Utilization of IoT and AI is a big chance not only for large enterprises but also for small-and-medium-sized enterprises (SMEs) and local communities. However, it is not easy for them to realize it. We have proposed "innovation design method." This method consists of frameworks such as value design (value proposition canvas), system design (SCAI graph), and strategic design (customer value chain analysis, open&closed canvas). In this paper, the innovation design method was applied to ideathon for solving regional challenges of Nomi City. Here, it is necessary to renovate the local communities and resources. We integrated the framework of "community renovation" into the existing innovation design method. In the "Nomi City × AI / IoT ideathon", 16 students participated in 4 days intensive group work based on the innovation design method, then proposed four
solutions utilizing IoT and AI. The effectiveness of the proposed innovation design method includes:(1) to efficiently diverge and converge ideas with short-term concentrated group work, (2) to recognize and claim the intrinsic value of the proposed solution, and (3) to visualize what kind of value should be provided to whom using the community renovation framework.
Human-centric Digital Twin Focused on ‘Gen-Ba’ KnowledgeNaoshi Uchihira
In recent years, companies and society have undergone a rapid digital transformation, and“digital twin” is the key technology for utilizing various information and knowledge in both physical and cyber spaces. However, sensor data that can be collected by IoT (Internet of Things) is only a small aspect of physical space. In particular, in on-site human working fields, such as nursing care, agriculture, manufacturing, maintenance, and inspection (we call them “Gen-Ba”), there exists a vast amount of knowledge (“Gen-Ba knowledge”) that humans possess and cannot be captured by IoT sensors. Since Gen-Ba knowledge includes not only explicit but also latent and tacit knowledge, it has been difficult to utilize it in cyberspace, and there is still a large gap between the two spaces in the digital twin. This study aims to make it possible to capture, systematize, and utilize the vast amount of Gen-Ba knowledge in the human-centric digital twin. The smart voice messaging system (SVM), which has been used for ten years in actual fields, can be used to capture Gen-Ba knowledge. This study proposes a conceptual model of a human-centric digital twin with a focus on Gen-Ba knowledge according to several experiments of real-world applications of SVM.
Human-centric Digital Twin Focused on ‘Gen-Ba’ KnowledgeNaoshi Uchihira
In recent years, companies and society have undergone a rapid digital transformation, and“digital twin” is the key technology for utilizing various information and knowledge in both physical and cyber spaces. However, sensor data that can be collected by IoT (Internet of Things) is only a small aspect of physical space. In particular, in on-site human working fields, such as nursing care, agriculture, manufacturing, maintenance, and inspection (we call them “Gen-Ba”), there exists a vast amount of knowledge (“Gen-Ba knowledge”) that humans possess and cannot be captured by IoT sensors. Since Gen-Ba knowledge includes not only explicit but also latent and tacit knowledge, it has been difficult to utilize it in cyberspace, and there is still a large gap between the two spaces in the digital twin. This study aims to make it possible to capture, systematize, and utilize the vast amount of Gen-Ba knowledge in the human-centric digital twin. The smart voice messaging system (SVM), which has been used for ten years in actual fields, can be used to capture Gen-Ba knowledge. This study proposes a conceptual model of a human-centric digital twin with a focus on Gen-Ba knowledge according to several experiments of real-world applications of SVM.
Success Mechanisms of Smart Factories in Small and Medium-Sized EnterprisesNaoshi Uchihira
Although large companies have progressed in digital transformation (DT) of factories using Internet of Things (IoT) and artificial intelligence (AI), small and medium-sized enterprises (SMEs) have not progressed enough. However, considering the efficiency and value creation of the entire supply chain, it is important to promote a smart factory in not only large companies but also SMEs. This study examines the success factors and mechanisms based on the case studies of Japanese SMEs that have successfully implemented smart factories. Then, its characteristics are compared with those of large companies. Specifically, in SMEs, where the top management and the factory floor are in close proximity, if the purposes and vision of DT are clear and the top management understands its possibilities and limitations, successful experiences can spread throughout the company through trial and error using an in-house system optimized for the factory floor. This study reveals that the success mechanism makes it easier for SMEs to promote DX than for large companies under certain conditions, which is a new finding and a theoretical contribution. The practical contribution of this study is that it guides SMEs to promote DT in factories in SMEs based on this mechanism.
