It was not long ago that automation technologies such as RPA were claimed to be the last technological leap for supply chain businesses. 

With automation and the power of data analytics already in hand, what else could supply chain management benefit from? How about a technology that could combine both automation and analytics and improve existing business processes? Enter generative AI.

The integration of generative AI into supply chain management marks a new era in operational efficiency and strategic foresight. Generative AI for supply chains harnesses advanced algorithms to predict business outcomes and offer solutions to logistical challenges.

This innovative approach is reshaping how companies manage inventories, optimize routes, and mitigate risks.

One of the most compelling illustrations of generative AI’s impact on supply chains is IBM’s recent transformation in their supply chains. IBM created a cognitive supply chain that was resilient even amidst the upheaval caused by the COVID-19 pandemic.

By leveraging AI, cloud computing, and IoT, IBM achieved a 100% order fulfillment rate during the pandemic’s peak. It was accompanied by a significant reduction in supply chain costs – an incredibly high-return scenario.

Similar to IBM’s case, AI use cases in supply chain management are rapidly expanding as companies seek innovative ways to streamline operations and enhance efficiency. But how can your business adopt this technology and benefit from it? Let’s explore the top generative AI for supply chain use cases and how they can add value to your business.


Table of Contents

  1. What is Generative AI for Supply Chain?
  2. How Generative AI can Transform Supply Chain Operations in 2023
  3. Top 7 Use Cases of Generative AI for Supply Chain Management
  4. Choosing the Right Partner for Generative AI Implementation
  5. Kanerika – Pioneering the Future of Supply Chain with Generative AI
  6. FAQs


What is Generative AI for Supply Chain?

Generative AI for supply chain refers to the application of advanced artificial intelligence systems within the supply chain management industry.

Unlike traditional AI, which analyzes input to produce a predetermined output, generative AI can create novel patterns and trends within data. It can anticipate unforeseen scenarios and propose solutions that haven’t been explicitly programmed.

Moreover, generative AI can create new and original content, predictions, and data-driven strategies.

For example, in the supply chain sector, generative AI can simulate complex logistics networks to predict the outcomes of various strategies under different conditions.

Generative AI for supply chains can generate demand forecasts, optimize routing, and automate inventory management. But how can it transform the supply chain industry as we know it today? Let’s understand the scope of change through some statistics and examples.


How Generative AI can Transform Supply Chain Operations in 2023

How Generative AI can Transform Supply Chain Operations in 2023

Generative AI has the potential to transform supply chain operations by introducing levels of efficiency that traditional operations simply cannot match. 

According to a survey conducted by KPMG in March 2023, 77% of respondents agreed that the use of generative AI logistics in the supply chain is highly impactful.

Generative AI in supply chain planning also empowers businesses to anticipate outages. It has enabled early adopters to improve logistics costs by 15%, and inventory levels by 35%. Service levels also went up by 65%, compared with slower-moving competitors.


Here’s a breakdown of how generative AI-powered supply chains differ from traditional ones:


Traditional Supply Chain OperationsGenerative AI-Powered Supply Chain Operations
Reactivity – Traditional supply chains react to changes in demand, supply disruptions, or logistical challenges.Proactivity – Generative AI, on the other hand, can predict these changes before they happen. Allowing companies to be proactive rather than reactive is a huge shift.
Static Planning – Traditional supply chain operations rely on static planning models that do not change unless manually updated.Dynamic Optimization – Generative AI Logistics introduces dynamic optimization. It continuously learns and adapts to new data, optimizing operations in real-time.
Manual Analysis – Previously, data analysis was often manual, time-consuming, and prone to human error.Automated Insight Generation –  Generative AI for supply chain automates insight generation, processing vast amounts of data more quickly and accurately.
Generic Solutions – Traditional supply chains often implement one-size-fits-all solutions that are sub-optimal. Customized Strategies – Generative AI for supply chain can customize strategies tailored to the unique challenges and opportunities of each supply chain.


It is clear that generative AI for supply chain is creating disruptions in the market that are shifting more businesses towards enforcing genAI in their supply chain operations. 

But what are some of the key use cases of this technology? Let’s explore the top 7 use cases of generative AI solutions in supply chain management.


Top 7 Use Cases of Generative AI for Supply Chain Management


Top 7 Use Cases of Generative AI for Supply Chain Management


Use Case 1 – Inventory Optimization

Generative AI inventory management systems employ advanced algorithms to optimize stock levels, ensuring businesses maintain the right amount of inventory.

These systems can analyze historical sales data and consider factors like lead times, seasonality, and market trends. Generative AI inventory management solutions can create probabilistic models that simulate different demand scenarios based on this data. This helps improve accuracy in demand forecasting and inventory management.

By analyzing a variety of factors, such as buying patterns, seasonal trends, and sales promotions, the AI system can predict what to order and when.

This approach ensures that the right products are available at the right time, reducing the risk of stock outs.


