How Does AI Impact Supply Chain Effectiveness?

Dianyi Su

Art&Science, University of Kentucky, Lexington, KY, U.S.A.

Keywords: AI, Supply Chain, Development, Effectiveness.

Abstract: This paper looks into how artificial intelligence (AI) can be used in supply chain management by showing

how AI can improve inventory, enhance transparency and predict demand in which AI's role in real-time

monitoring and data-driven decision-making is illustrated by this paper's research and examples. The paper

discusses about how machine learning and deep learning affect automation and managing resources with some

of the benefits of AI that are discussed about are better operational efficiency, fewer errors, lower costs and

more satisfied consumers. It also discusses the problems that can happen when AI is used in supply chain

management such as making sure that data is kept safe and private and that AI decisions are clear and easy to

understand. More than that, it discusses about how human resources can adapt to new AI developments and

how important it is to keep the roles of humans and machines balanced. By showing professionals and

decision-makers how AI will change supply chains in the future this paper ends with some ideas for how AI

can be used in supply chains.

1 INTRODUCTION

Both business and academia are interested in how

artificial intelligence (AI) will change the way supply

chain management is done in today's fast-paced

world of global connectivity and new technologies.

The main goal of this paper is to look at all the

different ways that AI has changed supply chain

management from making operations more efficient

and accurate to improving inventory management

and logistics. This text combines previous research

and case studies to show how important AI is for real-

time monitoring, making decisions based on data and

ultimately changing supply chains into networks that

are more flexible, resilient while concentrating on the

customer.

Using AI technologies and especially machine

learning and deep learning in supply chain processes

is an important milestone towards making things

more automated, efficient and less likely to make

errors. These technologies make it possible to look at

very large sets of data, which helps make more

accurate predictions about demand, better

management of inventory and more effective

planning of logistics. Nevertheless, using AI also

comes with challenges like concerns about data

https://orcid.org/0009-0006-1156-2270

privacy, security and how to understand the choices

AI makes. The paper also looks at what happens to

human resources and stresses how important it is to

find a balance between technological progress as well

as the positions of individuals in the supply chain

ecosystem.

This paper looks at the applications and

implications of AI in supply chain management with

the goal of giving professionals and decision-makers

useful information. By looking at the advantages,

negatives and future benefits of AI in this area, we

pave the way for supply chains to continue to grow

which is powered by creative uses of AI technologies.

2 LITERATURE REVIEW

Artificial intelligence is used in many different ways

in the supply chain business in which within deep

learning, AI really shines especially with Recurrent

Neural Networks (RNN) and Long Short-Term

Memory Networks (LSTM) whereby these networks

are great at processing sequential data and picking up

on complicated patterns of changes in demand (State

of AI: 14 Charts, 2023). The powerful subset of

Machine Learning (ML) and Artificial Intelligence

628

Su, D.

How Does AI Impact Supply Chain Effectiveness?.

DOI: 10.5220/0012960900004508

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2024), pages 628-632

ISBN: 978-989-758-713-9

(AI) called Deep Learning (DL) does better than

traditional ML methods especially when working

with large, unstructured datasets (Ionescu & Licu,

2023). Deep learning, an area of AI that specialises in

automatic learning that doesn't need human

extraction can be trained with a lot of labelled data

and neural network architectures. Unlike traditional

machine learning, deep learning is great at working

with unstructured data like text, images and sounds in

which with enough time and data instances deep

learning algorithms can figure out how different

factors affect sales. These factors include the

marketing mix (price, promotions, discounts and

ads), seasonality, calendar events, weather forecasts,

lagging sales data (sales from the previous period),

and even the effect of social media (Cui, 2021). To

figure out how supply and demand will change across

regions and products, big retail chains look at POS

data, weather data, social media trends and economic

indicators. For retailers, this detailed analysis lets

them make changes to their stock and advertising

plans ahead of time which cuts down on inventory

backlogs and makes supply and demand work better.

