Artificial Intelligence in Quality Control and Product Development

Abstract

Product development and continuous improvement have become a high priority for the chemical industry due to factors such as increasing competition, new technologies, more demanding customers, greater product variety, and shorter product life cycles. The increasing pressure on product development has led chemical companies to evaluate the stages of the product development process carefully. In product development, preparation and technical analysis are particularly critical. The knowledge and experience gained in the preparation phase can eliminate risky and costly activities in the later stages of the process. In addition, the testing and evaluation phases are critical elements that can provide meaningful feedback before commercialization. Another issue that chemical manufacturers work on, both for new and existing products, is quality control (QC). Manufacturers need to ensure that the product’s quality meets customer expectations. Making QC processes more efficient is one of the issues the industry has been working on for a long time. Therefore, using artificial intelligence (AI) technologies in product development and QC has become inevitable. McKinsey asserted that the implementation of AI has the potential to enhance market fit by up to 50%, achieve product performance improvements ranging from 15 to 60%, attain up to 50% increased workplace productivity, and facilitate a reduction in time to market of up to 40%. According to the research conducted by Bain & Company, AI is predicted to result in a 20% reduction in engineering hours and a 5-30% reduction in costs. © 2026 Elsevier Inc. All rights reserved.