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AI in the Laboratory – Part III
Artificial Intelligence in Laboratory Practice - Part III
Scenario 2: Fast Identification of Supplier Issues with the Help of AI
In today‘s competitive market, ensuring the quality of raw materials from suppliers is critical for maintaining product standards. This scenario highlights how AI can assist in comparing different material suppliers, providing manufacturers with the insights needed to make informed decisions.
Scenario 1: Fast Identification of Correlations with the Help of AI
Handling and analyzing vast amounts of data can be extremely challenging. Traditional methods, such as manual data entry and analysis in Excel, are often time-consuming and prone to human error. This scenario illustrates how AI can streamline these processes, making it easier to identify critical correlations and trends in complex datasets.
The Problem
Fluctuations and quality problems with supplier material were detected too late or not at all due to a lack of data analysis and visibility. It was difficult to monitor suppliers over long periods of time, which meant that ever-increasing deviations from required standards went unnoticed. In worst case, this could lead to delayed or unauthorized release of production batches.
The Solution
With the AI assistant, different prompts were used to analyze the complete data sets
from all three suppliers, aimed at finding suspicious parameters from one or more suppliers.
With a single prompt, the AI assistant provided a table of all results for all 10 batches of polymer materials from all suppliers. From the table, it’s clear that there is an anomalous result in batch 4 from supplier C, where a material has been supplied that is of a higher viscosity than the rest of their batches which is 3 to 4 times more viscous than the rest of their batches. With additional queries, the plastics processor‘s laboratory was able to generate quality control charts for all batches and all suppliers in a matter of seconds. They confirmed that supplier C had supplied a material of inferior quality in one of its batches. This enabled the processor to make an informed decision on supplier selection.
With additional queries, the plastics processor‘s laboratory was able to generate quality control charts for all batches and all suppliers in a matter of seconds. They confirmed that supplier C had supplied a material of inferior quality in one of its batches. This enabled the processor to make an informed decision on supplier selection.
In the next upcoming blog post we will take a closer look at other prompts to compare the quality of several suppliers by AI-generated visualizations. Or just download our case study below.
AI in Laboratory Practice
Everyone is talking about artificial intelligence (AI). In this case study however, we show how AI can be used in the lab on a practical level.
Learn more in this case study:
- How AI-based solutions can integrate fragmented laboratory data into a cohesive system
- What AI can do in your lab to improve efficiency and to exploit all data
- Practical examples of how AI can streamline data analysis
- The benefits of an AI-powered data management platform in transforming laboratory operations, reducing errors, and saving time.
Download the case study now and discover what AI can do in your laboratory.