Artificial Intelligence (AI) in supply chain management builds on a rich history, dating back to the 1950s when the idea of AI started taking shape, and Alan Turing introduced the Turing Test, as a way to measure machine intelligence. Today, with the advent of powerful large language models (LLMs) like ChatGPT, AI has gained even more attention. In this blog post, we will explore the remarkable impact of AI in supply chain management. We’ll explore its applications, some real-world examples, and the challenges it brings as it transforms the logistics and supply chain industry.
Unleashing the Potential of AI in Supply Chain Management
The modern supply chain is a complex system influenced by numerous internal and external factors, generating vast amounts of data that continue to grow exponentially. However, human capabilities to analyze and process this data quickly and effectively are limited, creating an opportunity for AI to intervene and optimize supply chain operations. In fact, Gartner predicts that by 2026, over 75% of commercial supply chain management application vendors will deliver integrated artificial intelligence (AI) as part of their application.
AI in Supply Chain: From Data to Action
1. Demand Forecasting and Inventory Management
AI has revolutionized product demand forecasting and inventory management, addressing the challenges organizations face in predicting future demand accurately. Traditional forecasting methods rely on limited data sources, such as sales history, which often fail to reflect actual demand accurately. AI algorithms, on the other hand, can analyze vast amounts of data, whether structured or unstructured, including complex historical patterns, market trends, and external factors like weather, events, and social media sentiments. This comprehensive analysis enables more accurate demand forecasts, which helps protect companies against unexpected spikes or drops in demand.
Such scalability of AI proves particularly impactful for large-scale supply chains, as evidenced by a case study from Remi AI, where one of the world’s largest beverage manufacturers achieved an average forecast accuracy improvement of 9%. For billion-dollar companies, such an increase in accuracy translates into substantial savings worth millions of dollars.
2. Route Optimization
In today’s climate-conscious and fast-paced delivery landscape, route optimization is vital for transportation and logistics. AI revolutionizes route optimization by dynamically determining the most efficient delivery routes, considering real-time traffic data, weather conditions, and road incidents. Machine learning algorithms use historical delivery data to learn and predict optimal routes, continually adapting to changing conditions and improving efficiency. AI also optimizes vehicle load, considering package sizes, weight restrictions, and delivery priorities, maximizing resource utilization. Furthermore, AI enables dynamic routing and rescheduling, adapting to unforeseen delays in real-time, and ensuring efficient allocation of resources. Notable examples include the Google Cloud Fleet Routing API, which utilizes AI to solve routing problems, predict ETAs (Estimated Time of Arrival) accurately, and reoptimize plans adapting to changes.
3. Warehouse Automation
The integration of AI and robotics has transformed traditional warehouses into highly efficient operations. Undoubtedly, you’ve come across captivating videos on the internet showcasing near-autonomous warehouses with agile robots moving seamlessly and computer vision systems accurately and rapidly scanning and then categorizing cases or parcels moving in different lines to fulfill specific orders.
Consider the Sparrow intelligent arm robot by Amazon. This robot can handle items with millions of different sizes and shapes before they are packaged. This will not be possible without AI and deep learning for computer vision particularly. Through sophisticated analysis, the robot can meticulously examine each item, allowing it to adjust pressure and maintain precise control, ensuring successful manipulation.
AI’s influence on supply chain management is vast and ever-expanding. AI will also play key roles in quality management, predictive maintenance, risk management, supplier relationship management, sustainability, and the list goes on. As we explore the impact of AI on supply chain management, it’s essential to understand the challenges that come with its implementation. Successfully adopting AI-driven solutions and unlocking its full potential requires addressing many issues that we will delve into some of them in the next section.
Navigating Challenges to AI Implementation
Data Quality: The Pillar of AI Success
Have you ever heard of “Garbage in Garbage out”? Well, it’s a famous concept in AI that highlights the importance of data quality. Inaccurate or outdated data can lead to flawed predictions and decision-making, squandering the true potential of AI. Unfortunately, according to IBM, 73% of business executives are unhappy with data quality. To mitigate this challenge, companies must invest in robust data governance practices, data validation, and regular audits to ensure accurate, secure, and relevant data. Also, specialized teams have to work closely with AI systems in the first stage of their deployments to measure their performance and adjust what’s wrong with the inputs accordingly.
Trust Issue: AI for Decision-Making in Supply Chains
While AI displays an impressive ability to analyze huge amounts of data, companies often hesitate to grant AI full decision-making authority. This reluctance is understandable, as sophisticated AI models utilizing deep learning and neural networks often operate as black boxes. As a result, stakeholders are unable to fully comprehend the decision-making process behind the AI system, which raises concerns about transparency and interpretability. AI can also exhibit behavior where it quotes non-existent sources of information that it has simply made up, or makes recommendations that are absurd or even dangerous. This AI behavior is known as “hallucinating”.
The lack of transparency and concern about hallucinations becomes especially important as companies push to integrate multiple independent AI solutions and benefit from the efficiencies and cost savings AI can provide. To reap the full benefits of AI, companies must grant AI a large amount of autonomy throughout the supply chain. Breaking down barriers between departments and companies within the supply chain will allow for the creation of a comprehensive AI system that can access a wealth of real-time data.
This holistic approach ensures that decisions are made based on information beyond what traditional operations and management teams can observe or consider. This use of enormous amounts of real-time data enables supply chains to become more resilient because AI can respond quickly, taking decisions in real-time, and only escalating the most critical decisions to human managers. To help human managers take these kinds of decisions, companies can then use explainable AI models that prioritize explainability during the AI model’s development phase. However, a trade-off has to be made between explainability and performance, and how companies make that trade-off will have important consequences.
Future of AI in Supply Chains
In conclusion, AI’s transformative impact on supply chain management is evident; it will enable optimizing operations and decision-making in unprecedented ways. From demand forecasting to warehouse automation, AI-driven solutions will revolutionize the logistics and supply chain industry.
Embracing AI’s potential while maintaining effective human involvement will be the key to success, enabling organizations to navigate challenges and seize opportunities in the ever-evolving landscape of supply chain management. Challenges like data quality and trust in AI decision-making must be addressed to fully unlock its benefits. There are many who talk about job replacement caused by AI, but the future of AI in supply chain management is distinctly focused on fostering collaboration and generating new job opportunities, rather than displacing human workers. For instance, in demand and inventory planning, AI systems can autonomously forecast demand and process orders, but human intervention is crucial when an exceptional context shift happens (i.e. something unexpected happens).
This collaborative human-machine approach enables teams of people to leverage AI’s capabilities and real-time decision making, while still applying their human expertise to address unique scenarios and unexpected circumstances (and the unexpected is becoming more common so human judgment is more important than ever).