Generative AI in the supply chain leverages advanced algorithms to autonomously create and optimize processes, enhancing efficiency and adaptability. This technology generates intelligent solutions, forecasts demand, and streamlines logistics, ultimately revolutionizing how businesses manage their supply chains by fostering agility and cost-effectiveness through data-driven decision-making.

1. 1/12
Generative AI in supply chain
leewayhertz.com/generative-ai-in-supply-chain
The supply chain, a vital cog in the success of businesses across various sectors, comprises
a complex network involving the production, distribution, and delivery of products and
services. With technological advancements, Artificial Intelligence (AI) has emerged as a
game-changing tool in refining supply chain operations.
The global supply chain is continuously evolving, aiming to enhance efficiency, reduce costs,
and satisfy customers. However, it faces mounting complexities which stem from escalating
customer expectations, rapid market changes, and an intensified call for sustainable
methods.
This is where generative AI, a subcategory of artificial intelligence, steps in, providing
innovative solutions to tackle these challenges. Through generative AI, supply chain
stakeholders can process immense volumes of data, extract valuable insights, and
streamline decision-making processes.
In March 2023, a significant stride was made when Microsoft announced Microsoft Dynamics
365 Copilot, an AI-driven assistant incorporated into CRM and ERP systems. Moreover, the
release of ChatGPT by OpenAI for public users in November 2022 was a groundbreaking
event that paved the way for anyone to explore the potential of generative AI.

2. 2/12
The expectations from generative AI in the supply chain sector are high. AI-powered
technology facilitates the smooth and sustainable flow of goods, components, and materials
in a global, data-driven business environment. According to IDC, by 2026, 55% of Forbes
Global 2000 OEMs are projected to have revamped their service supply chains with AI. This
will enable businesses to take proactive measures, ensuring a more efficient and smooth
supply chain operation. This proactive approach can help prevent delays, minimize
downtime, and improve overall supply chain performance.
In recent IDC surveys, global organizations have expressed the need for improved supply
chain visibility to mitigate challenges like cost increases and demand volatility. Generative AI
holds the capability to fulfill these needs and more, aiding businesses in enhancing their
transparency, efficiency, and overall resilience.
In this article, we will delve deeper into the applications and impact of generative AI within
the supply chain sector.
What is generative AI?
What sets generative AI apart from traditional AI in supply chain applications
Use cases of generative AI in supply chain
A real-life application of generative AI in supply chains: Microsoft Supply Chain Copilot
What is generative AI?
Generative AI is a type of artificial intelligence technology that focuses on generating new
content or data based on patterns it has learned from existing data. Unlike traditional AI
models that are designed for specific tasks, generative AI has the ability to create new and
original content. The emergence of Generative Adversarial Networks (GANs) in 2014
transformed the field, enabling the generation of realistic images, videos, and audio while
raising concerns about deepfakes. Modern generative AI interfaces allow plain language
requests, and the generated content can be adjusted based on feedback. They can also
generate synthetic data.
Transformers, a type of deep learning architecture, and Large Language Models (LLMs)
have been crucial in propelling generative AI into the mainstream. Transformers introduced
‘attention,’ enabling models to comprehend connections across extensive text volumes.
Progress in LLMs has led to generative AI creating engaging text, photorealistic images, and
even improvising sitcoms.
Despite its growth, generative AI still grapples with challenges like accuracy, bias, and
anomalous outputs. However, its potential to reshape industries, from code writing to supply
chain transformation, remains promising.