Project FMEA for Recognizing Difficulties in Machine Learning Application Sys...Naoshi Uchihira
Digital transformation (DX) is spreading across all industries. Artificial Intelligence (AI), especially machine learning, is inevitable for effective use of the data collected and stored in DX, and systems that utilize machine learning have been developed in various industries and companies. The development of machine learning application systems (MLASs) presents different difficulties from traditional IT system development. Therefore, software engineering (especially project management) for MLAS has become one of the most important issues in these days. In this paper, we have classified the difficulties of MLAS development based on various documents and interviews, and created a difficulty map consisting of 12 categories. This difficulty map has some unique features that include the introduction of the relationship between the difficulties and the dual MLAS development process (implementation process and exploitation process). In this paper, we then propose a method of expressing and sharing these difficulties among stakeholders, based on MLAS Project FMEA (Failure Mode Effect Analysis). The proposed method was evaluated using two illustrative MLAS examples.
The Nature of Digital Transformation Project Failures: Impeding Factors to St...Naoshi Uchihira
For successfully executing digital transformation (DX) projects in a company, collaboration among various internal and external stakeholders is intrinsically crucial and key to success. However, there are various factors that can impede such collaborations, six of which were identified in this paper based on interviews with DX project stakeholders. Six impeding factors include information gap, experience gap, perception gap of the future, incompatible evaluation criteria, conflict of interest, and lack of mutual trust. Although previous studies have discussed superficial factors which directly explain the success or failure of such projects, this paper isolates and explores them in greater depth than the traditional ones. These essential impeding factors are helpful to find solutions to eliminate them and lead DX projects to success.
Dialogue Tool for Value Creation in Digital Transformation: Roadmapping for...Naoshi Uchihira
With the rapid spread of digital technologies into industry and society, the collaboration between humans and machines (artificial intelligence and ma-chine learning) becomes an important issue, but it is not clear what kind of value can be created by the collaboration between humans and machines. Roadmapping is effective as a dialogue tool for clarifying the value among stakeholders. However, the traditional roadmapping methods are insufficient since collaboration between humans and machines is a socio-technical system and evolves together while influencing each other. This paper proposes the new co-evolutionary technology roadmapping method and reports the results of the roadmapping workshop for machine learning applications.
The document discusses knowledge transfer between farmers in Nomi City, Ishikawa Prefecture. Researchers interviewed farmers to understand the current situation of knowledge transfer and identify issues. They analyzed the interviews and proposed eight ideas for a system to facilitate knowledge transfer through videos between farmers growing round potatoes, which are a local specialty crop facing an aging farmer population. The researchers created demonstrations of the ideas to evaluate solutions for addressing the challenges of agricultural knowledge succession.
Innovation in the Internet of Things (IoT) provides various opportunities for large, medium, and small-sized companies; however, its realization is still challenging for these companies. Therefore, an engineering design methodology for IoT innovation is required, especially for non- information and communication technology experts. In this paper, we call the engineering design method for IoT innovation “IoT innovation design method,” and discuss its requirements and perspectives with reference to previous studies. Then, this paper proposes a concrete IoT innovation design method with an example. This paper contributes to existing studies not only by proposing a new specific method but also by clarifying the general requirements and perspective (viewpoints) of IoT innovation design methods.
Artificial Intelligence, Service Science, and Knowledge ScienceNaoshi Uchihira
This document discusses artificial intelligence, knowledge science, service science, machine learning, and their relationships. It focuses on how project managers can use results from machine learning models. Specifically, it notes that project managers can automatically use results, interpret results to make improvements or be aware of gaps, but interpretability of machine learning models is important. It also advocates for a hybrid approach combining machine learning, logical learning, and knowledge science for decision making.