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Abstract: The Stage-Gate® model has historically provided a systematic framework for New Product Development (NPD). However, the evolving landscape of innovation necessitates continuous enhancement. This paper redefines the model's foundational structure by advocating for the recognition of the Discovery Phase as Stage 1, emphasizing its essential role in aligning initial ideation with strategic goals, streamlining processes, and enhancing NPD efforts. Using a mixed-methods approach, including a systematic literature review, synthesis of illustrative examples and secondary data and case study analysis, the research demonstrates that formalizing the Discovery Phase improves earlystage decision-making, enhances alignment between front-end exploration and downstream execution and mitigates risks by supporting more informed project development. Synthesised sectoral examples show that incorporating the Discovery Phase improves feasibility, reduces risk, and boosts efficiency. For example, simulation planning early in innovation process increased manufacturing throughput by 52 %, while early IP checks lowered infringement risk. The proposed revision boosts the Stage-Gate® model's adaptability and integration with modern methodologies such as AI, Agile, Lean Startup, Design Thinking and TRIZ. The findings highlight how this change promotes a comprehensive approach to NPD. The implications extend to practical applications and future research, offering organizations a flexible framework that meets modern market and technological demands.
Keywords: Stage-Gate® model, fuzzy front end of innovation (FFEI), new product development (NPD), innovation management, discovery phase, agile, TRIZ, design thinking, large language model (LLM), sustainability
Published in DKUM: 03.11.2025; Views: 0; Downloads: 5
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Abstract: This paper presents a new approach for modelling nonlinear dynamic processes (NDP). It is based on a nonlinear autoregressive with exogenous (NARX) inputs model structure and a deep convolutional fuzzy system (DCFS). The DCFS is a hierarchical fuzzy structure, which can overcome the deficiency of general fuzzy systems when facing high dimensional data. For relieving the curse of dimensionality, as well as improving approximation performance of fuzzy models, we propose combining the NARX with the DCFS to provide a good approximation of the complex nonlinear dynamic behavior and a fast-training algorithm with ensured convergence. There are three NARX DCFS structures proposed, and the appropriate training algorithm is adapted. Evaluations were performed on a popular benchmark—Box and Jenkin’s gas furnace data set and the four nonlinear dynamic test systems. The experiments show that the proposed NARX DCFS method can be successfully used to identify nonlinear dynamic systems based on external dynamics structures and nonlinear static approximators.
Keywords: process identification, input-output modelling, NARX model, decomposed fuzzy system, hierarchical fuzzy system, deep convolutional fuzzy system
Published in DKUM: 30.11.2023; Views: 422; Downloads: 17
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Abstract: This paper presents a new neuro-fuzzy system based model, which is useful for the modelling of nonlinear dynamic systems. The new proposed model constitutes a soft computing method, namely, reasoning with a fuzzy inference system (FIS) and an optimisation by the neural-network learning algorithm. A structure, named the decomposed neuro-fuzzy ARX model is proposed. This structure is based on decomposition of the FIS. An evolution of a learning algorithm for the decomposed fuzzy model is suggested. A comparative study of dynamic system identification using conventional FIS models and the proposed neuro-fuzzy ARX model is presented for Box-Jenkins data set.
Keywords: input-output modelling, fuzzy ARX model, neuro-fuzzy system
Published in DKUM: 01.06.2012; Views: 2871; Downloads: 102
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