How can Businesses use AI to Improve Supply Chain Resilience?

The concept of artificial intelligence (AI) emerged in the 1950s, but it was in ChatGPT, launched in late 2022, amassing over 100 million users within just two months, that it gained widespread attention. Similarly, "supply chain management," a term coined in the 1980s, was largely overlooked until the COVID-19 pandemic highlighted its critical importance due to prolonged shortages of various products, from personal protective equipment to semiconductors. 

Today, an increasing number of companies are turning to AI to manage their global supply chains, raising questions about its potential to enhance supply chain resilience and its impact on employment in supply chain management.

How can businesses leverage AI to enhance supply chain resilience? This question has recently gained significant interest and concern as modern supply chain management strategies become increasingly complex due to the interconnected nature of global markets. AI offers powerful tools and solutions, including advanced analytics, predictive insights, and robust automation. 

AI-driven decision-making systems help organizations anticipate challenges in advance and contribute to overall improvements in supply chain effectiveness. AI can provide a competitive advantage and ensure continued success in dynamic economic environments by empowering organizations with efficient and resilient systems. 

The role of AI in mapping supply chains

Imagine having a real-time window into your supply chain, pinpointing the location of every product, and reacting instantly to disruptions. Artificial intelligence (AI) makes this a reality, offering immense potential to streamline internal operations.

Here's a troubling fact: a 2021 survey revealed that only 2% of companies have visibility beyond their immediate suppliers. This lack of transparency exposes them to a multitude of threats—natural disasters, pandemics, political tensions, trade barriers, and product recalls, to name a few.

This is where AI steps in as a powerful ally. AI tools excel at gathering and analyzing data from diverse sources like purchase orders, customs documents, and freight bookings, often riddled with inconsistencies in format and language. Leveraging an LLM for data analytics can further enhance these capabilities, providing deeper insights and more accurate predictions for supply chain management. AI algorithms act as data ninjas, extracting crucial information from structured and unstructured documents with impressive accuracy.  llm use cases, companies can further optimize their supply chain processes, leveraging natural language processing to enhance data comprehension

Let's take GBRS, an innovative AI startup, as an example. They've developed a game-changing tool that uses public and private data to map a company's supply chain. This is further enhanced by a large language model (LLM) assistant that understands plain language, allowing employees to get answers to their questions quickly. Additionally, Altana utilizes document processing systems to streamline the capture, analysis, and sharing of vital documents like invoices and purchase orders. This fosters improved efficiency, accuracy, and communication across the supply chain network.

By leveraging AI, companies can gain a clear picture of their supply chain, making them proactive and resilient in disruptions. It's time to harness the power of AI and unlock a future of smoother operations and a more robust supply chain. 

Using AI to detect changes in demand and supply

AI offers valuable assistance to firms in assessing market demand and customer sentiment. AI-powered tools like Google's Video AI can analyze text, images, and videos by leveraging scanner data from point-of-sale locations and vast troves of customer reviews and social media content. Google Video AI then constructs a real-time end-to-end supply chain dashboard that generates alerts for abnormal demand fluctuations due to competitive factors or product issues. Moreover, AI can detect early signs of panic buying by mining extensive data sources, enabling the dashboard to identify the root causes behind such anomalies.

It is also worth mentioning that AI tools excel in compiling and analyzing data on traffic conditions across various supply chain tiers, including ports and warehouses. These tools can swiftly identify disruptions from supply shortages, labor scarcities, factory closures, and shipping delays. For example, during the unprecedented delays at West Coast ports in September 2021, the US Department of Transportation introduced a national transportation supply chain dashboard. This dashboard tracked three real-time critical indicators of goods movement from ports to retail stores: imported container volumes, US retail inventory levels, and on-shelf availability of consumer goods. Monitoring these indicators allowed for the prompt detection and response to aberrant patterns as they arose.