Also read - 5 Benefits of Data Analytics In Logistics And Supply Chain Management

Use Case 2 – Predictive Maintenance

Generative AI logistics models aid in predictive maintenance by identifying anomalies and patterns that signal equipment failure. These models analyze factors, such as vibration, temperature, and sound from machinery, to detect irregularities.

Furthermore, by preventing unscheduled halts, the system maintains a steady production flow. The business can meet delivery deadlines and keep customer satisfaction intact.

One industry example of this is the District of Columbia Water and Sewer Authority’s utilization of predictive maintenance. It pinpoints possible water main and sewer pipe failures. They employ an artificial intelligence solution known as Pipe Sleuth. The system can analyze video footage of sewer pipes and promptly evaluate their maintenance requirements.

This approach not only optimizes maintenance operations but also extends the lifespan of costly equipment. The use of generative AI logistics in the supply chain was instrumental in achieving greater cost-efficiency and precision for the organization.

Take your Business to the Next Level (1)


Use Case 3 – Fraud Detection


Also read - 5 Benefits of Data Analytics In Logistics And Supply Chain Management


Through the use of Generative Adversarial Networks (GANs), generative AI is becoming a powerful tool in the fight against fraud.

GANs consist of two neural networks, the generator and the discriminator, which work against each other. This is achieved in the following manner: 

The generator generates data, such as images, while the discriminator evaluates whether the generated data is real. Through this adversarial process, GANs can create highly realistic data simulations.

This adversarial process makes GANs excellent for identifying anomalies or patterns that could indicate fraudulent activity. GAN powered generative AI for supply chain solutions is therefore highly sought after by businesses that deal with large transactions.

A leading company using GANs for fraud detection is Mastercard. They have developed a system called Decision Intelligence, which uses AI, including techniques like GANs, to analyze every transaction in real time.


Also read - Generative AI’s Impact on Agile Supply Chains

Read More – Why Businesses Should Embrace Generative AI Automation

Use Case 4 – Supply Chain Risk Management

The use of generative AI in supply chain planning significantly enhances supply chain risk management by analyzing historical data and external factors to pinpoint vulnerabilities.

By processing information, AI models can identify risk patterns and predict potential disruptions. It can even take into account supplier histories, weather patterns, and market trends. This predictive power allows companies to develop robust contingency plans.

For instance, if the generative AI in supply chain planning model detects a high risk, it can simulate the impact on supply routes. Further, it can suggest alternative sourcing or inventory relocations to mitigate delays.

Generative AI for supply chains can also help in crafting strategic action plans by generating ‘what-if’ scenarios. This results in a more resilient supply chain, where companies can minimize disruptions and maintain operational continuity.

Read More – Generative AI Consulting: Driving Business Growth With AI

Use Case 5 – Transportation Optimization

Greater transportation efficiency is possible through generative AI for supply chains. By analyzing real-time data to optimize delivery routes, AI ensures fast and cost-effective paths.

For instance, a logistics company can use generative AI to manage its fleet of delivery trucks. The AI system continuously takes in data from various sources, including GPS traffic updates, weather forecasts, and current locations. 

By processing this information, generative AI for supply chains can generate and adjust routes in real time. Generative AI logistics solutions are revolutionizing the transportation and delivery aspects of the supply chain.

A real-world example of this is UPS’s (United Parcel Service) On-Road Integrated Optimization and Navigation (ORION) system. 

ORION uses advanced algorithms to analyze delivery routes and has reportedly saved UPS millions of gallons of fuel and reduced delivery miles by several million each year. The system processes the delivery information for the day and optimizes each driver’s route.


Take your Business to the Next Level

Read More – Generative AI for Healthcare: Benefits and Use Cases

Use Case 6 – Demand Forecasting

Generative AI is drastically improving demand forecasting in supply chain management. By utilizing advanced algorithms, generative AI logistics models analyze vast amounts of historical sales data, market trends, and other relevant variables to create highly accurate, real-time demand models. 

These AI-driven forecasts are essential for determining optimal inventory levels, production schedules, and distribution plans, enabling businesses to meet customer demands more efficiently.

A recent industry example of generative AI being used for demand forecasting would be the case study of Domino’s Pizza UK & Ireland. They shifted from traditional spreadsheets to AI and analytics for demand prediction using Dynamics 365. This move aimed to enhance the quality of demand forecasting, improve the customer experience, and ensure timely product delivery.

Post implementation, the company has seen improvements in forecasting quality and continues to use a blend of AI and analytics technologies for their demand forecasting.

Read More – Decoding Generative AI vs LLM: A Comparative Analysis

Use Case 7 – Warehouse Layout Optimization

Generative AI for supply chains can enhance warehouse layout optimization by analyzing several factors that affect operational efficiency.

In a traditional warehouse setting, the layout is often static and designed based on historical data and infrequent assessments.

Generative AI can dynamically adjust the layout, arranging parts and products by size or alphabetically. The AI models can simulate various configurations and their impact on travel times within the warehouse.

For instance, generative AI for supply chains can identify the most frequently accessed items and suggest placing them closer to packing stations.