3. 3/12
What sets generative AI apart from traditional AI in supply chain
applications
Conventional AI methods typically utilize statistical models and historical data analysis.
Techniques like time series analysis, regression models, and machine learning algorithms
are employed to discern patterns and correlations in historical data. Predictions are made
based on identified trends, seasonality, and other data-driven factors.
While these methods are adept at capturing long-term trends and patterns, they often
struggle to adapt to abrupt changes or consider external factors not explicitly represented in
the historical data. Moreover, they require substantial amounts of accurate historical data for
precise forecasting.
Generative AI, however, adopts a different approach. Rather than solely depending on
historical data, it creates new data that mirror the training dataset. Generative AI algorithms,
such as GANs or Variational Autoencoders (VAEs), learn the underlying patterns and
characteristics of the data, utilizing this understanding to generate new data points.
Generative AI possesses an advantage in handling situations where there is a lack of
sufficient historical data. In such cases, generative AI can generate synthetic or artificial data
points to supplement the existing dataset. This capability is especially valuable when dealing
with new products or markets that have limited or no historical data available.
Additionally, generative AI can simulate alternative scenarios and produce “what-if” analyses.
This enables businesses to examine different demand scenarios, test the influence of
various factors, and make more informed decisions.
Generative AI truly excels in capturing complex relationships and adjusting to dynamic
conditions, which sets it apart from traditional AI in supply chain applications.
Use cases of generative AI in supply chain

4. 4/12
Demand forecasting and planning
Generative AI in supply chain transforms demand forecasting in supply chain management,
enhancing inventory streamlining, reducing product shortages, and elevating customer
satisfaction. It applies intricate algorithms to past data, market shifts, and external factors to
augment the precision of demand predictions.
The power of generative models lies in their ability to process multiple variables concurrently,
unraveling complex patterns and correlations often overlooked by traditional forecasting
techniques. This accuracy boosts a business’s ability to anticipate demand changes,
optimize production, and adjust inventory levels, driving operational efficiency and financial
gains.
The strength of generative AI shines when digesting vast historical sales data, incorporating
cyclical changes, marketing drives, and the wider economic climate. As the AI model learns
from this rich data, it becomes proficient at generating accurate demand forecasts. The
result? Businesses can expertly manage stock levels, allocate resources tactically, and brace
for future market trends.
Inventory optimization
Inventory management is a critical balancing act between averting product shortages and
mitigating costs associated with excess inventory. Generative AI in supply chain proves
pivotal in maintaining this balance, determining optimal inventory levels using analysis of

5. 5/12
historical data, demand patterns, and external variables. This technology helps businesses
to reduce surplus inventory, prevent overstocking, and enhance supply chain
responsiveness.
Generative AI models can identify optimal distribution strategies and storage practices
considering delivery times, transportation costs, and demand fluctuations. The result is
maximized operational efficiency and substantial cost reduction. By proposing to reorder
points and safety stock levels, generative AI aids businesses in improving warehouse
management, leading to reduced product shortages, minimized surplus inventory, and lower
holding costs.
Managing supplier selection and relationships
By analyzing comprehensive data sets, including performance indicators, quality
assessments, and cost structures, generative AI in supply chain allows businesses to identify
optimal suppliers, thereby enhancing supply chain resilience. Moreover, it aids in proficiently
managing supplier relationships by analyzing past interactions, contracts, and performance
records. These insights help identify potential risks, improvement areas and propose
negotiation strategies, enabling proactive management of supplier-related issues and
fostering beneficial collaborations.
Generative AI’s ability to process extensive data like past performance, financial records, and
news helps foresee potential disruptions and implement preemptive measures, such as
diversifying supplier networks or devising contingency plans. Hence, it helps improve the
overall dependability and performance of the supply chain.
Predictive maintenance
Equipment malfunctions and unexpected downtime can significantly disrupt supply chain
operations, often leading to considerable financial loss. Generative AI becomes an
invaluable asset for implementing predictive maintenance strategies in this context. It
achieves this by analyzing information such as sensor readings, historical maintenance
documentation, and equipment performance indicators.
Generative AI models can predict when maintenance is needed by identifying data
anomalies and patterns. This allows organizations to plan for repairs or replacements
proactively, consequently reducing downtime, prolonging equipment life, boosting operational
efficiency, and curbing maintenance expenses.
Optimizing routes and managing logistics
Generative AI in supply chain significantly enhances route optimization and logistics
management in supply chain operations. It can devise optimal transportation strategies by
considering traffic patterns, weather forecasts, vehicle capacities, and customer needs,

