This blog explains how using Large Language Models (LLMs) with tools like Microsoft Power Automate and SharePoint can improve backorder management. By automatically pulling details like product IDs, delivery dates, and order quantities, LLMs reduce the need for manual data entry, making workflows faster and more accurate.
Managing lengthy email correspondences with suppliers can quickly become a frustrating and time-consuming task, especially when you're trying to track down critical information like the status of backorders. Picture this: you’re scrolling through a long chain of emails, trying to piece together scattered details about material names, IDs, delivery dates, and other key information. Each email might contain a snippet of what you need, but finding it requires you to go through repetitive, irrelevant, or outdated messages one by one. This process not only eats up valuable time but also increases the risk of overlooking essential details, leading to errors and delays that ultimately reduce service levels.
Wouldn’t it be great if there was a way to automatically scan through your entire email thread and extract only the data you need? Imagine having all the relevant information, like material names, IDs, and delivery dates, delivered to you in a clear and organized format—without the hassle of manually combing through each and every email one by one.
To address this challenge LLMs offer a powerful and efficient solution. When applied to the task of backorder management, they can be trained to recognize specific types of information within an email thread, such as product IDs, delivery dates, order quantities, pricing details, supplier contact information, shipment tracking numbers, and more. These models can then compile this information into a structured format, such as an Excel spreadsheet, JSON, CSV, etc. which are much easier to review, utilize and integrate with ERP systems.
The process begins with inputting the entire email correspondence into the LLM. The model then processes the text, using its advanced understanding of language to determine which details are important. It identifies and extracts relevant information, considering the context and relationships between various pieces of data. The LLM can handle a wide range of formats, including paragraphs, bullet points, and tables, ensuring that no critical information is overlooked.
The video above shows how LLMs are integrated into backorder management workflows, using Microsoft Power Automate and SharePoint to streamline the process.
Instead of spending hours going through each email and manually extracting data, the LLM performs this task quickly and efficiently. This automation not only saves time but also reduces the risk of mistakes a human might make, leading to more accurate and reliable data management.
As they are used more frequently, these models can be fine-tuned to better understand the specific workflows, industry terminology, and communication styles relevant to a particular business. This adaptability ensures that the LLM remains effective as businesses change and evolve.
These models can be connected to email systems, ERP software, or other business tools, allowing for real-time data extraction and automatic updates. This integration enables a faster and more responsive workflow, where important data is captured and organized as soon as it arrives.
By automating the tedious task of data extraction, LLMs free up valuable time and resources, allowing team members to focus on more strategic and value-added activities. This shift not only increases productivity but also enables businesses to better allocate their human resources to tasks that require creativity, critical thinking, and decision-making.
With the ability to consistently and accurately extract important information from email correspondences, businesses can trust that their backorder data and other critical details are up-to-date and accurate. This reliability reduces the risk of missed deadlines, overlooked details, and miscommunications with suppliers or customers.
Utilizing LLMs allows companies to enhance their workflows, increase precision, and elevate productivity, all while minimizing the effort required for manual data management. This leads to a more efficient, dependable, and agile operation that enables faster and better customer service.
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