AI in International Logistics Unit - IV - AI-Driven Supply Chains, Workforce Transformation, and Cybersecurity

This unit analyzes how AI transforms supply chain management and its implications for employment patterns. It explores the rise of AI-powered supply chains, ethical challenges in AI-based decision-making, and the dual impact of automation on job displacement and creation. Additionally, it introduces key cybersecurity tools—such as SIEM, EDR, and MFA—used to protect digital logistics platforms from cyber threats

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WORKFORCE DISPLACEMENT
Workforce displacement refers to the situation where employees permanently lose their jobs
because of technological, economic, or organizational changes, most notably the adoption
of automation and Artificial Intelligence (AI).
It is distinct from a typical layoff because the job itself is often eliminated, rather than just the
person filling it, meaning the displaced workers may not be able to return to their former
occupation and must seek work in a different field.
Key Characteristics of Displacement:
• Cause: Primarily driven by technology (e.g., AI, robotics) that makes human labor
unnecessary for specific tasks or roles.
• Effect on Job: The job role is fundamentally changed or ceases to exist, making it a
permanent loss of employment in that occupation for the worker.
• Worker Requirement: Displaced workers must often undergo reskilling to acquire new
competencies for a different career path, as their existing skill set has become
obsolete.
AI-POWERED SUPPLY CHAIN
An AI-powered supply chain is a modernized logistics and operations network that uses
Artificial Intelligence (AI) and Machine Learning (ML) algorithms to automate processes,
generate predictive insights, and make complex, real-time decisions with minimal human
intervention.
It transforms the traditionally reactive and fragmented supply chain into a proactive, highly
efficient, and resilient system by integrating intelligence across all stages, from sourcing raw
materials to delivering the final product.
Core Functions of AI in the Supply Chain
AI enhances supply chain management by addressing common challenges related to visibility,
volatility, and inefficiency:
1. Advanced Demand Forecasting
• Method: AI algorithms analyze massive amounts of data, including historical sales,
market trends, seasonality, competitor actions, social media sentiment, and even
weather patterns.

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• Benefit: This provides highly accurate predictions of future customer demand,
allowing companies to avoid costly stockouts (understocking) or excessive
overstocking.
2. Intelligent Inventory Management
• Method: AI continuously monitors stock levels and uses demand forecasts to
automatically recommend optimal inventory levels and reorder points.
• Benefit: It reduces carrying costs, minimizes waste (especially for perishable goods),
and ensures the right product is in the right location at the right time.
3. Dynamic Logistics and Route Optimization
• Method: AI systems analyze real-time data on traffic, road conditions, weather, and
vehicle capacity.
• Benefit: It determines the most efficient delivery routes instantly, leading to reduced
fuel consumption, lower operational costs, and improved on-time delivery
performance.
4. Predictive Maintenance and Quality Control
• Method: AI analyzes data from IoT sensors on machinery, trucks, and equipment to
predict potential failures before they occur. Computer vision systems inspect products
for defects on assembly lines.
• Benefit: This prevents costly downtime, reduces maintenance expenses, and improves
overall product quality and safety compliance.
5. Proactive Risk Management
• Method: AI constantly monitors external data sources—including geopolitical events,
financial news, and natural disaster forecasts—to identify potential supply chain
disruptions in real-time.
• Benefit: It provides early warnings and suggests mitigation strategies, such as
rerouting shipments or switching to alternative suppliers, enhancing the entire chain's
resilience.
ETHICAL ISSUES IN DECISION-MAKING USING AI
AI raises significant ethical issues in decision-making primarily concerning fairness,
transparency, and accountability, especially when its decisions directly impact people's lives
in high-stakes areas like hiring, lending, or criminal justice.

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The core ethical challenges are:
1. Algorithmic Bias and Discrimination
The most critical issue is that AI systems are only as good (or as biased) as the data they are
trained on.
• Data Bias: If the historical data used to train the AI reflects past societal biases (e.g.,
historical hiring records favoring one gender or race), the AI will learn and amplify
those prejudices.
• Discrimination: This can lead to unjust, discriminatory outcomes, such as an AI
recruitment tool unfairly penalizing women's resumes, or a predictive policing
algorithm disproportionately flagging minority neighborhoods.
• Reinforcement of Inequality: By automating biased decision-making, AI can make
existing social and economic inequalities more persistent and harder to correct.
2. The "Black Box" Problem (Opacity)
Many advanced AI models, particularly deep neural networks, are so complex that even their
designers cannot easily explain the logic or precise factors that led to a specific decision.
• Lack of Transparency: When a loan application is rejected or a medical diagnosis is
given by an AI, the affected person often has no way of knowing why the decision was
made.
• Inability to Contest: This lack of explainability (XAI) makes it nearly impossible for
individuals to challenge or appeal a decision, undermining the fundamental rights to
due process and fairness.
3. Accountability and Liability
When an autonomous AI system makes an error that causes significant harm, it is difficult to
determine who should be held responsible.
• Blurred Responsibility: Is it the developer who coded the algorithm, the company that
deployed it, the user who provided the input data, or the AI itself?
• High-Stakes Decisions: This is especially problematic in autonomous systems like self-
driving cars that cause accidents or AI-driven medical devices that recommend
incorrect treatment. The lack of a clear chain of accountability hinders legal recourse
and regulatory oversight.
4. Privacy and Surveillance
AI relies on vast amounts of data, which raises fundamental concerns about personal privacy.

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• Mass Data Collection: AI systems in security, finance, and marketing continuously
collect and process personal data, often without clear or fully informed consent.
• Surveillance Risk: The sophisticated analytic power of AI, especially when paired with
technologies like facial recognition, creates a potential for ubiquitous and intrusive
surveillance, which can stifle individual freedoms and autonomy.
IMPACT OF AI ON EMPLOYMENT PATTERNS IN LOGISTICS
AI is profoundly reshaping employment patterns in the logistics sector, introducing a mix of
automation-driven job displacement for routine tasks and a growing demand for new,
specialized skills related to AI management and data analysis. This transformation is moving
the industry towards more strategic, technology-driven roles.
1. Automation of Routine and Manual Labor
AI-powered robotics and autonomous systems are increasingly taking over physically
demanding, repetitive, or hazardous tasks, leading to the displacement of certain roles.
• Warehouse Operations:
o Displaced Roles: Manual pickers, packers, forklift operators, inventory clerks.
o AI Impact: Automated guided vehicles (AGVs) and robotic arms now handle
sorting, picking, and moving goods with greater speed and accuracy. AI-driven
inventory systems eliminate the need for manual stock counts.
• Transportation & Delivery:
o Displaced Roles: Long-haul truck drivers (eventually), delivery drivers (for
specific routes).
o AI Impact: Autonomous trucks and drones are beginning to perform deliveries,
particularly in controlled environments or for last-mile logistics. AI-driven route
optimization reduces the number of vehicles and drivers needed.
2. Augmentation of Existing Roles
Rather than outright replacement, many roles are being augmented by AI, allowing human
workers to perform more efficiently and focus on higher-value tasks.
• Logistics Planners & Dispatchers:
o Augmentation: AI-powered systems quickly analyze real-time traffic, weather,
and shipment data to optimize routes and schedules. This frees human
planners to focus on strategic decisions, complex problem-solving, and
customer relationship management, rather than manual calculation.