6. 6/12
thereby reducing fuel usage and delivery times and increasing customer satisfaction.
Additionally, it can dynamically adapt to unexpected circumstances in real-time, enhancing
overall supply chain resilience. Generative AI analyses extensive data from various sources,
aiding in route optimization and transportation, resulting in time and cost savings and overall
improvements in logistical efficiency. Its capabilities span route enhancement, vehicle and
fleet optimization, and dynamic routing, contributing to a more robust and resilient supply
chain.
Fraud detection
Generative AI in supply chain significantly elevates fraud detection in supply chain
management by analyzing financial data for irregular patterns indicative of fraud. It uses
machine learning algorithms, particularly deep learning neural networks, to examine past
transactional data, invoice information, shipping details, and more, spotting anomalies that
could signal fraud.
Specifically, Generative Adversarial Networks (GANs) are employed, comprising a generator
network creating synthetic fraudulent transactions and a discriminator network identifying
them. This dual system enhances fraud detection accuracy.
Additionally, these AI models, equipped with predictive capabilities, can forecast potential
fraudulent activities using historical data. This helps detect and proactively prevent fraud,
bolstering supply chain security and reliability.
Managing risks and enhancing resilience in the supply chain
Generative AI is a powerful tool for businesses to identify and manage risks within their
supply chains. By scrutinizing historical data and external variables such as meteorological
conditions, political uncertainties, or disruptions from suppliers, generative AI models can
pinpoint potential hazards.
Generative AI models, through their capacity to process substantial volumes of data
encompassing past supplier performance, financial statements, and news coverage, can
discern patterns and trends associated with supplier-related risks. This enables businesses
to assess supplier dependability, foresee possible interruptions, and take preemptive
measures to manage risks. Such measures could include diversifying their roster of suppliers
or creating contingency strategies, contributing to a more robust and resilient supply chain.
Product design and innovation
Generative AI proves instrumental in advancing product design and innovation by conceiving
new ideas, refining product arrangements, and modeling various scenarios. It supports
creating inventive and tailored products that meet distinct customer needs while considering

7. 7/12
supply chain limitations and financial considerations.
Generative AI in supply chain is adept at processing market data, customer opinions, and
competitor information to yield insights about potential market gaps or opportunities. This can
direct businesses to create new products or services that respond to trends or customer
satisfaction standards.
Focusing on sustainability and environmental footprint
Generative AI can significantly promote sustainable supply chain management by refining
transportation pathways to decrease fuel usage and emissions. Additionally, it can aid in
enhancing the use of packaging materials, cutting down waste, and endorsing
environmentally conscious practices across the supply chain.
Technically, generative AI works by analyzing vast amounts of transportation, waste
management, and resource usage data. It uses machine learning algorithms to identify
patterns and predict outcomes that can lead to more sustainable practices.
In transportation, for instance, generative AI in supply chain can analyze traffic data, vehicle
capacities, and delivery routes to optimize logistics and minimize environmental impact.
Choosing the most fuel-efficient routes and schedules can reduce carbon emissions and
contribute to sustainability goals.
Generative AI can analyze product dimensions, fragility, and other factors for packaging
optimization to suggest the most efficient and environmentally friendly packaging solutions.
This reduces material usage and waste and can lead to cost savings.
Furthermore, generative AI can analyze resource consumption and waste production data
across the supply chain. Identifying inefficiencies and suggesting improvements can help
companies adopt greener practices and reduce their environmental footprint.
Reverse logistics and returns management
Generative AI in supply chain can streamline the reverse logistics process, which is a crucial
aspect of supply chain management, by evaluating data related to returns, repairs, and
refurbishments. This AI technology aids in identifying the best routes for returned products,
deciding on repair or disposal actions, and optimizing inventory distribution for refurbished
items.
For returned goods, AI can evaluate factors like the cost of transportation, the condition of
the product, and the demand for refurbished items. This data can predict whether a product
should be repaired, refurbished, recycled, or disposed of. This process helps reduce
unnecessary costs and waste.