We are now going to get into 10 uses of AI to Improve Supply Chain Resilience 

How technology can improve supply chain

 Technology is pivotal in enhancing efficiency, visibility, and resilience within the supply chain. Here are three key ways technology can drive improvements:

• Optimized Operations: Advanced technologies such as Internet of Things (IoT) devices, RFID tags, and sensors provide real-time data on inventory levels, shipment statuses, and environmental conditions. This data enables businesses to optimize operations by minimizing stockouts, reducing excess inventory, and streamlining logistics processes. For example, IoT sensors can track the location and condition of goods throughout the supply chain, allowing companies to identify bottlenecks and inefficiencies and take corrective actions promptly. Additionally, robotic process automation (RPA) automates repetitive tasks such as order processing and inventory management, freeing human resources to focus on more strategic activities. By leveraging technology to optimize operations, businesses can improve productivity, reduce costs, and enhance customer satisfaction.
• Enhanced Visibility: Supply chain visibility is crucial for identifying risks, anticipating demand, and responding to disruptions effectively. Advanced technologies like blockchain and cloud computing enable transparent and secure information sharing across supply chain partners, resulting in improved visibility and collaboration. For example, blockchain technology provides a tamper-proof record of transactions and product movements, enabling stakeholders to track the journey of goods from the point of origin to the end consumer. Cloud-based supply chain management systems offer real-time access to data from anywhere with an internet connection, allowing stakeholders to monitor inventory levels, track shipments, and communicate with partners seamlessly. By enhancing visibility across the supply chain, technology enables businesses to make informed decisions, mitigate risks, and swiftly adapt to changing market conditions.
• Predictive Analytics and Forecasting: Predictive analytics leverages historical data, machine learning algorithms, and statistical models to forecast future demand, identify trends, and optimize inventory levels. By analyzing large datasets, businesses can anticipate fluctuations in demand, optimize production schedules, and reduce stockouts and overstocking. For example, predictive analytics can help retailers forecast seasonal demand patterns, enabling them to stock the right products in the right quantities at the right time. Additionally, technologies like artificial intelligence (AI) allow predictive maintenance by analyzing equipment performance data to identify potential issues before they occur. By leveraging predictive analytics and forecasting, businesses can minimize costs, improve inventory management, and enhance customer satisfaction by ensuring product availability and timely delivery.

Supply chain technology examples

Here are some examples of supply chain technology:

• Blockchain: Blockchain technology offers transparent and secure tracking of transactions and goods throughout the supply chain. It enables traceability, reduces fraud, and enhances trust among supply chain partners.
• Internet of Things (IoT): IoT devices such as sensors and RFID tags provide real-time data on the location, condition, and movement of goods. This information helps optimize inventory management, minimize delays, and improve overall supply chain visibility.
• Artificial Intelligence (AI): AI-powered algorithms analyze large datasets to predict demand, optimize routes, and identify potential risks in the supply chain. AI also enhances decision-making processes and enables proactive problem-solving.
• Robotic Process Automation (RPA): RPA automates repetitive tasks such as data entry, order processing, and inventory management, freeing up human resources for more strategic activities. This technology improves efficiency and reduces errors in supply chain operations.
• Predictive Analytics: Predictive analytics uses historical data and statistical algorithms to forecast future trends and outcomes in the supply chain. It helps companies anticipate demand, optimize inventory levels, and mitigate risks effectively.
• Cloud Computing: Cloud-based supply chain management systems offer scalability, flexibility, and accessibility from anywhere with an internet connection. They enable real-time collaboration, data sharing, and seamless integration with other business systems.
• Digital Twins: Digital twins create virtual replicas of physical assets and processes within the supply chain. They allow for simulation, optimization, and predictive maintenance, leading to improved efficiency and cost savings.
• Augmented Reality (AR) and Virtual Reality (VR): AR and VR technologies enhance training, maintenance, and troubleshooting processes within the supply chain. They provide immersive experiences and enable remote assistance, improving productivity and reducing downtime.

These supply chain technologies empower businesses to optimize operations, reduce costs, enhance customer satisfaction, and adapt to rapidly changing market conditions.

Use of AI to Improve Supply Chain Resilience 

Here is how to improve supply chain resilience, and use AI to improve it.

