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Traceability and quality management through Artificial Intelligence

by Mosaniy Editorial
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This Post has been sponsored by MOSANIY ENTERPRISES (https://mosaniy.com/)

For today’s businesses to ensure output quality, optimize processes, control inventory, and guarantee on-time deliveries, the entire supply and production chain must be transparent. In any industry, traceability and quality control are crucial to business performance.

The goal of the artificial intelligence-powered traceability system

The goals of the traceability system can vary depending on the situation because we can keep track of the many production events:

  • Identify each product and learn about its past
  • Quickly spot items that don’t meet quality standards and remove only the necessary batches from the supply chain
  • Improve all of the operational procedures
  • Boost productivity: We can know the performance of the machine at different stages or when different goods have been made thanks to traceability controlled by intelligent software and act accordingly to enhance the process.

It is important to decide how to apply the system in each particular situation in order to select the actually important events and accomplish your goals. Some people have the idea that artificial intelligence-based traceability can only be used in sectors where product safety must be ensured, like medicines or catering. To increase the quality and effectiveness of the production chain, transparency at all stages of a traceability process is valuable and essential for all businesses. This is because it gives real-time visibility to all performed operations and provides the necessary data for effective control. Any industry can swiftly identify incidents and take the necessary action with instant information.

Traceability with sophisticated management software

It is crucial to have a system that enables us to recognize and keep track of the various events that take place throughout the industrial flow, enabling us to gather both quantitative and qualitative data:

  • The many stages the product is in
  • Composition/materials list
  • Equipment used
  • Batch no (raw material and final products)
  • The operators who take part in each stage of production
  • Duration
  • Put in shifts
  • The outcomes of the various quality controls

This list is only a portion of all the production-related events that can be used for further research. An intelligent system uses IoT connectivity to transfer this data automatically from the assets to the information management platforms.

The monitoring and tracking of quality

The management of traceability must place a strong emphasis on quality control. The purpose of quality control is to guarantee that the final product complies with predetermined criteria. For instance, some of these metrics in package quality control can be tensile/compressive strength, hardness, gas permeability, or hygroscopicity.

We can make sure that the items we supply to our clients fulfill the aforementioned requirements by doing quality control using clever software that connects the entire production process. Additionally, expenses and waste are decreased since, if a product needs to be thrown from manufacturing, intelligent traceability utilizing machine learning enables the identification of its detailed history and the rapid discarding of only the pertinent batches.

Finally, data from various sources and systems are cross-referenced to contextualize the greatest amount of information into a more complete picture in order to have a good traceability of the entire manufacturing process. Therefore, it is crucial that the solutions in charge of managing traceability be integrated with systems that connect to and assess each equipment and process’ performance using real-time data analysis.

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