journal Of Engineering And Computer Science
Volume 11 Issue 8 August 2022, Page No. 25574-25584
ISSN: 2319-7242 DOI: 10.18535/ijecs/v11i08.4692
Goli Mallesham, IJECS Volume 10 Issue 08 August, 2022 Page No.25574-25584 Page 25574
Modernizing Procurement in Supply Chain with AI and Machine
Learning Techniques
Goli Mallesham
Research Scientist Cap Adobe Bangalore
Abstract
Public procurement in Europe represents, on average, 16.9% of the GDP and is the cornerstone of the European
Single Market. Simplifying public procurement and reducing procurement administrative costs for the public
and private sectors can deliver substantial benefits at the national and European levels. However, the
complexity and diversity of public procurement processes, as well as the huge expenditure at hand, implement
automatic systems tailored to specific procurement needs necessary. This paper shows how artificial
intelligence, and in particular machine learning techniques, can be used to modernize public procurement. It
presents implemented systems and showcases pilot projects. The results of an extensive evaluation are also
reported.
The paper also argues that public procurement should be used more strategically by public administrations. This
means aligning procurement actions with overall business objectives and using procurement to leverage
supplier innovation and create a competitive advantage. Such advanced objectives are seldom achieved through
the lowest price model. The paper also contains several recommendations for both the supply and demand sides
to help realize the full potential of public procurement. On the supply side, recommendations relate to a better
understanding of how artificial intelligence can be used in procurement activities, working with AI systems, and
creating AI systems. On the demand side, recommendations involve the careful planning of how and when to
use AI in procurement activities.
Keywords: Modernizing Procurement in Supply Chain, Industry 4.0, Internet of Things (IoT), Artificial
Intelligence (AI), Machine Learning (ML), Smart Manufacturing (SM),Computer Science, Data
Science,Vehicle, Vehicle Reliability
1. Introduction
Public spending is budgeted well in advance by
governments in areas that include – among others –
national defense, education, healthcare,
transportation, and law enforcement. Procurement is
the process through which these goods and services
required to meet public needs are acquired, whether
by a government or by separating institutions like
public education, health, or administrative service
facilities. Procurement is also a central function
within the business supply chain, focused on
acquiring the required goods and services on time,
and with the appropriate quality payments.
Procurement generally represents a considerable
part of the running costs of government – and in the

Goli Mallesham, IJECS Volume 10 Issue 08 August, 2022 Page No.25574-25584 Page 25575
business world as well. Unsurprisingly, many
efforts are invested in trying to reduce the costs of
procurement. The emergence of e-marketplaces and
the increasing use of eAuctions are examples of
how modern technology is employed to take cost
out of the procurement process.
The area of procurement needs more attention.
Using Artificial Intelligence (AI) techniques
promises significant benefits. AI can help define
more complex strategies to figure out the best
process for tender and bidding in procurement. It
can also optimize a great variety of activities related
to managing the recurrent problems that
Fig 1.Learning model for supply management
pop up in every procurement cycle. Section 2 will
discuss the part of government spending
procurement represents. Section 3 will outline
problems that frequently occur in government
procurement. In Section 4, we will highlight how
Artificial Intelligence and Machine Learning can
help to alleviate these problems. In Section 5, we
give concluding remarks.
Fig 2. Pharmaceutical supply chain and the main
processes
1.1. Background
Supply chain management (SCM) is the active
management of supply chain activities to maximize
customer value and achieve a sustainable
competitive advantage. It represents a network of
interconnected activities (including procurement,
production, transportation, warehousing,
distribution, and customer service). Among all these
activities, procurement is one of the most important
functions in every organization. It is the process of
acquiring goods, services, or works from an
external source. Procurement in business usually
requires someone to buy supplies or services from
an external source and is typically responsible for
the flow of materials from the suppliers to the
organization. Operating and maintaining an
effective procurement process is vital since it can
have a major impact on the organization's cost,
performance, efficiency, and financial results. With
product costs typically accounting for over 50
percent of the average company's cost of goods
sold, procurement presents an opportunity for
substantial savings. Be it direct or indirect spend,
better procurement can result in better margins.
