Technology peripherals
AI
Application of intelligent technology in supply chain management: artificial intelligence and intelligent document processing technology
Application of intelligent technology in supply chain management: artificial intelligence and intelligent document processing technology
Artificial intelligence (AI) has irrefutable potential to improve business operations, but not always in the ways people imagine. For some, artificial intelligence in supply chains conjures up images of robots manning conveyor belts or drones speeding up delivery times. While this may eventually become a reality, the application of AI in modern supply chain management strategies is much more practical.
Supply chains are under intense pressure to deliver on time, whether to other organizations or directly to consumers. This situation is further exacerbated by staff shortages across the country, with fewer employees available for day-to-day business tasks.
AI-driven Intelligent Document Processing (IDP) can replace manual data entry with automated data capture, enabling digital extraction and export of information in minutes, simplifying customs compliance and reducing Backlog. By integrating AI applications to optimize user experience and provide immediate, measurable results, the supply chain industry can streamline daily operations, streamline manual data entry, and save businesses time and expense.
Here are some examples of the best use cases for integrating intelligent document processing into supply chain management operations, and the obstacles this technology can overcome:
Manual data entry errors
Gartner predicts that poor data quality costs businesses an average of $12.9 million per year. Many factors contribute to this statistic, with manual data entry playing a large role. Not only is this time consuming, but it also increases the likelihood of introducing human error. The more errors there are, the worse the data quality is, leading to wrong business decisions. Additionally, manually entering data can leave supply chains with outdated information because employees can’t keep up with the volume of data. Rushing to catch up can get ahead of input data quality, leaving businesses with inaccurate information and outdated data, leading to inefficiencies and poor decision-making.
In 2020, a study ranked manual data entry as one of the most hated office tasks among employees, leading to high employee turnover. Intelligent document processing eliminates manual data entry, allowing employees to focus on high-value tasks. Data quality improves and data processing speeds up, saving businesses money and time.
Data Inconsistency
If the company has a manual data entry position, there is a good chance that more than one person is responsible for this position. Adding more people may reduce the time it takes to log this data into the system, but it may also lead to inconsistencies in the data. For example, each employee responsible for manual data entry may define categories differently and interpret the data differently. As a result, information may be entered correctly but shifted or sequenced inconsistently, thus worsening the quality of the data available to the company. While this can be reduced with proper training, it does not eliminate the possibility of this inconsistency.
Intelligent Document Processing (IDP) provides consistency and quality of data input. The system can read documents like a human, but it does a better job of identifying and sorting content rather than blindly analyzing formats. As AI systems are used more, it will get better at data capture, making all entries more accurate. This can significantly reduce the number of data conflicts in the supply chain.
Continuing Backlog
Backlogs and bottlenecks continue to cause delays in transportation and logistics. This problem at individual companies could have a negative impact on the global economy. Companies can work around this by pausing sales and orders while they work through the backlog, but a continued revenue stream is needed to keep the company afloat. From here, the backlog continues to pile up, exacerbating the problem and frustrating customers and employees alike. As supply chains expand, it becomes increasingly impractical for one person to be responsible for handling these backlogs.
Intelligent document processing greatly shortens the time to deal with the backlog and speeds up the delivery of goods. Invoices will have a faster output, errors in documentation will be identified more quickly, and the system can incorporate real-time error correction feedback. Inaccuracies can be resolved immediately and the need for further traceback processes is eliminated.
Smart file handling becomes even more powerful with the addition of email integration. Imagine being able to proactively keep suppliers in the loop with automated email notifications and status updates. It is now possible to automate notifications and alerts, send payment and invoicing information, confirm receipts, provide status and follow-up updates via email.
According to data released by IDC, the global intelligent document processing (IDP) market will grow at a compound annual growth rate of 23.1% in the next five years. Almost all industries are beginning to recognize the importance of integrating IDPs into their business models.
However, advances in artificial intelligence in supply chains or any industry will not happen overnight. When designing new technology, improvement is always a gradual process. To ensure that the supply chain gets the best AI, AI must be implemented from the foundational level. Intelligent file processing provides the artificial intelligence elements needed to automate and streamline workflows for greater operational flexibility. This technology eliminates tedious manual data entry while providing a portal to the collective future that can support the drones and robots that capture everyone’s attention.
##
The above is the detailed content of Application of intelligent technology in supply chain management: artificial intelligence and intelligent document processing technology. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
AI Hentai Generator
Generate AI Hentai for free.
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
1384
52
Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions
Jun 28, 2024 am 03:51 AM
This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP
Context-augmented AI coding assistant using Rag and Sem-Rag
Jun 10, 2024 am 11:08 AM
Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application
Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations
Jun 11, 2024 pm 03:57 PM
Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it
Seven Cool GenAI & LLM Technical Interview Questions
Jun 07, 2024 am 10:06 AM
To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text
Five schools of machine learning you don't know about
Jun 05, 2024 pm 08:51 PM
Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing
To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework
Jul 25, 2024 am 06:42 AM
Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A
SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time
Jul 17, 2024 pm 06:37 PM
Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S
SK Hynix will display new AI-related products on August 6: 12-layer HBM3E, 321-high NAND, etc.
Aug 01, 2024 pm 09:40 PM
According to news from this site on August 1, SK Hynix released a blog post today (August 1), announcing that it will attend the Global Semiconductor Memory Summit FMS2024 to be held in Santa Clara, California, USA from August 6 to 8, showcasing many new technologies. generation product. Introduction to the Future Memory and Storage Summit (FutureMemoryandStorage), formerly the Flash Memory Summit (FlashMemorySummit) mainly for NAND suppliers, in the context of increasing attention to artificial intelligence technology, this year was renamed the Future Memory and Storage Summit (FutureMemoryandStorage) to invite DRAM and storage vendors and many more players. New product SK hynix launched last year
