Defining Batches under Consideration of Quality-Related Factors for Improved Failure and Scrap Analysis

Abstract

In order to satisfy upcoming needs for detailed traceability of products, components and manufacturing conditions, identification of every part is required. As for bulk goods applied marks are generally not appropriable due to variable costs, aggregation of individual parts manufactured under similar conditions to batches is carried out and these batches are identified and linked to information for tracking and tracing. Currently there is no standard for dividing bulk goods into batches. Hence, there are varying, company-specific approaches regarding rules for batch segregation and batch number definition. Thereby, transparency for subsequent stages of the supply chain is almost non-existent.In this paper we developed a method for batch segregation and number definition considering quality-related impacts. By identifying relevant influences and associated characteristics affecting events, basic parameters regarding segregation of batches can be found. As, especially in the automotive industry, FMEA and similar tools already are required, deriving this information is possible at low effort. Based on the factors identified, a meaningful batch ID may be generated including information on changes in particular parameters by encoding each parameter into the ID. While the overall objective should be outright informational transparency, sharing manufacturing data is not realistic in the short term. Therefore, our approach increases information sharing between members of the supply chain whilst protecting manufacturing know-how.The proposed systematic for batch IDs is supposed to enable subsequent participants in the supply chain to identify failure and scrap causes by communicating meaningful ID including relevant parameters. Hence, data analysis should be able to track down issues to changes in the manufacturing process at the supplier much faster and at lower effort than before. Additionally this may be a first step to pro-actively identify expectable quality issues due to information given in the ID and knowledge about correlation between different components and conditions with automated algorithms or some sort of artificial intelligence.