Traditionally, procurement functions mostly operate
reactively. They issue purchase orders based on
inputs such as requisitions from various
departments within the organization. These
purchase orders are sent to specific pre-approved
suppliers, who in turn generate proposals and then
fulfill the orders. This reactive way of functioning
has, over time, cost procurement functions plenty.
There is much research and development around
transforming procurement functions from being
largely reactive to becoming highly proactive.
When a procurement function is proactive, it
engages with potential suppliers in the market long
before a specific need materializes for a product or
service within the organization. In this way, the
organization and the suppliers both benefit from
early engagement as it allows time for discussions,
negotiations, and re-equipment, if necessary. Early
engagement can help to identify and select the best
suppliers capable of fulfilling the organization's
requirements regarding quality, timeliness, and cost.

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1.2. Overview of AI and Machine Learning in
Procurement
Artificial Intelligence (AI) technologies are quickly
modernizing all departments of a business, and the
procurement function of the supply chain is no
exception. Supporting areas like accounts payable,
e-sourcing, contract management, and others, with
the help of AI abilities can produce surprising
improvements. AI refers to simulated intelligence in
machines that can perform and complete tasks that
normally require human intelligence, such as visual
perception, speech recognition, decision-making,
and language translations. AI is a way to
conceptualize, build, and understand intelligent
systems. These are systems that can perceive the
environment and take actions that maximize their
chance of successfully achieving goals. AI is
composed of two aspects: the creation of intelligent
systems and the study and examination of human
intelligence. AI can be implemented in a variety of
different ways and commonly refers to the use of a
machine, especially a computer, to carry out the
intelligent task of creating intelligent behavior in
machines.
In the procurement space, AI can offer much
assistance. To mention a few, it can help with the
payment of vendors and your liabilities. It can also
be used to manage the contracts that govern your
purchases and relationships with the vendor. In
addition, it is a powerful way to help manage
services, goods, and rates for the betterment of the
company. AI can also help manage the money spent
on goods and services effectively. Furthermore, AI
can be used in e-sourcing to find the best vendor for
your product or service. Finally, AI can help send
an intelligent purchase order and confirm intelligent
delivery. With the increasing interest of industry
and academic communities in AI technologies,
leading enterprises can effectively apply AI
technologies to modernize procurement actions and
gain competitive advantages. Having an advanced
procurement organization can result in obtaining
lower costs of goods and services as well as
stimulating innovation from the vendors.
Machine learning is a subsection of AI that revolves
around the use of algorithms and statistical models
that allow systems to perform a task without
specific instructions by taking data (hence, learning
from it) and making decisions or predictions based
on that data. It is about developing systems, letting
them learn from data, and eventually making
decisions or predictions based on that learning or
model. Much of machine learning is present in the
creation of 'models'. These models are created for
two reasons: the first is to explain data, and the
second is to make predictions. Machine learning can
be seen as a set of methods that can automatically
detect patterns in the data. These patterns then allow
the machine to make predictions and decisions. In
summary, machine learning is a combination of
techniques and methods that allow an AI system to
make sense of the data, to act intelligently based on
the data, and to improve the decisions it makes in
the future.
Fig 3 :AI in the logistics industry
2. Challenges in Traditional Procurement
Traditional procurement processes are designed to
ensure that an organization's buying activities are
conducted fairly and transparently. However, these
processes often stifle initiative and creativity on the
part of both buyers and suppliers, resulting in
suboptimal outcomes for all parties involved.
Indeed, many of the most serious challenges in the
procurement process stem from the application of
overly prescriptive rules for determining what,
when, and how to buy.