8. 8/12
Regarding routing, generative AI can analyze transportation data to determine the most
efficient route for returning products. This can minimize transportation costs and time,
resulting in a more efficient reverse logistics process.
Furthermore, for inventory management, generative AI can use historical sales data and
demand forecasts to optimize the allocation of refurbished items. This can prevent
overstocking or stockouts of refurbished goods and help ensure that these items are
allocated where they are most likely to sell, improving overall supply chain efficiency.
Financial optimization
Applying generative AI in financial services and operations offers significant advantages in
supply chain management by enhancing efficiency, curtailing risks, and refining decision-
making procedures.
Applying generative AI to the financial elements of the supply chain can provide solutions to
numerous challenges:
Credit risk evaluation: Generative AI can process vast quantities of data, including
credit histories, financial reports, and market data. This allows for assessing the credit
reliability of suppliers, partners, or customers. Supply chain stakeholders can leverage
these insights to manage financial risks, make educated decisions about providing
credit, and pinpoint potential defaults or disruptions within the supply chain.
Fraud detection and mitigation: Generative AI models can scrutinize transaction data
to identify patterns and irregularities and flag potential instances of fraud within the
supply chain. This ability enables businesses to limit financial losses, safeguard their
reputation, and uphold the integrity of their supply chain operations.
Risk management: Generative AI can assess various risks, from currency oscillations
and interest rate shifts to geopolitical events. By generating valuable insights, AI can
aid businesses in developing strategies to mitigate these risks. This helps supply chain
stakeholders manage financial risks more effectively and maintain stability.
Automating the creation of logistics and customs papers
Generative AI holds significant potential in automating the creation of customs and other
logistics-related documents, a task traditionally associated with considerable manual effort.
This is accomplished by applying Natural Language Generation (NLG), a branch of AI
dedicated to creating text that mirrors human writing based on the data or input provided.
The system requires training on an extensive dataset comprising existing customs
documents to use generative AI in this context. These documents might encompass a variety
of forms, declarations, and regulations, providing the AI with a comprehensive understanding
of the specific language, patterns, and structures typically present in such documentation.

9. 9/12
Ensuring compliance with the stringent regulations governing these documents is paramount
in this process. Precise and accurate generation is crucial to avoid miscommunication or
violation of these regulations. Generative AI can maintain compliance across all documents it
generates, providing an effective, efficient, and reliable means of automating this crucial
aspect of supply chain management.
A real-life application of generative AI in supply chains: Microsoft
Supply Chain Copilot
Microsoft Supply Chain Copilot, empowered by generative AI, provides organizations with
unmatched visibility and critical insights to anticipate and mitigate potential disruptions.
Copilot leverages generative AI to proactively identify external issues, such as severe
weather, financial fluctuations, or geopolitical events, that could impact critical supply chain
operations. It offers predictive insights, emphasizing affected orders, and presents a platform
for quick action using context-specific email responses.
Supply chain users can collaborate with impacted suppliers to promptly set new delivery
timelines and redirect purchase orders if needed. Firms can thus fulfill high-priority customer
orders via alternate distribution centers, streamlining operations and saving time.
Its use can address the following problems:
Mitigating risks
Microsoft Supply Chain Center’s Copilot feature uses generative AI to detect potential supply
chain issues arising from external factors like weather or geopolitical events. It also provides
predictive insights into potential impacts on materials, inventory, and distribution networks.
Using Dynamics 365 Copilot, users can quickly act on these insights, sending context-driven
emails to streamline operations and collaborate with affected suppliers. They can promptly
set new Estimated Times of Arrival (ETAs), redirect Purchase Orders (POs) due to weather
disruptions, or use alternate distribution centers to fulfill orders amidst geopolitical instability.
This showcases the efficacy of Microsoft Supply Chain Center’s Copilot in minimizing supply
chain risks.
Streamlining order fulfillment processes
Microsoft Supply Chain Copilot, an integral component of the Dynamics 365 Intelligent Order
Management (IOM) system, provides businesses with the tools to manage and optimize their
order fulfillment procedures. The system applies the power of AI and machine learning to
orchestrate fulfillment operations intelligently and automatically. This is achieved through a
rule-based framework that leverages real-time data from multiple channels and
comprehensive inventory insights.

