The Future of Logistics and Supply Chain industry: 25 AI Use Cases and Applications Disrupting the Industry in 2023

They use AI to solve challenges in inventory management, demand forecasting, and optimization of packaging sizes. AI can be used to develop predictive models to anticipate future customer demand and help organizations plan their supply chain accordingly. AI-generated models can analyze customer trends to detect any potential problems and generate actionable insights that can help to prevent disruptions in the supply chain.

Gartner predicts that “The rise of IIoT will allow supply chains to provide more differentiated services to customers, more efficiently”. Gramener is a design-led data science company that helps solve complex business problems with compelling data stories using insights and a low-code analytics platform. We help enterprises large and small with data insights and storytelling by leveraging Machine Learning, Artificial Intelligence, Automated Analysis, and Visual Intelligence using modern charts and narratives. Additionally, we have solutions for performance improvement, smart warehousing, defect detection, cargo delay reduction, downtime prevention, and supply chain visibility projects. Time-based pricing linked to market demands and competitor plans can help companies remain competitive.

Using AI for Supply Chain Management

We grow your business by getting you closer to your customers with guaranteed 2-day delivery. Tasks such as document processing can be automated thanks to intelligent automation or digital workers that combine conversational AI with RPA. Chatbots and virtual assistants can also help ecommerce customers through the returns process, taking on a large volume of customer inquiries and allowing human workers to focus on higher-value tasks. In addition, AI can reduce product return rates by analyzing customer data and making personalized product recommendations. AI’s ability to spot the rare, but costly, anomaly also applies to equipment used in the supply chain, from materials handling systems to tractor-trailers to railcars. For example, a major auto manufacturer is piloting nuVizz’s RoboDispatch Solution in its inbound logistics operations.

Delivery management software can plan routes taking into account variables beyond the reach of any human team. The improved planning that AI facilitates will also prevent overbuying by introducing less wasteful practices such as product bundling. Rolls Royce, in partnership with Google, creates autonomous ships where instead of just replacing one driver in a self-driving car, machine learning and artificial intelligence technology replaces the jobs of entire crew members. Machine Learning serves as a robust analytical tool to help supply chain companies process large sets of data. With mounting pressures to deliver products on time to keep the supply chain assembly line moving, maintaining a dual check on quality as well as safety becomes a big challenge for supply chain firms. It could produce a big safety hazard to accept substandard parts not meeting the quality or safety standards.

What are the challenges of artificial intelligence in supply chain management?

In hot weather, for instance, people drink more Gatorade, which can create a sudden explosion in demand, so there could be a 10 to 15% spike in demand for bottles. There could be more fish in the ocean suddenly, which increases the demand for packaging to accommodate additional tons of fish. “Even though we try to forecast, it’s very difficult because we don’t always know our customers’ needs ahead of time,” says Ranchin.

With advancements in artificial intelligence, particularly in the domain of generative AI, the potential for revolutionizing the supply chain industry is immense. In this article, we will delve into some of the transformative applications of generative AI across the supply chain, from product design to logistics. However, we shouldn’t get carried away, it’s also important to weigh the pros and cons of AI in logistics as we’ve done in this article.

Those nearest the center of the wafer tend to have the best power performance profile. Intel has a quality threshold against which chips are measured to determine whether they should be kept or thrown out. FlowspaceAI for Freight is a new tool for DTC and B2B brands powered by artificial intelligence (AI) and machine learning.

This can then be used, for example, to determine probabilities of occurrence for events and to recognise behavioural patterns [4]. Selecting reliable suppliers and maintaining strong relationships is crucial for a smooth supply chain operation. Generative AI can assist in supplier selection by analyzing a wide range of data, such as supplier performance metrics, quality records, pricing, and geographical considerations. By leveraging Generative AI, organizations can identify the most suitable suppliers based on predefined criteria and enhance their supply chain resilience.

Let’s take a quick look at the benefits you will get after implementing artificial intelligence in your supply chain. Increasing supply chain volatility exacerbates the urgency for organizations to enable AI within their supply chain and drive business impact. One global retailer was able to achieve $400 million in annual savings and a 9.5% improvement in forecasting accuracy3. Enabling AI in the supply chain empowers organizations to make decisions with confidence, adjust business practices quickly, and outpace the competition. Supply Chain Matters highlights for readers a well written perspective on Generative AI and its eventual use in supply chain business process and decision making areas.