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• Customer Service:
o Augmentation: AI chatbots handle routine inquiries and provide instant
updates, allowing human customer service representatives to address more
complex issues, build rapport, and handle exceptions.
3. Creation of New, Specialized Jobs
The rise of AI in logistics generates a demand for new skills and entirely new job categories
focused on managing, maintaining, and developing these advanced systems.
• AI & Robotics Technicians:
o New Roles: Specialists needed to install, maintain, troubleshoot, and repair AI-
powered robots, autonomous vehicles, and automated warehouse systems.
• Data Scientists & Analysts:
o New Roles: Experts who can extract insights from the vast amounts of data
generated by AI systems, informing strategic decisions for optimizing supply
chain efficiency, predicting demand, and identifying bottlenecks.
• AI Implementation & Integration Specialists:
o New Roles: Professionals who bridge the gap between AI development and
practical application, ensuring AI systems integrate seamlessly with existing
logistics infrastructure and workflows.
• Supply Chain AI Strategists:
o New Roles: Leaders who identify opportunities for AI implementation,
evaluate its impact, and steer the company's long-term AI strategy within the
logistics context.
CYBERSECURITY TOOLS FOR PROTECTING LOGISTICS PLATFORMS
Cybersecurity tools are essential for protecting logistics platforms, which handle a massive
amount of sensitive data and rely on complex, interconnected systems for everything from
inventory to transport. These platforms, including Warehouse Management Systems (WMS),
Transportation Management Systems (TMS), and IoT devices in the supply chain, are
vulnerable to attacks like ransomware, data breaches, and service disruption.
Here is an analysis of how key cybersecurity tools can be used to protect logistics platforms,
with examples.
1. Security Information and Event Management (SIEM)

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Function: A SIEM system collects, aggregates, and analyzes log and event data from various
security devices, applications, and network hosts across the entire logistics infrastructure. It
uses correlation rules and behavioral analytics to detect suspicious patterns and alert security
teams in real-time.
Logistics Application Example: A global shipping company uses SIEM to monitor its network.
One day, the SIEM system notices a sudden, massive spike in login attempts to the central
TMS server from an IP address in a country where the company has no employees or
operations. This activity is highly unusual for a system that typically sees only local access. The
SIEM correlates this anomaly with logs from the firewall, which show multiple connection
attempts to the TMS's default port. The system triggers a critical alert for a brute-force attack,
allowing the security team to block the malicious IP and prevent a credential compromise
before the attacker gains access.
2. Vulnerability Scanners and Management
Function: These tools proactively scan networks, applications, and operating systems to
identify, classify, and prioritize security weaknesses like unpatched software,
misconfigurations, and weak passwords. They are crucial for maintaining a strong security
posture.
Logistics Application Example: A logistics firm operating a large automated warehouse uses a
vulnerability scanner monthly. The latest scan of their Industrial Internet of Things (IIoT)
network reveals that a fleet of automated guided vehicles (AGVs) is running an outdated
version of its operating firmware with a publicly known vulnerability. The scanner's report
ranks this as a high-severity risk. The security team uses this report to immediately initiate a
patch management process, updating the AGV firmware to close the security gap, thus
preventing a potential hacker from exploiting the flaw to remotely disrupt warehouse
operations or take control of the robots.
3. Endpoint Detection and Response (EDR)
Function: EDR tools continuously monitor end-user devices (endpoints) like laptops, servers,
and specialized workstations used for dispatch and planning. They record activity, analyze
behaviors for threats, and can automatically respond to isolate compromised devices.
Logistics Application Example: A dispatcher at a trucking company clicks on a phishing link in
an email, and an unknown file is executed on their desktop computer, which is connected to
the scheduling system. While traditional antivirus software might miss this zero-day malware,

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the EDR agent installed on the dispatcher's workstation detects an unusual sequence of
events: the new file attempts to access and encrypt a large number of route manifest files.
The EDR tool immediately flags this as potential ransomware activity, automatically isolates
the dispatcher's PC from the network to prevent lateral spread, and notifies the security team
for forensic investigation.
4. Multi-Factor Authentication (MFA)
Function: MFA requires a user to provide two or more verification factors to gain access to a
resource, making it exponentially harder for an attacker to log in even if they steal a password.
Logistics Application Example: A logistics platform requires all employees and external
partners (like freight brokers) to use MFA to access the sensitive cloud-based customer
relationship management (CRM) and billing systems. An attacker manages to steal the login
credentials of a freight broker through a data leak. When the attacker tries to log in, the system
prompts for a one-time code sent to the broker's mobile phone. Since the attacker doesn't
have the physical phone, they are locked out, and the sensitive customer financial data
remains secure.
5. Next-Generation Firewalls (NGFW)
Function: NGFWs go beyond traditional firewalls by incorporating features like deep-packet
inspection, intrusion prevention, and application awareness to enforce security policies and
block sophisticated threats at the network perimeter.
Logistics Application Example: A third-party vendor's system tries to connect to a logistics
company's API gateway to update shipment statuses. The NGFW, configured with application
control policies, inspects the network traffic not just by port and protocol, but also by the
specific application. It detects that the vendor's data packet contains code that violates the
predefined API security rules (e.g., an unauthorized attempt to run an executable file). The
NGFW automatically drops the malicious traffic and blocks future connections from that
vendor's IP address, preventing an API-based attack or data injection.
AUTOMATION AND THE DUAL IMPACT ON EMPLOYMENT: DISPLACEMENT AND CREATION
The impact of AI-based automation on employment trends is a complex, dual-sided
phenomenon involving both the displacement of certain jobs and the creation of new ones.
This transformation is fundamentally reshaping the structure of the labor market.
Job Displacement in Routine Tasks

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AI excels at automating tasks that are repetitive, predictable, and data-intensive. This leads
to the displacement or significant reduction of human roles across various sectors.
• Vulnerable Occupations: Jobs involving a high degree of routine work are most at risk.
Examples include:
o Data Entry and Clerical Work: AI can quickly process and organize large
volumes of data.
o Customer Service Representatives: Chatbots and virtual assistants are
increasingly handling routine queries.
o Bookkeepers, Accountants, and Auditors: Automation is streamlining tasks
like data reconciliation and report generation.
o Manufacturing and Warehouse Labor: Robotics and automated systems
perform assembly and logistics tasks with greater speed and consistency.
• Focus on Tasks, Not Just Jobs: The shift often affects specific tasks within a job role
rather than eliminating the entire occupation. For instance, a paralegal's document
review might be automated, freeing them to focus on complex legal strategy.
The Rise of New Roles and Industries
Automation isn't just a subtractive process; it's a powerful engine for efficiency and
innovation that enables entirely new forms of work and creates unprecedented demand for
new skills.
• Emerging AI-Centric Roles: A new category of jobs is focused on developing,
managing, and maintaining AI systems. Examples include:
o AI and Machine Learning Engineers: Responsible for building and deploying AI
models.
o Data Scientists and Data Analysts: Needed to interpret the massive datasets
generated and used by AI.
o Prompt Engineers: Specialists who optimize the inputs given to generative AI
models to achieve desired outputs.
o AI Ethics and Governance Specialists: Crucial for ensuring AI systems are fair,
transparent, and aligned with human values.
• Augmented Human Capabilities: Many existing roles are being enhanced rather than
replaced. AI serves as a "force multiplier," handling mundane tasks and allowing
professionals to focus on higher-level activities that require creativity, critical thinking,

