One of many widespread issues of organizations with turn-key tasks is the excessive scrap fee. There exist such conventional strategies as Lean Six Sigma (LSS) and DMAIC instruments that analyze causes and counsel options. New rising clever applied sciences ought to affect these strategies and instruments as they have an effect on many areas of our life. The aim of this paper is to current the progressive Lean Six Sigma methodology for the Small Blended Bathes (SMB) manufacturing system. The usual set of LSS instruments is prolonged by clever applied sciences akin to synthetic neural networks (ANN) and machine studying. The proposed methodology makes use of the data-driven high quality technique to enhance the turning course of on the bakery machine producer. The case research reveals the step-by-step DMAIC process of essential to high quality (CTQ) traits enchancment. Findings from the info evaluation result in a change of measurement instrument, coaching of operators, and lathe machine set-up correction. Nevertheless, the scrap fee didn’t lower considerably. Subsequently the superior mathematical mannequin primarily based on ANN was constructed. This mannequin predicts the CTQ traits from the inspection certificates of the enter materials. The prediction mannequin is part of a newly designed course of management scheme utilizing machine studying algorithms to cut back the variability even for enter materials with totally different properties from new suppliers. Additional analysis will likely be centered on the validation of the proposed management scheme, and purchased experiences will likely be used to help enterprise sustainability.