What 'chemical reactions' will occur when AI meets ERP?

Generative artificial intelligence (GenAI) is a new technology that has attracted much attention and has been widely used in various business fields such as marketing and sales. Through data analysis, it improves operational efficiency and supply chain flexibility, making enterprises more competitive. The emergence of GenAI has brought more development opportunities to enterprises, helping them better respond to market challenges and changes, and achieve more efficient business management and innovative development.

What is Generative Artificial Intelligence in the Supply Chain

In the consumer use case, the GenAI process involves generating data in text, image or video fields Enter a command or question into the AI ​​to start the process of generating new content. Typically, GenAI models are trained on large-scale data sets, and when users enter new data, the application combines this new data with previously learned knowledge to generate entirely new content.

Users can train GenAI using data from all aspects of the supply chain, including inventory, logistics and demand. By analyzing company information, GenAI helps improve the efficiency and flexibility of supply chain management.

7 application scenarios of generative artificial intelligence in supply chain

1. Demand forecast

Consumers will shift their spending from services to goods, causing supply chain operations to become Hard to predict. Enterprises need to pay attention to predicting demand changes to cope with the challenges of rapidly changing markets.

Enterprises can use GenAI models to analyze historical sales data, market trends and other related factors to simulate future supply and demand scenarios and improve the accuracy of demand forecasts. By tracking demand patterns, companies can better respond to market fluctuations, reduce potential disruptions, and effectively avoid inventory issues. This accurate demand forecast helps companies develop more targeted production plans and inventory management strategies, thereby improving operational efficiency, reducing costs, and enhancing competitiveness. At the same time, based on Gen

2, inventory assessment

GenAI can also help improve inventory management.

By training on key data such as inventory levels, warehouse capacity and manufacturing times, GenAI can suggest ways to optimize inventory processes, including suggesting when to replenish or reduce inventory, helping to reduce redundant storage. Maintaining appropriate inventory levels ensures a smooth supply chain while avoiding losses due to out-of-stocks. Accurately managing inventory to meet demand without overstocking products helps improve the company's operational efficiency. Because excess inventory increases capital costs and warehousing expenses, by optimizing inventory processes, companies can save costs and become more competitive. GenAI's data analysis and intelligent algorithms can help companies better plan inventory and achieve precise inventory management

3. Communication between suppliers and customers

Between the company, suppliers and customers Close communication is key to ensuring that the supply chain operates efficiently, however, ensuring that communication is effective can face some challenges.

GenAI automates messaging to make employees work more efficiently. Leveraging large language models and natural language processing technology, GenAI can analyze data such as market events and traffic delays to inform suppliers about risks. Additionally, the GenAI chatbot is capable of handling customer inquiries, such as handling return requests or tracking delivery progress.

4. Operations

New technologies and fluctuating demands can create operational challenges, and GenAI can suggest how to improve.

GenAI can assess operational aspects such as supplier performance and manufacturing speed, then suggest ways to optimize processes. Among other benefits, these optimizations can save companies money.

5. Logistics

Logistics disruption may cause various problems. Traffic accidents can delay shipments, and extreme weather events can cause unexpected shortages, making it difficult to maintain on-time shipping schedules.

The GenAI model incorporates data such as historical weather patterns, traffic maps and fuel prices to identify the best travel routes and highlight potential upcoming disruptions and alternative routes if necessary. Doing this helps shipments stay on schedule and improves customer service because orders aren't delayed.

6. Sustainability and Scalability

Sustainability is currently a major focus for many businesses, and GenAI can potentially highlight areas for improvement.

Training a GenAI model based on a company’s current material usage as well as market forecasts for renewable materials can provide insights into how to make processes more sustainable, taking into account cost-effectiveness and long-term scalability.

7. Analysis

GenAI can run simulations and potential what-if scenarios, assess risks, and compile the results into reports.

As with all other GenAI supply chain use cases, caution is required when using this technology as GenAI and the models it drives are still evolving. Current concerns include incorrect data and imperfect output, also known as AI hallucinations, which can hinder effective use.

The above is the detailed content of What 'chemical reactions' will occur when AI meets ERP?. For more information, please follow other related articles on the PHP Chinese website!