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emotional intelligence, and complex strategy. This includes roles like teachers, nurses,
and creative directors.
• New Economic Activity: AI-driven efficiency makes previously impossible or
prohibitively expensive projects viable, unlocking new business models and services
that require human oversight, customization, and implementation.
The Critical Role of Reskilling and Upskilling
The overall net effect on employment is often projected to be a job gain globally, but this
transformation will create significant churn and a growing skills mismatch. Therefore,
workforce adaptability is the most crucial factor in navigating the AI era.
• Upskilling: The process of acquiring new skills to improve performance and remain
relevant in a current role (e.g., a marketer learning to use AI tools for content
generation).
• Reskilling: The process of learning entirely new skills to transition to a new job or
career path, typically when an old role is at high risk of obsolescence.
• In-Demand Human Skills: As AI handles technical computation, skills unique to
humans—such as complex communication, collaboration, original creativity,
emotional reasoning, and systems-level problem-solving—become increasingly
valuable. Organizations that invest heavily in strategic reskilling programs will be better
positioned for growth and will help mitigate the social disruption caused by job
displacement.
AUTONOMOUS VEHICLES IN LOGISTICS
Autonomous vehicles (AVs) are vital to the future of logistics because they promise to deliver
unprecedented efficiency, safety, and cost reductions across the entire supply chain, from the
warehouse floor to the final delivery.
Importance of Autonomous Vehicles in Logistics
The integration of AVs—including self-driving trucks, drones, and robots—solves critical
challenges in the logistics sector:
1. Increased Efficiency and Throughput
• 24/7 Operations: Unlike human drivers who require mandated rest breaks, AVs can
operate continuously, significantly reducing transit times and increasing the overall
flow of goods.

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• Optimal Performance: AI-driven systems ensure consistent, optimal driving patterns
and speeds, leading to predictable scheduling and a higher volume of freight moved
per day.
2. Significant Cost Reduction
• Lower Labor Costs: AVs eliminate the largest operating cost in trucking: driver wages
and benefits.
• Fuel Efficiency: Sophisticated algorithms and technologies like truck platooning
(convoy driving) maintain steady speeds and minimize aerodynamic drag, substantially
reducing fuel consumption.
3. Enhanced Safety
• Reduced Human Error: Human error, fatigue, and distraction are the cause of over
90% of all vehicle accidents. AVs, with their constant vigilance and rapid reaction times,
have the potential to make long-haul transport far safer.
• Predictive Maintenance: Onboard sensors and AI can predict mechanical failures
before they happen, reducing costly breakdowns and downtime.
4. Addressing Labor Shortages
• The global logistics industry faces a chronic shortage of truck drivers. AVs provide a
scalable solution to maintain and increase capacity, ensuring the supply chain remains
resilient as demand grows.
5. Sustainability
• Optimized routing and improved fuel efficiency directly lead to a lower carbon
footprint for the transportation sector.
Examples of Autonomous Vehicles in Logistics
Autonomous technology is being deployed across all three main stages of the logistics journey:
Stage of Logistics Autonomous Vehicle
Type
Example of Application
Intralogistics
(Warehouse/Yard)
Autonomous Mobile
Robots (AMRs) and
Automated Guided
Vehicles (AGVs)
Inventory Management: Robots move
pallets, restock shelves, and sort packages
within large fulfillment centers, integrating
seamlessly with the warehouse
management system (WMS).

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Middle-Mile / Long-
Haul
Self-Driving Trucks
and Truck Platooning
Inter-Hub Transport: A logistics company
uses a self-driving truck (with a human
monitor) to haul goods autonomously along
a major highway corridor (e.g., between two
distribution centers) overnight, ensuring
faster, continuous delivery.
Last-Mile Delivery Delivery Drones and
Autonomous
Sidewalk Robots
Direct-to-Consumer: Small, electric
sidewalk robots deliver groceries or hot
food to customers within a specific
neighborhood or on a college campus,
bypassing road traffic congestion.

THE PRIMARY PURPOSE OF REGULATORY COMPLIANCE IN AI SYSTEMS
The primary purpose of regulatory compliance in AI systems is to ensure that the
development, deployment, and use of Artificial Intelligence is safe, fair, transparent, and
legally sound, thereby mitigating risks to individuals and society.
It essentially creates a set of guardrails to manage the powerful and sometimes unpredictable
nature of AI technology.
Key Goals of AI Regulatory Compliance
AI regulations, such as the EU's AI Act, are designed to achieve the following critical goals:
1. Protect Fundamental Rights and Safety
• Prevent Bias and Discrimination: AI systems can inherit and amplify biases present in
their training data, leading to unfair outcomes in critical areas like hiring, lending, or
criminal justice. Compliance requires systems to be tested and designed to ensure
fairness and non-discrimination.
• Ensure Safety and Robustness: Especially for high-risk applications (e.g., in healthcare
or autonomous vehicles), compliance mandates rigorous testing and risk management
to ensure the AI system operates reliably and doesn't cause harm.
2. Uphold Transparency and Accountability
• Explainability (XAI): Regulations require a degree of transparency so that users and
regulators can understand how an AI system reached a specific decision (the "black
box" problem). This is crucial for accountability.

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• Accountability: It establishes clear responsibility for the outcomes of an AI system. If
an AI makes a harmful or erroneous decision, the compliance framework clarifies who
(the developer, the deployer, etc.) is legally liable.
3. Safeguard Data Privacy and Security
• Data Protection: AI models require massive amounts of data. Compliance ensures that
all data used for training and deployment is collected and processed in adherence to
existing privacy laws (like GDPR or HIPAA).
• Cybersecurity: It ensures that AI systems are not vulnerable to cyber threats or
adversarial attacks that could be used to manipulate their decisions or steal sensitive
data.
4. Build Trust and Mitigate Risk
• Build Stakeholder Trust: Adherence to ethical and legal standards demonstrates an
organization's commitment to responsible AI, which is essential for building and
maintaining consumer and public trust.
• Prevent Legal and Financial Penalties: Non-compliance exposes organizations to
substantial fines, legal actions, and significant reputational damage. Compliance acts
as a necessary risk management strategy to avoid these outcomes.
In essence, AI compliance is the mechanism that translates broad ethical principles for AI into
concrete, auditable, and enforceable technical and organizational practices.
THE ROLE OF GOVERNMENT IN REGULATING “AI IN LOGISTICS”
The government's role in regulating AI in logistics is multifaceted, focused on balancing
innovation with the need to manage significant safety, economic, and ethical risks. Given that
logistics involves physical movement and critical infrastructure, the regulation is often more
specific and stringent than in other sectors.
Key Areas of Government Regulation
The government's role is to establish the necessary guardrails and standards for AI use across
the supply chain:
1. Safety and Infrastructure Standards (Physical AI)
• Autonomous Vehicles: This is the most critical area. Governments set and enforce
regulations for autonomous trucks, drones, and delivery robots to ensure public
safety. This includes:

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o Defining safety and performance requirements (e.g., collision avoidance, fail-
safe mechanisms).
o Establishing licensing and testing frameworks for autonomous systems to
operate on public roads or in shared airspace.
o Setting standards for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure
(V2I) communication.
• Critical Infrastructure: Regulating AI used for managing ports, traffic control, air
freight, and railways to ensure their robustness and resilience against cyberattacks or
system failures that could disrupt the entire economy.
2. Data Governance and Cybersecurity
• Data Privacy: Logistics AI relies on vast amounts of data, including personal data (e.g.,
driver routes, delivery times, customer addresses). Governments enforce privacy laws
(like GDPR) to ensure this data is collected, stored, and used lawfully and ethically.
• Cybersecurity: Mandating robust cybersecurity standards for AI systems that manage
logistics, supply chain finance, and inventory to prevent hacking, data breaches, and
malicious manipulation that could lead to physical harm or theft.
3. Economic and Social Impact (Algorithmic AI)
• Fairness and Anti-Discrimination: Regulating AI algorithms used in logistics for
decisions like hiring, scheduling, and labor management. This is to prevent bias
against protected groups or unfair practices like algorithmic wage setting that exploit
workers.
• Transparency and Explainability: Requiring a degree of transparency for AI systems,
especially those that make decisions impacting human employment (e.g., a system
deciding a driver's termination for inefficiency) or access to services.
The Dual Role of Government
The government doesn't just restrict; it also acts as a facilitator and user of AI in logistics:
Role Description
Regulator
(Guardrail
Setter)
Establishes the "rules of the road" to ensure AI is safe, ethical, and
compliant with existing laws (consumer protection, labor, environment).

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Enabler
(Innovation
Promoter)
Provides regulatory sandboxes or designated testing environments to
allow companies to safely develop and test new AI technologies (e.g.,
drone delivery routes) without immediate, full-scale regulatory burden.
Adopter (System
User)
Governments themselves use AI for public-facing logistics, such as
optimizing city traffic lights, managing public transport fleets, and
coordinating disaster relief supply chains. This sets a standard for
"trustworthy AI" practices.

THE ROLE OF DATA SECURITY IN AI-BASED LOGISTICS
The role of data security in AI-based logistics is paramount, moving beyond simple IT
protection to encompass the integrity, confidentiality, and availability of the vast and sensitive
data that powers the entire supply chain.
In essence, data security ensures the reliability and trust in the AI systems that manage critical
physical processes.
1. Protecting the "Brain" of the AI System (Integrity)
• Safeguarding AI Models: Security is vital for protecting the machine learning models
and their underlying training data from malicious manipulation, which is a unique AI-
specific threat.
o Preventing Data Poisoning: Maliciously injecting false or biased data into the
training set to corrupt the AI's logic, potentially causing it to misroute
shipments, improperly reject a vendor, or fail to detect a threat.
o Defending Against Adversarial Attacks: Using subtly altered inputs (e.g., a tiny
change to a barcode or a traffic sign image) to force an autonomous vehicle or
sorting robot to make an incorrect, and potentially catastrophic, decision.
2. Ensuring Operational Continuity and Safety (Availability)
• Preventing Supply Chain Disruption: A cyberattack can halt an entire logistics network.
Security measures (like encryption and access control) are critical to defend against
threats like ransomware, which encrypts essential operational data (e.g., shipping
schedules, inventory levels) and stops all movement.
• Maintaining Trust in Automation: For AI-driven processes like autonomous trucking
or drone delivery, data security ensures the systems are always available and

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trustworthy, preventing remote hijacking or manipulation that could lead to accidents
or cargo theft.
3. Compliance and Protecting Sensitive Information (Confidentiality)
• Adhering to Privacy Laws: Logistics AI handles massive amounts of sensitive data,
including customer addresses, employee records, financial transactions, and
competitive pricing models. Robust security ensures compliance with regulations like
GDPR or industry-specific standards, avoiding massive fines and legal liabilities.
• Preventing Industrial Espionage: Logistics data reveals proprietary business strategy,
such as optimal routes, specific customer volumes, financial terms, and predictive
forecasts. Security protects this competitive intelligence from being stolen by rivals or
bad actors.
THE APPLICATION OF AI ETHICAL PRINCIPLES TO A REAL-TIME DELIVERY SYSTEM
The application of AI ethical principles to a real-time delivery system (like those used for last-
mile logistics and the gig economy) is critical because the algorithms make automated, high-
stakes decisions that directly affect workers, customers, and communities.
The core ethical principles—Fairness, Transparency, and Accountability—are challenged by
the system's focus on maximizing efficiency.
1. Fairness and Non-Discrimination
In a real-time delivery system, fairness applies primarily to the delivery personnel and the
customers being served.
Principle
Applied
Description and Ethical Challenge Mitigation Strategies
Worker
Allocation
& Pay
The system's optimization goal (e.g.,
maximizing profit/speed) may lead
to algorithmic bias in task
assignment, shift scheduling, or
dynamic pricing for pay. For example,
favoring drivers with a higher
completion rate, which may be
unachievable for drivers operating in
high-traffic or lower-income areas,
Regular Audits of the algorithm to
ensure pay and task allocation do not
correlate with protected
characteristics (e.g., zip code, which
may proxy for race/socioeconomic
status). Base pay guarantees to
mitigate the financial precarity caused
by dynamic pricing.

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thus perpetuating economic
disadvantage.
Service
Equity
AI may inadvertently prioritize
service to densely populated or
wealthier areas where delivery is
more profitable or efficient, leading
to slower, more expensive, or
unavailable service for other
communities (a digital divide).
Inclusion constraints in the route
optimization model to ensure a
minimum service level or delivery
window for all geographic areas, even
if less profitable.
2. Transparency and Explainability (XAI)
Transparency addresses the "black box" nature of complex AI, especially concerning the gig
workers whose livelihoods depend on the system.
Principle
Applied
Description and Ethical Challenge Mitigation Strategies
Decision-
Making
Delivery personnel often lack visibility
into why they were penalized,
deactivated, or given fewer high-value
deliveries. The opaque nature of the
algorithm creates an extreme power
imbalance.
Explainable AI (XAI) interfaces that
provide clear, human-readable
explanations for critical decisions.
For example, "Your score was
reduced because you were 5
minutes late for 3 deliveries this
week."
Data
Usage
The AI collects vast amounts of real-
time personal data (location, speed,
dwell time, etc.). Workers and
customers need to know what data is
collected, how it affects their
rating/service, and for how long it is
retained.
Clear, accessible privacy policies
and mechanisms for workers to
access and contest the data used to
calculate their performance metrics.
3. Accountability and Safety
Accountability ensures that when an error occurs, a responsible party can be identified and
harm can be redressed. Safety covers physical security and well-being.