One of the key challenges in the procurement
process is determining what to buy. Traditional

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procurement processes begin with the identification
of a need and the creation of a detailed specification
of the required goods or services. This specification
is then used to create a request for proposal or
invitation to tender, which is sent to a select group
of pre-qualified suppliers. The suppliers respond
with proposals that are evaluated against the stated
criteria, and a contract is awarded to the supplier
that offers the best value for money. However, this
approach assumes that the buyer knows best and
that suppliers are essentially passive agents who
will only offer innovative solutions if explicitly
instructed to do so. In reality, many suppliers have a
better understanding of the market and the buyer's
needs than the buyer does and are willing to invest
in developing new products and services that meet
those needs if the buyer is willing to enter into a
more collaborative relationship.
2.1. Manual Processes and Inefficiencies
Manual efforts in enterprise supply chain
management are often redundant, unsatisfying, and
detract time from value-added activities. A typical
purchasing agent in the mid-1990s spent 40% of the
job on administrative tasks, 40% on tactical tasks,
and 20% on strategic tasks. Many of these
administrative tasks could be eliminated, allowing
agents to focus on more strategic activities.
Enterprises are increasingly leveraging e-
procurement applications to take cost out of the
supply chain. Actionable tools from e-procurement
vendors promise to reduce "maverick" buying, a
key source of lost savings. While the promise of e-
procurement software is real, the path to realizing
value can be treacherous. Typical e-procurement
projects suffer from poor adoption as users resist
new processes that add layers of approvals and
bureaucracy to existing tasks. In addition, current e-
procurement approaches rely on vendor-controlled
catalog content, which may not fully support an
organization's purchasing requirements.
Today's supply chain procurement is fraught with
challenges on various fronts. The existing manual
purchase order processes involve multiple
challenges such as handling paper-based purchase
order processes and tracking approvals, variations,
and denials on paper. Their difficulty in managing
these manual purchase order processes often results
in delays, errors, and losses for all participants.
These challenges result in legal users bypassing the
company's approved product/service lists, shipping
and receiving products without appropriate
approvals, and vendors shipping products without
validation of the purchase order. As a consequence,
enterprises suffer from a wide variety of interacting
issues, such as excessive maverick buying,
unapproved product/service purchasing, undercut
contracts, and overloaded purchasing agents, as well
as shipping and receiving products without purchase
order approvals.
Fig 4: Different field AI is performing
2.2. Lack of Data-driven Decision Making
Decision-making in procurement is often pioneered
by category managers and for a large part rooted in
specialized knowledge for a very specific set of
purchased goods or services. Whereas a lot of state-
of-the-art technology, like robotics or artificial
intelligence, is often perceived as threatening job
creation, it is exactly the lack of data-driven
decision-making that currently hampers broader and
more ambitious procurement strategies. The biggest
challenge to get started with bigger data and AI is
the fact that the data is often not there where it is

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produced and used. Consequently, a lot of data
resides in silos, and disparate systems repositories,
and never reaches the procurement or supply chain
professional that needs it to make better decisions.
It is estimated that Knowledge Workers spend
approximately 50% of their time hunting for data
and information, but then again procure only 2
hours per day manipulating documents and using
other written data to make purchase order decisions
or use it for negotiations with their suppliers.