We can get a better idea of how AI and deep

learning can be used in supply chain management by

looking closely at the research that has already been

done which lays a solid foundation for future research

and also shows areas that need more attention like

how readable deep learning models are, how to

protect privacy and security and how cross-domain

applications might work.

Within my future research, I want to first look into

how deep learning can be used in the supply chain

especially for forecasting and improving inventory

management and secondly to think about how to use

data effectively while protecting users' privacy and

thirdly I want to look into cross-domain applications

such as using deep learning techniques in supply

chain management. It is my hope that these studies

will help spread the use of AI technology in supply

chain management and make the chain more

responsive and efficient.

3 OVERVIEW OF THE

DEVELOPMENT OF AI

The field of artificial intelligence (AI) is new and

changing quickly whereby technology and

applications have come a long way in AI with

Language processing, image and sound recognition,

automated decision support systems and predictive

analytics are a few of the many areas where AI is

used. Large-scale language models are getting bigger

and more expensive according to Stanford University

whereby the finance, entertainment, healthcare and

transportation industries have all been transformed by

these technological advances (Figure 1). Concerns

about privacy, security, job security and moral issues

have been raised by the progress of AI and based on

the fact that AI algorithms and computing power

continually become better, it's likely that in the future

AI will be able to learn on its own and do more things.

Figure 1: Estimated training costs of large models.

4 APPLICATIONS OF AI

Artificial intelligence (AI) is used in many fields right

now such as finance, healthcare, transportation,

manufacturing, retail, education, entertainment,

media, furniture and even the Internet. In the

entertainment industry, AI is also becoming more and

more important with an instance of TikTok which

uses AI to create profiles of users and put them into

groups so that they can be shown relevant content.

The journal Social Sciences article discusses about

how the TikTok algorithm changes how a user sees

themselves and their personal values whereby

research has shown that TikTok uses a complicated

algorithm to look at how users act and what they like,

put them into groups and suggest personalised

content. These algorithms look at how users interact

with content like how long they watch, comment, like

and share to figure out what they like and don't like

(A, 2021; A and B, 2021; A et al., 2021). A

(2021) notices that because AI is growing so quickly,

it's hard to keep people's daily lives separate from AI

services and also because our world is becoming

more and more connected, the supply chain system is

now an important way for all industries to cut costs

and as a result, AI development and use have become

important parts of the supply chain system.

How Does AI Impact Supply Chain Effectiveness?

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Figure 2 Framework.

5 AI AND THE SUPPLY CHAIN

5.1 Reinforcement Learning

Artificial intelligence can also be used in a unique

manner to help with managing inventory in which

smart inventory management using AI-based

Reinforcement Learning (RL) helps it interact with

the environment to find the best strategy. Fixed

ordering quantities, safety stocks and cycle stocks are

used in traditional inventory models although the

reinforcement learning (RL) can teach situations-

specific ways to order. RL clearly provides a clear

benefit when dealing with real supply chain issues

like changing demand, unknown delivery times and

incomplete data (Rolf).

Using more complex algorithms for artificial

intelligence can make it better at improving logistics

and transportation with an instance of ant colony

optimization and genetic algorithms can be used to

solve problems like planning routes and making

schedules for vehicles. Natural-looking AI

algorithms that act like social animals can solve

problems well with an instance of the ant colony

algorithm can be used to find the shortest route for

vehicles (VRP) by using a certain number of ant

colonies with the individual ants releasing a unique

pheromone when the colony is active. Over time, the

accumulation of pheromones increases as the shortest

path is consistently chosen. The substance can be

sensed by other ants, who will then choose their path

based on its concentration. This process ultimately

affects the direction of the entire colony's activities.

Combined with the ant colony algorithm, we can

determine the shortest distribution path while

minimizing pollutant emissions, thus achieving the

goal of green logistics and distribution (Zhang, Liang

& Zhang).