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Principle
Applied
Description and Ethical Challenge Mitigation Strategies
Liability
in Errors
If an AI-optimized route leads to a
traffic violation, a physical accident,
or a severe delivery delay that causes
significant loss, who is responsible?
The driver, the platform, or the
algorithm developer?
Establish clear lines of responsibility
for AI outcomes before deployment.
Implement human oversight and an
easy-to-use appeal and redress
mechanism for workers and customers
to challenge an AI's decision.
Worker
Well-
being
The AI's relentless pursuit of
efficiency (e.g., fastest route,
shortest time) can pressure workers
to drive unsafely, skip breaks, or
violate traffic laws to meet metrics,
risking their health and public safety.
Implement "safety breaks" and
"maximum work hours" as hard
constraints within the optimization
algorithm, overriding the efficiency
goal to ensure legal compliance and
driver well-being.

THE ROLES OF DRONES (UNMANNED AERIAL VEHICLES OR UAVS) AND AUTONOMOUS
VEHICLES (AVS) IN LOGISTICS
The roles of drones (Unmanned Aerial Vehicles or UAVs) and Autonomous Vehicles (AVs) in
logistics are complementary, but they are fundamentally distinguished by the type of journey,
cargo capacity, and environment in which they operate. They address different challenges
within the overall supply chain.
Here is a comparison of their primary roles and characteristics:
Comparison of Roles in Logistics
Feature Autonomous Vehicles (AVs) Drones (UAVs)
Primary Role in
Supply Chain
Middle-Mile & Long-Haul Freight
(Autonomous Trucks/Vans),
Warehouse Management (AGVs,
Forklifts), and Last-Mile in
Urban/Suburban Areas (Sidewalk
Robots).
Last-Mile & Specialty Delivery
in air. Internal Warehouse
Inventory Management (Stock-
taking).

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Typical Cargo Large, Heavy, and Bulk Goods. Pallets,
containers, large parcels (e.g., semi-
trucks, vans) or small parcels (e.g.,
sidewalk robots).
Small, Light, and Time-
Sensitive Items. Medical
supplies (blood, vaccines), food,
small e-commerce packages
(typically under 5-10 lbs/2-5
kg).
Operating
Environment
Existing Ground Infrastructure.
Highways, streets, sidewalks, and
dedicated warehouse floors. Limited
by traffic, road conditions, and
congestion.
Airspace (Uncongested Path).
Can fly directly, bypassing
ground obstacles, traffic, and
difficult terrain (e.g.,
mountains, flooded areas).
Distance &
Endurance
Long Distance. Autonomous trucks can
operate continuously, 24/7, for
thousands of miles (with
charging/refueling stops).
Short-to-Medium Distance.
Limited by battery life and
payload capacity; ideal for trips
of just a few miles (e.g., last-
mile from a local hub).
Detailed Roles and Use Cases
1. Autonomous Vehicles (AVs) in Logistics
AVs, which include self-driving trucks, delivery vans, and small sidewalk robots, are focused
on high-volume and continuous ground movement.
AV Type Primary Role Key Advantage
Autonomous
Trucks (Level
4/5)
Long-Haul/Middle-Mile: Moving
freight hub-to-hub on major
highways.
Operational Cost Reduction:
Eliminates human labor costs for
the longest, most monotonous
legs; enables continuous, 24/7
operation; improves fuel efficiency
via "platooning."
Autonomous
Vans/Small
Vehicles
Last-Mile: Delivery in
urban/suburban neighborhoods
(on roads).
Capacity & Reliability: Can carry
larger loads than drones and
operate with established ground-
based regulations.

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Automated
Guided Vehicles
(AGVs)
Intra-Logistics/Warehouse:
Moving goods within a factory or
distribution center (e.g.,
autonomous forklifts, pallet
trucks).
Safety & Efficiency: Reduces
human error and improves worker
safety by automating repetitive
material handling tasks.
2. Drones (UAVs) in Logistics
Drones are primarily focused on speed, accessibility, and bypassing obstacles in the final
stage of delivery or for critical missions.
Drone Type Primary Role Key Advantage
Last-Mile Delivery
Drones
Customer Delivery: Rapid, direct-to-
consumer delivery of small packages
from a local hub.
Speed & Congestion
Avoidance: Fastest
method for short-distance
delivery in congested
areas by using the air
path.
Specialty/Emergency
Drones
Humanitarian/Medical Logistics:
Delivering life-saving supplies
(blood, vaccines, AEDs) to remote,
rural, or disaster-stricken areas.
Accessibility: Can reach
areas with no or
destroyed road
infrastructure where
ground vehicles cannot
operate.
Warehouse/Inventory
Drones
Internal Operations: Flying through
large warehouses to scan inventory,
check stock levels, and monitor
infrastructure.
Accuracy & Worker
Safety: Automates the
high-risk, time-consuming
process of climbing to
check high-bay racks.
Conclusion: Synergy, Not Competition
The ultimate future of logistics will rely on the synergy between these two technologies in a
multimodal system:
• AV Trucks handle the long-haul transport of bulk cargo to regional distribution centers.
• AV Vans or Sidewalk Robots handle the dense urban last-mile delivery.

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• Drones launch from the distribution centers, or even from the top of an autonomous
truck ("mothership" concept), to perform the final, rapid aerial drop-off to the
customer's home, especially for time-critical or lightweight parcels.
The combined use of drones and AVs aims to create a fully autonomous, end-to-end supply
chain that drastically reduces costs, increases speed, and improves service reliability across all
distances and terrains.
"RESKILLING" IN LOGISTICS
"Reskilling" in logistics is the strategic process of training the existing workforce to acquire
entirely new competencies and skills to transition from manual, repetitive, and routine tasks
to new, technology-centric roles.
It is driven by the rapid adoption of automation, Artificial Intelligence (AI), and digital tools
(like autonomous vehicles, robotics, advanced analytics, and Warehouse Management
Systems) that are transforming traditional logistics jobs.
Key Aspects of Reskilling in Logistics:
1. Shift from Manual to Technical:
o Automation Displacement: Roles centered on manual labor (e.g., forklift
operation, routine package sorting, long-haul driving, manual inventory
counting) are being automated by robots, autonomous vehicles, and AI.
o New Roles Created: Displaced workers are being retrained for new jobs that
involve overseeing, maintaining, troubleshooting, programming, and
analyzing the new automated systems.
2. Focus on Digital and Analytical Skills:
o The primary new skills required are digital literacy and data competence.
o Workers need to be proficient in:
▪ Data Analytics: Interpreting real-time data from IoT sensors, optimizing
routes using AI software, and performing predictive analysis.
▪ Technology Management: Operating and maintaining complex
automated warehouse equipment (AGVs, robotic arms) and advanced
supply chain software (WMS, TMS).
▪ Cybersecurity: Understanding and protecting the highly connected,
digitized supply chain networks.
3. Emphasis on Human-Centric Skills:

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o As machines handle routine tasks, human value shifts to roles requiring
uniquely human capabilities.
o These include: critical thinking, complex problem-solving, change
management, leadership, negotiation, and cross-functional collaboration
across the supply chain.
4. Strategic Business Imperative:
o Reskilling is viewed by logistics companies as a cost-effective way to bridge the
growing skill gap and future-proof their workforce, often being more
affordable than continually hiring new, specialized talent.
o It also improves employee retention and engagement by showing a
commitment to their career development.
CONCEPT OF LOGISTICS AUTOMATION
Logistics automation is the application of technology and software to execute and optimize
logistics operations—such as warehousing, inventory management, transportation, and order
fulfillment—with minimal or no human intervention. Its core concept is to replace manual,
repetitive, and time-consuming tasks with automated systems to achieve greater efficiency,
accuracy, and cost savings across the entire supply chain.
Here is an outline of the concept of logistics automation:
I. Core Definition and Goal
• Definition: The use of hardware (robotics, machinery) and software (AI, advanced
systems) to streamline and improve the execution of logistics and supply chain
processes.
• Primary Goal: To optimize the flow of goods, information, and funds from point of
origin to point of consumption.
• Key Drivers: Rising labor costs, the explosive growth of e-commerce (requiring faster
delivery), the need for real-time visibility, and complex global supply chains.
II. Key Areas of Application
Logistics automation is applied across three main areas:
A. Warehouse & Fulfillment Automation
Focuses on the internal movements and storage of goods.
• Physical Automation (Hardware):

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o Robotics: Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots
(AMRs) for transporting materials.
o Automated Storage and Retrieval Systems (AS/RS): Computer-controlled
systems (cranes, shuttles) that automatically place and retrieve inventory in
high-density storage racks.
o Sorting & Conveyors: High-speed systems for directing packages to specific
loading docks or destinations.
o Robotic Picking: Robotic arms or collaborative robots (cobots) for high-volume
picking and packing.
• Digital Automation (Software):
o Warehouse Management Systems (WMS): Software for optimizing inventory
placement (slotting), workflow, and order picking paths.
o Goods-to-Person (G2P): Systems that bring the inventory item directly to the
human picker, eliminating travel time.
B. Transportation & Last-Mile Automation
Focuses on the movement of goods between locations.
• Transportation Management Systems (TMS): Software that automatically plans and
optimizes delivery routes (AI-powered route optimization), manages fleet, selects
carriers, and audits freight bills.
• Real-Time Tracking & Visibility: Using GPS, IoT sensors, and telematics to monitor
vehicle location, cargo condition, and estimated time of arrival (ETA).
• Autonomous Vehicles: Piloting self-driving trucks, delivery bots, and drones for long-
haul and last-mile delivery (currently in testing/early adoption).
C. Information & Process Automation
Focuses on the flow of data and administrative tasks.
• AI/Machine Learning: Used for predictive analytics, such as demand forecasting, to
optimize inventory levels and prevent stockouts or overstocking.
• Robotic Process Automation (RPA): Software robots that automate repetitive back-
office tasks like data entry, invoice processing, order validation, and generating
shipping notices.
• End-to-End Systems: Integration of ERP, WMS, and TMS to ensure seamless data flow
across finance, inventory, and operations.

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III. Major Benefits
Logistics automation delivers quantifiable improvements in three core business areas:
1. Efficiency and Speed: Faster order fulfillment, quicker processing times, and increased
throughput with 24/7 operational capability.
2. Accuracy and Quality: Significant reduction in human error (e.g., mispicks, incorrect
data entry), leading to higher order accuracy and fewer returns.
3. Cost Reduction: Lower operational expenses from reduced labor reliance, optimized
routes (fuel savings), better resource utilization (vehicle and warehouse space), and
minimized error costs.
4. Scalability: The ability to handle sudden peaks in demand (like holiday seasons or e-
commerce surges) without proportional increases in human resources.
IV. The Role of the Human Worker
Automation does not eliminate all jobs; it re-skills and re-purposes the workforce. Humans
shift from manual labor (picking, driving, sorting) to higher-value roles that complement
technology, focusing on:
• System maintenance and troubleshooting
• Data analysis and strategic decision-making
• Exception handling and problem-solving
• Management and oversight of the automated fleet and systems

THE FUNCTIONS OF DRONES IN SUPPORTING LOGISTICS
The functions of drones, or Unmanned Aerial Vehicles (UAVs), in logistics can be summarized
across two main operational categories: Internal/Warehouse Operations and
External/Transportation Operations.
I. Internal (Warehouse and Intralogistics) Functions
Drones significantly improve efficiency and accuracy within large logistics facilities:
• Inventory Management and Cycle Counting:
o Equipped with barcode/RFID scanners and cameras, drones can fly
autonomously through tall warehouse racks to quickly and accurately count
and locate inventory.
o This automates the highly manual and time-consuming process of cycle
counting, ensuring real-time stock visibility and reducing human error.

24. AI IN INTERNATIONAL LOGISTICS UNIT - IV

• Inspections and Surveillance:
o They are used to inspect high-up areas, roofing, or hard-to-reach infrastructure
for maintenance issues, damage, or safety concerns without requiring
personnel to work at dangerous heights.
o Security drones can patrol large outdoor storage areas or facility perimeters,
often using thermal imaging, to monitor for theft or unauthorized access 24/7.
• Material Transfer (Intralogistics):
o Drones can transport small, urgent spare parts, tools, or documents between
different areas of a large factory or warehouse campus faster than ground
transport.
II. External (Transportation and Delivery) Functions
Drones are primarily focused on revolutionizing the delivery aspect of logistics:
• Last-Mile Delivery:
o This is the most publicized use, involving the rapid delivery of small, lightweight
packages directly to the customer's location.
o Benefit: They bypass road traffic, allowing for a shorter, more direct delivery
route, which drastically cuts transit time and lowers fuel consumption (being
battery-powered).
• Delivery to Remote/Inaccessible Areas:
o Drones excel at reaching locations with challenging geography (mountains,
islands, poor road infrastructure) or areas affected by natural disasters.
o They are crucial in humanitarian and medical logistics for urgently delivering
blood, vaccines, and essential medical supplies to remote clinics.
• Route Scouting and Monitoring:
o Drones can be used to survey delivery routes for congestion, road damage, or
blockages before a truck is dispatched, aiding in dynamic route optimization.
• Inter-Facility Transport:
o Used for quick transfers of packages, samples, or documents between two
fixed facilities (e.g., a distribution center and a smaller hub).
In essence, drones transform logistics by providing a fast, highly accurate, and often more cost-
effective "air corridor" solution, complementing traditional ground-based methods.

