Amidst the post-pandemic challenges faced by many industries, the supply chain industry stands out as an example of adaptation and progress. Embracing modern technologies at a significant scale, such as Artificial Intelligence (AI), has empowered this sector to revolutionize processes, enhance decision-making, and boost overall efficiency.
AI emerges as a top advanced technology that businesses should leverage to maintain a competitive edge. The adoption of AI in the supply chain solution market has yielded benefits like improved inventory management, smart manufacturing, dynamic logistic systems, and real-time delivery controls, driving its widespread implementation.
In short, AI is here to transform the way things were before. From planning to execution to optimizing goods movement, businesses can unveil greater value by leveraging AI and machine learning (ML).
AI Use Case in Supply Chain and Logistics
AI is rapidly weaving into each industry with an aim to innovate and transform existing operations for greater scalability and growth. Global logistics leaders are embracing AI to bring change and expand quickly. It is expected that the market size of AI-fueled logistics and supply chains will be growing at an exponential rate of 46.5% (2023 to 2030).
Artificial Intelligence can play a vital role in streamlining warehouse processes. Let's dive into a few of the trending and noticeable changes AI brings:
Quality Inspection and Control
Maintaining a standard quality for your goods is a must to retain and attract new consumers in today’s competitive landscape. Your product quality decides whether you are worthy or not. Defective products not only increase the chances of reverse logistics (more expense) but also harm the brand image. Thus, it’s super important for the logistics domain to perform quality inspection and control.
Reverse logistics is the process that involves the return delivery movement of goods away from the point of consumption (for capturing value) to the point of origin. Return delivery expenses in North America alone were 363 billion US dollars in 2019, with worldwide return costs exceeding one trillion US dollars, as per a Statista report.
Solely relying on human efforts for such repetitive tasks can be tedious and lead to serious errors. Errors like missing significant defects that can directly affect your customer’s experience. By leveraging AI capabilities like deep inspections using digital image processing and computer vision, logistics companies can ensure quality at speed without comprising on accuracy.
Three major problems that the logistics sector can overcome using AI:
- Better tracking of incomplete or delayed shipments with image classification and segmentation
- Object detection got one step ahead with AI trained model to spot damages or drops during shipping
- Optimized packaging with an AI system to ensure your product stays at its best
AI with human supervision can create the overall inspection process more efficient and minimize the percentage of delivering defective products to customers.
Warehouse Automation and Inventory Management
When we are discussing AI in logistics and supply chain, we can’t leave automation out of the picture, RIGHT? Automation allows us to avoid those timid labor-intensive tasks (data entry etc.) and fatigues while parallelly boosting productivity and saving a few bucks.
Warehouse automation technologies can range from simple replacement tasks, such as using conveyor belts in the production line, to complex activities using AI & machine learning automation capabilities or autonomous mobile robots (AMR). Let’s take a tour of some essential ways AI is transforming warehouse automation and management processes:
- Automated Guided Vehicles in Plants — The state of a manufacturing supply chain is known for its volatility, pushing companies to innovate their inventory management approach for efficiency. Automated guided vehicles (AGVs) use sensors (present in the warehouse) to self-guide themselves to move around the warehouse. It requires a small amount of computing power to transport materials and goods from one point to another. It provides improved accuracy of inventory tracking as the routes are fixed and speed up the process. For instance, BMW automates intralogistics in its production warehouses with automated guided vehicles (AGVs). These devices deliver supplies and goods along predetermined paths, providing the organization with more insight into its inventory.
- Automated Storage and Sortation System — Utilizing an automated storage system helps companies to overcome complex supply chain challenges. The essence of automated storage technology is so large that Walmart acquired Alert Innovation in Oct’22. Likewise, the implementation of an automated sortation system is necessary to enhance order fulfillment and reduce the need for human interaction.
It has even been highlighted in a recent report that the warehouse automation market will continue to grow at a CAGR of 16.2%, a significant growth from $22.15 billion in 2023 to $46.93 billion by 2028.
Protection Against Theft and Intrusion
The addition of AI in logistics and supply chain can greatly increase security. AI-driven systems help companies to detect theft and intrusions by utilizing security camera footage and analyzing it for any suspicious activities. With such efficient monitoring and improvement in overall warehouse operations, AI helps enterprises to be cost-effective and quick.
Final Say…
Enhancing supply chain performance becomes effortless with AI integration in logistics.
The versatility of AI technology offers a multitude of applications, from using computer vision to prevent inventory shrinkage and deter theft to enhancing quality control processes and optimizing shipment load monitoring.
AI-driven predictive analysis improves risk management and provides logistics companies with invaluable demand prediction insights. Overall, AI in logistics and supply chain management leads to reduced error rates, lowered operational costs, and a seamless experience for you and your customers with minimal stockouts.