Real-life applications of this technology can be seen in Amazon’s fulfillment centers. The company has been a pioneer in adopting AI and robotics to optimize warehouse operations.

Read More – Five Generative AI Trends to Watch Out for in 2024!

Also read - Supply Chain Insights_ How Manufacturing Analytics Improves Logistics


Choosing the Right Partner for Generative AI Implementation

When incorporating generative AI into sectors like supply chain management, it’s crucial for businesses to select a partner that understands their unique needs and industry-specific challenges. The right AI consulting partner offers:

  1. Proven Strategies: Leveraging their experience, they can streamline AI integration and mitigate risks.
  2. Customized Tools: They provide tailored tools for every stage, from data analysis to AI monitoring.
  3. Industry Expertise: With deep knowledge in both generative AI and your sector, they can develop solutions addressing your specific issues.
  4. Support for Transition: A good partner facilitates smooth adoption, offering training and change management assistance to ensure your team adapts well to the new AI technologies.

With an adept partner on board, supply chain businesses can maximize their operational output with well-designed AI systems that automate numerous activities. Here is a quick overview of Kanerika’s achievements in the industry with AI implementations.


Case Study 1 – Predictive Maintenance for Fleet Managers 

Case Study - Predictive Maintenance for Fleet Managers_

Challenges Encountered by Client:

  • Assimilating real-time data from diverse vehicle brands and models was compromising the efficiency of fleet operations.
  • Scheduling conflicts and recurrent maintenance dilemmas were causing excessive vehicle downtime.
  • The absence of preemptive maintenance strategies was a significant barrier.

Strategic Solutions Implemented by Kanerika:

  • An artificial intelligence (AI) algorithm was developed to scrutinize sensor outputs, and driving patterns.
  • A mobile application was deployed to deliver instant maintenance alerts and monitor driver conduct.
  • Decision-making processes regarding vehicle maintenance schedules and potential replacements were refined.

Technology Used:

  • AWS | Postman  Tableau | TensorFlow

Success Metrics Achieved after Implementation:

  • Maintenance expenses were curtailed by 16%, attributed to the AI system’s capacity to foresee and avert potential issues.
  • The overall performance of the fleet saw a 20% enhancement, bolstered by the AI-powered insights that enabled more effective vehicle utilization.
  • There was a notable 26% decline in vehicular accidents as the system’s proactive maintenance notifications promoted safer driving practices.


Read More – Best Generative AI Tools For Businesses in 2024

Case Study 2 – Synthetic Data Generator for Telemetry Analytical Applications


Case Study - Synthetic Data Generator


Challenges Encountered by Client:

  • Difficulty in handling millions of data combinations for validation delayed platform development.
  • Manual validation methods were time-consuming and prone to errors.
  • Existing systems struggled with the large scale of data, affecting performance.

Strategic Solutions Implemented by Kanerika:

  • An AI-driven telemetry synthesizer was developed for extensive scenario testing, ensuring robust platform quality.
  • Platform tracking and verification were automated, enhancing reliability and user satisfaction.
  • Cloud solutions and Kubernetes were implemented for efficient, large-scale data management, supporting growth.

Technology Used:

Kubernetes | AWS | TensorFlow

Success Metrics Achieved after Implementation:

  • Customer satisfaction increased by 32% due to improved platform performance.
  • Infrastructure issues were cut by 45%, leading to more stable operations.
  • Testing time was reduced by 74%, accelerating the development cycle.


Kanerika – Pioneering the Future of Supply Chain with Generative AI

With over two decades of expertise in data management and AI/ML, Kanerika delivers robust, ethically responsible, and regulation-compliant end-to-end solutions. 

Kanerika’s team of 100+ experts in cloud technology, BI, AI/ML, and generative AI has successfully integrated AI-driven solutions across diverse supply chain sectors, maximizing AI’s potential for efficiency and innovation.

Partner with Kanerika and leverage the latest generative AI for supply chain advancements for your business.





How can generative AI be used in supply chain?

Generative AI can be used in supply chain management for various tasks like demand forecasting, inventory optimization, risk management, predictive maintenance, transportation optimization, supplier performance monitoring, and warehouse layout optimization.

What is generative AI in procurement and supply chain?

In procurement and supply chain, generative AI refers to the use of advanced AI technologies to generate and simulate multiple scenarios, aiding in decision-making, optimizing procurement processes, and enhancing supply chain efficiency.

How to use generative AI in logistics?

In logistics, generative AI for supply chain can be utilized for optimizing delivery routes, forecasting demand, generative AI inventory management, and predictive maintenance of logistics infrastructure, ensuring efficiency and reducing costs.

How does generative AI affect supply chain management?

Generative AI transforms supply chain management by introducing proactive approaches, dynamic optimization, and automated insight generation. It allows for more customized and efficient strategies, enhancing overall supply chain resilience and performance.

Can generative AI be used for forecasting?

Yes, generative AI is highly effective for forecasting in supply chains. It analyzes large datasets to create accurate demand models, considering factors like market trends, seasonality, and consumer behavior, leading to improved decision-making and generative AI inventory management.