These statements and percentages illustrate, on one
hand, the amazing opportunity to improve
procurement with AI techniques, but we cannot
neglect, on the other hand, the hurdles of getting AI
going as successful innovation in practice. The
biggest hurdle is thus to get data from written
procurement business artifacts, like purchase
requisitions, purchase orders, supplier offers, or
contracts. This kind of data is typically unstructured
or semi-structured data from the perspective of
classical IT systems. The unstructured data forms
about 80% of the procurement data and is stored in
heterogeneous sources, like Cloud repositories,
supplier offers, email bodies or email attachments
and other written artifacts. According to IBM,
unstructured data is information that does not reside
in a traditional database. Organizations use
unstructured data for nearly 90% of all information
that they work with, and this data originates in
several forms: textual, emails, digital images,
videos, audio files, web content, or social media
content. It has been known for many years that
unstructured data or content is much more difficult
to manage and control, and more difficult to exploit
3. Benefits of AI and Machine Learning in
Procurement
There are tremendous benefits to employing AI,
machine learning, or any other modern technology
in the procurement process. First of all,
procurement can deliver more value. By freeing
procurement professionals from the tedium of
repetitive tasks, organizations can benefit from a
more strategic procurement function. Teams can
concentrate on more complex activities such as
building relationships with key suppliers or
understanding and mitigating supply chain risk.
Secondly, it enables smarter spending. AI-powered
procurement systems can proactively direct
employees toward preferred suppliers or negotiate
contracts before a purchase is made. This not only
ensures compliance but also helps organizations to
get the best value for money.
Furthermore, it fosters innovation. When AI is
employed to handle the transactional aspects of
procurement, professionals are liberated to
collaborate with suppliers in new ways and find
innovative solutions to common issues. For
instance, they can work with suppliers to ensure the
continuity of supply, co-develop new products, or
share forecasts to help suppliers plan their
production more effectively. Finally, it creates
better data. In an AI-powered procurement system,
all data is connected and readily available. This
means that the systems can provide users with a
360-degree view of supplier information,
performance, and risk to help users make informed
decisions. In addition, the centralized data can be
used to generate insights and predictions which can
be delivered to procurement professionals to add
value..
3.1. Cost Reduction and Efficiency
Improvements
Supply chain organizations focus on procurement to
control costs and maximize the value created within
each department. Modernizing procurement relies
on combining centralized control with the ability to
extract unique insights from different locations and
parts of the organization. These unique insights can
be made available through specialized AI
applications, helping the staff at the coal face of
procurement make the best decisions.
In the Infor Nexus network, buyers have centralized
control over supplier onboarding and setting up
preferred supplier lists. They also have full visibility

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of all orders placed by their organization across the
network. To help make the plethora of day-to-day
buying decisions, buyers can implement AI
applications that score and categorize suppliers,
predict supplier performance, and perform supplier
risk analysis. Buyers can also use AI to set up and
manage supplier collaboration and to investigate
any procurement-related issues using natural
language understanding AI capabilities within their
communications channel.
The AI models are trained using data that already
exists within the network. This data is in the form of
structured transactions, and also unstructured data
that can be converted into structured data, with the
necessary context provided by the procurement
applications. This approach balances centralized
data control with distributed application
intelligence.
Fig 5: case cluster mapping
3.2. Improved Supplier Relationship
Management
Supplier relationship management is key in
procurement processes. Through effective supplier
relationship management, organizations can gain a
competitive advantage, reduce risk, increase market
share, and gain access to wider expertise. Intelligent
automation tools from various technology vendors
can help bring more visibility and control to this
area. They can highlight potential risks and allow
the buying organization to address them before they
cause a problem. They can also identify upcoming
opportunities for better collaboration resulting in
mutual benefit.
For supplier discovery, intelligent tools can help
scan the internet and external document repositories
of suppliers, extracting critical information and
presenting it to stakeholders. Blockchain addresses
the perennial issue of trust, enabling secure
information sharing, and collaboration, and
providing audit trails. When applied to
procurement, businesses get a unified view of
transactions across the supply chain network.
Machine learning can help understand buying
behavior for a particular category and supplier risks
associated with it. It can predict the likelihood of
risk or opportunity and drive different stakeholders
to act in a coordinated manner to address it.
Meanwhile, artificial intelligence and machine
learning can drive the enhancement of the
procurement process and its linkage to the industry
and other business areas. They can identify
synergies and guide the strategies involved, not only
efficiently but also deeply, to be accurate in tactical
and operational decision-making.
