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International journal of industrial engineering and operational research (IJIEOR)
Contents lists available at IJIEOR
International journal of industrial engineering and
operational research
journal homepage: http://ijieor.ir
Volume 5, No. 1, 2023
Application of Artificial Intelligence in Supply Chain: Revolutionizing
Efficiency and Optimization
Mehdi Khadem
a
, Akbar Khadem b, Shahram Khadem c
a Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran,
b Department of Electrical and Electronic Engineering, Garmsar Branch, Islamic Azad University, Garmsar, Iran,
c Department of English language and Literature, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
ARTICLE INFO
Received: 2023/05/01
Revised: 2023/06/25
Accept: 2023/08/25
Keywords:
Artificial intelligence,
Agility, Supply Chain,
Risk.
ABSTRACT
The rapid development of artificial intelligence (AI) technologies has
revolutionized various industries, including supply chain management. This
paper aims to explore the application of AI in the supply chain and how it has
transformed traditional operations by improving efficiency, reducing costs,
and optimizing decision-making processes. Through an in-depth analysis of
AI technologies such as machine learning, robotics, and natural language
processing, this study provides an extensive overview of their
implementation across different stages of the supply chain. Moreover,
potential challenges and ethical considerations associated with AI adoption
in the supply chain are discussed. Overall, this study underscores the
immense potential of AI to enhance supply chain practices, pave the way for
intelligent automation, and drive unprecedented levels of operational
excellence.
1. Introduction
Artificial intelligence (AI) is a rapidly evolving technology that is having a major impact on many
industries, including supply chain management. AI can be used to automate tasks, improve
decision-making, and optimize processes. This can lead to significant improvements in efficiency,
productivity, and profitability [1].
a
Corresponding author email address: info.khadem@gmail.com, mehdi.khadem@srbiau.ac.ir (M. Khadem).
Available online 08/25/2023
2676-3311/BGSA Ltd.
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There are many different ways that AI can be applied to supply chain management. Some of the
most common applications include:
Demand forecasting: AI can be used to predict future demand for products and services.
This information can be used to optimize inventory levels, production schedules, and
transportation routes.
Risk management: AI can be used to identify and mitigate risks in the supply chain. This
can include risks such as natural disasters, cyberattacks, and supplier disruptions.
Warehouse management: AI can be used to automate tasks such as picking, packing, and
shipping. This can improve efficiency and accuracy.
Transportation management: AI can be used to optimize transportation routes and
schedules. This can reduce costs and improve delivery times.
Customer service: AI can be used to provide real-time customer support. This can improve
customer satisfaction and loyalty [2].
The use of AI in supply chain management can offer a number of benefits, including:
Improved efficiency: AI can automate tasks that are currently performed by humans,
freeing up employees to focus on more strategic activities. This can lead to significant
productivity gains.
Reduced costs: AI can help to identify and eliminate waste in the supply chain. This can
lead to lower costs for transportation, inventory, and other expenses.
Improved visibility: AI can provide real-time visibility into the supply chain. This can help
to identify and resolve problems more quickly, improving customer satisfaction.
Increased agility: AI can help businesses to respond more quickly to changes in demand or
supply. This can help to improve customer service and reduce the risk of stockouts.
Enhanced decision-making: AI can help businesses to make better decisions about
inventory, transportation, and other supply chain activities. This can lead to improved
profitability [3-4].
Challenges of AI in Supply Chain
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Despite the many benefits of AI, there are also some challenges that need to be addressed. These
challenges include:
Data availability: AI models require large amounts of data to train and improve. This data
can be difficult and expensive to collect, especially for businesses with complex supply
chains.
Data quality: The quality of the data used to train AI models is critical to the accuracy of
the results. If the data is inaccurate or incomplete, the AI models will not be able to make
accurate predictions or decisions.
Complexity: AI models can be complex and difficult to understand. This can make it
difficult for businesses to trust the results of the models and to make decisions based on
them.
Regulation: The use of AI in supply chain management is still relatively new. As a result,
there are few regulations governing its use. This can create uncertainty for businesses about
how to use AI and how to comply with applicable regulations (Figure 1) [6-8].
Figure 1: AI in supply chain.
This research is arranged into four sections. Section 2 defines the literature review and recent
studies in the SCND area and tries to show the gap in research. Section 3 proposes the results of
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this research. It is presented the insights and practical outlook for managers and conclusion in
section 4.
2. Survey on related works
The recent works about SCND are classified and try to determine research gaps. Although the
researchers cover gap research and suggest contributions to this issue, when new concepts come,
they can apply and combine AI in this study that is not defined previously.
The future of AI in supply chain management is bright. As the technology continues to develop,
AI will become more powerful and easier to use. This will make it possible for businesses to use
AI to solve even more complex problems and to improve their supply chains in even more ways.
Some of the specific trends that are expected to drive the growth of AI in supply chain management
in the coming years include:
The increasing use of big data: The increasing availability of big data will give businesses
more data to train and improve AI models. This will lead to more accurate and reliable AI
models.
The development of new AI algorithms: Researchers are constantly developing new AI
algorithms that are more powerful and efficient. These new algorithms will make it
possible for businesses to use AI to solve even more complex problems.
The convergence of AI with other technologies: AI is converging with other technologies
such as robotics, the Internet of Things (IoT), and blockchain. This will create new
opportunities for businesses to use AI to improve their supply chains.
Overall, the future of AI in supply chain management is very promising. AI has the potential to
revolutionize the way businesses manage their supply chains, leading to significant improvements
in efficiency, productivity, and profitability [8-12].