10. 10/12
With this technology, Copilot can refine order fulfillment strategies by automating, identifying
and implementing the most efficient fulfillment decisions. Furthermore, the Copilot offers an
invaluable feature for enhancing its AI models. In scenarios where the AI’s recommendations
fall short of the ideal, the system uses a unique training, feedback, and improvement method
to adapt and optimize its decision-making processes continually.
Enhancing forecast precision through collaborative demand planning
Microsoft Supply Chain Copilot, with its AI capabilities, greatly enhances the precision of
demand forecasting, an area already employing machine learning. However, due to recent
supply chain disruptions, there remains a need for careful manual review, leading to
substantial time investments from demand planners in manual analysis and demand plan
adjustments.
Next-generation AI models promise to change this landscape. By providing explainable AI
forecasts and natural language queries, these models help demand planners accelerate their
analyses, shortening adjustment periods from days to just minutes.
Additionally, AI enriches demand review meetings, using natural language for informed
decision-making, identifying risks and opportunities, summarizing plan assumptions,
providing real-time analyses, and generating meeting transcripts and action items. This new
era of AI-in-demand planning aims to make the process more streamlined, accurate, and
collaborative.
Leveraging data quality assurance to alleviate order delivery risks
Monthly supplier reviews often involve considerable time and effort as procurement teams
gather and analyze performance data. This process can become more efficient with
conversational AI.
With Dynamics 365 Copilot, an analyst could request a list of orders not delivered on time
and in full (OTIF) in the past month, an estimation of the backlog impact, and
recommendations to rectify the issue. The analyst could also ask AI to generate a request for
the supplier’s involvement in monthly reviews until their OTIF rate is above 97%. This
illustrates how generative AI can democratize data access and retrieval through
conversational interactions with AI chatbots.
Moreover, AI can expedite supplier onboarding by fast-tracking internal legal reviews. AI’s
assistance in reviewing master supplier agreements could be invaluable to roles such as
purchasing managers and supply chain directors.
Harnessing autonomous, self-regulated supply chains

11. 11/12
Microsoft’s Supply Chain Copilot leverages advanced AI to transform the complex supply
chain management arena. It creates adaptive, automated supply chains that use
reinforcement learning to work collaboratively toward improved resilience, profitability, and
customer service.
Supply Chain Copilot integrates historical trends and supply chain events to evaluate
scenarios, analyze business impacts, and determine optimal strategies. For example, it
might propose a combined manufacturing and buying strategy, providing a comprehensive
metrics and cost scorecard.
The Copilot system also automates tasks, generates alerts, suggests actions based on
exceptions, and learns from user interactions. It enhances decision-making capabilities over
time, marking a significant leap forward in intelligent supply chain management.
Intelligent inventory visibility and optimization
Microsoft Supply Chain Copilot’s application allows businesses to optimally balance their
inventory, reducing stockouts and enhancing customer satisfaction.
Take, for instance, a scenario where AI predicts an inventory shortage in the following
quarter. The root cause is an imbalance between supply and demand in a specific region,
further worsened by a scheduled factory maintenance halting production. With Copilot
insights, analysts can identify the impacted products and locations and enact corrective
measures such as rebalancing inventory from other locations or employing a contract
manufacturer.
Moreover, Copilot-enabled inventory visibility is reshaping how businesses manage their
stocks, providing users with rapid access to data. Users can quickly check stock levels or
product availability by using natural language queries, eliminating the need for complicated
navigation or memorization of product details. Additionally, it simplifies data-mining
processes and provides easy-to-understand dashboards and text reports, freeing analysts to
focus on strategic initiatives.
Endnote
Incorporating generative AI promises to be a game-changer for supply chain management,
propelling it into an era of unprecedented innovation. By harnessing the power of generative
models, businesses can evaluate various scenarios, model diverse strategies, and fine-tune
their decision-making mechanisms. For instance, generative AI could be the key to
architecting highly streamlined warehouse layouts, fine-tuning production lines, or
formulating creative packaging approaches. Through constant trial and innovation,
generative AI equips businesses with the tools to discover novel efficiencies, pinpoint
opportunities, and promote ongoing refinement within their supply chains.

12. 12/12
As technology continues to advance, the integration and adoption of generative AI will have
a significant role in shaping the future of supply chain management, transforming how supply
chains are managed, leading to improved competitiveness and sustained growth for
businesses.
Unlock superior supply chain efficiency with generative AI solutions. Contact LeewayHertz’s
AI experts to transform your operations and drive unparalleled business growth.