AI’s assistance in reviewing master supplier agreements could be invaluable to roles such as purchasing managers and supply chain directors. The system requires training on an extensive dataset comprising existing customs documents to use generative AI in this context. These documents might encompass a variety of forms, declarations, and regulations, providing the AI with a comprehensive understanding of the specific language, patterns, and structures typically present in such documentation. In March 2023, a significant stride was made when Microsoft announced Microsoft Dynamics 365 Copilot, an AI-driven assistant incorporated into CRM and ERP systems. Moreover, the release of ChatGPT by OpenAI for public users in November 2022 was a groundbreaking event that paved the way for anyone to explore the potential of generative AI.

Distribution node planning

The global supply chain is a complex web of interconnected processes that involve numerous parties, including manufacturers, suppliers, logistics service providers, and retailers. Fortunately, the advent of artificial intelligence (AI) is transforming logistics and supply chain management. AI algorithms can automate routine tasks, freeing up time for logistics professionals to focus on more strategic aspects of their job. Additionally, AI can help identify inefficiencies in the supply chain, allowing companies to optimize their operations and save costs.

In the past five years, analytics and AI have become increasingly important to many companies’ business. These powerful tools are enabling companies to automate tasks they never could before while providing much deeper insights companies can use to make faster, better decisions to improve business performance. And “business performance” today requires delivering simultaneously against traditionally competing KPIs like customer satisfaction, revenue, efficiency, cost control and carbon emissions. As a result, companies are better positioned to meet demand, avoid being surprised by disruptions or changes in conditions, and even eliminate unnecessary shipments and, thus, fuel use and emissions. A digital twin can be created for the end-to-end supply chain or for specific functional areas for targeted improvements. Thus, most supply chains have manual quality inspections to find damage during transit.

Customer service and marketing

It offers predictive insights, emphasizing affected orders, and presents a platform for quick action using context-specific email responses. Furthermore, for inventory management, generative AI can use historical sales data and demand forecasts to optimize the allocation of refurbished items. This can prevent overstocking or stockouts of refurbished goods and help ensure that these items are allocated where they are most likely to sell, improving overall supply chain efficiency. Regarding routing, generative AI can analyze transportation data to determine the most efficient route for returning products. This can minimize transportation costs and time, resulting in a more efficient reverse logistics process. For returned goods, AI can evaluate factors like the cost of transportation, the condition of the product, and the demand for refurbished items.

One AI-enabled solution is BlueNode, which measures carbon and Scope 3 emissions from ports, terminal operators, maritime and rail carriers, shippers, and trade authorities. The AutoScheduler.AI platform was developed with P&G and implemented at P&G, Unilever, General Mills, and other companies. AI can help companies plan loads and create a more balanced transportation plan so they can work with preferred carriers and ensure adequate storage space and labor availability across their sites. Because RiskGPT’s model is trained on Overhaul’s data, shippers can get an answer with details and contextual accuracy when they ask RiskGPT how to respond to a specific event.

There is a significant disruption in logistics, shortages in materials, skills, and labor, not to mention a rise in prices due to the COVID-19 pandemic and the war in Ukraine. This has forced companies to reimagine and reinvent the use cases for the supply chain with AI and ML. AI can optimize energy use in warehouses and other supply chain facilities and also track and analyze energy consumption data in order to identify inefficiencies and reduce energy costs. Through AI-driven analytics, it is possible to identify potential risks with suppliers earlier and help to reduce the risk of supply chain interruptions. Sentiment Analysis – every company needs feedback from the customer, this usually comes from the review section for each product. Going through each and every review manually and assigning into good, bad and anything in between can be a tedious job.

• Like other thought leaders, Keith indicates that this is impressive technology, but it is in the beginning stages of a journey toward the ability for providing increasing value.
• The results demonstrated successful energy efficiency optimization based on estimated time of arrival.
• Picking, packaging, assembly, and quality control can be optimized, especially when combined with cameras.

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