Therefore, it can be concluded that genetic

algorithms and ant colony algorithms can optimize

cargo distribution routes by leveraging their

strengths. These algorithmic systems consider

vehicle capacity limitations, customer time window

requirements, and environmental performance,

resulting in reduced logistics costs and improved

distribution efficiency(Figure 2).

5.2 Risk Management

Supply chain risk management is a crucial aspect of

supply chain management. Companies face various

risks, particularly in supplier selection and

management. The most challenging risk is supply

chain disruption. Predictive analytics and sentiment

analysis techniques can be used to identify the level

of risk, while AI can predict market trends and

potential supply chain disruptions by analyzing

online content, news reports, and economic

indicators. To provide a specific example, in the case

of P&G, their Global Supply Chain 3.0 program is

achieving cost savings through AI-enhanced

automation and data analytics which enables them to

collaborate more effectively with retailers throughout

the supply chain. P&G has adopted an assertive

approach to utilizing AI technology to enhance

dynamic supply chain activity and optimize sourcing

with a cost savings of $200 million to $310 million

are expected to be realized through increased

productivity back to pre-outbreak levels. P&G's "AI

Factory" is also driving product innovation by

making data scientists faster and more efficient with

an instance of AI technology is being used to control

the creation of digital scents which is bettering

product development and design in Crown

City(Johnston, 2024). A global company uses AI

analytics tools to find global events like natural

disasters and political conflicts and figure out how

they can impact the supply chain. By identifying risks

ahead of time the business can take steps to lessen

their effects and make sure that production and

supply don't discontinue.

Clearly, Artificial Intelligence (AI) has had a wide

range of effects on the growth of the supply chainas

it can be used for predictive analysis which looks at

past data and market trends to guess what supply and

demand will be in the future. This can help businesses

better plan their production and inventory which can

cut down on backlogs and shortages and in the

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end save costs. Additionally, AI can make warehouse

operations more efficient by using automation and

smart technology to lower the risk of human error and

improve the layout of inventory and it can also predict

demand and keep track of inventory. AI can look at a

lot of different factors to find the best way to organize

the inventory, predict demand and manage the stock

whereby Artificial intelligence (AI) can figure out

how changes in seasonality, market trends and

consumer behavior affect demand. AI is better able to

adapt quickly to the constantly changing market in

situations that are challenging to predict and also to

meet consumer demand and enhance their

experience, AI can also be extremely valuable. To

improve human service, create a more personalized

shopping experience and improve customer care, AI

technology can offer a robot chat and voice

recognition system which could make customers

more satisfied and more likely to remain loyal.

5.3 AI and Supply Chain Efficiency

Artificial intelligence (AI) and the efficiency of the

supply chain are deeply linked whereby modern

supply chain management relies on AI technology to

cut costs and improve efficiency. Using AI, supply

chain data can be collected and analysed in real

time which helps businesses make better decisions

more quickly. AI can forecast market demand,

optimise production plans, prevent supply chain

backlogs or shortages and increase the speed as well

as adaptability of the supply chain by deep mining

sales data, inventory data, logistics data and other

data. Automating tasks in the supply chain is made

easier by AI in which AI technology can replace some

manual tasks such as order processing, inventory

management and logistics planning while also

reducing human error and delay and enhancing

processing speed and accuracy. Besides lowering the

cost of labor, this automation also improves the

stability and efficiency of the supply chain.

AI may additionally employ smart algorithms to

make the best use of the resources in the supply chain

whereby AI can help businesses save time as well as

funding on transportation by choosing the best routes.

AI can also help businesses keep an eye on and

predict inventory levels in real time so they can make

smart decisions about when to restock and when to

schedule so they don't waste resources or run out. AI

is a key part of making the supply chain work better

whereby the supply chain becomes smarter as a

result and businesses are better able to adapt to

changes in the market and customer demand. AI

technology is always getting more effectively so it is

thought that AI will play a bigger role in supply chain

management in the future giving businesses more

value and a competitive edge[7].

