Prof. David Milla is analyzing the third element of deep analytics - structure i.e. the backbone of a system associated with a specific technological innovation [Figure 1.4]. What are the basic elements of the system architecture associated with a technology innovation? It has two critical viewpoints: system architecture and organization structure. The first one considers technological aspects of the system architecture in terms of topology, smart grid and various components of industrial control system such as SCADA, Expert system, DCS, PCS, SIS, BAS and EMS. The topology of a system should be analyzed in terms of nodes, connectivity, type of connections such as P2P or multipoint, layers, interfaces between layers and organization of layers.
For example, OSI model is a layered framework for the design of communication networks of information systems. It has seven layers from bottom to top : physical, data link, network, transport, session, presentation and application layers. A data communication system has five basic components such as message, sender, receiver, transmission medium and protocol. On the basis of nodes and links, the physical topology of a communication network can be classified into four categories such as mesh, ring, star and bus. The second viewpoint is organization structure – what type of structure is suitable for specific technological innovation; it may be functional, divisional, matrix or network structure. Is there any link between technology and organization structure? It depends on the characteristics of business model.
Another view of structure should be explored in terms of organization structure, size of a firm, economies of scale in R&D, access to complementary resources such as capital and market, governance mechanisms and organizational learning. There are various types of organization structure such as divisional and networked models. The efficiency and creativity of innovation model is closely associated with different types of structural dimensions such as formalization, standardization, centralization, decentralization and loosely coupled networks within and between firms. Global firms should consider several critical factors such as knowledge, resources and technological diffusion to conduct R&D activities.
Figure 1.4: Structure analytics
How is it possible to represent the structure of a system associated with a technology innovation correctly and transparently? Digital twins may be an interesting solution; it integrates the concept of industrial IoT, AI, machine learning and software analytics to optimize the operation and maintenance of physical assets, systems and manufacturing processes. A digital twin is the digital replica of a living or non-living physical entity (e.g. physical asset, process, agent, place, system, device); it is expected to bridge and support data sharing between the physical and virtual entities. Digital twins can learn from multiple sources such as itself through sensors, historical time series data, experts and other nodes of the networking schema of the system and get updated continuously to represent real-time status, working conditions or positions.
The concept of digital twins are expected to be useful for manufacturing, energy (e.g. HVAC control systems), utilities, healthcare and automotive industries in terms of connectivity, digital traces and product life-cycle management. The concept can be used for 3D modeling to create digital companions of the physical objects i.e. an up-to-date and accurate copy of the properties and states of the objects (e.g. shape, position, gesture, status, motion) based on the data collected by the sensors attached to the system. It may be useful for the maintenance of power generation equipment such as turbines, jet engines and locomotives; monitoring, diagnostics and prognostics to optimize asset performance and utilization through root cause analysis and to overcome the challenges in system development, testing, verification and validation for automotive applications. The physical objects are virtualized and can be represented as digital twin models seamlessly and closely integrated in both physical and cyber spaces. Digital twins should represent the structure of a product innovation intelligently through various phases of the product life-cycle.
Another interesting technology for exploring innovative structure is V-commerce through virtual (VR), mixed (MR) and augmented reality (AR). A virtual entity may not exist physically but created by software in a digital environment. VR and AR are sophisticated, creative and powerful tools to show complex structures and offer a complete computerized digital experience by integrating AI, computer vision, graphics and automation in various applications such as manufacturing, retail, healthcare, entertainment, furniture and interior decoration.
Structure Analytics
Agents: System analysts, business analysts, technology management consultants;
Objects / entities: sustainable smart cities, smart villages, communities, smart world, smart universe;
Moves: Design and configure /* refer to scope and system analytics, sections 1 and 2 */
Level 1: digital technology, information technology, computer science, earth science, environmental engineering, agriculture engineering, genetic engineering, electrical, electronics, telecommunication, sensor, renewable energy, power plant, instrumentation, biomedical, biotechnology, pharmacy, chemical, mechanical, nanotechnology, mechatronics, automobile, civil, construction, architecture, chemical, petroleum, oil and gas, metallurgy;
Level 2 : Identify technology classification and technology association in the technology tree.