1. Real-Time Data Analysis for Proactive Management 

​The AI technology in real-time data analysis has an impact on the way companies do business and control their supply chains. AI monitoring gives the businesses possibility to engage in the flow of supply chain activities for 24/7. In other words, the firms see the changes in supply chain processes and take a proactive approach instead of a reactive one. 

This is a process that takes place by continuous supervision and which covers everything starting from the raw materials procurement to the final delivery, therefore making sure that each step is made as efficient as possible and responsive.

The AI systems can be linked with real-time data analysis features that enable them in detecting trends, predicting upcoming challenges, and offering quick corrections. For instance, AI will discover holdups in ships, or production in real time. This enables the managers to deal with the problems in the primary stage, hence, it prevents the problems from being escalated to more critical problems. 

This element becomes particularly important when an entity is dealing with intricate, multi-stage supply chains where it is mandatory to act with promptitude in order to keep the line of activities uninterruptedly.

2. Predictive Analytics for Anticipating Disruptions 

AI-integrated predictive analytics is no longer a science fiction fantasy that a business of today has to deal with, instead it allows a company to manage supply chain disruptions more efficiently. Contrary to traditional risk management approaches which are people based, this data-driven AI approach is based on the analysis of historical data and current trends to discover patterns that may signal imminent dangers or disruptions. The use of intelligent monitoring systems by companies has helped them to prevent potential snags in their supply chain such as demand fluctuation, supplier issues, and logistical issues.

The proactive approach enables businesses to make contingency plans, to make their choices based on the information that is available to them, and to make these risks manageable. Predictive analytics has been proven to be one of the most necessary tools in this case for the identification and measurement of profound multidimensional disasters like geopolitical changes or global health crises, the consequence of which is the bringing of corrective measures in advance. 

3. Automated Inventory Management 

AI-based automated inventory management translates into better supply chain operations with higher efficiency. 

The AI is unsurpassed in the sphere of inventory monitoring and management because it is not constrained by anything ever. AI technology is capable of closely monitoring stock levels with a calculation of the replenishment requirements that are needed to eliminate stock outs without the excessive stocking. This is a vital point that a business should consider when it comes to maintaining a well-balanced inventory so that it has enough stock to meet demand, but not so much that it is not able to liquidate the unsold goods.

The AI based smart inventory control, though, is not restricted to mere monitoring; rather it also considers the sales trends, seasonal fluctuations, and market dynamics and automatically adjusts the levels of inventory accordingly. The business can, then, be able to forecast demand more precisely so that they can also execute more precise planning of inventory. 

On the other hand, the AI can also help to find the ineffective steps of the process of inventory management and fix them, for instance, recommending that some products require promotional measures or the stocking levels should be changed.

4. Enhanced Supplier Selection and Management 

Employing AI for advanced supplier selection and control implies for businesses an evolving paradigm of how they perceive the supply chains. 

AI's in-depth analytics and data processing provision companies with a swift and plausible way of measuring the reliability and performance of suppliers. AI through the examination of such examples as previous delivery times, quality indicators, and responsiveness determines the most trustworthy sellers for companies. Such an AI-guided approach would then give a more strategic and data-driven decision-making process and the result is a supply network which will be strong and will not collapse. 

AI implementation in supplier management has brought about better procurement strategies and it has also contributed greatly to the overall resilience of the supply chain.

5. AI-Driven Demand Forecasting ​

AI-led demand forecasting is undoubtedly the game-changer that helps companies to see the market demands and adjust their supply chain management operations. Through using AI as a tool of prediction, companies can analyze large data sets - that include the historical sales, market trends, consumers' behavior, and even the economic indicators, and estimate the future demand with an incredible precision. 

This intelligence enables companies to control the amount of stocks they keep in stock, plan their production schedules effectively and allocate their resources properly so that they are always ready to meet customers' needs without straining the resources.

AI is not just with the sales projections but its predictive power covers much more. Thus, businesses can predict the seasonal variations, promotional activities and market swings that may be unpredictable. Through the linkage of AI and demand forecasting, the companies can adopt a proactive strategy of supply chain from the reactive one, and thus will be able to eliminate waste and increase the level of customer satisfaction. 