25. AI IN INTERNATIONAL LOGISTICS UNIT - IV

THE IMPORTANCE OF RESKILLING IN THE AGE OF AI
The rise of Artificial Intelligence (AI) is fundamentally reshaping the world of work, making
reskilling not just beneficial, but an absolute necessity for career stability and economic
growth.
The Reskilling Imperative in the Age of AI
1. The Automation-Driven Obsolescence of Routine Tasks
AI is designed to efficiently automate tasks that are repetitive, rule-based, and data-intensive.
This is leading to the obsolescence of entire components of many jobs, particularly in fields
like data entry, basic customer service, and routine analysis.
• The Illustration: A data entry clerk's primary role is largely taken over by an AI system
that processes invoices and records with near-perfect accuracy and speed. Reskilling
is crucial for that clerk to transition into a new, higher-value role, such as a "process
manager" who audits the AI's output, handles exceptions, and designs more efficient
workflows. Without reskilling, that individual's job security is severely compromised.
2. Shifting from Task Doer to AI Collaborator
AI isn't just taking jobs; it's transforming them. The new workforce will increasingly need to
be fluent in working with AI as a powerful tool, not just competing against it.
• The Illustration: A graphic designer is initially worried about generative AI creating
images instantly. Their company, however, invests in upskilling (a component of
reskilling) them in prompt engineering and AI art direction. The designer's new role is
elevated: they use the AI to rapidly generate dozens of concepts, freeing them up to
spend their time on higher-level creative strategy, client-facing consultation, and
refining the final product with unique human creativity. Reskilling turns a potential
threat into a productivity multiplier.
3. Emphasizing Uniquely Human Skills
As AI takes on technical, repetitive, and analytical tasks, the demand for skills that are difficult,
if not impossible, for a machine to replicate skyrockets. Reskilling efforts must focus on these
"power skills."
• The Illustration: In healthcare, an AI can process lab results and suggest a preliminary
diagnosis faster than a human. The role of the human nurse or doctor shifts to
demanding more critical thinking, ethical judgment (e.g., deciding when to override
an AI recommendation), and, most importantly, empathy and complex

26. AI IN INTERNATIONAL LOGISTICS UNIT - IV

communication when dealing with anxious patients and their families. Reskilling in
these "human" soft skills ensures that professionals remain the essential core of
service delivery.
4. Creating and Securing New Job Opportunities
The adoption of AI creates entirely new roles that require new expertise. Reskilling programs
are the bridge that connects workers to these emerging opportunities.
• The Illustration: The demand for new roles like AI Ethicists, Machine Learning
Engineers, Data Governance Specialists, and AI System Maintenance Technicians is
rapidly growing. A former IT specialist who was reskilled in cloud computing and basic
machine learning principles can step into a newly created role managing the
integration and deployment of AI models—a job that simply didn't exist in a
meaningful way a decade ago. Reskilling is the engine of career mobility in the new
economy.
THE LAWS GOVERNING AI USING IN INTERNATIONAL SHIPPING OPERATIONS
The legal framework for governing AI use in international shipping operations is currently in a
state of flux, characterized by the adaptation of old laws and the creation of new, technology-
specific guidelines. The core challenge is that virtually all existing maritime law is predicated
on the presence of a human Master and crew.
Analysis of Laws Governing AI in International Shipping
1. International Maritime Organization (IMO) Initiatives (The Global Standard Setter)
The IMO, the UN specialized agency responsible for the safety and security of shipping and
the prevention of marine and atmospheric pollution by ships, is the key global body.
• Regulatory Scoping Exercise (RSE): The IMO has completed an RSE on Maritime
Autonomous Surface Ships (MASS) to identify which parts of major treaties—like the
International Convention for the Safety of Life at Sea (SOLAS), the Convention on the
International Regulations for Preventing Collisions at Sea (COLREGs), and the
International Convention on Standards of Training, Certification and Watchkeeping for
Seafarers (STCW)—are currently barriers to, or require amendment for, the use of AI
and autonomous vessels.
• The MASS Code: The IMO is developing a non-mandatory, goal-based MASS Code,
which is intended to become mandatory by around 2028. This code will provide the

27. AI IN INTERNATIONAL LOGISTICS UNIT - IV

first comprehensive international legal framework for autonomous vessels, classifying
them by degrees of autonomy (from automated processes to fully autonomous ships).
• Interim Guidelines: While the full Code is developed, IMO has adopted Interim
Guidelines for MASS Trials to allow member states to test autonomous ships safely,
requiring at least the same degree of safety and environmental protection as
conventional ships, and mandating appropriate cyber risk management.
2. Core Legal Challenges and Conflicting Conventions
The fundamental conflict lies with established conventions:
Convention Traditional Requirement AI/Autonomous Conflict
SOLAS Requires certain safety equipment,
emergency controls, and minimum
manning.
AI systems replace watchkeeping
crew; the requirement for a human
Master is challenged.
COLREGs Mandates maintaining a "proper
lookout by sight and hearing as well
as by all available means" and
requires human judgment for
collision avoidance.
The AI must be proven to interpret
and act on these rules with an
equivalent level of judgment. The
concept of "in sight" is redefined by
sensors.
STCW Specifies the qualifications, training,
and certification for the human
Master and crew.
AI requires certifications for remote
operators (shore-based) and the AI
software itself, a concept not covered
in the original convention.
UNCLOS Requires a "Master" to be in charge
of the ship and imposes a duty on
the flag State to ensure proper
manning.
Who is the "Master" of a fully
autonomous vessel? Is it the remote
operator, a corporation, or the AI?
3. Emerging Focus Areas for AI-Specific Law
• Liability: Traditional liability (shipowner, Master, crew negligence) shifts to product
liability (manufacturer, software developer) in case of an accident due to an AI failure
or flaw.

28. AI IN INTERNATIONAL LOGISTICS UNIT - IV

• Cybersecurity: Given the heavy reliance on digital systems and satellite
communication, robust cybersecurity protocols are legally required to prevent hacking
or disruption of the AI control system.
• Data Governance: Rules are needed to govern the collection, storage, and use of the
massive amounts of data generated by AI navigation and performance systems.
Illustrative Example: Collision Avoidance by an Autonomous Vessel
Scenario
A fully autonomous container ship, the "AI Navigator," is traveling through a busy strait in
international waters. The ship's AI navigation system detects a small, manually operated
fishing vessel that is not displaying proper lights and is moving erratically.
The AI system processes the data using its programming, which is based on COLREGs and an
optimization algorithm for fuel efficiency.
Legal Challenge Illustrated
1. COLREGs Compliance (Rule 7 & 8):
o The Rule: COLREGs require avoiding a collision and taking action "in ample
time."
o AI's Action: The AI calculates the minimal course correction required to
maintain speed and save fuel, narrowly missing the fishing vessel.
o The Legal Conflict: A human Master might have chosen a much larger, more
obvious course change ("action taken should be large enough to be readily
apparent"). If the fishing vessel suddenly changes course and a collision occurs,
the AI's "optimal" but minimal maneuver could be deemed negligent because
it lacked the human margin of safety and caution that current laws implicitly
expect.
2. Liability and Accountability (UNCLOS & Traditional Law):
o The Incident: A system bug in the AI causes it to misclassify the erratic fishing
vessel as a fixed navigational buoy, and it fails to take any evasive action,
leading to a minor collision.
o The Question: Who is liable?
▪ Current Law: The shipowner/operator is traditionally liable.
▪ AI Law (Future Focus): The primary fault lies with the AI software
developer or manufacturer for a latent defect in the code. A future