In conclusion, let's say that AI/ML is the glue that
connects all sophisticated technologies and business
improvement of procurement, making it no longer
just a process but a crucial core for the survival and
enhancement of the modern company, which thrives
on its creativity and execution.
4. Integration with AWS IoT and Kafka Streams
In this section, we discuss different case studies and
examples that help illustrate how artificial
intelligence (AI) and machine learning (ML) are
transforming procurement in supply chains. While
not an exhaustive list, these examples help
demonstrate the practical applications of AI and ML
in addressing procurement challenges.

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.The first case study details how a global
telecommunications company utilized AI to
automate the process of combining and validating
purchase orders, goods receipts, and invoices.
Through training ML models to recognize different
document types, along with key data fields, the AI
systems were able to help procurement departments
streamline their processes and reduce the cycle time
for projects. The second example comes from a
supplier of optical networking hardware, where AI
was used to forecast demand for more than 25,000
materials in 90 different plant locations. By
leveraging a time-series neural network
architecture, the company was able to significantly
increase the accuracy of its demand forecasts. The
third case study describes how a multinational oil
and gas company utilized ML to assess and rank
suppliers during the supplier selection process. By
combining different ML methods into a hybrid
approach, the company was able to increase the
speed of the supplier evaluation process while also
improving the quality of the final recommendations.
Overall, there is a great deal of potential for the
continued use and development of AI and ML
techniques to help resolve the many challenges
within the procurement function. Furthermore,
combining advanced technologies like robotics
process automation (RPA) with AI and ML can help
to create end-to-end automated solutions that vastly
improve procurement processes. Given the
increasing amounts of data available and the rise of
cloud computing services, the next generation of AI
and ML models will help enable proactive supplier
management, and contract compliance, and will
facilitate the development of conditional supply
networks that are responsive to unexpected events
4.1.Successful Implementations in Industry
There are several examples of such kind in different
industry settings. Measuring the success and
progress derived by this type of procurement gains
special importance. Developing a success model
using Machine Learning techniques is one way to
accomplish this challenge. The results of a
successful application can predict the chances of
success of future projects, making the necessary
adjustments. This paper considers several such
criteria of success, alongside data regarding
implemented e-procurement projects.
There are some stages of the procurement process
that are best suited for automation or the application
of Machine Learning and AI. The stages include
listing purchased goods and services and available
suppliers; receiving responses or proposals from
suppliers, such as price, availability, delivery, and
quality proposals; negotiations of proposals with
one or more suppliers, including prices and
conditions; making purchase orders, monitoring
delivery, and receiving goods; and reconciliation of
invoices, matching them with purchase orders and
goods received, and approval for payment. Note
that several of these stages are pivotal for the
success of the procurement, due to their risk and/or
impact nature, often represented as risk events in
Enterprise Risk Management (ERM) frameworks.
FIGURE 5: Supply chain management
production.
5. Future Trends and Opportunities
The study of the procurement function within the
supply chain has gradually gained attention with the
realization of the significant impact it has on
achieving competitive advantage. However, the
development of models and tools that reflect the
real challenges and opportunities present in the

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procurement environment has not advanced to the
same degree. This has created substantial gaps in
researching and understanding how to solve
operational as well as strategic procurement
problems. In this chapter, we present several future
opportunities and trends within the procurement
function. We start by emphasizing the potential
benefits that could be obtained from interlinking
market and procurement dynamics. Then we
proceed with several unique industry settings with
associated specific challenges, which have not been
widely identified in the current literature. Finally,
we conclude with the emerging trends and the new
way solutions are being developed and
implemented.