Such a foresighted approach is important to ensure that the company can remain actively competitive and resistant to change in the highly dynamic marketplace of nowadays. 

6. Optimizing Logistics and Routing 

AI is in the frontiers of optimal logistics and routing and this is changing the supply chain's efficiency. Companies will eventually discover that the usage of AI algorithms will make the route optimization and logistics operations efficient. This will in turn lead to an improvement in both time and cost. AI assists in analysis of historical data, current conditions, existing capacities, and priority deliveries to determine the best routes. 

This not only enhances speed but also conservation of fuel and environment. AI's ability in adjusting its routing procedure in an instant accommodates the undesired events such as traffic jams or delivery delays that may happen, leading to a system that stays fluid and flexible. In order to optimize the business process, this level of optimization is very much needed for the business that wants to smoothen the supply chain and increase customer satisfaction.

7. Intelligent Risk Assessment and Mitigation ​

Utilizing AI for intelligent risk assessment and mitigation is a key factor that contributes to the increased supply chain endurance. AI is a data processing tool that gives it the capacity to identify several risks that may disrupt the supply chain, this includes supplier instability, logistical bottlenecks, or market volatility. AI forecasts these risks (for example, all possible problems) and therefore, the businesses get an opportunity to develop well-thought-out measures. 

AI-powered systems are able to propose alternatives in terms of suppliers, regulate the stock level, or reroute the shipment in order to avoid possible disruptions. 

AI, through its proactive approach to risk management, makes it possible for the business to maintain a smooth operation and mitigate any eventuality that could have a negative impact on their supply chains.

8. Enhancing Communication and Collaboration ​ 

AI as the main driver tool makes the chain of supply easy to coordinate and partners who are involved as well. AI-based platforms can assist in the smooth exchange of information that will in turn create unity of purpose and minimize the chances of misinformation. Communication, therefore, becomes more sophisticated and allows for more synchronized processes, from the factory to the stores. 

AI assists in the forecasting and timely communication of probable problems thus enabling quicker problems resolution and ultimately contributing positively to supply chain network efficiency and dependability. 

Utilizing this multi-faceted approach provides for a more cohesive and adaptable supply chain system which is critical for satisfying the various market demands equally and maintaining a competitive edge.

9. Quality Control and Compliance Monitoring ​

  AI is undoubtedly one of the top ranking factors that contribute to the improvement of quality and compliance inspections in the supply chain. The AI checks and monitors the AI-driven processes of product checks at every stage of the supply chain to ensure product quality and regulation compliance. AI algorithms may notice any deviation from normal quality standards and may take actions that correct them on the spot. 

This makes us to do effortlessly and to keep the clients' satisfaction on this highest level. One more what AI is good tool the company uses to stay regulatory compliant with updated changes by having the access to the new updates and automatically changes the process. 

Besides being risk-averse, this attitude of the business to quality and compliance helps not only to maintain its reputation of being steadfast and reliable but contributes to its excellence too.

10. Customized and Flexible Supply Chain Solutions ​

AI implementation in supply chain management enables the development of customized and flexible solutions that could be modified to help each business deal with its unique and individual issues. Using the modern technology of data analytics and machine learning functions which AI provides, companies can now build up supply chain strategies that are quick to react and hard to dismantle. 

AI systems are capable of giving the business a most appropriate decision-making support based on a wide range of parameters, such as the market trend, consumer behavior, and logistics. 

Artificial intelligence powered supply chains not just help the businesses to keep their supply chains resilient and dynamic even in the presence of volatile environments but also allow minimizing the loss of efficiency and continuity.

Wrapping up 

AI has become an essential element of supply chains nowadays, which allows enterprises to both withstand and enhance their performance. AI gives us solutions like instant data analysis and individual supply chain solutions among other tools useful for companies in the strategic tackling of problems, predicting market changes and improving their operations. 

The heart of the matter is that success is achieved by using AI to strengthen decision making, teamwork, and continuous preparedness for the unpredictability of the environment. 

Many more enterprises would move to AI-driven solutions, in which way business would be prepared to handle the complexity of modern supply chains with the added advantage of flexibility and confidence.