Smart Agri-Region and Value Engineering - Raúl Pastor; Pablo G. Rodriguez; Antonio Lecuona; Juan Pedro Cortés (Systems)
Agriculture and silviculture offer interesting opportunities for food, energy, and construction sectors, but to transform such raw materials into valuable products, multiple engineering works must be carried out within R&D, innovation projects, and programs. The classical official decision to promote or supervise such projects involves many agents and criteria but rarely considers engineering quality, reusability, or other valuable and measurable attributes considered in ISO 25.000 or in value engineering guidelines. Missing them would increase technological, business, and programmatic risks, potentially wasting public money or credibility. Large projects are not free from these risks, and it is not a kind of madness to derive R&D and innovation funds to enable access to such valuable knowledge comprehensively, with models. In this context, communications and services, construction, and renewables play a crucial role in smart rural environments. Model-Based Systems Engineering (MBSE) and generative Artificial Intelligence (AI), combined with Natural Language Processing (NLP), are expected to help Knowledge Management (KM) in engineering and governance to supervise value engineering and their relationship with other metrics. Starting with a motivational and multidisciplinary framework for a smart rural transformation for System of Systems (SoS), the authors conduct specific bibliographic research on MBSE-NLP-AI use for automatizing systems engineering supervision at program governance levels.