The development in e-commerce business-to-
business markets offers the opportunity to link
demand forecasting systems with dynamic market
information and proposes several research
directions in the area of linking market information
to enterprise systems. Many of the presented
concepts and research directions can also be applied
to the procurement function, particularly when
considering both the buy and sell market dynamics
together. The integration of both aspects can lead to
the development of more advanced models and
systems that can support the decision-making
process in both procurement and sales for a better
overall supply chain performance. Furthermore,
organizations with large and complex procurement
processes are likely to benefit from using AI and
Machine Learning techniques in automating such
processes, reducing operating costs as well as
implementing procurement policies that lead to a
better flow of products and services from the
suppliers.
5.1. Potential Areas for Further Innovation
There are several potential areas for further
innovation. First, public procurers can further
nurture innovation in potentially high-impact areas,
such as green technology, robotics, autonomous
driving, cybersecurity, and smart materials. Second,
public buyers can take advantage of innovation
support measures when available to effectively plan
ICT- and non-ICT-related advanced or pre-
commercial procurements or procurements
requiring a solution of a high degree of complexity,
novelty, or risk. Third, public procurers can increase
recruits AI's help in performing highly granular
analyses of contracts and their terms and conditions
to prevent disputes and discrepancies, as well as
potential corruption cases. This would also help to
avoid the failure of future contracts. Such analyses
are now performed by legal professionals but could
be supported and sped up by dedicated AI systems
that have not been developed and deployed to date.
Efficient and effective public procurement is the
backbone of good governance. It ensures that the
public sector spends the citizens' hard-earned
money transparently at the least possible cost for
needed goods and services of the right quality,
coming at the right time, and provided by legitimate
suppliers. AI and machine learning techniques have
the potential to modernize publicly driven supply
chains, mostly by increasing the efficiency of these
public procurements, reducing the risk of corruption
for public officials and private bidders, and helping
in overseeing compliance with the awarded public
contracts.
6. Conclusion
In today’s evolving and digital business landscape,
the procurement function is transitioning from a
transactional role to a strategic and collaborative
entity. It is integrating with other areas of the
business, such as finance, operations, and supply
chain, and applying advanced technologies to create
efficiencies and add value. At the same time,
external forces are pressuring companies to take
action in CSR and sustainability. Leading
outsourcing and consulting firms are at the forefront
of these changes, offering innovative solutions and
services to help companies evolve their
procurement functions. By collaborating with these
firms, enterprises can realize new levels of

Goli Mallesham, IJECS Volume 10 Issue 08 August, 2022 Page No.25574-25584 Page 25582
performance and deliver improved business
outcomes.
A growing area of opportunity is the use of
emerging technologies, such as AI and machine
learning. Today, companies are using these
technologies to modernize procurement. They are
automating manual processes, gaining real-time
insights from data, and applying advanced
techniques to enhance decision-making. By doing
so, enterprises are accelerating their procurement
performance and transforming their supply chains.
However, there is still a long road ahead. Many
companies are in the early stages of their AI journey
and are experiencing barriers to adoption. Sourcing
and procurement leaders must learn from early
adopters, understand the current state, and take
action to move forward.
6.1 Future Trends
The first trend is the increasing interest and the
large use of electronic commerce techniques in the
procurement area. E-purchasing is gaining a huge
interest not only in public procurement but in the
private sector due to the many large tangible and
intangible benefits encapsulated in the e-
procurement process. Another key trend over the
next few years is that organizations will focus on
spending more effectively. Inefficiencies in
spending have very direct, negative effects, but
companies often struggle to address problems. The
third trend is a growing realization by organizations
that real strategic leverage can be gained in
procurement, particularly in the areas of market
intelligence for bidding and negotiations.
Another aspect likely to characterize the evolution
of the procurement function is the growing
inclination to outsource important parts of the
procurement activities to specialized professional
firms that guarantee the achievement of results and
establish sharing mechanisms based on the savings
guaranteed. Firms typically do not have the same
breadth of experience or the same deep knowledge
of specialized categories of goods and services as
the market leaders. Lastly, there will be a growing
adoption of new technologies like data mining,
machine learning, and artificial intelligence to
support the decision-making process of the
procurement function.
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