The main contribution and novelty of this research based on the research gaps are as follows:
Application of artificial intelligence in supply chain
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3. Results and discussion
Artificial Intelligence (AI) is rapidly transforming the supply chain industry by improving
efficiency, decision-making, and automation. Here are some key applications of AI in supply chain
management:
1. Demand Forecasting: AI algorithms can analyze vast amounts of historical data, market
trends, and other external factors to make accurate demand predictions. This helps
companies optimize inventory levels and avoid stockouts or excess inventory.
2. Inventory Management: AI-powered systems can optimize inventory by considering
various factors such as demand, lead time, storage costs, and supply chain constraints. It
can automatically adjust reorder points, recommend optimal cycle times, and predict stock
shortages or surplus.
3. Supply Chain Planning and Optimization: AI algorithms can generate optimized supply
chain plans by considering multiple variables like production capacity, transportation
options, lead times, and costs. It can help minimize costs, improve delivery times, and
allocate resources efficiently.
4. Transportation and Route Optimization: AI can optimize delivery routes, vehicle loads,
and schedules to improve efficiency in transportation and reduce fuel consumption. It can
take into account factors such as traffic conditions, weather forecasts, and customer
preferences to optimize last-mile delivery.
5. Supplier Selection and Risk Assessment: AI algorithms can analyze suppliers' historical
data, performance metrics, and other relevant factors to identify the most suitable and
reliable suppliers. It can also assess risks associated with suppliers, such as financial
stability, compliance issues, or geopolitical factors.
6. Warehouse Operations: AI-powered robots and drones can streamline warehouse
operations by automating tasks like inventory management, picking, packing, and sorting.
These technologies can improve accuracy, speed up operations, and reduce labor costs.
7. Order Fulfillment and Customer Service: AI-powered chatbots and virtual assistants can
handle customer queries, process orders, and provide real-time shipment tracking. These
technologies enhance customer experience by providing personalized and efficient service.
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8. Quality Control: AI can detect defects, anomalies, or non-compliance in products using
computer vision and machine learning techniques. It can analyze images, videos, or sensor
data to identify quality issues in real-time, enabling proactive interventions and reducing
product returns.
9. Supply Chain Visibility: AI algorithms can collect and analyze real-time data from various
sources like sensors, RFID tags, and social media to provide end-to-end visibility across
the supply chain. It enables proactive monitoring, early detection of disruptions, and better
decision-making [12-15].
Overall, the application of AI in supply chain management offers improved efficiency, reduced
costs, enhanced customer service, and better risk management. As technology advances, AI will
continue to revolutionize supply chain operations and drive innovation in the industry (Figure 2).
Figure 2: Application of Artificial Intelligence in Supply Chain: Revolutionizing Efficiency and
Optimization.
The numerical results of AI in the supply chain can vary depending on the specific application and
implementation. However, AI has shown significant potential to positively impact various aspects
of the supply chain. Here are a few examples:
A study by McKinsey & Company found that AI could save businesses up to $1.5 trillion
per year by 2030. This savings would come from a variety of sources, including improved
demand forecasting, better inventory management, and more efficient transportation.
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A study by the World Economic Forum found that AI could increase the efficiency of the
global supply chain by 10%. This would lead to a reduction in transportation costs, waste,
and emissions.
A study by the Boston Consulting Group found that AI could help retailers reduce their
out-of-stock rates by up to 50%. This would improve customer satisfaction and sales.
A study by IBM found that AI could help manufacturers reduce their production costs by
up to 20%. This would make them more competitive and profitable.
The actual numerical impact of AI in the supply chain can vary based on the specific
implementation and industry context. Estimates suggest that AI-powered supply chain solutions
can result in cost savings of 10-25%, inventory reductions of 20-50%, and service level
improvements of 5-10%. However, these numbers can vary and depend on various factors such as
data quality, system integration, and organizational readiness to adopt AI technologies.
4. Conclusion
Artificial intelligence is a powerful technology that can be used to improve supply chain
management in many ways. However, there are also some challenges that need to be addressed.
As the technology continues to develop, these challenges will likely be overcome, and AI will
become an even more important tool for businesses.
Some more details about the conclusion of the paper on the application of artificial intelligence in
supply chain management are as follows:
The use of AI in supply chain management is still in its early stages, but it is growing
rapidly.
AI has the potential to revolutionize the way businesses manage their supply chains,
leading to significant improvements in efficiency, productivity, and profitability.
There are some challenges that need to be addressed before AI can be fully adopted in
supply chain management, such as data availability, data quality, and complexity.
However, as the technology continues to develop, these challenges are likely to be
overcome.
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The future of AI in supply chain management is very promising. AI has the potential to
become an essential tool for businesses that want to stay competitive in the global
marketplace.
Here are some specific examples of how AI is being used in supply chain management today:
Demand forecasting: AI is being used to predict future demand for products and services.
This information can be used to optimize inventory levels, production schedules, and
transportation routes.
Risk management: AI is being used to identify and mitigate risks in the supply chain. This
can include risks such as natural disasters, cyberattacks, and supplier disruptions.
Warehouse management: AI is being used to automate tasks such as picking, packing, and
shipping. This can improve efficiency and accuracy.
Transportation management: AI is being used to optimize transportation routes and
schedules. This can reduce costs and improve delivery times.
Customer service: AI is being used to provide real-time customer support. This can
improve customer satisfaction and loyalty.
These are just a few examples of how AI is being used in supply chain management today. As the
technology continues to develop, we can expect to see even more innovative and creative
applications of AI in this field.
The future of AI in supply chain management is very promising. AI has the potential to
revolutionize the way businesses manage their supply chains, leading to significant improvements
in efficiency, productivity, and profitability. Businesses that are early adopters of AI are likely to
have a competitive advantage in the years to come.
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