Beyond the buzzword, discover practical applications of Gen AI in supply chain and logistics.
Beyond the buzzword, discover practical applications of Gen AI in supply chain and logistics.
While traditional AI excels at automation, optimization, and basic forecasting based on historical precedents, generative AI goes beyond. It can uncover hidden patterns, predict disruptions, and even design innovative solutions. By embracing generative AI in the supply chain, leaders can unlock new possibilities, making their operations a competitive advantage, mitigating risks, bringing products to market faster, and personalizing the customer experience. This blog will help supply chain leaders understand how generative AI can augment traditional AI capabilities and move beyond automation into the future of intelligent and adaptive supply chains.
Traditional AI vs Generative AI – Supply Chain perspective
In a constantly shifting supply chain landscape, markets fluctuate, consumer demands evolve, and global events can throw even the best-laid plans into disarray. In this volatile environment, success depends on more than strict adherence to a set of rules—it requires flexibility and creativity to adapt quickly. Let’s start by understanding the difference between traditional AI and generative AI.
Traditional AI systems have been in existence and evolving for more than 50 years and are typically rule-based or use machine learning techniques like decision trees, neural networks, etc.
One example of how traditional AI systems are widely used in supply chain management is in demand forecasting. These systems analyze historical sales data, factoring in things like seasonality and promotions, to predict future demand for products. This allows businesses to optimize their inventory levels, ensuring they have enough stock to meet customer needs without incurring unnecessary inventory carrying costs.
However, these systems are limited to narrow, well-defined tasks. The outputs of traditional AI systems are typically deterministic, meaning they will always produce the same result given the same inputs. This leaves limited room for originality and adaptability in dynamic supply chain environments.
On the other hand, Generative AI models are based on large neural networks that are trained on vast amounts of data to generate novel content, such as text, images, audio, or code. In the context of supply chain management, these models can generate open-ended, creative solutions to complex problems. For example, they can generate multiple demand forecasting models based on different combinations of factors, like historical sales data, current trends, and even social media buzz. Also, the evolution of generative AI in its ability to process text, audio, and video as a multi-modal interface enables it to offer a human-like interaction augmenting human-machine collaboration.
One good example is this use case by retail giant Walmart' to optimize inventory management and offer personalized shopping recommendations to its customers based on the customer context Generative AI models, like large language models, can learn from unlabeled data and develop more general, flexible capabilities through unsupervised learning. The outputs of these models can be varied and unpredictable, as they are not limited to a fixed set of rules or patterns. Generative AI utilizes deep learning architectures, such as Generative Adversarial Networks (GANs) and Transformers, enabling it to understand and manipulate complex data relationships. This allows for more innovative and adaptable solutions in the landscape of supply chain management.
How Gen AI can become your trusted sidekick in the supply chain
The applications of generative AI in supply chain management are in their early stages and evolving as organizations augment their traditional AI-led initiatives with generative AI to orchestrate their operations with greater control and offer a personalized experience to their customers. Here are some of the evolving use cases in this space.
1. Hyper-accurate demand forecasting:
Traditional AI models require structured data around historical sales data and are limited in their ability by statistical methods to predict demand. With generative AI, you can augment historical data with external factors like social media buzz, economic trends, weather forecasts, and any other real-time but unstructured data. Given its ability to do enhanced pattern recognition and advanced scenario analysis, leveraging generative AI can lead to significant improvements in forecast accuracy and enable optimal inventory levels. Generative AI can also create inventory plans, production schedules, and distribution planning making it a smooth sail to meet the customer demand efficiently.
2. Supply planning with foresight:
Traditional AI excels in predictions based on historical data. Generative AI's ability to analyze vast datasets and simulate various scenarios makes it a powerful tool for anticipating potential disruptions. By modeling and generating new scenarios around unexpected demand spikes, supplier issues, or shipping delays, businesses can proactively develop contingency plans. This proactive risk management approach can make supply chains anti-fragile by dynamically generating alternate production schedules, inventory allocation plans or transportation routing consistently. Also, wherever there are data gaps, generative AI can plug in synthetic data based on real-world scenarios and can offer a conversational ‘what if' analysis enabling an expert co-pilot-like experience for supply chain planning.
3. Augmenting human-machine collaboration:
Traditional AI can classify documents and extract data with high accuracy. Generative AI can go beyond classification in terms of identifying unusual patterns or inconsistencies that may indicate error or fraud. Generative AI can also refer to external data sources to generate meaningful documentation summaries to enable informed decisions. It can analyze similar documents to auto-complete or predict missing information fields to streamline data entry across shipping documents or invoices. It can also auto-generate entire documents based on existing templates like bill of lading or customs documents augmenting human expertise and eliminating any risk of errors or omissions. Generative AI offers a human-like interactive copilot experience empowering your teams with decision intelligence and progressively helping you build an autonomous supply chain.
4. Smarter sourcing:
Traditional AI can analyze supplier data to identify past performance and potential risks. Generative AI can augment this risk profiling by its understanding of market trends, financial data, and geopolitical risks. On a similar note, generative AI goes beyond historical sourcing data to analyze vast amounts of external data, including news articles, social media, and industry reports, to predict emerging market trends and identify new sourcing opportunities. Generative AI can analyze successful contracts from past negotiations and current market data to suggest alternative contract terms, pricing models, and risk mitigation clauses to help the teams negotiate better. It can also analyze communication patterns and past interactions to suggest strategies for improved supplier relationship management.