Understanding how AI affects the efficiency of

the supply chain is a complicated process involving

many links and levels working together and

interacting with each other in which by gathering and

analysing data, AI greatly assists in making decisions

in the supply chain. In real time, AI can get data from

many parts of the supply chain using big data and

machine learning technologies which includes data

on sales, inventory, logistics and more. Artificial

intelligence (AI) can find patterns and trends in these

data and use them to help businesses predict market

demand, make better use of their resources and come

up with more accurate and effective supply chain

strategies (Worldwide Computer Products News,

2023).

Optimise processes in the supply chain by

automating them and using smart processing as a lot

of boring, time-consuming tasks have to be done by

hand in traditional supply chain management which

can lead to mistakes andtime delays. This task like

order processing, inventory management, logistics

planning and more, can be done automatically by AI

technology thus reducing the need for human

intervention and enhancing processing speed and

accuracy. At the same time, AI can also improve the

efficiency of the supply chain by using smart

algorithms to reduce inventory backlogs, optimize

transportation routes and other tasks.

5.4 AI and Supply Chain Reliability

The ability of AI to monitor and send early warnings

in real time helps to keep the supply chain running

smoothly as AI can get real-time information about

the status of different links in the supply chain and do

real-time analysis with the help of IoT technology

and sensor devices. When AI finds strange things or

possible risks it can quickly send out warnings so

businesses can act quickly to stop problems from

getting worse. With this real-time monitoring and

early warning system, supply chain risks are cut

down and the chain is made more reliable and stable.

Integrating other cutting-edge technologies to the

supply chain can also make it work better with an

instance whereby AI can be combined with

blockchain technology to make information about the

supply chain clear and easy to track. AI can also be

combined with robotics technology to improve the

speed and accuracy of storage and sorting and thus

the supply chain will be smarter and work better when

these technologies are used together (Yuan, 2022).

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6 CONCLUSION

In conclusion, studying the relationship between AI

and supply chain efficiency helps us understand how

important AI is for making the chain work better

whereby AI greatly improves supply chain decision

making by collecting and analyzing data to help

businesses understand market trends and maximize

resources. At the same time, AI's automation and

smart processing have greatly improved the supply

chain process, reduced human error and holdups and

increased overall operational efficiency as real-time

monitoring and early warning from AI keep the

supply chain running smoothly and reduce risks. The

combination of AI and other advanced technologies,

such as blockchain and the Internet of Things, further

promotes innovation and development in supply

chain management, making the supply chain more

intelligent and transparent. The application of these

technologies not only improves the reliability and

sustainability of the supply chain, but also brings

more competitive advantages to enterprises.

However, we should also recognize that the

application of AI in supply chain management still

faces some challenges, such as data security issues,

technological maturity issues, and so on. These issues

require us to strengthen technological research and

regulatory construction while promoting AI

applications, to ensure the healthy and sustainable

development of AI technology.

Based on the above conclusions, this article

proposes the following targeted policy

recommendations: the government should increase

investment in research and development of AI

technology, and promote continuous innovation and

progress of AI technology. At the same time,

encourage enterprises to actively introduce and apply

AI technology to enhance the intelligence level of

supply chain management. Establish and improve

relevant regulations and standards to ensure data

security and privacy protection. In the process of

promoting AI applications, it is necessary to pay

attention to data security and privacy protection to

prevent data leakage and abuse in which the

government should establish strict regulations and

standards to regulate the data collection, storage, and

use behavior of enterprises.

Strengthen talent cultivation and introduction to

provide strong talent support for the development of

AI technology in which the research and application

of AI technology require high-quality talent support

and the government should increase the training and

introduction of AI talents to provide a continuous

source of talent power for the development of AI

technology. Promote the digital transformation of

supply chain management and enhance the

intelligence level of the supply chain. The

government should encourage enterprises to

strengthen digital transformation, shift traditional

supply chain management towards digitalization and

intelligence and improve the efficiency and reliability

of the supply chain.

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