In recent months, I have been watching with a mix of trepidation and curiosity as the introduction of Large language models (LLMs) has become mainstream in academic research and technical development. As a practitioner and educator in systems engineering and mechanical design, it is my responsibility to keep my knowledge and skill set up-to-date and reflect this in my research and teaching. Part of this includes evaluating new tools and technology within my area of expertise and providing realistic professional guidance based on my experiences. This reflection outlines my thoughts and professional opinion on the use of LLMs to support systems engineering and mechanical design. As a designer with training in classic mathematical optimization techniques and large-scale systems engineering, I first worked to understand LLMs and how they relate to my area of expertise in systems engineering and mechanical design. From an engineering perspective, an LLM is an artificial intelligence (AI) or optimization model which has been designed and trained using human language. Similar to how most of these kinds of tools are trained using labeled images or real-world technical data, LLMs recognize patterns and interactions between words and pieces of written text. The initial models are only as good as their training data, but continue to improve over time as they encounter more and more data. As with many other types of AI models, the initial training is done with a massive dataset (text in the case of LLMs) using an unsupervised learning algorithm. Once the baseline model is trained using the general dataset, it can then be refined using more specialized training data to develop more specialized tools. It is clear that LLMs are not some kind of new technology or “silver bullet” tool but simply a different application and logical progression/development of existing design and AI tools which derive from classic mathematical optimization. This is reassuring for those nervous about the emergence of a technology which seems to blur the lines between human and machine. In reality, it is simply a mathematical model which mimics human speech and writing by learning patterns.

This article explored the spiral development process, sometimes called "evolutionary acquisition" in military sectors, and reviewed major non-software applications in the engineering literature. The spiral development process was originally developed for the design of software while minimizing and managing risk, but the principles can be applied to a wide variety of systems engineering problems where risk management is a priority. The major application domains discussed in this review were product design and development, robotics, agriculture and construction systems, product-service and human-technology systems, medical systems and devices, military and aerospace systems, and data management, enterprise systems, and information technology systems. This exploration and accompanying discussion is useful for system designers, systems engineering educators, and other major stakeholders as it shows successful applications in a wide variety of non-software technology sectors and provides guidance for application in new areas. Several new and expanded application sectors were identified during the review.

Purpose: This study delves deep into systematically integrating AI, Blockchain, and IoT within manufacturing, guided by Lean Six Sigma (LSS) philosophy, aiming to promote higher precision, human safety, sustainability, reduced errors, and wastage while maintaining minimal human involvement. Design/methodology/approach: The study rigorously studies cases of the manufacturing industry integrating these concepts into real industrial or experimental setups and discusses their potential implications. It explores the step-by-step integration, control, and regulation of intricate manufacturing aspects. Findings: Artificial Intelligence is beneficial for real-time regulation and prompt corrective measures during manufacturing operations. The Internet of Things provides real-time feedback, ensuring synchronization between teams and departments to maintain flaw detection and correction. Blockchain offers security and transparency in task performance, supply chain management, and payments, resulting in a seamless and efficient manufacturing experience. Originality: This study is not a mere overview or surface-level juxtaposition of Industry 5.0 concepts. It offers a comprehensive analysis of the holistic integration of advanced technologies within manufacturing, linking developments to sustainable manufacturing. It provides new insights and perspectives not discussed in current literature found in databases like Scopus and Web of Science. Research limitations/implications: The study encourages ongoing research and development to meet modern economic needs and environmental challenges. It emphasizes that the integration is not the endpoint, but continuous improvement (kaizen) should prevail. Practical implications: This study serves as an authoritative source of information for the scientific community to further advancements in the field, guiding them to develop evidence-based opinions. Social implications: The findings support the vision of sustainable digital manufacturing, promoting economic efficiency and environmental sustainability, crucial for the current and future industrial landscape.