Modularity Design Rules for Architecture Development: Theory, Implementation, and Evidence from the Development of the Renault–Nissan Alliance “Common Module Family” Architecture

In this paper, we propose a set of rules for developing modular architectures. We first consider the well-known concept of “Design Rules” advanced by Baldwin and Clark. We then propose a broader conceptualization called “Modularity Design Rules” that is derived from later studies of the strategic, managerial, and organizational processes that must also be undertaken to implement successful modular development projects. We elaborate the critical role that the proposed Modularity Design Rules play in strategically grounding, organizing, and managing modular architecture development processes. We also identify key roles that top management must fulfill in supporting implementation of the proposed rules. We then provide evidence in support of the proposed Modularity Design Rules through a case study of the Renault–Nissan Alliance’s successful development and use of a modular “Common Module Family” architecture between 2009 and 2014. We then suggest some important implications of the Modularity Design Rules for open innovation processes in new product development.

Aircraft Engine Performance Monitoring and Diagnostics Based on Deep Convolutional Neural Networks

The rapid advancement of machine-learning techniques has played a significant role in the evolution of engine health management technology. In the last decade, deep-learning methods have received a great deal of attention in many application domains, including object recognition and computer vision. Recently, there has been a rapid rise in the use of convolutional neural networks for rotating machinery diagnostics inspired by their powerful feature learning and classification capability. However, the application in the field of gas turbine diagnostics is still limited. This paper presents a gas turbine fault detection and isolation method using modular convolutional neural networks preceded by a physics-driven performance-trend-monitoring system. The trend-monitoring system was employed to capture performance changes due to degradation, establish a new baseline when it is needed, and generatefault signatures. The fault detection and isolation system was trained to step-by-step detect and classify gas path faults to the component level using fault signatures obtained from the physics part. The performance of the method proposed was evaluated based on different fault scenarios for a three-shaft turbofan engine, under significant measurement noise to ensure model robustness. Two comparative assessments were also carried out: with a single convolutional-neural-network-architecture-based fault classification method and with a deep long short-term memory-assisted fault detection and isolation method. The results obtained revealed the performance of the proposed method to detect and isolate multiple gas path faults with over 96% accuracy. Moreover, sharing diagnostic tasks with modular architectures is seen as relevant to significantly enhance diagnostic accuracy.

Magnetic Micro- and Nanoagents for Monitoring Enzymatic Activity In Vivo

Enzymes are appealing diagnostic targets because of their centrality in human health and disease. Continuous efforts spanning several decades have yielded methods for magnetically detecting the interactions of enzymes with exogenous molecular substrates. Nevertheless, measuring enzymatic activity in vivo remains challenging due to background noise, insufficient selectivity, and overlapping enzymatic functions. Magnetic micro- and nanoagents are poised to help overcome these issues by offering possible advantages such as site-selective sampling, modular architectures, new forms of magnetic detection, and favorable biocompatibility. Here, we review relevant control and detection strategies and consider examples of magnetic enzyme detection demonstrated with micro- or nanorobotic systems. Most cases have focused on proteolytic enzymes, leaving ample opportunity to expand to other classes of enzymes. Enzyme-responsive magnetic micro- and nanoagents hold promise for lowering barriers of translation and enabling preemptive, point-of-care medical applications. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Modular Hardware Architecture for the Development of Underwater Vehicles Based on Systems Engineering

This paper addresses the development of a modular hardware architecture for the design/construction/operation of a remotely operated vehicle (ROV), based on systems engineering. The Vee model is first presented as a sequential process that emphasizes the validation processes with stakeholders and verification plans in the development and production stages of the ROV’s life cycle. The conceptual design process starts with the mapping of user requirements to engineering specifications, using the House of Quality (HoQ), a quality function deployment tool that allows executing a functional-division-based hardware design process that facilitates the integration of components and subsystems, as desired for modular architectures. Then, the functional division and hardware architectures are described, and their connection is made through the proposed system architecture that sets the foundation for the definition of a physical architecture, as it involves flows that connect abstract functions with a real context. Development and production stages are exemplified through the design, construction, and integration of some hardware components needed for the remotely operated vehicle Pionero500, and the operational stage briefly describes the first sea trials conducted for the ROV. Systems engineering has shown to be a very useful tool for the development of marine vehicles and marine engineering projects that require modular architectures.