5. Adaptive logistics optimization:
Unlike traditional AI, Generative AI can analyze visual data from surveillance cameras, and sensors to detect anomalies and augment this with access to real-time data around traffic patterns, weather conditions, and road networks in conjunction with delivery constraints. This helps them to generate adaptive routing strategies in their ability to respond to disruptions faster and reduce transit times significantly. Given its ability to generate human-like text/ video or audio, it can enable seamless collaboration across logistics stakeholders and ecosystem partners in its ability to interpret customer requests, generate delivery instructions or offer updates on shipment status improving customer experience.
6. Personalized Customer Service:
Traditional AI has transformed customer service by automating tasks, and updates, and providing basic support. Generative AI enhances this experience through personalized communications based on the customer journey and its ability to handle complex inquiries through pattern recognition across similar issues and the resolutions accomplished. Generative AI can also tap into customer reviews or social media sentiment to perform sentiment analysis, proactively identify gaps in the fulfillment process, and address customer concerns to improve customer delight. Imagine these available to your customers through a human-like personalized assistant!
7. Accelerated product innovation:
Traditional AI can analyze product designs to proactively detect manufacturing issues. Generative AI can go beyond this to analyze vast amounts of external data to suggest & generate design modifications to ensure faster manufacturability and minimize waste from a circular economy perspective. In a similar vein, Generative AI can analyze product characteristics, shipping data, space optimization needs, environmental impact, etc. to suggest sustainable packing solutions. Given its ability to generate multiple production scenarios, it can assist in identifying potential bottlenecks, production delays or quality issues to mitigate potential risks and accelerate go-to-market
But wait, tread carefully
The benefits of Generative AI are undeniable – cost savings, better efficiency, smarter decision-making, and happier customers. But, like any powerful tool, Generative AI comes with its own set of challenges that CSCOs need to consider:
- Bias and inaccuracy: Like traditional AI models, generative AI models can also inherit biases from the massive data used in training, leading to inaccurate or skewed outputs. For example - This could translate to favoring a few suppliers in specific regions or overlooking more sustainable routing options from a logistics context. To mitigate this risk, start by using diverse data sets that represent the variety of your supply chain and its partners. This ensures that the AI system is learning from a wide-ranging dataset, reducing the risk of systemic biases. Additionally, keep human oversight as a crucial part of your AI workflows, especially for critical decision-making areas. This human review process helps validate AI outputs and correct any errors or inconsistencies that may arise.
- Over-reliance and lack of transparency: Relying too heavily on AI can create a lack of understanding about how AI systems arrive at their conclusions. This lack of transparency can be problematic, particularly when decisions have significant impacts. To address this, use explainable AI models that provide insights into the reasoning behind their outputs. These models help build trust and clarity around AI-driven decisions. Alongside this, ensure your supply chain teams are trained to understand AI operations and can collaborate effectively with technology. Upskilling these teams improves their capacity to make informed decisions and reduces the risk of over-dependence on AI.
- Regulatory and compliance concerns: Generative AI in sensitive areas, such as financial reporting, may raise compliance and regulatory issues. To manage these concerns, conduct regular compliance audits to ensure AI applications align with relevant laws and industry regulations. This ongoing assessment can help identify potential compliance risks early and prevent legal complications. Partner with legal experts to navigate complex regulatory landscapes and ensure your AI strategies meet all necessary compliance requirements.
- Cybersecurity risks: Generative AI models are susceptible to cyber threats, potentially compromising sensitive data and disrupting supply chain operations. To minimize these risks, implement robust cybersecurity measures, including strong encryption and access controls to protect your AI systems. Consistently conduct vulnerability testing to detect and address security weaknesses. This proactive approach to cybersecurity can help safeguard your AI infrastructure and maintain the integrity of your supply chain data.
A guide for CSCOs to get teams started with GenAI for logistics execution
By responsibly adopting generative AI, CSCOs can unlock a future-proof supply chain with unparalleled agility, efficiency, and foresight. Here's how to turn your vision into reality by building a roadmap for implementation
👉 Pilot programs: Start small with focused pilot programs targeting specific logistics challenges or use cases from your organization context. This allows you to test the capabilities of generative AI in a controlled environment, measure the impact, and refine your approach before full-scale deployment.
👉 Invest in data quality: Generative AI is only as good as the data it's trained on. Focus on building a robust data infrastructure (both internal and access to market data) to ensure high-quality, unbiased data sets. This will minimize the risk of skewed results and ensure reliable AI outputs.
👉 Foster human-AI collaboration: View generative AI not as a replacement, but as a powerful tool to augment human expertise. Upskill your supply chain team to interpret AI outputs and collaborate effectively with the technology. This human-AI partnership unlocks the true potential of generative AI.
👉 Embrace continuous learning: The field of generative AI is rapidly evolving. Stay updated on the latest advancements by attending industry conferences or partnering with AI solution providers. By continuously exploring new applications, you can ensure your supply chain remains at the forefront of innovation.
Harnessing Gen AI for smarter logistics
Generative AI has the potential to transform your logistics operations, offering unprecedented efficiency, flexibility, and innovation by augmenting capabilities offered by Traditional AI models By leveraging this technology, you can improve demand forecasting, optimize supply planning, and even enhance customer service, leading to reduced costs and building a more resilient supply chain. However, success with Generative AI isn't just about adopting new tools—it's about integrating them thoughtfully and responsibly across your teams and ecosystem partners within the construct of your supply chain landscape.
