In this era of digitalization, it’s a fact that PPAPs are considered to be documents – not data. The process of maintaining and verifying the accuracy of these documents is recursive and tedious. Many organizations have taken the effort of building portals that act as an extensive document management system to house and store all PPAPs submitted by suppliers. It is difficult for quality practitioners to transform these documents from heterogeneous forms, such as spreadsheets and PDFs, into usable data. The process of automatically analyzing and predicting supplier and product quality trends is still an unrealized dream.
This 30-minute Webinar examines a state-of-the-art digital solution called O-BOTs, which uses artificial intelligence (AI), machine learning, and deep learning algorithms to help evaluate PPAP documents. For example, O-BOTs can be used to have an entire PPAP submission reviewed and analyzed with feedback for the product team. The software automatically analyzes DFMEA, PFMEA, control plans, MSA, and SPC documents. In fact, there can be a trial submission for initial feedback so that the final submission is acceptable. In addition, O-BOTs can provide accurate reporting on the overall quality of PPAP submissions, and PPAP reviewers can train O-BOTs to accommodate internal rules and exclusions. O-BOTs, as a recommendation and validation system, can also guide product development engineers to develop better APQP/PPAP documents.
An audience Q&A follows the technical presentation.