Modular Approach to Ultra-fast Charging Stations

Road transport electrification is essential for meeting the European Union's goals of decarbonization and climate change. In this context, an Ultra-Fast Charging (UFC) system is deemed necessary to facilitate the massive penetration of Electric Vehicles (EVs) on the market; particularly as medium-long distance travels are concerned. Anyway, an ultra-fast charging infrastructure represents the most critical point as regards hardware technology, grid-related issues, and financial sustainability. Thus far, this paper presents an impact analysis of a fast-charging station on the grid in terms of power consumption, obtained by the Monte Carlo simulation. Simulation results show that it is not economical convenient size the assumed ultra-fast charging station for the maximum possible power also considering its high impact on the grid. In view of the results obtained from the impact analysis, the last part of the paper focuses on finding a method to reduce the power installed for the DC/DC stage while keeping the possibility for the electric vehicle to charge at their maximum power. To achieve this goal a modular approach is proposed. Finally, two different modular architectures are presented and compared. In both the solutions, the probability of having EVs charging at limited power is less than 5%.

Design, Development and Evaluation of a telemedicine platform for patients with sleep apnea: A Design Science Research approach (Preprint)

BACKGROUND With an aging population and escalating cost of care, telemedicine alternatives are increasingly becoming a societal imperative. Such a case is especially true of patients seeking care for sleep apnea, with its prevalence approaching 1 billion worldwide. Increasing awareness has led to a surge in demand for sleep apnea care; however, there is a short supply of resources and expertise necessary to cater to this rising demand. OBJECTIVE The study aimed to explore the design, development, and evaluation of a telemedicine platform for the consultation, diagnosis, and treatment of patients suffering from sleep apnea called ‘Ognomy.’ METHODS The telemedicine platform was developed using the framework in the design science research paradigm. The context was analyzed using activity theory to map requirements and features. Subsequently, low and high-fidelity prototypes were developed using collaborative design sprints. Following that, iterative software development methods and layered modular architectures were used to build the platform. Finally, the platform was evaluated through several levels of validation using usability testing, design review and the system testing. RESULTS The telemedicine platform ‘Ognomy’ comprises four artifacts 1. Mobile application for patients 2. Web application for providers 3. Dashboard for reporting, and 4. AI-based chatbot for customer onboarding and support. Ognomy went live in April 2020 and caters to patients in the region. The patient-facing application has been more than 4000 downloads and has received excellent reviews and ratings on Android and iPhone app stores. CONCLUSIONS Sleep apnea is an underdiagnosed and undertreated condition. However, with increasing awareness, the demand for quality sleep apnea care will surge, and creative alternatives will be needed. The results from this study demonstrate the successful application of a framework in the design science research paradigm to design, develop, and evaluate a telemedicine platform for patients suffering from sleep apnea and their providers.

Decision Support Method for Upgrade Cycle Planning and Product Architecture Design of an Upgradable Product

An upgradable product is a product in which the valuable life is extended by exchanging or adding components. An upgradable product is both environmentally and economically advantageous compared with products requiring replacement because its functions can be improved by adding only a few components. Therefore, the design and sale of upgradable products represent effective methods for attaining a sustainable society. Previous studies of upgradable product design methods have assumed that products have a modular architecture, in which all components are functionally independent. However, actual products have both integral architectures and modular architectures. Achieving high-performance products through component optimization is easier with an integral architecture than with a modular architecture. However, the integral architecture makes it difficult to disassemble and replace individual components. It is difficult to achieve high levels of performance in products with modular architecture, but it is easy to disassemble and replace components. Therefore, upgradable product design must determine the most appropriate product architecture. Hence, this paper focuses on the product architecture of upgradable products and proposes a decision support method that yields the appropriate combination of product architecture and upgrade cycle. In addition, the authors propose evaluation models for the environmental load, cost, and customer dissatisfaction, as well as a comprehensive evaluation index based on these models. The overall model, which gives the evaluation index, considers the differences in the evaluated values resulting from differences in the product architecture and the number of upgrades. The proposed method was applied to a motherboard module design problem for a laptop computer. The results of this case study confirm that the proposed method successfully supports the designer during upgradable product design by deriving the most suitable combination from a set of product architectures and upgrade cycle candidates.

The Data Imperative

Companies across all industries are engaging in digital transformation to harness the power of advanced information technologies. Building on interviews and diverse case studies, this book describes how data and algorithms are reshaping management practices, organizational structures, corporate culture, and work roles. The book develops a broad framework for understanding digitalization not as a technological change, but as a new normative mindset, ‘the data imperative’. New managerial ideals compel companies to pursue digital omniscience and omnipotence—the abilities to represent and understand the world through real-time data flows and to control customer experiences, physical equipment, and workers with software. The efforts to complement and replace human expertise with data and smart algorithms are associated with shifts in strategic priorities, adoption of powerful modular architectures, new organizational structures, and introduction of artificial intelligence into diverse work roles. Surveying the changes in management and the workplace, this book offers an integrative and balanced account of the ongoing changes. It elaborates how artificial intelligence is changing work at all levels of the hierarchy and envisions how the emerging artificially intelligent organization will change how professionals work. The frameworks and ideas espoused in this book will help the reader understand the ongoing changes in the workplace that affect everyone from executives and professionals to frontline workers.