29. AI IN INTERNATIONAL LOGISTICS UNIT - IV

MASS Code would have to determine if the owner was negligent in
deploying a known-faulty system, or if liability rests squarely on the
product liability of the AI vendor. The absence of a human Master to
override the decision removes the traditional "negligence of the crew"
element.
This example highlights that current international maritime law is ill-equipped to handle
algorithmic failure and product-based liability, necessitating the comprehensive reform
being undertaken by the IMO through the new MASS Code.
WORKFORCE RESKILLING STRATEGIES
Workforce reskilling strategies are crucial for logistics companies, acting as a direct facilitator
of their adaptation, efficiency, and future competitiveness, especially in the face of rapid
digital transformation and automation (Industry 4.0).
Reskilling provides a pathway for logistics companies to address the emerging skills gap by
preparing their current employees for new, technology-driven roles, rather than relying solely
on external hiring.
Key Ways Reskilling Facilitates Logistics Companies
Reskilling strategies facilitate logistics companies across several critical dimensions:
1. Adapting to Automation and Technology
• Transitioning Roles: It prepares employees in roles being automated (e.g., manual
sorting, basic inventory tracking) for new positions that involve managing and
maintaining the automation technology.
• Developing Tech-Specific Skills: Workers are reskilled for roles like robotics and
mechatronics technician, warehouse systems manager, data analyst, and IT support
specialist to handle automated guided vehicles (AGVs), conveyor systems, and
complex warehouse management systems (WMS).
• Enhancing Data Literacy: Employees are trained in data analytics and interpretation
to leverage the massive amounts of data generated by connected supply chains (IoT,
GPS, sensors) for better decision-making, route optimization, and forecasting.
2. Improving Operational Efficiency and Agility
• Streamlining Processes: A reskilled workforce can implement and manage new, more
efficient, digitized processes, reducing errors and cycle times.

30. AI IN INTERNATIONAL LOGISTICS UNIT - IV

• Cross-Functional Expertise: Reskilling can provide employees with a broader
understanding of the entire supply chain, breaking down departmental silos and
allowing them to handle a wider range of responsibilities, which improves the
company's adaptability to market changes or disruptions.
• Focusing on Value-Add Tasks: As automation takes over repetitive tasks, reskilled
workers shift their focus to higher-value activities like risk management, sustainability
planning, customer relationship management (CRM), and complex problem-solving.
3. Enhancing Talent Management and Retention
• Bridging the Skills Gap: Reskilling is generally more cost-effective and faster than
recruiting and onboarding new specialized talent in a competitive labor market.
• Boosting Employee Retention and Morale: Investing in an employee's professional
development demonstrates a commitment to their career growth, leading to higher
job satisfaction, engagement, and loyalty, thereby reducing expensive turnover.
• Cultivating a Learning Culture: By making reskilling a strategic priority, the company
fosters an environment of continuous learning and growth, which is essential for
sustained innovation and competitiveness.
4. Critical Skills for Reskilling in Logistics
Reskilling programs in logistics focus on developing a blend of technical and human skills:
Category Examples of Skills New Roles Supported
Technical/Digital Data Analytics, AI/ML concepts,
IoT/Sensor Management, Cybersecurity,
Warehouse Systems Management
(WMS), Autonomous Vehicle
Maintenance, Cloud Computing
Supply Chain Analyst,
Automation Technician,
Digital Logistics Specialist,
Cybersecurity Analyst
Human/Soft
Skills
Critical Thinking, Complex Problem
Solving, Leadership, Emotional
Intelligence, Cross -functional
Collaboration, Adaptability
Logistics Coordinator, Risk
Manager, Team Leader,
Customer Service Specialist
Export to Sheets

31. AI IN INTERNATIONAL LOGISTICS UNIT - IV

REFERENCES
• Upskilling and reskilling requirement in logistics and supply chain industry for the
fourth industrial revolution - A journal paper discussing the impact of IR 4.0 and the
resulting skill requirements in logistics.
o URL:
https://www.researchgate.net/publication/353261168_Upskilling_and_resk
illing_requirement_in_logistics_and_supply_chain_industry_for_the_fourth
_industrial_revolution
• Workforce Development and Sustainability in Logistics: The Role of HR - An abstract
and discussion on HR's role in fostering talent development for sustainability and
adapting to new technologies in logistics.
o URL:
https://www.researchgate.net/publication/391643437_Workforce_Develop
ment_and_Sustainability_in_Logistics_The_Role_of_HR
• Reskilling and Upskilling the Future-ready Workforce for Industry 4.0 and Beyond - A
paper with a blueprint for reskilling and upskilling, noting the large percentage of
workers globally who will need new skills due to technology adoption.
o URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC9278314/
• The Impact of Automation on Employment: Just the Usual Structural Change? - A
study discussing how job loss from automation is often counterbalanced by job
creation in new sectors and the importance of a skilled workforce.
o URL: https://www.mdpi.com/2071-1050/10/5/1661
• Beyond hiring: How companies are reskilling to address talent gaps (McKinsey &
Company) - A survey and analysis on skill gaps and the tactics companies, including
those in logistics-related fields, use to close them through reskilling.
o URL: https://www.mckinsey.com/capabilities/people-and-organizational-
performance/our-insights/beyond-hiring-how-companies-are-reskilling-to-
address-talent-gaps
• What Supply Chain Leaders Need to Know About Upskilling and Reskilling (ASCM -
Association for Supply Chain Management) - An article highlighting the benefits of
reskilling for cost savings, employee retention, and future-proofing the supply chain
workforce.

32. AI IN INTERNATIONAL LOGISTICS UNIT - IV

o URL: https://www.ascm.org/ascm-insights/what-supply-chain-leaders-
need-to-know-about-upskilling-and-reskilling/
• Reskilling of Workforce for the Supply Chain of Tomorrow: The Human Element
(Medium) - Discusses essential future skills for the supply chain workforce (e.g., data
analytics, technology management) and strategies for reskilling.
o URL: https://medium.com/@dixitjigar/the-human-element-reskilling-your-
workforce-for-the-supply-chain-of-tomorrow-482abcb287ef
• Understanding the impact of automation on workers, jobs, and wages (Brookings
Institution) - An examination of automation's impact, the types of jobs created and
displaced, and the necessity of improved education and training in sectors like retail
logistics.
o URL: https://www.brookings.edu/articles/understanding-the-impact-of-
automation-on-workers-jobs-and-wages/
• Reskilling and Upskilling: Differences, Importance, and HR's Role (AIHR) - Defines and
differentiates reskilling and upskilling, and outlines the benefits for business agility and
talent retention.
o URL: https://www.aihr.com/blog/reskilling-and-upskilling/
• Close the workforce skills gap with upskilling and reskilling (SAP) - Details the steps
for strategic workforce planning using upskilling and reskilling to navigate changing
technology and maximize internal talent.
o URL: https://www.sap.com/hk/resources/upskilling-and-reskilling-the-
workforce
• Reskilling and Upskilling | Meanings and Main Differences (Iberdrola) - Discusses how
companies use reskilling and upskilling to address the digital divide and the need for
both hard and soft skills in the digital transformation era.
o URL: https://www.iberdrola.com/talent/reskilling-upskilling