Artificial Intelligence and Digital Twin-Based Framework for Automated New Product Introduction Management and Innovative Charger Prototyping: The InnovaNP Case at Salcomp

Abstract

New product introduction (NPI) in contract manufacturing environments is traditionally slowed by manual, paper-based requirement gathering and fragmented information systems, producing costly divergences between quoted and actual costs and delaying time-to-market. This paper reports the ongoing applied research and experimental development project "InnovaNP", conducted through a technical cooperation agreement between Salcomp, a global manufacturer of power supply solutions for mobile and electronic devices, and the Conecthus Institute of Technology and Biotechnology of Amazonas, under the incentive regime of the Manaus Free Trade Zone (SUFRAMA, Decree No. 10,521/2020). The project integrates two complementary research fronts: (i) a software module that applies artificial intelligence — including time-series forecasting, Random Forest, and neural network models — together with Digital Twin concepts to support demand prediction, production planning simulation, and automated governance of the NPI process; and (ii) a hardware module that applies the Design for Six Sigma (DFSS) methodology, using the Define-Measure-Analyze-Design-Validate (DMADV) model, to design and prototype a family of electronic battery chargers of multiple power ratings (12W, 16W, 18W, 24W, and 45W). An extensive literature review situates the project within Industry 4.0, Digital Twin, machine learning forecasting, DFSS, and power-electronics design research streams. Partial results obtained through March 2026 show that the software platform has reached a stage of functional, integration, performance, and usability validation, while two hardware prototypes (P1 and P2) have undergone laboratory test campaigns whose preliminary findings point to the need for further specialized validation of electrical stability, thermal behavior, and electromagnetic interference. The paper discusses the expected contributions of InnovaNP to NPI management theory and practice and outlines the remaining validation milestones toward project closure.